THE RESEARCH BEHAVIOR OF INDIVIDUAL INVESTORS

个人投资者的研究行为

Toomas Laarits

Jeffrey Wurgler

Working Paper 33625 http://www.nber.org/papers/w33625

NATIONAL BUREAU OF ECONOMIC RESEARCH

美国国家经济研究局

1050 Massachusetts Avenue

马萨诸塞大道 1050 号

Cambridge, MA 02138

马萨诸塞州 剑桥市 02138

March 2025

2025年3月

We thank Valerie Baldinger, Myeongrok Doh, and Cody Wan for outstanding research assistance and Fulin Li, Terry Odean, Raghu Rau, Noah Stoffman (discussant), Paul Tetlock, Edward Tufte, and participants at NYU Stern, the University of Texas at Austin, Texas A&M University, the NBER Conference on Big Data and High-Performance Computing for Financial Economics, and the Colorado Finance Summit for helpful comments. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

我们感谢 Valerie Baldinger、Myeongrok Doh 和 Cody Wan 出色的研究协助，并感谢 Fulin Li、Terry Odean、Raghu Rau、Noah Stoffman（评论人）、Paul Tetlock、Edward Tufte 以及纽约大学斯特恩商学院、德克萨斯大学奥斯汀分校、德州农工大学、NBER大数据与高性能计算金融经济学会议和科罗拉多金融峰会的参会者提出的有益评论。本文所表达的观点仅为作者观点，不一定反映美国国家经济研究局的观点。

NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

NBER工作论文旨在促进讨论和评论。它们未经同行评审，也未经NBER董事会（其评审是NBER官方出版物的必要环节）的审查。

© 2025 by Toomas Laarits and Jeffrey Wurgler. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.

© 2025 Toomas Laarits 和 Jeffrey Wurgler 版权所有。保留所有权利。在注明完整出处（包括©标记）的情况下，无需明确许可即可引用不超过两段的简短文本。

The Research Behavior of Individual Investors

个人投资者的研究行为

Toomas Laarits and Jeffrey Wurgler

NBER Working Paper No. 33625

NBER 工作论文第 33625 号

March 2025

2025年3月

JEL No. G02, G11, G12, G4, G40, G41, G5, G50, G53

JEL 分类号：G02, G11, G12, G4, G40, G41, G5, G50, G53

ABSTRACT

摘要

Browser data from an approximately representative sample of individual investors offers a detailed account of their search for information, including how much time they spend on stock research, which stocks they research, what categories of information they seek, and when they gather information relative to events and trades. The median individual investor spends approximately six minutes on research per trade on traded tickers, mostly just before the trade; the mean spends around half an hour. Individual investors spend the most time reviewing price charts, followed by analyst opinions, and exhibit little interest in traditional risk statistics. Aggregate research interest is highly correlated with stock size, and salient news and earnings announcements draw more attention. Individual investors have different research styles, and those that focus on short-term information are more likely to trade more speculative stocks.

来自近似代表性个人投资者样本的浏览器数据，详细描述了他们的信息搜索行为，包括他们在股票研究上花费多少时间、研究哪些股票、寻求哪些类别的信息，以及相对于事件和交易，他们在何时收集信息。中位数个人投资者对所交易的股票代码，每次交易大约花费六分钟进行研究，且大多在交易前进行；平均数投资者则花费大约半小时。个人投资者花费最多时间查看价格图表，其次是分析师意见，而对传统的风险统计数据兴趣不大。总体研究兴趣与股票市值高度相关，显著的新闻和盈利公告会吸引更多关注。个人投资者有不同的研究风格，那些关注短期信息的人更倾向于交易投机性更强的股票。

Toomas Laarits

Stern School of Business

斯特恩商学院

New York University

纽约大学

44 West Fourth Street

西四街 44 号

Suite 9-190

9-190 室

New York, NY 10012

Jeffrey Wurgler

Stern School of Business, Suite 9-190

斯特恩商学院，9-190 室

New York University

纽约大学

44 West 4th Street

西四街 44 号

New York, NY 10012

纽约州 纽约市 10012

and NBER

及 NBER

I. Introduction

I. 引言

Asset pricing models make a wide range of assumptions about what investors know or what they think they know. Classic models assume interest in, and knowledge of, variances, covariances, and risk premia, e.g., Sharpe (1964); Merton (1987) highlights limits on attention to risk-return statistics. De Long, Shleifer, Summers, and Waldmann (1990) assume that some investors have information about fundamental values while others, generally individual investors, trade on noise. Hong and Stein (1999) assume that some investors follow the news and others react to price patterns. Microstructure models and limited attention models consider still other information setups. In addition to being fundamental to modeling investor beliefs, “information set” concepts are central to empirical work: Countless studies have tested how prices reflect the textbook breakdown of information into past prices, public information, and public plus private information.

资产定价模型对投资者知道什么或他们认为自己知道什么做出了广泛的假设。经典模型假设投资者对（且了解）方差、协方差和风险溢价感兴趣，例如 Sharpe (1964)；Merton (1987) 强调了对风险回报统计数据的注意力限制。De Long, Shleifer, Summers, 和 Waldmann (1990) 假设一些投资者拥有关于基本价值的信息，而另一些投资者，通常是个人投资者，则基于噪音进行交易。Hong 和 Stein (1999) 假设一些投资者关注新闻，而另一些投资者对价格模式做出反应。微观结构模型和有限注意力模型则考虑了其他的信息设置。除了作为投资者信念建模的基础外，“信息集”概念对于实证研究也至关重要：无数研究测试了价格如何反映教科书中将信息分解为历史价格、公共信息以及公共加私人信息的分类。

This diversity of approaches is understandable not just because models and tests are designed to illustrate different mechanisms, but because there are relatively few comprehensive facts about the information that investors gather. In this paper, we use browser history data on an approximately representative sample of U.S. individual investors to address a set of first-order questions: How much time do individual investors spend on stock research? Which sites do they use? Which stocks do they focus on? When do they do their research relative to their trades or corporate events? And, perhaps most importantly, what types of information do individual investors care about—and what do they ignore?

这种方法的多样性是可以理解的，不仅因为模型和测试旨在阐释不同的机制，还因为关于投资者收集信息的全面事实相对较少。在本文中，我们使用来自近似代表性的美国个人投资者样本的浏览器历史数据来解决一系列首要问题：个人投资者在股票研究上花费多少时间？他们使用哪些网站？他们关注哪些股票？相对于他们的交易或公司事件，他们在什么时候进行研究？而且，也许最重要的是，个人投资者关心哪些类型的信息——又忽略了哪些？

These questions can be addressed through browser history data, also known as clickstream data, because URL addresses include details about the page visited. Gargano and Rossi (2018) were the first to exploit this type of data in finance. We build on and substantially extend their work. Significantly, the data we use here have a number of important qualities. First, it contains much more detail. Second, it is comprehensive of sample investors’ browsing activity, not being limited to activity at a single brokerage domain, so we can observe the investors’ entire information diet. Third, the sample is balanced by the data provider against the panel against U.S. households at the time of the sample, allowing for tentative generalization of numerical estimates to the population of individual investors.

这些问题可以通过浏览器历史数据（也称为点击流数据）来解决，因为 URL 地址包含了所访问页面的详细信息。Gargano 和 Rossi (2018) 是最早在金融领域利用此类数据的。我们在他们的工作基础上进行了实质性的扩展。值得注意的是，我们在此使用的数据具有许多重要特性。首先，它包含更多细节。其次，它全面涵盖了样本投资者的浏览活动，不限于单一经纪商域名的活动，因此我们可以观察到投资者的完整信息获取情况。第三，数据提供商根据样本采集时的美国家庭情况对样本进行了平衡，使得数值估计可以初步推广到全体个人投资者。

The raw data for the households that we focus on—those that trade any individual U.S. stocks or ADRs within an online brokerage account—include over 8 million clicks and 60,000 hours of Internet use in four months of 2007. We identify 484 such households and they make 2,911 stock trades over the course of the sample. For conciseness, and following prior literature, we usually refer to household units that trade stocks as individual investors. Importantly, the data provide insights into current practices by individual investors because online research and online trading was already the norm as of the time of the sample.

我们关注的家庭（即那些在网上经纪账户内交易任何美国个股或美国存托凭证的家庭）的原始数据包括 2007 年四个月内超过 800 万次点击和 6 万小时的互联网使用。我们识别出 484 个这样的家庭，他们在样本期间进行了 2911 次股票交易。为简洁起见，并遵循先前文献，我们通常将交易股票的家庭单位称为个人投资者。重要的是，这些数据为了解个人投资者当前的做法提供了见解，因为在样本采集时，在线研究和在线交易已经成为常态。

Our main conclusions include the following:

我们的主要结论包括：

• The subsample that provides the best estimates indicates that the median individual investor spends six minutes per trade on research about the tickers traded. The mean individual investor spends 29 minutes. The median investor conducts most of this stock research in the 24 hours before a trade, and most of that time in a burst immediately before the trade.

• 提供最佳估计的子样本表明，中位数个人投资者对所交易的股票代码，每次交易花费六分钟进行研究。平均数个人投资者花费 29 分钟。中位数投资者大部分股票研究在交易前的 24 小时内进行，且其中大部分时间是在交易前瞬间集中完成。

• Typical investors spend only a fraction of their research time at their broker’s domain. Disparate finance news sites, and especially Yahoo Finance, constitute a majority of stock-related research.

• 典型投资者仅将其一小部分研究时间花在其经纪商的域名上。各种金融新闻网站，尤其是雅虎财经，构成了大部分与股票相关的研究活动。

• Market capitalization is the most important determinant of individual investor research interest. Salient news, such as Apple’s introduction of its iPhone, can temporarily launch a smaller stock to the highest ranks of aggregate attention. Individual investors also pay more attention around earnings announcements.

• 市值是个人投资者研究兴趣的最重要决定因素。显著的新闻，例如苹果推出其 iPhone，可以暂时将一只较小的股票推向总体关注度的最高行列。个人投资者在盈利公告前后也会给予更多关注。

• Most trades are preceded by the presentation of a snapshot page which includes a set of brief price and fundamental statistics and a graph of intraday prices. Many investors do not pursue research beyond this page.

• 大多数交易之前都会展示一个快照页面，其中包含一组简要的价格和基本面统计数据以及日内价格图表。许多投资者在此页面之后不再进行深入研究。

• When individual investors do go beyond the snapshot page, they spend by far the most time on price charts and price-related information. Analysts’ estimates are consulted less frequently, followed by assorted other fundamental and technical information. Risk statistics such as beta or volatility are of little apparent interest.

• 当个人投资者确实超越快照页面进行研究时，他们绝大多数时间都花在价格图表和与价格相关的信息上。分析师的估计被查阅的频率较低，其次是各种其他基本面和技术信息。诸如贝塔系数或波动率之类的风险统计数据显然引不起什么兴趣。

• Individual investors have different research styles. Some spend more time on research, and those who research more speculative stocks tend to focus on price charts, news, and simple snapshots as opposed to slower-moving fundamentals such as earnings and dividends. Unexplained heterogeneity is considerable.

• 个人投资者有不同的研究风格。有些人花费更多时间进行研究，而那些研究更具投机性股票的人倾向于关注价格图表、新闻和简单的快照，而不是像盈利和股息这样变化较慢的基本面因素。无法解释的异质性相当大。

• The data confirm that Google Trends accurately measures variation in individual investor interest. This addresses concerns in prior research that it may be too noisy due to its inclusion of all Google users; it also speaks to the representativeness of our own sample in light of the massive random sample underlying Google data.

• 数据证实，谷歌趋势 (Google Trends) 准确地衡量了个人投资者兴趣的变化。这解决了先前研究中对其可能因包含所有谷歌用户而噪音过大的担忧；鉴于支撑谷歌数据的大规模随机样本，这也说明了我们自己样本的代表性。

These results provide new facts about individual investor beliefs and behavior, complementing those from, for example, Lease, Lewellen, and Scharblaum (1974), Sicherman, Loewenstein, Seppi, and Utkus (2016), and Gargano and Rossi. Lease et al. study individual investor demographics, investment strategies, and sources of information from a survey of a retail brokerage’s clients. Sicherman et al. study online account logins and trading activity as a function of market conditions and investor characteristics. Gargano and Rossi’s innovative paper, the closest to ours, analyzes clickstream data from 2013-2014 from an online brokerage and documents time spent across broad activities (research, trading, balances etc.) and explore drivers of investor attention. We are able to compare some of their results with our own.

这些结果提供了关于个人投资者信念和行为的新事实，补充了例如 Lease, Lewellen, 和 Scharblaum (1974)、Sicherman, Loewenstein, Seppi, 和 Utkus (2016) 以及 Gargano 和 Rossi 的研究成果。Lease 等人通过对一家零售经纪公司客户的调查，研究了个人投资者的基本人口特征、投资策略和信息来源。Sicherman 等人研究了在线账户登录和交易活动与市场状况及投资者特征的关系。Gargano 和 Rossi 的创新性论文与我们的研究最为接近，他们分析了 2013-2014 年来自一家在线经纪公司的点击流数据，记录了在广泛活动（研究、交易、查看余额等）上花费的时间，并探讨了投资者注意力的驱动因素。我们能够将他们的一些结果与我们自己的结果进行比较。

Since clickstream data is a direct measure of investor attention, our results also bear on that large literature. It includes Barber and Odean (2008), who show that stocks with news or extreme daily returns appear to capture the attention of retail investors. Kaniel, Saar, and Titman (2008) and Barber, Odean, and Zhu (2008) find retail trader contrarianism against recent returns. Tetlock (2010) shows the impact of news on return reversal and volume-induced momentum. Da, Engelberg, and Gao (2015) find that aggregate Google search volume about economic downturns predicts market volatility and fund flows. Subsequent work has sought to further disaggregate types of information commanding the attention, such as Kwan et al. (2025), study news article accesses and relate them to portfolio allocations of institutional investors.

