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Limited Attention and the Earnings Announcement Returns of Past Stock Market Winners

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... Given the critical role of the previous returns of stocks, our main research question is: do the investors pay more attention to CEO appointment announcements in the context of investment style and major past price movements. For example, stocks with sharp run-ups, as argued by Aboody et al. (2010), tend to attract the attention of individual investors before earning announcements, and correspondingly with returns for the postannouncement period. To the best of our knowledge there has been no discussion about major price movements as a central aspect for CEO appointment announcement in the investor attention literature. ...
... attention 6 , and sometimes neglect relevant public information signals, which leads to stock mispricing (Hirshleifer & Teoh, 2003). The majority of studies testing the inattention hypothesis in the context of relatively small corporate events concentrate on earnings announcements (DellaVigna & Pollet, 2009;Hou et al., 2009;Aboody et al., 2010;Boehmer & Wu, 2013;DeHaan et al., 2015;Ben-Rephael et al., 2017;Lawrence et al., 2018;Chapman, 2018). Louis & Sun (2010) find that inattention drives a muted market reaction to Friday merger announcements 7 . ...
... Consequently, we should expect less market attention in the case of 6 Nowadays investors need to constantly collect up-to-date information in order to keep up with changes (Jiang, 2016). Limited attention is a core concept of behavioural finance, suggesting that limitations on investor time and energy cause a lack of market information understanding in real time (Aboody et al., 2010). De Souza et al. (2018) point out that attention to a particular factor results from the natural human incapacity to process a large amount of information. ...
... While we highlight the importance of extrapolative beliefs in explaining EA return patterns, other research highlights the importance of investor attention. For example, an important related paper is by Aboody et al. (2010), who study pre-EA and post-EA return patterns conditional on trailing 12-month returns. Consistent with an investor attention hypothesis, firms with high trailing 12-month returns have positive pre-EA returns and negative post-EA returns. ...
... Moreover, whereas individual investors are more likely to purchase stocks in the top decile of both our extrapolated return measure and a measure of trailing returns before EAs, investors are also more likely to sell stocks in the top decile of trailing returns. This latter finding is consistent with the investor attention hypothesis of Aboody et al. (2010) but inconsistent with extrapolation. Indeed, investors are even less likely to sell stocks in the top decile of our extrapolated return measure before the EA, which confirms a unique prediction of extrapolation. ...
... Indeed, investors are even less likely to sell stocks in the top decile of our extrapolated return measure before the EA, which confirms a unique prediction of extrapolation. In this sense, relative to Aboody et al. (2010), we offer distinct evidence of investors' extrapolative beliefs based on individual investor trading and the cross-section of returns around EAs. Our findings are complementary in that extrapolative expectations are based on investors paying attention to returns during recent EAs. ...
Article
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We propose that extrapolative beliefs about earnings announcement (EA) returns may contribute to the understanding of EA return patterns. We construct a theoretically motivated measure of extrapolative investors’ expectations based on a stock’s recent history of EA returns. We then show that this measure explains cross-sectional variation in stock returns and investor behavior around EAs. Stocks expected to have high EA returns, according to our measure, experience predictable increases in prices before EAs and predictable decreases afterward. These patterns are economically significant: investors that buy (sell) a portfolio that is long firms with high recent EA returns and short firms with low recent EA returns in the pre-EA (post-EA) period earn daily five-factor abnormal returns of 16.1 bps (18.3 bps). Using individual investor trades data and a measure of institutional trading, we find that individual and institutional investors are more likely to purchase stocks with high recent EA returns, consistent with at least a subset of investors forming extrapolative beliefs about EA returns.
... Textual analysis algorithms can detect sentiment in corporate announcements, mergers and acquisitions, annual reports, and prospectuses. Many scholars have confirmed a relationship between investor attention and financial markets (Aboody et al., 2010;Li et al., 2019;Loh, 2010). Empirical evidence demonstrates that public climate concern can be regarded as investor sentiment and thus affects the returns on carbon-intensive stocks (Choi et al., 2020;Ye & Xue, 2021) and the cost of insuring carbon tail risk (Ilhan et al., 2021;. ...
... First, Google is the world's largest information search engine to provide valuable information for Internet users worldwide. Second, the problems that indirect agents may encounter (e.g., returns, turnover, and news) can be avoided through the Google search engine (Aboody et al., 2010;Loh, 2010;Yuan, 2015). Finally, Google's search intensity provides a good measure for obtaining publicly available information from various sources. ...