由于点击流数据是投资者注意力的直接衡量标准，我们的结果也与该庞大的文献相关。这包括 Barber 和 Odean (2008)，他们表明有新闻或极端日收益率的股票似乎能吸引散户投资者的注意力。Kaniel, Saar, 和 Titman (2008) 以及 Barber, Odean, 和 Zhu (2008) 发现散户交易者对近期收益表现出逆向投资行为。Tetlock (2010) 展示了新闻对收益反转和交易量驱动的动量效应的影响。Da, Engelberg, 和 Gao (2015) 发现关于经济衰退的谷歌总搜索量可以预测市场波动和资金流向。后续研究试图进一步细分吸引注意力的信息类型，例如 Kwan 等人 (2025) 研究了新闻文章的访问情况，并将其与机构投资者的投资组合配置联系起来。

As others have pointed out, one challenge for this literature is going beyond associations and observing, at an investor level, the full line from attention to action. The literature is also piecemeal in terms of focusing on particular determinants of attention. If the aggregate information set of individual investors was a pot of alphabet soup, research on investor attention often attempts to establish the existence of particular letters in this soup. Our data allows us to estimate the full distribution of letters in the aggregate pot, directly observe the distribution within 484 particular bowls, and also directly observe trading activity. In this way the analysis supports some of the associations observed in the attention literature.

正如其他人指出的那样，该文献面临的一个挑战是超越关联性，在投资者层面观察从注意力到行动的完整链条。该文献在关注特定注意力决定因素方面也显得零散。如果将个人投资者的总体信息集比作一锅字母汤，那么关于投资者注意力的研究通常试图确定这锅汤中是否存在特定的字母。我们的数据使我们能够估计这锅汤中所有字母的完整分布，直接观察 484 个特定碗中的分布情况，并直接观察交易活动。通过这种方式，该分析支持了在注意力文献中观察到的一些关联性。

More generally, our results contribute to asset pricing modelling, especially in behavioral finance. As noted above, finance models make the full gamut of assumptions about investor information sets. The paper contributes facts that will be useful when it is important to be accurate about what information interests individual investors.

更广泛地说，我们的结果对资产定价建模，尤其是行为金融学领域做出了贡献。如上所述，金融模型对投资者信息集做出了各种各样的假设。本文贡献的事实将在需要准确了解哪些信息能引起个人投资者兴趣时发挥作用。

Section II describes the data and the sample of investors. Section III gives basic statistics on total stock research. Section IV reviews most-consulted domains, and Section V investigates which stocks are of most research interest. Section VI studies the timing of research vis-à-vis firm news and investor trading. Section VII documents which categories of information are of most interest. Section VIII explores heterogeneity in research approaches. Section IX concludes and comments on future directions, such as connecting research behavior to performance and portfolio formation.

第二部分描述了数据和投资者样本。第三部分给出了关于总股票研究的基本统计数据。第四部分回顾了最常访问的域名，第五部分调查了哪些股票最受研究关注。第六部分研究了相对于公司新闻和投资者交易的研究时机。第七部分记录了哪些类别的信息最受关注。第八部分探讨了研究方法的异质性。第九部分总结并评论了未来的研究方向，例如将研究行为与业绩和投资组合构建联系起来。

II. Individual investor browsing data

II. 个人投资者浏览数据

We begin with an introduction to the data set, including a stylized example, and discuss its unique advantages and remaining limitations. We then describe the specific household (“individual investor”) sample of interest.

我们首先介绍数据集，包括一个程式化的例子，并讨论其独特的优势和仍然存在的局限性。然后，我们描述我们感兴趣的特定家庭（“个人投资者”）样本。

A. Data source

A. 数据来源

Clickstream data like ours are collected by multiple companies. The data was made available by an online research company and was used in an academic legal context by Bakos, Marotta-Wurgler, and Trossen (2011) and Marotta-Wurgler (2011, 2012) and in a law and finance context by Laarits, Marotta-Wurgler, and Wurgler (2024). It includes the browsing behavior of tens of thousands of U.S. households across January, February, March, and June 2007. To paraphrase Bakos et al., the panel of households, recruited using random-digit-dialing distributions, installed a plug-in that collected the timing and sequence of URLs visited. All computers within the household had their browser data gathered and aggregated in this manner; the timing of the sample happened to coincide with the announcement but not the widespread use of Apple’s iPhone. Confidential or personally identifiable data, such as account numbers, addresses, passwords, and the like, have been removed by the data provider. To date, only a few studies have exploited clickstream data in finance research, including Gargano and Rossi (2018) and Benamar, Foucault, and Vega (2021).

像我们这样的点击流数据由多家公司收集。该数据由一家在线研究公司提供，并被 Bakos, Marotta-Wurgler, 和 Trossen (2011) 以及 Marotta-Wurgler (2011, 2012) 用于学术法律背景，被 Laarits, Marotta-Wurgler, 和 Wurgler (2024) 用于法律和金融背景。它包含了 2007 年 1 月、2 月、3 月和 6 月期间数万个美国家庭的浏览行为。引用 Bakos 等人的话来说，这些家庭样本是通过随机数字拨号分布招募的，他们安装了一个插件来收集访问 URL 的时间和顺序。家庭内的所有计算机的浏览器数据都以这种方式被收集和汇总；样本的时间恰好与苹果 iPhone 的发布时间吻合，但当时 iPhone 尚未广泛使用。机密或个人身份信息，如账号、地址、密码等，已被数据提供商删除。迄今为止，只有少数研究在金融研究中利用了点击流数据，包括 Gargano 和 Rossi (2018) 以及 Benamar, Foucault, 和 Vega (2021)。

Inclusion in the data set is voluntary, but the data provider makes extensive efforts to reduce selection biases. Our focus in this paper is on the subset of households that trade individual stocks. Henceforth, where the context allows, we refer to these household units as individual investors, following Barber and Odean (2002) and others. Subject to remaining selection bias and sampling noise, the data appear to offer a reasonably representative picture of U.S. individual investors trading online at the time—young and old, wealthy and less so, active and less active, and geographically balanced.

纳入数据集是自愿的，但数据提供商付出了大量努力来减少选择偏差。本文的重点是交易个股的家庭子集。此后，在上下文允许的情况下，我们遵循 Barber 和 Odean (2002) 等人的做法，将这些家庭单位称为个人投资者。在考虑到剩余的选择偏差和抽样噪音的情况下，这些数据似乎提供了当时在线交易的美国个人投资者的一个相当具有代表性的图景——包括年轻和年长、富裕和不那么富裕、活跃和不那么活跃的投资者，并且在地理上是平衡的。

A stylized extract of raw data illustrates the level of detail that it may include. Table 1 reports fourteen minutes of a browsing session. The session may begin at any link. This particular investor is a motorsports fan but soon switches to CNBC.com, where she clicks a link providing current data on several U.S. and international stock market indices, the US 10-year Treasury yield, and the USD-Euro exchange rate.

原始数据的程式化摘录说明了其可能包含的细节程度。表 1 报告了一次浏览会话的十四分钟。会话可以从任何链接开始。这位特定的投资者是赛车运动爱好者，但很快切换到 CNBC.com，在那里她点击了一个链接，该链接提供了几个美国和国际股票市场指数、美国 10 年期国债收益率以及美元-欧元汇率的当前数据。

After a minute at that page, the investor logs into a brokerage account. Within her online broker’s research pages, she seeks out the day’s most active names, which on that day included ImClone (IMCL). Based on our own investigation, IMCL enjoyed good news for its cancer drug’s prospects. Our investor then consults the quarterly earnings performance for IMCL. From the link, we can infer that earnings were presented for the prior three years and estimates were shown for the next two years. In a separate panel, the chart showed the daily volume for IMCL over the prior three years, as well as a 13-day moving average. Further analysis of IMCL took place on the highly popular Yahoo Finance website. The investor obtains a quote on symbol (“?s=”) IMCL and then looks up the latest analysts’ estimates (“ae”).

在该页面停留一分钟后，投资者登录了经纪账户。在其在线经纪商的研究页面内，她查找当天最活跃的股票名称，当天包括 ImClone (IMCL)。根据我们自己的调查，IMCL 因其抗癌药物的前景而获得了好消息。我们的投资者随后查阅了 IMCL 的季度盈利表现。从链接中，我们可以推断出呈现了过去三年的盈利情况，并显示了未来两年的预测。在另一个面板中，图表显示了 IMCL 过去三年的日交易量，以及 13 天移动平均线。对 IMCL 的进一步分析在非常受欢迎的雅虎财经网站上进行。投资者获取了代码为 (“?s=”) IMCL 的报价，然后查找最新的分析师估计 (“ae”)。

Following a check-in with race results, the investor returns to her brokerage’s page and takes a different tactic. This time, she looks for trading ideas through a stock screener, in particular stocks with expected EPS growth of at least fifty percent over the next fiscal year. This would have yielded many results, Google (GOOG) among them. A click or two later, the investor comes to a snapshot page, which presents many types of financial and price information on the given stock in an abbreviated form. After looking at a price chart, the investor enters a market order to buy (“ordertype=1”) Google shares. Then she logs into e-mail and proceeds with other activities.

在查看了比赛结果后，投资者返回到她的经纪商页面并采取了不同的策略。这一次，她通过股票筛选器寻找交易思路，特别是寻找下一财年预期每股收益增长至少百分之五十的股票。这会产生许多结果，其中包括谷歌 (GOOG)。点击一两次后，投资者来到了一个快照页面，该页面以缩略形式呈现了给定股票的多种类型的财务和价格信息。在查看了价格图表后，投资者输入了一个市价单购买 (“ordertype=1”) 谷歌股票。然后她登录电子邮件并进行其他活动。

While every domain has a different directory layout and requires tedious processing to extract all the available information from query strings, as described in the Internet Appendix, this example illustrates how the data can provide rich detail about the research process of individual investors. This level of detail is not present in Gargano and Rossi’s (2018) data, which indicates only that the investor in question is doing some research, but not what type.

尽管每个域名的目录布局都不同，并且需要繁琐的处理才能从查询字符串中提取所有可用信息（如互联网附录中所述），但这个例子说明了数据如何能够提供关于个人投资者研究过程的丰富细节。这种细节水平在 Gargano 和 Rossi (2018) 的数据中是不存在的，他们的数据仅表明相关投资者正在进行某种研究，但没有说明是哪种类型的研究。

Accordingly, this paper takes a first and high-level look at this data set. By implication, the data also shed light on the categories of information that are not interesting to individual investors despite being a click away.

因此，本文对该数据集进行了初步和宏观的审视。这意味着，这些数据也揭示了尽管只需点击一下即可获得，但个人投资者并不感兴趣的信息类别。

It is worth discussing what links do and do not reveal. By definition, we cannot directly observe what investors know or learn from other sources; in particular, we don’t observe any information the investors gather offline. Although this may appear to be a severe limitation, its impact is lessened by the fact that, as of 2007, the Internet was not just the easiest but often the only practical way to gather most of the data items that we track. Consensus earnings, beta, insider selling, institutional ownership, recent price trends, the day’s most active stocks, forward P/E ratios—such metrics were easily accessible via free, continuously updated, one-stop-shop data feeds just a click or two away from the Buy and Sell buttons. Consider the wide range of information obtained instantly by the investor in Table 1. The Internet was, and as of this writing remains, the obvious venue for stock research for individual investors.

值得讨论的是链接揭示了什么以及没有揭示什么。根据定义，我们无法直接观察投资者从其他来源知道或学到了什么；特别是，我们无法观察到投资者在线下收集的任何信息。尽管这似乎是一个严重的局限性，但其影响因以下事实而减弱：截至 2007 年，互联网不仅是收集我们追踪的大多数数据项最简单的方式，而且通常是唯一切实可行的方式。共识收益、贝塔系数、内幕交易、机构持股、近期价格趋势、当日最活跃股票、预期市盈率——这些指标都可以通过免费、持续更新、一站式的数据源轻松获取，距离买入和卖出按钮只有一两次点击。想想表 1 中的投资者瞬间获取的广泛信息。互联网过去是，并且在撰写本文时仍然是个人投资者进行股票研究的显而易见的场所。

We also cannot observe an investor’s accumulated “stock” of knowledge as opposed to the flow of information exhibited by the clickstream. In a stock-specific trading context, this is often not a major issue because many of the statistics consulted by our investor in Table 1 would be useless if stale. An investor interested in a one-week-lookback price chart cannot store that information for future trades. We can investigate these issues to see how research “ramps up” prior to the trade; if an investor was relying heavily on a stock of unobserved information, we might not expect a sudden burst of research on that stock prior to the trade, but it turns out that is often what we observe.

我们也无法观察投资者积累的知识“存量”，而只能观察点击流所展示的信息“流量”。在特定股票的交易背景下，这通常不是一个主要问题，因为表 1 中我们的投资者查阅的许多统计数据如果过时就会变得毫无用处。对一周回顾价格图表感兴趣的投资者无法将该信息存储起来用于未来的交易。我们可以研究这些问题，看看研究在交易前是如何“逐步增加”的；如果投资者严重依赖未观察到的信息存量，我们可能不会预期在交易前对该股票的研究会突然爆发，但事实证明这通常是我们观察到的情况。

These limitations are more significant when it comes to soft information about products, corporate reputation, and word of mouth from friends and family. “Apple makes cool products, and a growing number of young people seem to be using them” is an abstract intuition but may be relevant in cases where investors are also customers or have come in contact with the product line. Informal information gathering is much less likely to be important for investments in defense contractors, oil drilling equipment manufacturers, and indeed most of the thousands of exchange-traded stocks.

当涉及到关于产品、公司声誉以及来自朋友和家人的口碑等软信息时，这些局限性就更为显著。“苹果制造很酷的产品，而且似乎有越来越多的年轻人在使用它们”是一种抽象的直觉，但在投资者同时也是客户或接触过该产品线的情况下可能具有相关性。对于国防承包商、石油钻探设备制造商以及实际上数千种交易所交易股票中的大多数而言，非正式信息收集的重要性要小得多。

Other limitations cause browser data to exaggerate, rather than understate, the scope of investor information. One cannot determine how well investors actually understand the information they access. If they struggle to interpret a detailed analyst report, the time spent perusing such data may give the impression that they value these items more than they actually do. Furthermore, without eye-tracking software, we cannot observe which specific parts of pages containing a mix of information, such as “snapshots” pages that list multiple statistics, capture attention. We take three approaches to dealing with this issue as described below. This noise may average out somewhat if different investors focus on different items of a multidimensional page, as the evidence of diverse research approaches suggests that they do.