Article
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This study explores the predictive effect of climate change attention on carbon futures returns. Using climate‐related Google Trends and news, we construct five dimensions of the public climate attention index and media climate attention index. After feature selection, we incorporate the optimized combination with lagged order into the machine learning model to predict EU Emission Allowance futures returns. Our empirical results show that the forecasting models with climate attention outperform the corresponding benchmark models, indicating that climate attention does provide predictive information for carbon futures returns. In addition, we carry out trading simulations to investigate the economic performance of the forecast results. It turns out that the market strategies based on the prediction models with climate attention can deliver more benefits than the counterpart market strategies. More specifically, the cumulative returns reach 140% during the out‐of‐sample period, much higher than 79% of the cumulative returns of the buy‐and‐hold strategy.
... Barber and Odean used the level of current trading volume relative to previous trading volume, the level of current return relative to previous return, and the amount of media coverage on the stock to measure investor attention [9]. Aboody, Lehavy and Trueman measure investor attention through the cumulative return of the past year, and find that the higher the cumulative return of the past, the higher the investor attention [1]. Li and Yu measure investor attention by how close the current price of the Dow Jones Index is to the highest price in the past 52 weeks and the highest price in the past year [37]. ...
... Finally, we chose five indicators of investor attention. We refer to the extreme returns [9], abnormal trading volume [9], past returns [1], proximity to the 20-day high [37] and Baidu Search Index [27,78,85]. Baidu search is currently the largest search platform in China, and the Baidu Search Index represents the frequency of searches for a term over a period. ...
... Similarly, extreme returns are calculated as the ratio of returns at the end of the month to average past 12-months returns for each stock (Barber & Odean, 2008). Past returns are computed as previous 12-months cumulative returns for each stock (Aboody et al., 2010). ...
... Extreme returns are calculated as the ratio of returns at the end of the month to average past 12-months returns for each stock (Barber & Odean, 2008). Past returns are computed as previous 12-months cumulative returns for each stock (Aboody, Lehavy & Trueman, 2010). Piotroski (2000) F-score is the composite indicator of a firm's fundamental strength consisting of nine fundamental measures. ...
Article
This paper investigates the impact of investor attention on the dynamics of the value premium. We find superior return differences to value-growth strategy conditioned as low degree of investor attention. In contrast, return differences to the value-growth strategy conditioned as high investor attention are indifferent from zero. We show that return differences to low degree of investor attention across value and growth firms are attributed to mispricing explanation using common risk factors, mispricing factors, sentiment analysis, multivariate analysis, and market expectation errors approach. The findings suggest that investor attention contributes to generating superior return differences to standard value-growth strategy. Our finding concludes that long-short investment strategy in value stocks and growth stocks conditioned as low investor attention generate superior value premium.
... Legged returns (LRETs) include LRET [t,t] , LRET [t−5,t−1] , and LRET [t−11,t−6] , where for example, LRET [t−5,t−1] is the lagged cumulative stock return over the past five months. It is important to control for lagged returns in our analysis since Aboody et al. (2010) show that stocks with the highest prior 12-month returns experience significantly negative market-adjusted returns immediately following earnings announcements. Moreover, the results in Table 2 show that change of reporting lag has a significant inverse relation with past stock performance. ...
... Coefficients of SUE interactions with FCs and industry fixed effect are not reported for brevity. Consistent with Aboody et al. (2010), stock returns over different horizons generally have a negative relation with lagged returns. The coefficient of reporting lag (RLAG) is also negative and highly significant. ...
Article
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Holding earnings surprises constant, investors react negatively to delayed earnings announcements. One standard deviation of delay (5 days) corresponds to about 21 bps negative abnormal returns over a two-day announcement window. We show that the results are robust after further controlling for various firm characteristics, earnings characteristics, and the industry effect. We examine alternative explanations, including the earnings manipulation hypothesis proposed in the literature, which suggests that delayed earnings announcements are susceptible to manipulation and are thus discounted by investors. We find no evidence supporting the earnings manipulation hypothesis. Instead, our results are consistent with the effect of concurrent information disclosure. We show that there is a cluster of bad news for late announcements. As investors react to not only a firm’s own announcements but also concurrent earnings announcements, the negative information of concurrent announcements contributes significantly to lower stock returns. In addition, information update by the management and analysts also has a significant effect on market reactions to delayed announcements. In particular, information update by analysts may help the market to incorporate future earnings information more efficiently into stock prices. We show direct evidence that negative market reactions to delayed earnings announcements contain information of future earnings.
... Extant studies argue that attention is a scarce resource (Barber & Odean, 2008;Hirshleifer & Teoh, 2003). Consequently, it is difficult for investors to fully pay attention to and process all available information, and their ability to interpret market information is relatively low, which makes it difficult for stock prices to fully reflect relevant information such as stock fundamentals (Aboody et al., 2010). ...
Article
Using listed firms from 16 heavy pollution industries in China, we show that investor attention can significantly improve the quality of corporate environmental information disclosure. By examining the heterogeneity effects of investor attention under different firm characteristics, locations, and external supervision, we reveal that this improvement effect is more pronounced for smaller firms, state-owned enterprises, firms with weaker corporate governance, and firms located in China's western regions (regions with poor economic development). We further find that public's attention to environmental issues promotes the investor attention level on firms in heavy pollution industries, thus improving firms' environmental information disclosure quality. Overall, our results emphasize the role of external investor attention on corporate environmental issues.
... Dellavigna and Pollet (2009) studied the impact of earnings announcement on investors' attention on Friday and other weekdays, and found that investors' attention-driven trading occurred on Friday, and the earnings announcement on Friday had low market response and delayed response. Aboody et al. (2010) found that in the five trading days before the release of earnings announcement, companies with good earnings performance in the past 12 months had higher excess return rate, but the situation would reverse in the five trading days after the announcement. ...
... Investor attention has been estimated by differing measures such as abnormal trading volumes (Barber & Odean, 2008;Peng et al., 2007), extreme returns (Da et al., 2011), past returns (Aboody et al., 2010), nearness to the 30-day high (Li & Yu, 2012), abnormal turnover (Cziraki et al., 2019;Da et al., 2011;Ying et al., 2015), Internet search volumes (Bijl et al., 2016;Da et al., 2011Da et al., , 2014Ding & Hou, 2015;Dzielinski, 2012), and media coverage (Da et al., 2011;Fang & Peress, 2009;Ying et al., 2015). ...
Article
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It is widely recognized that limited attention capacity of individual investors affects stock performance. We construct five aggregate investor attention indices for each stock by extracting common information components related to stock returns from various attention proxies using equal-weighted (EW), principal component analysis (PCA), partial least squares (PLS), gradient boosting decision tree (GBDT), and random forest (RF) methods. In a sample of all Shanghai Stock Exchange 50 constituent stocks, we identify two attention indices constructed by machine learning algorithms, RF and GBDT, that provide economically meaningful enhanced prediction of stock returns in both in-sample and out-of-sample periods. Moreover, these indices are negatively related to return volatility. Results suggest the utility of using machine-learning to form proxies of investor attention and reveal the excellent forecasting power of these proxies in asset pricing.
... muli") events have also been adopted as traditional indirect proxies to measure investor attention. In this study, we start with the selected four indirect proxies including extreme return (Koester et al., 2006;Seasholes and Wu 2007;Barber and Odean 2008;Yuan 2015), abnormal trading volume (Gervais et al., 2001;Barber and Odean, 2008), past return (Aboody et. al., 2010;Vozlyublennaia, 2014) and nearness to the 30-day high (Li and Yu, 2012). ...
Article
Investor attention is a scarce cognitive resource which affects investment decisions, and recent studies suggest that investor attention also have impacts on asset prices. Although Bitcoin is found to be one of the most unpredictable cryptocurrencies with excessive volatilities, researchers are still looking for determinants of Bitcoin prices. In this study, we firstly adopt the Long Short-Term Memory Networks (LSTM) approach to evaluate the effect of investor attention on Bitcoin returns by constructing an aggregate investor attention proxy. We combine both direct and indirect proxies for investor attention, in addition to the Bitcoin trading variables as the LSTM inputs. Our empirical results suggest that the including of attention variables could effectively improve the LSTM's prediction accuracy of Bitcoin returns, whereas direct proxies, i.e., Google Trends and Tweets, contain more valuable information to further improve the LSTM's forecasting capacity.
... Wealthier individual investors prefer stocks with growth potential, a history of good returns, high liquidity, and high volatility, while the investors with relatively little wealth have the opposite preference. Aboody et al. (2010) took excess return as a proxy variable for investors' attention. ...