其他局限性导致浏览器数据夸大而非低估了投资者信息的范围。我们无法确定投资者实际理解他们所访问信息的程度。如果他们难以解读详细的分析师报告，那么花在仔细阅读这些数据上的时间可能会给人一种印象，即他们对这些项目的重视程度高于实际情况。此外，没有眼动追踪软件，我们无法观察到包含混合信息（例如列出多个统计数据的“快照”页面）的页面中哪些特定部分吸引了注意力。我们采用下述三种方法来处理这个问题。如果不同的投资者关注多维页面的不同项目，这种噪音可能会在一定程度上被平均掉，正如多样化研究方法的证据所表明的那样。

Finally, spending only a few seconds viewing a bit of information does not always indicate a lack of understanding or a lack of interest. Dividend information will take only a moment to find and review and, because dividends are a stable characteristic, will be relevant for some time. Changes in analyst opinion, on the other hand, take more time to find and understand and also have a short-lived investment relevance. In light of these considerations, it is useful to tabulate not just the average amount of time investors spend on an item of information, but the fraction of investors that are interested at all.

最后，只花几秒钟查看少量信息并不总是表明缺乏理解或缺乏兴趣。股息信息只需要片刻时间就能找到和查阅，并且由于股息是一个稳定的特征，它将在一段时间内保持相关性。另一方面，分析师意见的变化则需要更多时间来查找和理解，并且其投资相关性也较为短暂。考虑到这些因素，不仅要统计投资者在某个信息项上花费的平均时间，还要统计对此感兴趣的投资者比例，这样做是有用的。

B. Sample of individual investors

B. 个人投资者样本

We focus on households where we observe at least one trade in a specific U.S. stock (or ADR) over the four-month sample. (Although some investors may do some activity one could regard as stock research yet not trade even a single time in four months, they are less interesting and could not be influential.) To maintain a well-defined focus on individual investors in stocks, we do not include the few investors who trade only in options, mutual funds, international stocks, bonds, or ETFs. Research on such instruments is also distinct. For example, options trading is more short-term and event-based, and fund-level investing does not involve as much companyspecific research. We leave this to future work.

我们关注的是在四个月的样本期内，我们观察到至少进行过一次特定美国股票（或 ADR）交易的家庭。（尽管有些投资者可能进行一些可以被视为股票研究的活动，但在四个月内甚至一次交易都没有，但他们不那么有趣，也不可能产生影响。）为了保持对股票个人投资者的明确关注，我们不包括那些只交易期权、共同基金、国际股票、债券或 ETF 的少数投资者。对此类工具的研究也是不同的。例如，期权交易更具短期性和事件驱动性，而基金层面的投资不涉及那么多的公司特定研究。我们将此留待未来的工作。

To identify traders, we start with a list of online brokerages operating in 2007. Six of them feature prominently in our data. In the links to pages in their domains, we search for words that suggest trading—buy, sell, ticker, order, trade, and so on—and proceed iteratively, manually inspecting each of thousands of potential trade links. Table 1 shows an example of a link that makes plain the details of a trade. See the Internet Appendix for additional detail about how raw data are processed into usable data.

为了识别交易者，我们首先列出了 2007 年运营的在线经纪商名单。其中六家在我们的数据中占据显著位置。在指向其域名下页面的链接中，我们搜索暗示交易的词语——如 buy（买入）、sell（卖出）、ticker（代码）、order（订单）、trade（交易）等等——并迭代进行，手动检查数千个潜在的交易链接。表 1 展示了一个清楚说明交易细节的链接示例。有关如何将原始数据处理成可用数据的更多详细信息，请参阅互联网附录。

This process identifies 484 investors in our data, and they make 2,911 total U.S. stock and ADR trades over the four-month period. The raw data for the associated households include around 8.5 million clicks in roughly 60,000 hours of Internet use. Individual households spend between one and four months in the data. The median household spends three months in the sample while close to 40% of the households are present for the entire four months. To maintain comparability across investors who may be in the sample for a different number of months, we report statistics on household-level data, normalized either by the months they spend in the sample or the number of trades they carry out. For the same reason, we construct count variables (such as the number of unique tickers researched) on the household-month level, and average to the household level.

这个过程在我们的数据中识别出 484 位投资者，他们在四个月期间总共进行了 2,911 次美国股票和 ADR 交易。相关家庭的原始数据包括大约 850 万次点击，涉及大约 60,000 小时的互联网使用。单个家庭在数据中的时间为一到四个月不等。中位数家庭在样本中停留三个月，而接近 40% 的家庭在整个四个月内都在样本中。为了保持在样本中停留月份数可能不同的投资者之间的可比性，我们报告基于家庭层面的统计数据，这些数据根据他们在样本中停留的月份数或他们进行的交易次数进行了标准化。出于同样的原因，我们在家庭-月份层面上构建计数变量（例如研究的唯一股票代码数量），然后平均到家庭层面。

Panel A of Table 2 shows basic demographic information. Head-of-household income is topcoded at $100,000/year ($155,000/year in 2025 dollars). The median is between $75,000 and $100,000. The average age of the head of household is 50 years. households are dispersed across the U.S. and are balanced to have a representative distribution of other characteristics including family size and life stage.

表 2 的 A 部分显示了基本的人口统计信息。户主的收入上限为每年 100,000 美元（按 2025 年美元计算为每年 155,000 美元）。中位数在 75,000 美元到 100,000 美元之间。户主的平均年龄为 50 岁。这些家庭分散在美国各地，并经过平衡以具有代表性的其他特征分布，包括家庭规模和生命阶段。

Panel B reports general browsing statistics for this sample. We define a browsing session as a series of clicks with a break of no longer than 15 minutes. The mean household in this sample carries out 103 such browsing sessions per month, and the median household comes in at 92 sessions per month. During these browsing sessions, the average household visits three unique broker or other finance websites in a month. It spends over three and a half hours on brokerage sites, and another one and a half hours on other finance websites. For comparison, all other browsing time adds up to just over 37 hours per month (2,239 minutes), indicating that these households spend over 10% of their total online time on finance-related sites. The time spent at brokerage and other finance sites is skewed. The median household-investor spends 1.4 hours per month at brokerage sites and the 95th percentile spends close to fifteen hours.

B 部分报告了该样本的一般浏览统计数据。我们将浏览会话定义为一系列点击，其间中断不超过 15 分钟。该样本中的平均家庭每月进行 103 次此类浏览会话，中位数家庭每月进行 92 次会话。在这些浏览会话期间，平均家庭每月访问三个独特的经纪商或其他金融网站。它在经纪网站上花费超过三个半小时，在其他金融网站上花费另外一个半小时。相比之下，所有其他浏览时间加起来每月仅超过 37 小时（2,239 分钟），这表明这些家庭将其总在线时间的 10% 以上用于金融相关网站。在经纪商和其他金融网站上花费的时间是偏斜的。中位数家庭投资者每月在经纪网站上花费 1.4 小时，而第 95 百分位的投资者花费接近十五个小时。

Panel C summarizes trading activity in individual stocks that we observe; we will now switch more fully from “per household” to “per investor” terminology. The average investor in our sample trades in 1.3 browsing sessions per month; the median investor records only half a browsing session with a trade in the average month. Recall that investors are required to trade once in the four-month period to be included in our sample, hence the theoretical minimum value of per month browsing sessions with a trade is 0.25. A closely related measure is the number of stock trades. The average investor in the sample trades 2.2 times per month, while the median makes 0.67 trades per month. Trading activity is skewed: the 95th percentile investor trades more than nine times per month. Roughly speaking, this activity resembles the activity in Gargano and Rossi’s sample. Trades are roughly split between buys and sells. The mean trade size is $13,540 and the median is $2,020 among the subset of 191 investors who use brokers where the URL format indicates the number of shares traded (we determine the trade price from CRSP and TAQ data). The average trading-related part of a session with at least one trade lasts under three minutes.

C 部分总结了我们观察到的个股交易活动；我们现在将更全面地从“每个家庭”转向“每个投资者”的术语。我们样本中的平均投资者每月在 1.3 个浏览会话中进行交易；中位数投资者在平均月份中仅记录半个包含交易的浏览会话。回想一下，投资者需要在四个月期间交易一次才能被纳入我们的样本，因此每月包含交易的浏览会话的理论最小值为 0.25。一个密切相关的衡量标准是股票交易次数。样本中的平均投资者每月交易 2.2 次，而中位数投资者每月交易 0.67 次。交易活动是偏斜的：第 95 百分位的投资者每月交易超过九次。粗略地说，这种活动类似于 Gargano 和 Rossi 样本中的活动。交易大致在买入和卖出之间平分。在使用 URL 格式指示交易股数的经纪商的 191 位投资者子集中，平均交易规模为 13,540 美元，中位数为 2,020 美元（我们根据 CRSP 和 TAQ 数据确定交易价格）。包含至少一次交易的会话中与交易相关的部分平均持续不到三分钟。

III. How much time is spent on stock research?

III. 股票研究花费了多少时间？

A. Categories of content

A. 内容类别

Asset pricing research often distinguishes between cash flow news and discount rate news. In reality, investment-related information is organized differently. Brokerage sites provide a broadly similar list of company-specific data items, and investors can then drill down into these within the same domain or follow hyperlinks to other domains. Given remaining heterogeneity in how websites present content, however, we must choose an appropriate level of granularity. If our content categories are too granular, we risk misinterpreting patterns if some investors, perhaps just by chance, connect to brokerages that offer a bit more or less detailed information; if the categories of interest are too coarse, we fail to exploit the full detail of the data.

资产定价研究通常区分现金流消息和贴现率消息。实际上，与投资相关的信息组织方式不同。经纪网站提供大致相似的公司特定数据项列表，投资者随后可以在同一域内深入研究这些数据项，或通过超链接访问其他域。然而，鉴于网站呈现内容的方式仍然存在异质性，我们必须选择适当的粒度级别。如果我们的内容类别过于细化，那么如果一些投资者（也许只是偶然）连接到提供稍多或稍少详细信息的经纪商，我们就有可能误解模式；如果感兴趣的类别过于粗略，我们就无法充分利用数据的全部细节。

Balancing these considerations led to a focus on the following content categories, which collectively represent “stock research” for purposes of the paper: (1) risk statistics, (2) earnings, (3) dividends, (4) other fundamentals (typically valuation ratios or financial statements), (5) analyst opinions, (6) informed ownership and trading, e.g., by insiders, funds, institutions, or short sellers, (7) price charts and price-related data, (8) technical signals, and (9) company website visits for research-relevant purposes. We classify links as “other” if the topic is determinable but of limited interest and as “indeterminate” if the link constitutes stock research but lacks the detail to further categorize. A number of links are to pages that present a mixture of content. Such links are handled separately as described below.

平衡这些考虑因素后，我们将重点放在以下内容类别上，这些类别共同构成本文目的下的“股票研究”：(1) 风险统计数据，(2) 收益，(3) 股息，(4) 其他基本面（通常是估值比率或财务报表），(5) 分析师意见，(6) 知情所有权和交易（例如，内幕人士、基金、机构或卖空者），(7) 价格图表和价格相关数据，(8) 技术信号，以及 (9) 出于研究相关目的访问公司网站。如果主题可确定但兴趣有限，我们将链接分类为“其他”；如果链接构成股票研究但缺乏进一步分类的细节，则分类为“不确定”。许多链接指向呈现混合内容的页面。如下所述，此类链接将单独处理。

We focus first on aggregate totals of research time before breaking research interest down by content category. Documenting how individual investors distribute attention across these categories is a unique opportunity provided by the detail in the data.

我们首先关注研究时间的总和，然后再按内容类别细分研究兴趣。记录个人投资者如何在这些类别之间分配注意力是数据细节提供的独特机会。

B. Content versus format

B. 内容与格式

The content categories we track are defined by content, not presentation format. Earnings information can be conveyed through a bar chart, a news article, or a text press release. Past prices and returns also represent content, while a chart or a table of prices represents different formats. Although links sometimes indicate the presentation format, and the manner in which information is presented might impact perception through behavioral salience and framing effects, in this paper we regard the content itself as the first-order input for investment decisions and the unit of increment to the information set.

我们追踪的内容类别是按内容定义的，而不是按呈现格式。收益信息可以通过条形图、新闻文章或文本新闻稿传达。过去的价格和回报也代表内容，而图表或价格表格则代表不同的格式。尽管链接有时会指示呈现格式，并且信息的呈现方式可能通过行为显著性和框架效应影响感知，但在本文中，我们将内容本身视为投资决策的首要输入和信息集的增量单位。

C. Total research time

C. 总研究时间

One of the most basic questions the data speak to is simply how much stock research individual investors do when all sources and types of online research are considered. Table 3 summarizes the total research activity conducted by investors in our sample, summing up over all the content categories. In order to maintain comparability between investors that spend a differential amount of time in the sample, we construct these measures as investor-level averages of monthly values, or normalize by the number of months in sample, or normalize by the number of trades by the investor.

数据回答的最基本问题之一就是，当考虑所有在线研究的来源和类型时，个人投资者究竟进行了多少股票研究。表 3 总结了我们样本中投资者进行的总研究活动，汇总了所有内容类别。为了保持在样本中花费不同时间的投资者之间的可比性，我们将这些指标构建为月度值的投资者层面平均值，或按样本中的月份数进行标准化，或按投资者的交易次数进行标准化。

Panel A reports that investing households spend anywhere between one to four months in the sample, with three months being the median value. Panel B provides a sense of overall research activity, where we count as research any clicks that could be categorized the groups listed in subsection A above. Recall we define as a “browsing session” a sequence of clicks with a break no longer than fifteen minutes. The average investor carries out close to 30 such sessions a month that have at least some research component, and in the course of these research sessions the average investor sees 30 unique tickers and visits 3.3 distinct brokerage or other finance sites. We construct these count measures on the investor-month level, average to the household level, and report the statistics of the resulting distribution of N=484 values. The median investor conducts about 15 research sessions per month and comes across 12 unique tickers. The average browsing session with a research component includes 3.6 minutes of research. Over the course of the month, the average household carries out about 118 minutes of research, with the median at 37.1 minutes and the 95th percentile clocking in at close to nine hours per month. (Note that the per session averages multiplied by average sessions per month do not have to exactly equal the research per month numbers.) We did not find a strong relationship across households between the average amount traded and the average research time per trade, which argues for giving attention to median behavior.

A 部分报告称，投资家庭在样本中花费的时间在 1 到 4 个月之间，中位数为 3 个月。B 部分提供了整体研究活动的概念，我们将任何可以归入上述 A 小节所列组别的点击都计为研究。回想一下，我们将“浏览会话”定义为一系列点击，其间中断不超过十五分钟。平均投资者每月进行近 30 次包含至少一些研究成分的此类会话，在这些研究会话过程中，平均投资者查看 30 个独特的股票代码并访问 3.3 个不同的经纪商或其他金融网站。我们在投资者-月份层面上构建这些计数指标，平均到家庭层面，并报告由此产生的 N=484 个值的分布统计数据。中位数投资者每月进行约 15 次研究会话，并接触到 12 个独特的股票代码。包含研究成分的平均浏览会话包括 3.6 分钟的研究。在一个月的时间里，平均家庭进行约 118 分钟的研究，中位数为 37.1 分钟，第 95 百分位的投资者每月研究时间接近九个小时。（请注意，每次会话的平均值乘以每月的平均会话数不一定精确等于每月的研究时间。）我们未发现家庭之间平均交易量与每次交易的平均研究时间存在强相关性，这支持了关注中位数行为的观点。

There are a few similar estimates in the modern literature to compare to these. One is Lease et al.’s (1975) survey of “long-term” customers of a “large national retail brokerage” from 1964-1970. The response rate to their survey was 40%. The median respondent reported spending three to five hours per month “in investment analysis and decision-making for your securities portfolio” and a mean of nine hours per month. In light of the characteristics of survey respondents, one expects a bias toward active investors, so it is less surprising that this amount of time per month would put the typical survey respondents in the highest percentiles of research per month in our sample.

现代文献中有一些类似的估计可以与这些进行比较。其中一个是 Lease 等人（1975 年）对 1964-1970 年间一家“大型全国性零售经纪公司”的“长期”客户进行的调查。他们调查的回复率为 40%。中位数受访者报告每月花费三到五个小时“用于证券投资组合的投资分析和决策”，平均每月花费九个小时。考虑到调查受访者的特征，可以预期存在偏向活跃投资者的偏差，因此，每月花费的这些时间将典型的调查受访者置于我们样本中每月研究时间的最高百分位数也就不足为奇了。

It is also possible to back out an estimate of average time spent on broker-site research from Gargano and Rossi (2018). We multiply their sample mean of 381 hours per weekday spent on “research” and divide by 11,000 investors in the sample, leading to a mean of 2.1 minutes per weekday of broker-site research. We estimate a mean of 1.8 minutes under the same restrictions (i.e., limiting the tabulated research to the investor’s broker’s website). In the next section, however, we will see that broker-site research represents less than half of the typical investor’s overall research time, so these estimates of research time also represent less than half of total research activity across all web domains.

也可以从 Gargano 和 Rossi（2018）的研究中推算出在经纪网站上进行研究的平均时间估计值。我们将他们样本中每个工作日花费在“研究”上的平均 381 小时乘以样本中的 11,000 名投资者，得出每个工作日在经纪网站上进行研究的平均时间为 2.1 分钟。我们在相同的限制下（即，将列表中的研究限制在投资者的经纪商网站上）估计的平均值为 1.8 分钟。然而，在下一节中，我们将看到经纪网站研究仅占典型投资者总研究时间的不到一半，因此这些研究时间的估计值也代表了所有网络域总研究活动的不到一半。

Although we focus on the activity of traders in this paper, this tabulation of the amount of research per month can also be benchmarked against investors in our data that do at least a few seconds of activity that we classify as stock-investing research but do not trade even once within the sample period. There are 9,042 such investors; they conduct a mean of 27.6 minutes of research per month (vs. 118 for traders) and a median of 3.8 minutes per month (vs. 37.1 for traders). Thus, not surprisingly, traders do several times more research than nontraders.

尽管本文重点关注交易者的活动，但每月研究量的这种列表也可以与我们数据中那些进行了至少几秒钟我们归类为股票投资研究活动但在样本期内甚至一次交易都没有的投资者进行基准比较。有 9,042 位这样的投资者；他们平均每月进行 27.6 分钟的研究（交易者为 118 分钟），中位数为每月 3.8 分钟（交易者为 37.1 分钟）。因此，毫不奇怪，交易者进行的研究比非交易者多几倍。

Finally, we report total research per trade: the average investor carries out 144 minutes of research per trade with the 95th percentile coming in close to eleven hours, while the 25th and 50th percentile are at 10 and 36 minutes, respectively. We highlight this dimension—research per trade—as the subsequent category-level analysis frequently employs this normalization to give a sense of how much incremental research is reflected in a typical trade by an individual investor.

最后，我们报告了每次交易的总研究时间：平均投资者每次交易进行 144 分钟的研究，第 95 百分位的投资者接近 11 小时，而第 25 和第 50 百分位分别为 10 分钟和 36 分钟。我们强调这个维度——每次交易的研究——因为随后的类别层面分析经常采用这种标准化方法，以了解个人投资者的典型交易中反映了多少增量研究。

By every measure, though, research efforts are notably right skewed.

然而，从各项指标来看，研究投入都显著地呈右偏态分布。

In Panel C we break down the per-trade research numbers further. Close to half of the 144 minutes of research per trade can be matched to some sort of identifier, be it a stock ticker, an index identifier, or any other sort of information about the security or series in question. An average of 52 minutes per trade is matched to an explicit stock ticker, while 9 minutes are matched to funds and indices and just over a minute is matched to other categories such as REITs or currencies.

在 C 部分，我们进一步细分了每次交易的研究数据。每次交易的 144 分钟研究中，接近一半可以匹配到某种标识符，无论是股票代码、指数标识符，还是有关所涉证券或系列的其他任何信息。平均每次交易有 52 分钟与明确的股票代码匹配，9 分钟与基金和指数匹配，略超过一分钟与 REITs 或货币等其他类别匹配。

Panel D gives a sense of time spent at brokerage or other finance sites for reasons other than explicit research. Here we report the number of browsing sessions that included time at any brokerage or finance site listed in Table 4 but did not include an explicit stock research component. We find an average of 23 such sessions per month and the non-research time at these sites adds up to about 155 minutes per month. See Gargano and Rossi for an extensive breakdown of non-research activities at a brokerage website.

D 部分给出了出于明确研究以外的原因在经纪或其他金融网站上花费的时间概念。在这里，我们报告了包含在表 4 所列任何经纪或金融网站上花费时间但未包含明确股票研究成分的浏览会话数量。我们发现平均每月有 23 次此类会话，在这些网站上花费的非研究时间总计约为每月 155 分钟。有关经纪网站非研究活动的详细分类，请参阅 Gargano 和 Rossi 的研究。

IV. Which websites are used for stock research?

IV. 哪些网站用于股票研究？

The next broad question the data can answer is where online stock research is done. The event study literature documents that marginal investors react to information of various types, but it is typically unclear where they obtain that information. Gargano and Rossi’s data are from a single brokerage; Table 4 tabulates how long individual investors spend on different domains, including but not limited to a brokerage. We can also identify time spent directly on Yahoo Finance, the SEC website, and the aggregate amount of time spent on the corporate websites of tickers traded within our sample. We anonymize most domains to protect the intellectual property of the data provider, but we can show the distribution of research time across every relevant individual domain.

数据可以回答的下一个广泛问题是在线股票研究在哪里进行。事件研究文献记录了边际投资者对各种类型的信息做出反应，但通常不清楚他们从哪里获得这些信息。Gargano 和 Rossi 的数据来自单一经纪公司；表 4 列出了个人投资者在不同域（包括但不限于经纪公司）上花费的时间。我们还可以识别直接在雅虎财经、SEC 网站上花费的时间，以及在我们样本内交易的股票代码的公司网站上花费的总时间。我们对大多数域进行了匿名化处理，以保护数据提供商的知识产权，但我们可以显示研究时间在每个相关个体域上的分布情况。

Yahoo Finance is by far the most popular finance research site. We can unmask it because it has previously been reported as such. The site reports financial data, news, analyst opinions, filings, and other information in a standardized format, and it does not require an account to use. More than half of our investors use it at least once across the four months of the sample—in some cases, apparently through a hyperlink from a brokerage’s domain, and in other cases more directly. Yahoo Finance also appears at or near the top of any Google search involving a particular stock, which in turn perpetuates its dominance. The mean investor spends 45.3 minutes per trade at the site on what we assess is stock research; the 53.5% of investors who actually use the site spend a mean of 85 minutes and a median of 7 minutes per trade. Attesting to the prominence of Yahoo Finance, Lawrence, Ryans, Sun, and Laptev (2018) conduct a field experiment and show that earnings announcement articles promoted to a mere one percent of Yahoo Finance users is enough to have observable impacts on abnormal returns and bid-ask spreads.

雅虎财经是迄今为止最受欢迎的金融研究网站。我们可以公开它，因为它之前已被报道过。该网站以标准化格式报告财务数据、新闻、分析师意见、备案文件和其他信息，并且无需帐户即可使用。我们超过一半的投资者在样本的四个月内至少使用过一次——在某些情况下，显然是通过经纪公司域的超链接，而在其他情况下则更直接。雅虎财经也出现在涉及特定股票的任何谷歌搜索结果的顶部或附近，这反过来又巩固了它的主导地位。平均投资者每次交易在该网站上花费 45.3 分钟进行我们评估为股票研究的活动；实际使用该网站的 53.5% 的投资者平均每次交易花费 85 分钟，中位数为 7 分钟。Lawrence、Ryans、Sun 和 Laptev（2018 年）进行的一项实地实验证明了雅虎财经的突出地位，该实验表明，仅向百分之一的雅虎财经用户推广盈利公告文章就足以对异常回报和买卖价差产生可观察的影响。

The remaining sets of columns shows that slightly more than half of the total time on Yahoo Finance is matched to specific stock tickers, and the third columns include only research links matched to tickers that were traded by that investor at least once within the sample. The gap reflects some combination of time spent researching stocks already owned but not traded within the sample (such stocks, like the rest of the portfolio, are unobservable by us), time perusing information about stocks that were ultimately not traded, and other factors.

剩余的列组显示，雅虎财经上总时间的略多于一半与特定的股票代码匹配，第三列仅包含与该投资者在样本内至少交易过一次的代码匹配的研究链接。这种差距反映了多种因素的组合，包括研究已持有但在样本内未交易的股票所花费的时间（此类股票，如同投资组合的其余部分，我们无法观察到）、仔细研究最终未交易股票信息所花费的时间，以及其他因素。

After Yahoo Finance, the research time across domains has a long tail. The next major sources of research are the brokerage sites themselves. We exclude the time spent trading and time on non-research activities at the same broker. Since investors typically use only their own broker for broker-domain research (few have accounts at multiple brokers), the columns for investors with nonzero time spent on that broker’s website are the most informative, because they exclude non-account holders who couldn’t access that broker’s domain anyway. From this perspective the relative dominance of Yahoo Finance again stands out.

在雅虎财经之后，跨域的研究时间呈现长尾分布。下一个主要的研究来源是经纪网站本身。我们排除了在同一经纪商处进行交易的时间和非研究活动的时间。由于投资者通常只使用自己的经纪商进行经纪域研究（很少有人在多个经纪商处拥有账户），因此，在该经纪商网站上花费非零时间的投资者的列信息量最大，因为它们排除了无论如何都无法访问该经纪商域的非账户持有人。从这个角度来看，雅虎财经的相对主导地位再次凸显。

Investors that spend any time on research at their broker’s website spend an average of around an hour per trade, but the median is far less. Many investors spend a minute or less time on broker-site research per trade. Although nearly every investor is doing at least a few seconds of broker-site research over the four-month sample, much of this is likely to be passive in the sense that the broker may make it impossible for an investor to enter an order without being presented with a cursory quote page containing some information. This page nonetheless presents relevant information, such as an intraday price chart, prior to the trade, and may still affect the decision whether and what quantity to trade.

在经纪商网站上花费任何时间进行研究的投资者平均每次交易花费约一小时，但中位数要少得多。许多投资者每次交易在经纪网站研究上花费的时间不超过一分钟。尽管几乎每个投资者在四个月的样本期内都至少进行了几秒钟的经纪网站研究，但这很可能大部分是被动的，因为经纪商可能让投资者在下单前必须看到一个包含某些信息的粗略报价页面。然而，该页面在交易前仍会呈现相关信息，例如日内价格图表，并且仍可能影响是否交易以及交易数量的决定。

Proceeding far down the list of importance, 6.6% of individual investors visited the SEC’s domain. This overstates interest in SEC.gov per se because visitors were typically directed by a link from another domain, such as a brokerage or Yahoo Finance. In addition, only a fraction of these investors obtained a document that we can match to a company large enough to be in the CRSP sample. Recall that we can tabulate here only the time spent on the SEC website, not the time spent reading downloaded documents or documents opened in applications outside the browser. The table suggests that these limitations may not be consequential: Among 484 investors who perform 2,911 trades, only a single investor reviewed information from the SEC website on a CRSP stock that he traded within the sample.

在重要性列表中往下看，6.6% 的个人投资者访问了 SEC 的域。这高估了对 SEC.gov 本身的兴趣，因为访问者通常是通过其他域（例如经纪公司或雅虎财经）的链接引导过来的。此外，这些投资者中只有一小部分获取了我们可以匹配到足够大以纳入 CRSP 样本的公司的文件。回想一下，我们在这里只能统计在 SEC 网站上花费的时间，而不是阅读下载文件或在浏览器之外的应用程序中打开文件所花费的时间。该表表明这些限制可能并不重要：在进行 2,911 次交易的 484 名投资者中，只有一位投资者查看了来自 SEC 网站的关于他在样本内交易过的 CRSP 股票的信息。

Another source of investment-relevant information is the websites of publicly traded U.S. corporations, specifically those matched to CRSP tickers. We group all such domains together for purposes of the table. Determining whether this browsing constitutes deliberate investmentfocused research requires assumptions based on detailed examination of the clickstream, since so much internet activity occurs on domains of public corporations that is not investment-relevant (e.g., shopping on amazon.com or reviewing bank balances at citibank.com). As described more fully in the Internet Appendix, we impose the requirement that a visit to a corporate website qualifies as “research” only when the same household-investor clicks at least one link on another domain that implies investment-related research on the appropriate ticker. We also impose a running constraint that company website research cannot sum to more than two times the investor’s overall investment-relevant research on that ticker.