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The increasing abundance of information leads to the scarcity of investor attention, which has become an important factor affecting the financial market. Search engines play the role of information retrieval and record the search behavior of investors, which is a direct and accurate measure of investor attention. This paper investigates the relationship between investor attention and China's stock market. Considering the relationship with stock returns as the mainline, we take the Baidu index as a substitute variable of investor attention to deeply study the correlation and the time-varying nature between investor attention and China's stock returns. To this end, we used quantile regression to examine the relationship over the period 2006–2021 to capture its evolution during calm and turbulent times. We thus investigated the effect of investor attention on the mean and other quantiles. Our findings show that the relationship between investor attention and China's stock returns exhibits time-variation as investor attention significantly impacts the dynamics of China's stock returns, but its sign and effect vary per quantile: investor attention is negatively correlated with stock returns at low quantiles, but it turns positive at high quantiles. In addition, to test the model's robustness, variable replacement method and model replacement method are used to conduct significance tests, respectively. The results are equally significant.
... Meanwhile, Lou (2014) documents a positive relation between a firm's advertising spending and its stock returns in the year of the spending due to increased net buying but stock price drops in subsequent years. Other investor attention proxies include past returns (Aboody et al., 2010) and price limits (Seasholes and Wu, 2007). ...
Article
The thesis encompasses three essays in empirical asset pricing and mainly concentrates on Chinese stock and futures markets. Traditional asset pricing models assume the arrival of information is immediately processed and incorporated into asset prices. In reality, there are numerous constraints that may distort the assumption, for instance the cross-border transmission and the cognitive limitation of humans. The three essays specifically explore the impact of these issues on asset prices. The first essay examines the instantaneous response of eight Chinese commodity futures to 19 different types of scheduled US macroeconomic news announcements after the introduction of night trading in China. Using intraday data from 2013 to 2016, we provide robust evidence that the surprise components of a number of news announcements exhibit a significant effect on returns, trading volume, and volatility of a majority of Chinese futures contracts, with gold and silver futures being the most sensitive. Moreover, we observe an asymmetric effect between positive and negative surprise components. A further examination of the responses to the US macroeconomic news announcements using US gold and silver futures over the same sample period provides qualitatively similar results with larger magnitudes. This evidence suggests a possible channel through which the impact of macroeconomic news announcements transmits from the US to the Chinese commodity futures market. The second essay develops a simple measure of investor attention by aggregating the days that a stock hits the upper price limit on a monthly basis. This attention proxy describes investor trading behavior and predicts the cross section of stock returns. Using data from the Chinese equity market from 2002 to 2017, we provide extensive evidence that the investor attention captured by the measure negatively predicts cross-sectional stock returns in the following months, and the long-short strategy based on this attention measure produces significant economic value. We argue that attention-motivated trading by individual investors is the main cause behind the return reversal. The third essay constructs novel market-level attention proxies using the abnormal limit-hits on a monthly basis in Chinese stock market, and examines their explanatory power on market excess return from 2005 to 2019. The abnormal limit-hits are calculated as the change in equal-weighted number of limit-hits that are aggregated from firm-specific ones. Notably, we find the attention proxies, constructed using upper limit-hits, have substantially negative predictability in subsequent two months for both in-sample and out-of-sample analyses. The forecasting power is also comparable to economic variables and perform better than other attention and sentiment variables. Additionally, the mean-variance investors can gain sizable economic gains in asset allocation based on the forecast of our predictors. Finally, we document the attention-motivated trading increases the price pressure temporarily but revert to fundamental subsequently and further lead to lower market returns.
... Other things being equal, a onepercent increase in LnNumber_index causes an increase of 0.202% in raw return. Individual investors only analyze the information that draws their attention, and then adjust their investment, resulting in a temporary increase in fund performance (Aboody et al, 2010). ...
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This study examines the impact of investor attention, measured by internet search volumes, on mutual fund flow and performance. In a sample from China between 2011 and 2017, we find that investor attention, proxied by targeted search in Baidu, positively contributes to contemporaneous mutual fund flow and performance, but fund performance will reverse in the following six to twelve months. Moreover, investor attention bolsters the positive relationship between fund flow and performance. The findings support wider application of attention data in decision making.
... Kahneman [35] proposes the theory of limited attention, who regards the attention of market participants as limited psychological resources that people use to perform tasks [37], arguing that the attention and information processing ability of decision-makers are limited and the information processing efficiency is affected by many factors. In the era of big data, facing huge amount of information with limited information processing ability, market participants can hardly pay attention to and understand all the information due to limited time and energy [38], which leads to incomplete understanding of information and deviation of economic behaviour. Limited attention has a significant impact on market participants' decision-making [39]. ...
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