另一个与投资相关的信息来源是美国上市公司的网站，特别是那些与 CRSP 代码匹配的公司。为了表格的目的，我们将所有此类域组合在一起。确定这种浏览是否构成有意的以投资为重点的研究需要基于对点击流的详细检查做出假设，因为在上市公司域上发生了大量与投资无关的互联网活动（例如，在 amazon.com 购物或在 citibank.com 查看银行余额）。正如互联网附录中更详细描述的那样，我们规定，只有当同一家庭投资者在另一个域上点击至少一个暗示对相应代码进行投资相关研究的链接时，访问公司网站才符合“研究”的资格。我们还施加了一个运行约束，即公司网站研究的总和不能超过投资者对该代码进行的总体投资相关研究的两倍。

Under these rules, 59.9% of investors in the sample consulted websites of publicly traded companies of demonstrated research interest to them. Among the 20% of investors that clicked on a company domain, conducted other research on the same stock, and traded the stock within our sample, the average spent about two minutes per trade on that domain and the median spent just a few seconds.

根据这些规则，样本中 59.9% 的投资者查阅了对他们具有已证实研究兴趣的上市公司的网站。在点击公司域、对同一只股票进行其他研究并在我们样本内交易该股票的 20% 的投资者中，平均每次交易在该域上花费约两分钟，中位数仅花费几秒钟。

V. Which stocks attract research interest?

V. 哪些股票吸引研究兴趣？

We enumerate the most-researched stocks in our sample in Table 5, which is inspired by a similar list in Gargano and Rossi. At the top of our list is Apple, Inc., with more than onequarter of individual investors “researching” Apple at least once in just four sample months. This is an astounding degree of aggregate research attention in a single stock which, at the time, was not even in the top twenty in market capitalization. Again, this is not simply counting households that peruse Apple’s corporate website for new laptop specs or replacement cables and adding that time to an investor’s research activity; as described above, we do not include time spent at a company website as research unless the same household-investor also studied the company from a more clearly investment-relevant perspective.

我们在表 5 中列举了我们样本中研究最多的股票，该表受到 Gargano 和 Rossi 类似列表的启发。我们列表的顶部是苹果公司 (Apple, Inc.)，在短短四个月的样本期内，超过四分之一的个人投资者至少“研究”过苹果一次。对于一只当时市值甚至未进入前二十名的单一股票来说，这种聚合研究关注度是惊人的。再次强调，这不仅仅是计算那些为了查看新笔记本电脑规格或更换电缆而浏览苹果公司网站的家庭，并将这些时间添加到投资者的研究活动中；如上所述，除非同一家庭投资者也从更明确的投资相关角度研究了该公司，否则我们不将花在公司网站上的时间计为研究。

As a crude assessment of the persistence of aggregate research attention, we can compare the list of most-researched firms in the 2007 sample with the most-researched firms in Gargano and Rossi’s 2013 sample. As mentioned before, our sample is more comprehensive in terms of types of research, and we also measure research somewhat differently. In any case, six stocks appear on both lists: Apple, Microsoft, General Electric, Sirius XM, Ford, and AT&T. By contrast, 15 out of the 20 highest market cap rank stocks in January 2007 remained in the top 20 cap as of January 2013. There is measurement error in research rankings but not in cap, but nevertheless we assess that relative market cap is more stable than relative research attention.

作为对聚合研究关注度持久性的粗略评估，我们可以将 2007 年样本中研究最多的公司列表与 Gargano 和 Rossi 的 2013 年样本中研究最多的公司列表进行比较。如前所述，我们的样本在研究类型方面更全面，并且我们对研究的衡量方式也略有不同。无论如何，有六只股票出现在两个列表上：苹果、微软、通用电气、Sirius XM、福特和 AT&T。相比之下，2007 年 1 月市值排名前 20 的股票中有 15 只截至 2013 年 1 月仍保持在前 20 名。研究排名存在测量误差，但市值排名没有，但我们仍然评估相对市值比相对研究关注度更稳定。

Returning to our sample, Figure 1 shows a scatterplot of stocks that are among the top 100 stocks by market cap or the top 100 by the number of investors that engaged in investmentrelated research during the sample period. There is in general a strong correlation between breadth of interest and market cap. Microsoft ranks second in breadth of research interest and third in market cap, and the top five companies by market cap all fall within the top ten in terms of breadth of interest. In general, large technology companies see especially high levels of research interest, while other large stocks, including oil and financial institutions, tend to attract less research attention relative to their size. Perhaps investors consider that the major driver of oil company profits is oil prices, or perhaps research on financial institutions seems complex. In retrospect, AIG and others involved in real estate lending could have used more scrutiny at this time, the eve of the global financial crisis.

回到我们的样本，图 1 显示了按市值排名前 100 或按样本期内参与投资相关研究的投资者数量排名前 100 的股票的散点图。总体而言，研究兴趣的广度与市值之间存在很强的相关性。微软在研究兴趣广度上排名第二，在市值上排名第三，市值排名前五的公司在研究兴趣广度方面都排在前十名之内。总的来说，大型科技公司尤其受到高度的研究关注，而其他大型股票，包括石油和金融机构，相对于其规模往往吸引较少的研究关注。也许投资者认为石油公司利润的主要驱动因素是油价，或者对金融机构的研究似乎很复杂。回想起来，在全球金融危机前夕的这个时候，AIG 和其他涉及房地产贷款的公司本应受到更严格的审视。

Table 6 uses regressions to relate the overall breadth of research interest and intensity as a function of stock characteristics, the volume of news articles, and other factors, which also follows Gargano and Rossi. We consider only stocks that receive any research interest by any investor in the sample. (This is a relatively modest screen, since 80% of stocks in the CRSP universe are present for the regressions even upon requiring all independent variables as well.) The first several columns focus on the overall percentage of investors with nonzero research time in that ticker-month; the last column uses the log of the total number of hours that sample investors spent on research in that ticker-month. All right-side variables are standardized unless otherwise indicated.

表 6 使用回归分析将研究兴趣的总体广度和强度与股票特征、新闻文章数量和其他因素联系起来，这也遵循了 Gargano 和 Rossi 的方法。我们只考虑样本中任何投资者对其产生过研究兴趣的股票。（这是一个相对温和的筛选，因为即使要求所有自变量都存在，CRSP 宇宙中 80% 的股票也出现在回归分析中。）前几列关注在该股票代码-月份内研究时间非零的投资者的总体百分比；最后一列使用样本投资者在该股票代码-月份内用于研究的总小时数的对数。除非另有说明，所有右侧变量均已标准化。

The first takeaway is the dominant effect of firm size in explaining breadth of research interest. This reflects the pattern in Figure 1. As a single factor, log market cap explains 19% of the variation of the breadth of research interest across stocks. A one-standard deviation increase in log market cap from its mean, which corresponds to an increase in cap from about $775 million to $5.4 billion, doubles the percentage of investors conducting some research within the sample, from an unconditional average of .50% up to 1.03% (=.50%+.53%). The second specification shows that the inclusion of Fama-French 49 industry dummies increases explanatory power only marginally, to 23%.

第一个要点是公司规模在解释研究兴趣广度方面的显着影响。这反映了图 1 中的模式。作为单一因素，对数市值解释了股票间研究兴趣广度变化的 19%。对数市值从其均值增加一个标准差，相当于市值从约 7.75 亿美元增加到 54 亿美元，将样本内进行某些研究的投资者百分比翻倍，从无条件平均值 0.50% 增加到 1.03% (=0.50%+0.53%)。第二个设定表明，包含 Fama-French 49 行业虚拟变量仅将解释力略微提高到 23%。

It is not hard to imagine many reasons why large stocks receive more research attention in aggregate. For example, based on print media article counts, Fang and Peress (2009) find that “firm size has an overwhelming effect on media coverage” (p. 2030). Van Nieuwerburgh and Veldkamp (2009) predicts that investors should pay more attention to higher-cap stocks. Liquidity, ETF and index inclusion, information availability, investment constraints, familiarity bias, basic equilibrium considerations—it is hard to think of a mechanism that would predict anything other than a strong positive relationship between aggregate investor attention and market cap.

不难想象大型股票在总体上受到更多研究关注的许多原因。例如，基于印刷媒体文章数量，Fang 和 Peress（2009）发现“公司规模对媒体报道具有压倒性的影响”（第 2030 页）。Van Nieuwerburgh 和 Veldkamp（2009）预测投资者应该更多地关注市值较高的股票。流动性、ETF 和指数纳入、信息可用性、投资限制、熟悉度偏差、基本均衡考虑——很难想出一种机制会预测聚合投资者关注度与市值之间存在强正相关关系之外的任何情况。

The third specification includes two clearly endogenous factors—the retail share of trading volume, as estimated by Boehmer, Jones, Zhang, and Zhang (2021), and the percentage of individual ownership, as measured by 100% minus the percentage of institutional ownership. We confirm the expected positive relationships: Controlling for size, stocks that individuals disproportionately hold and trade also disproportionately attract their research attention.

第三个设定包括两个明显的内生因素——零售交易量份额，由 Boehmer、Jones、Zhang 和 Zhang（2021）估计，以及个人持股百分比，以 100% 减去机构持股百分比衡量。我们确认了预期的正相关关系：在控制规模的情况下，个人不成比例持有和交易的股票也同样不成比例地吸引了他们的研究关注。

The fourth specification considers two time-varying news variables. The first is total news coverage, not necessarily investment focused, constructed as the number of unique news items (articles and press releases) in the RavenPack database. Logs again seem appropriate, as the median number of news articles per stock-month is 22, the mean is 83, and the 99th percentile is 1,061. Controlling for size, companies that are more often in the news are subject to more research attention. This specification also includes a dummy for whether the stock had an earnings announcement in the same month. Drake, Roulstone, and Thornock (2012) observe an elevated level of Google search volume around earnings announcements. In our data, both news factors are statistically significant, but do not much increase overall explanatory power.

第四个设定考虑了两个时变新闻变量。第一个是总新闻报道量，不一定以投资为重点，构建为 RavenPack 数据库中独特新闻项目（文章和新闻稿）的数量。再次使用对数似乎是合适的，因为每个股票月份的新闻文章中位数为 22，平均数为 83，第 99 百分位数为 1,061。在控制规模的情况下，更频繁出现在新闻中的公司会受到更多的研究关注。该设定还包括一个虚拟变量，用于指示该股票是否在同月发布了盈利公告。Drake、Roulstone 和 Thornock（2012）观察到盈利公告前后谷歌搜索量升高。在我们的数据中，两个新闻因素都具有统计显著性，但并未显著增加总体解释力。

The remaining specifications add a full list of stock characteristics. A few stand out in their magnitudes. In addition to market capitalization, stock characteristics that attract research interest include low nominal share price, high volatility, no dividends, high growth (as measured by sales growth or external finance over assets), number of analysts covering the stock (as in Gargano and Rossi), and S&P 500 Index membership. Although neither share price nor S&P 500 membership is directly related to stock fundamentals, they have particularly strong associations with research interest. In general, these relationships are intuitive in light of prior research such as Barber and Odean (2008) and Balasubramaniam et al. (2023).

剩余的设定添加了完整的股票特征列表。有几个特征在幅度上脱颖而出。除市值外，吸引研究兴趣的股票特征包括低名义股价、高波动性、无股息、高增长（以销售增长或外部融资与资产比衡量）、覆盖该股票的分析师数量（如 Gargano 和 Rossi）以及标普 500 指数成员资格。尽管股价和标普 500 成员资格都与股票基本面没有直接关系，但它们与研究兴趣有着特别强的关联。总的来说，鉴于先前的研究，如 Barber 和 Odean（2008）以及 Balasubramaniam 等人（2023），这些关系是直观的。

The last column uses the log of the total number of research hours by investors in that stock-month as the dependent variable, closest to Gargano and Rossi. As a measure of research interest, this variable can be quite noisy when, for example, a single investor focuses at great length on a particular stock, so we winsorize it at the 95th percentile. Overall, the same factors and characteristics drive this measure of research interest as well.

最后一列使用投资者在该股票月份的总研究小时数的对数作为因变量，这与 Gargano 和 Rossi 最接近。作为研究兴趣的衡量标准，当例如单个投资者长时间专注于特定股票时，该变量可能会非常嘈杂，因此我们对其进行 95% 的缩尾处理。总体而言，相同的因素和特征也驱动着这种研究兴趣的衡量标准。

VI. When is research conducted? Research around events and trades

VI. 研究何时进行？围绕事件和交易的研究

A. Research around events

A. 围绕事件的研究

Panel regressions from Table 6 suggest that elevated news coverage can lead to elevated research activity, but the limited length of the panel precludes the inclusion of firm fixed effects. In-depth study of research activity around specific events provides more compelling evidence into both the nature of our sample and actual investor research behavior.

表 6 的面板回归表明，新闻报道增多可能导致研究活动增加，但面板长度有限，无法包含公司固定效应。对特定事件周围研究活动的深入研究为我们样本的性质和实际投资者研究行为提供了更有力的证据。

In Figure 2, we analyze news-intensive periods for three companies listed at the top of Table 5, and we compare the breadth of research interest in our data to search intensity measured by Google Trends. Note that there is no mechanical relationship because Google search activity, like all search engine activity, is excluded from our research time tabulations.

在图 2 中，我们分析了表 5 顶部列出的三家公司的新闻密集期，并将我们数据中研究兴趣的广度与谷歌趋势衡量的搜索强度进行了比较。请注意，两者之间没有机械关系，因为谷歌搜索活动，像所有搜索引擎活动一样，被排除在我们的研究时间统计之外。

Researchers have increasingly used Google Trends as a proxy for investor attention. Da, Engelberg, and Gao (2011) create indices of investor attention using Google search data. They find that increased search attention predicts higher prices in the subsequent weeks, eventual reversals over the long run, and IPO underperformance. Da et al. (2015) find that aggregate Google search volume in topics related to economic downturns has predictive power for market volatility and fund flows. Subsequent work on investor attention has sought to further disaggregate types of information commanding the attention of various investors. As mentioned, Drake et al. document an increase in Google searches around earnings announcements, with elevated interest persisting long after the earnings news is released.

研究人员越来越多地使用谷歌趋势作为投资者关注度的代理指标。Da、Engelberg 和 Gao（2011）使用谷歌搜索数据创建了投资者关注度指数。他们发现，搜索关注度的增加预示着随后几周价格上涨、长期最终反转以及 IPO 表现不佳。Da 等人（2015）发现，与经济衰退相关主题的谷歌搜索总量对市场波动和资金流向具有预测能力。随后关于投资者关注度的研究试图进一步分解吸引不同投资者注意力的信息类型。如前所述，Drake 等人记录了盈利公告前后谷歌搜索量的增加，并且在盈利消息发布后很长一段时间内，这种 повышенный интерес 持续存在。

What does more detailed browser data at the level of individual investors tell us? As an illustrative example, Microsoft is the second-most researched stock in our sample. Panel A looks at a month of activity that includes a quarterly earnings announcement. We plot research behavior in the clickstream data, measured as the number of investors engaged in investmentrelevant research, alongside Google Trends search volume for the ticker symbol “MSFT” as a proxy for investment-focused search activity.

个人投资者层面的更详细浏览器数据告诉我们什么？举例来说，微软是我们样本中研究第二多的股票。面板 A 展示了一个包含季度盈利公告的月份活动。我们将点击流数据中的研究行为（以参与投资相关研究的投资者数量衡量）与谷歌趋势中股票代码“MSFT”的搜索量（作为以投资为重点的搜索活动的代理）并列绘制。

There is a strong agreement between the two methodologies, supporting the approach of prior authors using Google search volume. In both data sets, breadth of interest peaks around the quarterly earnings announcement. This indicates that our sample can capture general investmentrelated interest despite being smaller by orders of magnitude than the Google Trends random sample of a large fraction of all search activity. Also, in the other direction, our sample validates that Google Trends can accurately measure time variation in individual investor interest and attention in individual tickers, which is not obvious due to Google’s normalization approach. This finding is relevant for other research because Google Trends data are available over the history of Internet usage and across search terms.

这两种方法之间存在很强的一致性，支持了先前作者使用谷歌搜索量的方法。在两个数据集中，研究兴趣的广度都在季度盈利公告前后达到峰值。这表明，尽管我们的样本比谷歌趋势对所有搜索活动的一大部分进行的随机抽样小几个数量级，但它仍能捕捉到普遍的投资相关兴趣。此外，反过来看，我们的样本验证了谷歌趋势可以准确衡量个人投资者对个别股票代码的兴趣和关注度的时间变化，这由于谷歌的标准化方法而并不明显。这一发现对其他研究具有相关性，因为谷歌趋势数据在整个互联网使用历史中以及跨搜索词都可用。

Apple is the most-researched company in our sample period. Panel B shows a month of activity that includes both the announcement of the iPhone and a regular quarterly earnings announcement. We again observe a local maximum in clickstream data interest around the earnings announcement and another spike around the product announcement. Here, Google Trends data have the advantage that separate search terms can be used to isolate iPhone product interest from other investment-related interest, while clickstream data have the advantage that one can identify and isolate investors who do other investment-related research on Apple from those who are simply interested in a new product.

苹果是我们样本期内研究最多的公司。面板 B 展示了一个月的活动，其中包括 iPhone 的发布和一次常规的季度盈利公告。我们再次观察到点击流数据兴趣在盈利公告前后出现局部最大值，在产品发布前后出现另一个峰值。在这里，谷歌趋势数据的优势在于可以使用不同的搜索词将对 iPhone 产品的兴趣与其他投资相关兴趣区分开来，而点击流数据的优势在于可以识别并区分那些对苹果进行其他投资相关研究的投资者和那些仅仅对新产品感兴趣的投资者。

Our third example, in Panel C, involves Sirius, which Table 6 shows is an outlier with far greater research interest than its capitalization would suggest. This traces to a merger announcement during the sample period. Extrapolating the number of investors in our sample who “research” Sirius to the population implies an enormous focus of investor attention on this small stock around this specific event. The popular talk show host Howard Stern moved to Sirius in January 2006, likely fueling attention to corporate events. Here again, investor interest based on browser data supports the use of Google Trends and, in concert with this particular episode, Drake et al. find that acquisition announcements are particularly strong events in term of boosting Google search volume for a ticker symbol.

我们的第三个例子，在面板 C 中，涉及 Sirius，表 6 显示它是一个异常值，其研究兴趣远超其市值所暗示的水平。这可以追溯到样本期内的一次合并公告。将我们样本中“研究”Sirius 的投资者数量推断到总体，意味着围绕这一特定事件，投资者对这只小盘股给予了极大的关注。受欢迎的脱口秀主持人霍华德·斯特恩于 2006 年 1 月加入 Sirius，这可能加剧了人们对公司事件的关注。同样，基于浏览器数据的投资者兴趣支持使用谷歌趋势，并且结合这一特定事件，Drake 等人发现，就提升股票代码的谷歌搜索量而言，收购公告是特别强烈的事件。

B. Research in the run-up to a trade

B. 交易前的研究

The temporal pattern of overall research relative to actual trading is another aspect of individual investor behavior that our data can characterize. Theory gives no general guidance, but some investors clearly do respond to news to an extent, and some types of information that investors could access quickly become stale, e.g., short-lookback price charts or the Sirius merger announcement in Figure 2. Other investment-relevant information, such as dividend policy, is more persistent and needs to be consulted less often.

整体研究相对于实际交易的时间模式是我们数据可以刻画的个人投资者行为的另一个方面。理论没有给出普遍的指导，但一些投资者显然在一定程度上对新闻做出反应，并且投资者可以获取的某些类型的信息很快就会过时，例如，短期回顾价格图表或图 2 中的 Sirius 合并公告。其他投资相关信息，例如股息政策，则更具持久性，不需要那么频繁地查阅。

Conceptually, the simplest case is an investor that executes a single trade in a single ticker at t=0 and does no other trading throughout the time in the sample. If so, one can simply cumulate up research time related to the traded ticker, as well as untraded tickers if desired, and report the timing of the research relative to the trade at t=0. But in more general cases in which an investor makes multiple trades in the same ticker over time, or trades in multiple tickers in short order following a common burst of research, complexities arise.

从概念上讲，最简单的情况是投资者在 t=0 时对单个股票代码执行单笔交易，并且在样本期间内不进行其他交易。如果是这样，人们可以简单地累积与交易股票代码相关的研究时间，如果需要，也可以累积未交易股票代码的研究时间，并报告研究相对于 t=0 交易的时间安排。但在更普遍的情况下，即投资者随着时间的推移对同一股票代码进行多次交易，或者在一次共同的研究爆发后短时间内交易多个股票代码，就会出现复杂性。

In Figure 4, we allocate research time in a traded ticker to the next trade in that stock; if no further trade occurs, we count research time as “post-trade” research. With research effort classified as a distance to next trade in the given stock, we cumulate up time for each investortrade. In other words, cumulation of time for any given investor-trade in a specific stock starts either at the beginning of the sample, or immediately after a trade in the stock. In the cases where the investor trades multiple times over the course of our sample, we create an average cumulation of research time per trade. We plot the results for mean (and median) cumulative research time associated to any trade as well as buys and sells separately. We further limit the sample to the 50 investors whose links in the research-related clickstream can be fully or almost fully associated with specific tickers, as a result of the detail contained in the URLs of the domains they consulted (domains provide different degrees of detail in their URLs).

在图 4 中，我们将交易股票代码的研究时间分配给该股票的下一次交易；如果没有进一步的交易发生，我们将研究时间计为“交易后”研究。将研究投入归类为到给定股票下一次交易的距离后，我们为每个投资者-交易累积时间。换句话说，任何给定投资者对特定股票的交易时间累积要么从样本开始时开始，要么在该股票交易后立即开始。在投资者在我们的样本过程中多次交易的情况下，我们创建了每次交易的平均研究时间累积。我们绘制了与任何交易相关的平均（和中位数）累积研究时间的结果，以及买入和卖出的结果。我们进一步将样本限制在 50 位投资者，由于他们查阅的域名的 URL 中包含的详细信息（不同域名的 URL 提供不同程度的详细信息），他们在研究相关点击流中的链接可以完全或几乎完全与特定股票代码相关联。

The left panel on Figure 4 plots the mean research time in the ticker traded at t=0 from eight days prior to the trade to two days after the trade. Each vertical bar marks a 24-hour period, and the periodic flat spots reflect afterhours pauses in research activity. Note that cumulative research at eight days prior to the trade is already different from zero, reflecting the cumulation starting at the beginning of the sample or after the last trade in the stock. We see a substantial increase in overall research time prior to a trade being executed; that is, the “stock” of research knowledge is rapidly augmented in the hours just prior to a trade. Roughly about five minutes of the total 20 minutes of pre-trade research takes place in the hours immediately prior to trade, while half of the overall pre-trade research takes place in the week prior to trade. Research in buys is more concentrated in the immediate runup while research on sells is more spread out. This may relate to the fact that research on sells involves stocks that are already in the portfolio, but that is not the case for all buys.

图 4 的左侧面板绘制了在 t=0 交易的股票代码从交易前八天到交易后两天的平均研究时间。每个垂直条标记一个 24 小时周期，周期性的平坦点反映了盘后研究活动的暂停。请注意，交易前八天的累积研究已经不为零，反映了从样本开始或股票最后一次交易后开始的累积。我们看到在交易执行之前，整体研究时间大幅增加；也就是说，研究知识的“存量”在交易前的几个小时内迅速增加。交易前总共 20 分钟的研究中，大约有 5 分钟发生在交易前的几个小时内，而交易前整体研究的一半发生在交易前一周。买入研究更集中在交易前夕，而卖出研究则更分散。这可能与卖出研究涉及已在投资组合中的股票有关，但并非所有买入都是这种情况。

The median behavior in the right panel of Figure 4 is different. The median investor carries out no research at all until five days before a trade, and a considerable fraction of tickermatched research that does take place occurs in the last ten minutes prior to the trade. Comparing the median and the mean behavior per trade implies that research time is skewed, and a large number of trades are preceded by only a few minutes of ticker-specific research.

图 4 右侧面板的中位数行为有所不同。中位数投资者直到交易前五天才进行任何研究，并且相当一部分确实发生的与股票代码匹配的研究发生在交易前的最后十分钟内。比较每次交易的中位数和平均行为意味着研究时间是偏斜的，大量交易之前只有几分钟的特定股票代码研究。

VII. What information do individual investors seek? Research by content type

VII. 个人投资者寻求什么信息？按内容类型划分的研究

We next break down the research totals into types of underlying information observed, another unique opportunity afforded by the data. In keeping with prior, domain-level results, we report statistics on time spent in specific content categories, normalized by the number of trades that investor carries out over the course of the sample.

接下来，我们将研究总量分解为观察到的基础信息类型，这是数据提供的另一个独特机会。与之前的域级别结果保持一致，我们报告了在特定内容类别上花费时间的统计数据，并根据投资者在样本期间执行的交易数量进行了标准化。

At this level of analysis, two samples are particularly interesting. The full sample of 484 investors give us the best estimates of total time research breakdowns across categories. We are also interested in a subsample of 50 investors for which we are able to attach a very high fraction of the investors’ links to particular ticker symbols. Based on our investigation, these investors are not different from the full sample in any observable way—in particular, they trade with similar frequency, perform similar levels of overall research, and have similar age and income demographics. But their data provide the best estimates of research activity with respect to the specific stocks that the investor actually trades—in effect, a measure of the information that the average individual investor’s trades “inject” into prices.

在这个分析层面，有两个样本特别值得关注。484 名投资者的完整样本为我们提供了跨类别总研究时间分解的最佳估计。我们还对 50 名投资者的子样本感兴趣，对于这些投资者，我们能够将他们链接的很高比例附加到特定的股票代码上。根据我们的调查，这些投资者在任何可观察的方式上都与完整样本没有区别——特别是，他们的交易频率相似，进行相似水平的整体研究，并且具有相似的年龄和收入人口统计特征。但他们的数据提供了关于投资者实际交易的特定股票的研究活动的最佳估计——实际上，这是衡量普通个人投资者交易“注入”价格的信息。

A. Mixed-content pages

A. 混合内容页面

The first complexity to address is that some research pages present multiple types of content. For example, investors frequently access detailed quote pages, often referred to as “snapshots” by their broker site or Yahoo Finance, which contain a variety of information— earnings, price charts, dividends, and so on. Without eye-tracking software, we cannot observe attention to the different content types within such pages. News articles of indeterminate content and time spent on message boards present similar complexities. Although snapshots, news, and message boards are objective characterizations of a dimension of research, they are not specific types of content. After reviewing time spent on such pages, we discuss a methodology for attributing time to specific content categories.

首先要解决的复杂性是，一些研究页面呈现多种类型的内容。例如，投资者经常访问详细的报价页面，通常被他们的经纪商网站或雅虎财经称为“快照”，其中包含各种信息——收益、价格图表、股息等等。没有眼动追踪软件，我们无法观察到对此类页面内不同内容类型的关注度。内容不确定的新闻文章和在留言板上花费的时间也呈现出类似的复杂性。尽管快照、新闻和留言板是研究维度的一个客观特征，但它们并非特定的内容类型。在回顾了在此类页面上花费的时间后，我们讨论了一种将时间归因于特定内容类别的方法。

A.1. Snapshots

A.1. 快照

Snapshot pages, or detailed quote pages, are familiar. They often serve as starting points for deeper research through other links and tabs, and other times they may constitute the entirety of an investor’s research time. In some cases, investors who trade a given ticker cannot avoid some form of fairly detailed quote based on the structure of the broker’s website.

快照页面，或详细报价页面，是人们所熟悉的。它们通常作为通过其他链接和标签进行更深入研究的起点，有时它们可能构成投资者全部的研究时间。在某些情况下，根据经纪商网站的结构，交易给定股票代码的投资者无法避免某种形式的相当详细的报价。

Brokerage and finance research domains have largely converged on a format for the snapshot page, but the mix underlying content represented varies somewhat. Snapshots virtually always contain current prices, daily percentage return, open/bid/ask, today’s volume, 52-week range, market cap, dividend or yield, a brief earnings statistic, and a price chart. Beyond those statistics, practices vary. For example, beta was not in Yahoo Finance’s snapshot page until years after our sample period. Sites also differ in their use of lookback periods in price charts. Some include current headlines or detailed analyst forecasts, compare today's volume to a moving average, or report other fundamental data. Others are sparse or provide numerical data without context.

经纪和金融研究领域在快照页面的格式上已基本趋同，但所代表的基础内容的组合略有不同。快照几乎总是包含当前价格、每日百分比回报、开盘价/买入价/卖出价、当日交易量、52 周范围、市值、股息或收益率、简要收益统计数据和价格图表。除了这些统计数据之外，做法各不相同。例如，在我们样本期结束多年后，Beta 才出现在雅虎财经的快照页面中。网站在价格图表中使用回顾期的方式也不同。有些网站包含当前头条新闻或详细的分析师预测，将当日交易量与移动平均线进行比较，或报告其他基本面数据。其他网站则内容稀疏或提供没有上下文的数字数据。

In Table 7, which repays careful study, we report means and medians of snapshot research time across all 484 investors (in the left columns) or the high ticker match rate sample of 50 investors (in the right columns), the percentage of those investors spending any time on snapshots, and means and medians of research time conditional on the investor spending some time on snapshots.

在表 7 中（值得仔细研究），我们报告了所有 484 名投资者（左列）或 50 名高股票代码匹配率样本投资者（右列）的快照研究时间的平均值和中位数，在快照上花费任何时间的投资者百分比，以及在投资者在快照上花费一些时间的条件下研究时间的平均值和中位数。

The average investor in the full sample spends 37 minutes per trade on snapshot pages.

Nearly all investors (93%) spend some time on such a page, but the median investor spends only 6.5 minutes per trade. The mean investor in the high ticker match rate sample exhibits similar behavior. Since investors may click on several stocks’ snapshot pages around any given trade, the amount of time spent on the snapshot of a stock for which we actually observe a trade in the sample is lower. Referencing the last columns, our best estimate is that the average (median) investor spends 11.2 minutes per trade (1.2 minutes) on snapshots of the traded stocks.

几乎所有投资者（93%）都会在此类页面上花费一些时间，但中位数投资者每次交易仅花费 6.5 分钟。高股票代码匹配率样本中的平均投资者表现出类似的行为。由于投资者在任何给定交易前后可能会点击多个股票的快照页面，因此在我们实际观察到样本中有交易的股票快照上花费的时间量较低。参考最后一列，我们的最佳估计是，普通（中位数）投资者每次交易在交易股票的快照上花费 11.2 分钟（1.2 分钟）。

A.2. News and message boards

A.2. 新闻和留言板

We often observe that an investor is scrolling through a stock-related message board, but we do not know the content itself. We treat the message board as a format that reflects a mix of underlying content, and in this way resemble snapshots. News links present a similar challenge. The link may indicate that the article is about Intel but not whether it involves an earnings announcement or an analyst interview. In such cases we label the links as “Message Board” and “News – Indeterminate.” See also Kwan et al. (2025) for an extensive investigation of institutional investor attention to a large set of financial news articles that is matched to funds’ holdings.

我们经常观察到投资者在浏览与股票相关的留言板，但我们不知道其具体内容。我们将留言板视为一种反映混合基础内容的格式，在这方面类似于快照。新闻链接也带来了类似的挑战。链接可能表明文章是关于英特尔的，但并未说明是否涉及盈利公告或分析师访谈。在这种情况下，我们将链接标记为“留言板”和“新闻 - 不确定”。另请参阅 Kwan 等人（2025）对机构投资者对大量金融新闻文章关注度的广泛调查，这些文章与基金持股相匹配。

Of these two types of mixed-content links, news is the more important, although both are less important than snapshots. News of indeterminate nature occupies a median of 2.6 minutes per trade, and message boards are used by only a fifth of investors; among these, a few spend a good deal of time, but even the median message-board user spends only a minute per trade. The last columns show that the majority of news pertains to stocks that are not actually traded; the average investor who trades a certain ticker spends 1.2 minutes on news about that ticker but of otherwise unknown content.

在这两种混合内容链接类型中，新闻更为重要，尽管两者都不如快照重要。性质不确定的新闻每次交易的中位数占用时间为 2.6 分钟，只有五分之一的投资者使用留言板；在这些用户中，少数人花费了大量时间，但即使是中位数的留言板用户每次交易也只花费一分钟。最后一列显示，大部分新闻涉及的股票实际上并未交易；交易某个特定股票代码的普通投资者在该股票代码的新闻上花费 1.2 分钟，但这些新闻的内容在其他方面是未知的。

B. Imputing mixed-content links to specific content

B. 将混合内容链接归因于特定内容

In an attempt to provide a full, albeit somewhat noisy, accounting of the actual content of information presented to investors, we impute research time on these mixed-content pages to specific types of content. The method for which we report results in Table 7 is described more fully in the Internet Appendix, but the basic approach is as follows. We allocate snapshot time to specific categories of content based on investor interest in those specific categories when such attention can be observed, as reported in the third column of Table 7. We allocate news of otherwise indeterminate type to specific categories using breakdowns of present-day news articles constructed by RavenPack. And we allocate message board content using the distribution of content that we find on the present-day Reddit r/stocks message board. See Antweiler and Frank (2004) for an analysis of the content of message board postings and Neuhierl, Scherbina, and Schlusche (2013) for a detailed breakdown of the content of corporate press releases; it is not possible for us to capture this information, link-by-link, in such a granular manner.

为了提供一份完整（尽管有些干扰）的呈现给投资者的信息实际内容的说明，我们将这些混合内容页面上的研究时间归因于特定的内容类型。我们在表 7 中报告结果的方法在互联网附录中有更详细的描述，但基本方法如下。我们根据投资者对特定内容类别的兴趣（当可以观察到这种关注时，如表 7 第三列所示）将快照时间分配给这些类别。我们使用 RavenPack 构建的当前新闻文章的分类，将类型不确定的新闻分配给特定类别。并且，我们使用在当前 Reddit r/stocks 留言板上发现的内容分布来分配留言板内容。有关留言板帖子内容的分析，请参见 Antweiler 和 Frank (2004)；有关公司新闻稿内容的详细分类，请参见 Neuhierl、Scherbina 和 Schlusche (2013)；我们无法以如此精细的方式逐个链接地捕获这些信息。

C. Specific content

C. 特定内容

C.1. Risk statistics

C.1. 风险统计数据

The standard academic advice for individual investors is to allocate a fraction of portfolio wealth to a diversified portfolio and the rest to a riskless asset. Each investor in our sample has eschewed that advice, at least to some extent, since he or she is trading individual stocks. For such investors, academics offer the advice to at least be sure to distinguish alpha from beta. It is interesting to see the extent to which investors consult standard risk statistics, such as beta and volatility (or ignore our advice here as well).

标准的学术建议是，个人投资者应将一部分投资组合财富分配给多元化投资组合，其余部分分配给无风险资产。我们样本中的每个投资者都或多或少地回避了这一建议，因为他或她正在交易个股。对于这类投资者，学者们建议至少要确保区分阿尔法（alpha）和贝塔（beta）。有趣的是，投资者在多大程度上查阅了标准的风险统计数据，例如贝塔和波动率（或者也忽略了我们这里的建议）。

The data indicate that few investors display special interest in standard risk statistics. Only seven out of 484 investors demonstrated specific interest in beta or volatility, and only a few drilled down into risk information on a ticker that they traded. This is not quite fair, because risk statistics are sometimes presented as line items in snapshots. Upon imputing snapshot time to specific content types, our best estimate is that the mean investor spends 1.8 of her 144.2 minutes per trade of total research on risk statistics; the median investor spends a fraction of a minute on risk statistics in total and essentially no time at all on risk statistics of the stocks that she actually trades.

数据表明，很少有投资者对标准风险统计数据表现出特别的兴趣。在 484 名投资者中，只有 7 人对贝塔或波动率表现出特定的兴趣，只有少数人深入研究了他们交易的股票代码的风险信息。这不太公平，因为风险统计数据有时会作为快照中的项目呈现。在将快照时间归因于特定内容类型后，我们的最佳估计是，平均投资者在每次交易的总研究时间 144.2 分钟中，有 1.8 分钟用于风险统计数据；中位数投资者总共在风险统计数据上花费不到一分钟，并且基本上没有花时间在她实际交易的股票的风险统计数据上。

C.2. Dividends, earnings, and other fundamentals

C.2. 股息、收益和其他基本面

Investors may seek out “hard” fundamentals: dividends, earnings, valuation ratios, and the accounting statements and regulatory filings that underlie these figures. We find more interest in this type of research. Almost 40% of investors seek out some sort of earnings information, with just under two minutes of research for the mean investor. Dividends attract at least some direct attention by 17% of investors, and those with that particular interest tend to spend relatively more time on research. Intuition suggests that dividend information is faster to digest than earnings information.

投资者可能会寻找“硬”基本面：股息、收益、估值比率，以及支撑这些数字的会计报表和监管文件。我们发现人们对这类研究更感兴趣。近 40% 的投资者寻找某种收益信息，平均投资者为此进行的研究时间略低于两分钟。17% 的投资者至少对股息给予了一些直接关注，而那些有此特定兴趣的人往往在研究上花费相对更多的时间。直觉表明，股息信息比收益信息更容易消化。

Finally, a considerable fraction of investors—53%—explicitly observe some nonearnings, non-dividend fundamental information. This disparate category includes valuation ratios such as price to sales, annual reports, cash flow statistics, and regulatory filings. On average, we see a few minutes of such research per trade.

最后，相当一部分投资者（53%）明确观察了一些非收益、非股息的基本面信息。这个不同的类别包括市销率等估值比率、年度报告、现金流统计数据和监管文件。平均而言，我们看到每次交易有几分钟的此类研究。

As discussed below, earnings and sometimes dividend information is prevalent on snapshot pages, which present a mix of content. Upon adding this imputed time, the mean time spent on earnings, dividends, and other fundamentals rises considerably, but median behavior changes much less.

如下所述，收益信息，有时还有股息信息，在呈现混合内容的快照页面上很普遍。加上这个估算的时间后，花在收益、股息和其他基本面上的平均时间显著增加，但中位数行为变化要小得多。

C.3. Analysts

C.3. 分析师

Analyst reports and estimates are of comparatively popular interest. 71% of investors pursue this type of research in some measure over the course of the sample for at least a few minutes. The mean investor spends over 12 minutes per trade on analyst reports; conditional on doing any such research, the mean investor spends approximately 18 minutes. Time on analyst reports is quite skewed, with the median investor with an interest in analyst behavior carrying out only 3 minutes per trade.

分析师报告和估计相对来说更受欢迎。在样本期间，71% 的投资者在某种程度上进行了此类研究，至少花费了几分钟。平均投资者每次交易在分析师报告上花费超过 12 分钟；如果进行任何此类研究，平均投资者大约花费 18 分钟。花在分析师报告上的时间非常偏斜，对分析师行为感兴趣的中位数投资者每次交易仅进行 3 分钟的研究。

C.4. Ownership

C.4. 所有权

Ownership-related research includes information about short-selling, institutional or mutual fund ownership, or insider ownership or trading. This groups together market participants that the average individual investor might (or should) suspect to have comparatively superior information. The median investor who ever consults this information does so for under a minute per month. This does not necessarily indicate disinterest, since as mentioned above it would not take long to read the percentage of institutional ownership or short interest outstanding; it is more telling that only about a quarter of individual investors actively seek out such information. Some type of ownership information is included on most major brokers’ snapshot pages, although there was none on the Yahoo Finance site snapshot at the time. Overall, this category is among the least important in terms of research time share and breadth of interest.

与所有权相关的研究包括有关卖空、机构或共同基金所有权，或内部人所有权或交易的信息。这将那些普通个人投资者可能（或应该）怀疑拥有相对优势信息的市场参与者归为一类。曾经查阅过此类信息的中位数投资者每月花费不到一分钟。这并不一定表示不感兴趣，因为如上所述，阅读机构持股比例或空头头寸百分比并不需要很长时间；更有说服力的是，只有大约四分之一的个人投资者积极寻找此类信息。大多数主要经纪商的快照页面都包含某种类型的所有权信息，尽管当时雅虎财经网站的快照上没有。总的来说，就研究时间份额和兴趣广度而言，这一类别是最不重要的类别之一。

C.5. Prices, price charts, and technical signals

C.5. 价格、价格图表和技术信号

By far the most prevalent types of pages the investors see are price charts and snapshots. Price chart refers to any sort of page that shows a chart of prices or reports price information in a tabular form. Snapshot refers to a combination page of price information, as well as a host of other categories such as analysts or earnings. The average investor spends over 39 minutes per trade on price chart pages and another 37 minutes on snapshot pages. Nearly all investors do some of this research: both of the categories see 93% of investors with nonzero time.

到目前为止，投资者看到的最普遍的页面类型是价格图表和快照。价格图表指的是任何显示价格图表或以表格形式报告价格信息的页面。快照指的是价格信息的组合页面，以及分析师或收益等许多其他类别。平均投资者每次交易在价格图表页面上花费超过 39 分钟，在快照页面上花费另外 37 分钟。几乎所有投资者都进行此类研究：这两个类别都有 93% 的投资者花费了非零时间。

Most snapshot pages contain a chart showing the intraday evolution of the nominal share price while most price chart pages contain a price chart showing the evolution of the nominal share price over the prior month. Only one broker and one finance website deviate from this standard, having one-year default lookbacks on the price chart pages. Theory gives no guidance as to how far back investors should be looking into past prices. Intuitively, price charts provide some notion of risk, which is useful since we have already seen that investors are not seeking out detailed risk statistics; but, for full context, a stock’s price should be plotted against a market index, and that is generally not included in the default. Perhaps another plausible intuition is that investors with shorter investment horizons would be looking at shorter lookbacks.

大多数快照页面包含一个显示名义股价日内演变的图表，而大多数价格图表页面包含一个显示过去一个月名义股价演变的价格图表。只有一个经纪商和一个金融网站偏离了这个标准，其价格图表页面的默认回顾期为一年。理论没有指导投资者应该回顾多长时间的过去价格。直观地说，价格图表提供了一些风险概念，这很有用，因为我们已经看到投资者并未寻求详细的风险统计数据；但是，为了获得完整的背景信息，股票的价格应该与市场指数进行对比绘制，而这通常不包含在默认设置中。也许另一个合理的直觉是，投资期限较短的投资者会关注较短的回顾期。

Figure 3 shows the frequency of various lookback horizons that individual investors use. Panel A includes charts that are shown with default lookback windows and given that snapshots are the predominant type of research, over 70% of observed charts show intraday data. The rest of chart consultations are mostly made up of default chart pages with one month lookback windows. The bottom panel shows the frequency distribution of lookback periods when investors do opt out of the website’s default. When doing so, investors tend to look further back to gather more historical information. They are most likely to opt for lookback windows between one month and one year, with only a few zooming into more recent data or zooming out beyond one year. This pattern seems consistent with the fact that most individual investors have an average holding period longer than one month, but the main takeaway is that most investors observe past prices only to the default lookback. It is intriguing to think about how the one-month default lookback could relate to the stylized facts of reversal within one month (Jegadeesh (1990)) and momentum at longer horizons (Jegadeesh and Titman (1993)).

图 3 显示了个人投资者使用的各种回溯期的频率。面板 A 包括使用默认回溯窗口显示的图表，并且鉴于快照是主要的研究类型，超过 70% 的观察图表显示日内数据。其余的图表查阅主要由具有一个月回溯窗口的默认图表页面组成。底部面板显示了当投资者确实选择退出网站默认设置时回溯期的频率分布。这样做时，投资者倾向于回顾更长的时间以收集更多的历史信息。他们最有可能选择一个月到一年之间的回溯窗口，只有少数人放大到更近期的数据或缩小到一年以上。这种模式似乎与大多数个人投资者的平均持有期超过一个月的事实一致，但主要的结论是，大多数投资者仅观察到默认回溯期的过去价格。思考一个月默认回溯期如何与一个月内反转（Jegadeesh (1990)）和更长时期动量（Jegadeesh and Titman (1993)）的典型化事实相关联是很有趣的。

A category closely associated with price charts is technical analysis. We find that 45% of investors look at charts that include additional elements of technical analysis, such as moving averages, oscillators, Bollinger bands, or simpler return-based indicators such as 52-week highs.

与价格图表密切相关的一个类别是技术分析。我们发现 45% 的投资者查看包含技术分析附加元素的图表，例如移动平均线、振荡指标、布林带或更简单的基于回报的指标（如 52 周高点）。

Overall time spent on technical analysis averages about 5 minutes per trade for the investors who carry out any such research.

对于进行任何此类研究的投资者来说，花在技术分析上的总时间平均每次交易约为 5 分钟。

C.6. Company website visits

C.6. 公司网站访问

A potentially important source of stock market information is knowledge about the company’s products. To capture this aspect of investor familiarity with the stock, we track time the investor-households spend on the URLs associated with tickers that they separately research on any of the brokerage or finance sites. This means that an investor who has a Bank of America account, shops on Amazon.com, or routinely uses search websites such as Google or Yahoo does not get “research credit” for these tickers unless there is some other evidence of investmentrelated interest into those tickers. Specifically, we limit the running total of company website research time to no more than twice the running total of all other research on the ticker. Investors in general have some exposure to the websites of companies they research. 290 investor-households visit the website of a company that they also research in other investmentrelevant respects. However, as a reflection of our 2x constraint, the overall time spent by investors at company websites is modest, averaging only 1 minute per trade.

关于公司产品知识可能是股票市场信息的一个潜在重要来源。为了捕捉投资者对股票熟悉程度的这一方面，我们跟踪投资者家庭在与他们在任何经纪或金融网站上单独研究的股票代码相关联的 URL 上花费的时间。这意味着，拥有美国银行账户、在 Amazon.com 购物或经常使用 Google 或 Yahoo 等搜索网站的投资者，不会因为这些股票代码而获得“研究积分”，除非有其他证据表明对这些股票代码存在与投资相关的兴趣。具体来说，我们将公司网站研究时间的累计总和限制为不超过该股票代码所有其他研究累计总和的两倍。总的来说，投资者对他们研究的公司的网站有所接触。290 个投资者家庭访问了他们在其他投资相关方面也进行研究的公司的网站。然而，作为我们 2 倍约束的反映，投资者在公司网站上花费的总时间是适度的，平均每次交易仅为 1 分钟。

C.7. Other and indeterminate

C.7. 其他和不确定

The data include 13 minutes per trade of “Other,” on average. These are links where we can observe or infer the type of research-related information, but it is obscure both theoretically and empirically and doesn’t warrant its own primary category. We also count about 10 minutes per trade of research involving links that are truly “indeterminate,” meaning we are not able to confidently ascribe any primary content category based on the information we observe.

数据平均包括每次交易 13 分钟的“其他”时间。这些是我们能够观察或推断出研究相关信息类型的链接，但无论在理论上还是经验上都比较模糊，不足以构成其自身的主要类别。我们还计算出每次交易大约 10 分钟的研究时间涉及真正“不确定”的链接，这意味着我们无法根据观察到的信息自信地归属任何主要内容类别。

D. Summing up

D. 总结

Table 7 provides new details about the interest of individual investors in different types of information. The first-order takeaways are that investors spend a disproportionate amount on price charts. Much of the remaining information comes from the contents of the snapshot page. Overall, our best estimate is that the mean (median) investor spends around 144.2 (minutes (36.4 minutes) per trade on stock research on any ticker, fund, option, or other investment instrument, and 29.2 minutes (5.7 minutes) on stock research on the particular ticker that he trades. Thus, the median trade by individual investors is associated with about six minutes of observable, tickerspecific research. As indicated in the previous section, much of this information is reviewed just prior to the trade.

表 7 提供了关于个人投资者对不同类型信息兴趣的新细节。首要的结论是，投资者在价格图表上花费了不成比例的时间。其余大部分信息来自快照页面的内容。总的来说，我们的最佳估计是，平均（中位数）投资者每次交易在任何股票代码、基金、期权或其他投资工具的股票研究上花费约 144.2 分钟（36.4 分钟），在他交易的特定股票代码的股票研究上花费 29.2 分钟（5.7 分钟）。因此，个人投资者的中位数交易与大约六分钟的可观察到的、特定股票代码的研究相关联。如前一节所述，这些信息大部分是在交易前不久查阅的。

VIII. Heterogeneity in research behavior

VIII. 研究行为的异质性

There is clearly heterogeneity in the depth and types of research that investors carry out. We now look for patterns via principal components on investor-level research intensity in total and in different content categories.

投资者进行的研究深度和类型显然存在异质性。我们现在通过对投资者层面的总研究强度和不同内容类别的研究强度进行主成分分析来寻找模式。

For this analysis we restrict the data to research matched to tickers, and we create indicator variables for any investor-category that sees more than two minutes of research per month. This leads us to exclude the “risk statistics” category entirely as it is not of enough express interest to include in the estimation. The need to restrict to an investor’s ticker-matched research is to be able to augment these indicator variables with a measure that captures the nature of the stocks that the particular investor focuses on. To be specific, we construct a speculative stock focus variable based the investor’s propensity to research stocks across the characteristics included in Table 6; it is more positive for an investor who tilts her trading interest toward volatile, small, younger, lower nominal price “speculative” stocks and more negative for one with more interest in larger, older, dividend-paying “bond-like” stocks. The inclusion of this variable into the principal components analysis is a simple way to correlate how the information of interest depends on the nature of the stock of interest.

对于此分析，我们将数据限制为与股票代码匹配的研究，并为每月研究时间超过两分钟的任何投资者类别创建指标变量。这导致我们完全排除了“风险统计”类别，因为它没有足够的明确兴趣来包含在估计中。需要将研究限制在与投资者股票代码匹配的范围内，是为了能够用一个衡量特定投资者关注的股票性质的指标来增强这些指标变量。具体来说，我们根据投资者研究表 6 中包含的特征的股票的倾向，构建了一个投机性股票关注变量；对于倾向于将交易兴趣转向波动性大、规模小、成立时间短、名义价格低的“投机性”股票的投资者，该变量更偏正值，而对于更关注规模大、成立时间长、支付股息的“类债券”股票的投资者，该变量更偏负值。将此变量纳入主成分分析是一种简单的方法，可以将感兴趣的信息与感兴趣的股票性质相关联。

The first principal component reported in Table 8 is a level effect. Some investors do more research of any and all categories than other investors. This is not driven by imputation because the specification includes mixed-content categories such as snapshots, message boards, and news of indeterminate nature. In light of the skewness in overall research time in the summary statistics, it is expected that this is a strong differentiator of research behavior across individual investors, and it explains about 27% of the variation. In these data, the depth of research is independent of a focus on speculative stocks.

表 8 中报告的第一个主成分是水平效应。一些投资者比其他投资者在任何和所有类别上都进行更多的研究。这并非由归因驱动，因为规范包含了混合内容类别，例如快照、留言板和性质不确定的新闻。鉴于汇总统计数据中总研究时间的偏度，预计这是区分个体投资者研究行为的一个强有力因素，它解释了大约 27% 的变异。在这些数据中，研究深度与是否关注投机性股票无关。

The second principal component, which explains about 9% of the variation, is intuitive and interesting. It contrasts investors who focus on earnings, dividends, and other slow-moving fundamentals versus those who focus on news, message boards, brief summary statistics, and price charts. The latter investors also concentrate their research in speculative stocks. Speculative stocks are less likely to pay dividends, so there is less research to be done of that particular sort for such stocks. Speculative stocks are also more driven by rapidly decaying news and sentiment from message boards, and it is further intuitive that price charts are more likely to be of interest to investors who focus on speculative stocks.

第二个主成分解释了大约 9% 的变异，它既直观又有趣。它对比了关注收益、股息和其他缓慢变化的基本面的投资者与关注新闻、留言板、简要汇总统计数据和价格图表的投资者。后一类投资者也将其研究集中在投机性股票上。投机性股票支付股息的可能性较小，因此对此类股票进行的特定类型研究较少。投机性股票也更多地受到快速衰减的新闻和留言板情绪的驱动，并且更直观的是，关注投机性股票的投资者更可能对价格图表感兴趣。

Beyond these two components, there is a large amount of unexplained heterogeneity in research behavior. This helps to explain the survey evidence in Giglio, Maggiori, Stroebel, and Utkus (2021) that wealthy retail investors’ “beliefs are mostly characterized by large and persistent individual heterogeneity” (p. 1481). Table 8 is a first step, but there is much more to be done to connect the search for information, the formation of beliefs, the stocks of interest, and the style of trading.

除了这两个成分之外，研究行为中还存在大量无法解释的异质性。这有助于解释 Giglio、Maggiori、Stroebel 和 Utkus (2021) 的调查证据，即富裕散户投资者的“信念主要表现为巨大且持续的个体异质性”（第 1481 页）。表 8 是第一步，但在连接信息搜索、信念形成、感兴趣的股票和交易风格方面还有很多工作要做。

IX. Conclusions

IX. 结论

This paper documents a number of facts about the research interests of individual investors. These facts help flesh out the nature of beliefs that individual investor trades could plausibly inject into market prices and what information guides individual investor portfolios. The main findings include facts about how much stock-specific research is behind the median investor’s trade, which stocks are most researched, which types of information attract most attention, and heterogeneity in research approaches. These facts inform theory and empirical research on individual investor behavior.

本文记录了有关个人投资者研究兴趣的若干事实。这些事实有助于充实个人投资者交易可能合理地注入市场价格的信念性质，以及指导个人投资者投资组合的信息。主要发现包括关于中位数投资者交易背后有多少特定股票研究、哪些股票被研究最多、哪些类型的信息最受关注以及研究方法异质性的事实。这些事实为关于个人投资者行为的理论和实证研究提供了信息。

There are natural follow-up questions that can be answered with detailed browser data. Perhaps the most obvious is whether individual investors’ research behavior is associated with their performance. The contemporary trading environment does give some cause for skepticism. A contemporaneous and certainly a modern quantitative fund manager spends years and millions of dollars refining algorithms—and then execute trades, given fresh data, within minutes or seconds. And a traditional active manager might employ a number of industry specialists and spend days contemplating a large trade. It is rather optimistic to think that the average individual investor gains an edge based on their own occasional minutes of research, especially if focused on a stock like Apple that is simultaneously being watched by hundreds of thousands if not millions of other individual investors. When it comes to the typical individual investor, it true that “more research is needed”? If so, of what type or style? Gargano and Rossi take some first steps here using their brokerage’s clickstream data.

有一些自然的后续问题可以通过详细的浏览器数据来回答。也许最明显的是个人投资者的研究行为是否与其业绩相关。当代的交易环境确实让人产生一些怀疑。一个同时代的，当然也是一个现代的量化基金经理，花费数年和数百万美元来完善算法——然后在获得新数据后，在几分钟或几秒钟内执行交易。而一个传统的积极型经理可能会雇佣一些行业专家，并花费数天时间来考虑一笔大额交易。认为普通个人投资者凭借自己偶尔几分钟的研究就能获得优势，是相当乐观的，特别是如果他们关注的是像苹果这样的股票，同时有数十万甚至数百万其他个人投资者在关注。对于典型的个人投资者来说，“需要更多的研究”是真的吗？如果是，需要哪种类型或风格的研究？Gargano 和 Rossi 利用他们经纪公司的点击流数据在这方面迈出了一些初步的步伐。

A question of at least equal importance is how an individual investor gets from what she observes to what she trades. If she sees a particular price pattern in a chart, or certain statistics in a table, how does that affect the trading decision? Since information feeds so directly into beliefs and trading, browser data can shed new light why trades are made and how the portfolios that influence investor wealth are formed.

至少同等重要的一个问题是，个人投资者如何从观察到的信息转化为交易行为。如果她在图表中看到特定的价格模式，或在表格中看到某些统计数据，这如何影响交易决策？由于信息如此直接地影响信念和交易，浏览器数据可以为交易发生的原因以及影响投资者财富的投资组合是如何形成的提供新的见解。