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The “Dartboard” Column: Second-Hand Information and Price Pressure
Abstract
This study analyzes the effect of second-hand information on the behavior of security prices and volume using analysts' recommendations published in the monthly “Dartboard” column of the Wall Street Journal. For the two days following the publication of the recommendations, average positive abnormal returns of 4 percent—nearly twice the level of abnormal returns documented in previous research on analyst recommendations—and average volume double normal volume levels on the two days following publication of the recommendations are documented. The positive abnormal return on announcement is partially reversed within 25 trading days. The authors conclude that the positive abnormal return on announcement of the recommendations is a result of naive buying pressure as well as the information content of the analysts' recommendations.
... We conduct a standard event study analysis, following Brown and Warner (1980); Brown and Warner (1985); Boehmer et al., (1991), and Corrado (2011). We designate the event date as day 0 and use a trading day window of [-125, -26] for estimation according to Barber and Loeffler (1993), Zhang et al., (2016), and Shen and Zhang (2021). We choose [-5, 30] as the event window to investigate the market reaction to the climate risk reports. ...
... However, post the short term, there will be a reversal, with the respective stock price returning to its basic value. Our research is consistent with Barber and Loeffler (1993) who find that positive abnormal returns tend to partially reverse within 25 trading days. 2 Our results do not support Albert and Smaby (1996), who concludd that the positive abnormal returns of announcements support an information diffusion hypothesis (IDH). ...
... These include the firm being state-owned, negatively perceived by investors (negative investor sentiment), and being the focus of heightened investor sentiment. Additionally, as we find that positive abnormal return reverse in the short term, we evidence support for the price pressure and information diffusion hypotheses (Barber and Loeffler, 1993;Albert and Smaby, 1996;Zhang et al., 2016;Chu et al., 2023). Results will be of wide interest to scholars interested in transparency, firm reactions, climate finance, and investor sentiment. ...
... Consistent with this narrative, the stocks with the biggest increase in users on the popular Robinhood app tend to earn poor returns (Barber, Huang, Odean, and Schwarz (2022)). Similarly, several studies document price reversals following attention-grabbing events: Jim Cramer's stock recommendations (Keasler and McNeil (2010), Bolster, Trahan, and Venkateswaran (2012), Engelberg, Sasseville, and Williams (2012)), the WSJ Dartboard Column (Barber and Loeffler (1993), Liang (1999)), Google stock searches (Da, Engelberg, and Gao (2011), Da, Hua, Hung, and Peng (2022)), and repeat news stories (Tetlock (2011)). Barber, Lin, and Odean (2021) discuss the role of media in directing investor attention. ...
... We show that the concentration of trading in the stocks that subsequently underperform means that on average retail investors lose from trade. The concentration of retail buying in underperforming stocks is consistent with the literature that documents investors earn predictably poor returns following attention-grabbing events (Barber and Loeffler (1993), Liang (1999), Keasler and McNeil (2010), Da, Engelberg, and Gao (2011), Tetlock (2011), Bolster, Trahan, and Venkateswaran (2012, Engelberg, Sasseville, and Williams (2012), Barber, Huang, Odean, and Schwarz (2022), and Da, Hua, Hung, and Peng (2022)). Prior studies have hypothesized that retail order imbalance positively predicts shortterm returns because retail investors are informed or profit from providing liquidity. ...
... For example, scholars have analyzed Cramer's Mad Money(Keasler and McNeil (2010),Bolster, Trahan, and Venkateswaran (2012), and Engelberg, Sasseville, and Williams (2012)), the WSJ Dartboard Column(Barber and Loeffler (1993),Liang (1999)), Google stock searches(Da, Engelberg, and Gao (2011),Da, Hua, Hung, and Peng (2022)), repeat news stories(Tetlock (2011)), and the trading of Robinhood investors (Barber, Huang, Odean, and Schwartz(2021)).Barber, Lin, and Odean 31 https://doi.org/10.1017/S0022109023000601 Published online by Cambridge University Press ...
Retail order imbalance positively predicts returns, but on average retail investor trades lose money. Why? Order imbalance tests equal-weighted stocks, but retail purchases concentrate on attention-grabbing stocks that subsequently underperform. Long–short strategies based on extreme quintiles of retail order imbalance earn dismal annualized returns of −14.8% among stocks with heavy retail trading but earn 6.6% among other stocks. Our results reconcile the literatures on the performance of retail investors, the predictive content of retail order imbalance, and attention-induced trading and returns. Smaller retail trades concentrate more on attention-grabbing stocks and perform worse.
... Existing literature has extensively examined the impacts of traditional media information and social media information on financial markets. Many studies state that traditional media information has a significant effect on the firm's performance or valuation, i.e., to increase stock returns and firm values, predict future earnings, and decrease the cost of capital [1,[4][5][6]. However, other research papers find only a weak effect of traditional media coverage [2,7]. ...
... Previous literature [3][4][5][6]13] states that news and sentiment have a significant effect on firms' performance (trading volume and return) because they contain inside information and market (investor) expectations that help affect firms' performance (trading volume and return). Compared with traditional financial markets (stock and bond markets), innovative and less regulated markets (such as the P2P lending market) present a more serious information asymmetry problem. ...
... In this research, we examine the effect of media and social media sentiments on the default probability and cost of capital of China's peer-to-peer lending platforms. The results show the asymmetry effect between improving and deteriorating sentiment on default probability and cost of capital after using the PSM method 4 . The results prove that only improved media and social media sentiment could help reduce the default probability and cost of capital. ...
This paper uses supervised machine learning (sentiment analysis) to analyze the sentiments of social media information in the P2P lending market. After segmentation, filtering, feature word extraction, and model training of the text information captured by Python, the sentiments of media and social media information were calculated to examine the effect of media and social media sentiments on default probability and cost of capital of peer-to-peer (P2P) lending platforms in China (2015–2019). We find that only positive changes in media and social media sentiment have significantly negative effects on the platform’s default probability and cost of capital, while negative changes in sentiment do not have any effects. We conclude the existence of an asymmetric effect of media and social media sentiments in the Chinese peer-to-peer lending market.
... Typically, attention-grabbing events can be classified as abnormal trading volume (Gervais et al. 2001;Barber and Odean 2008), extreme return (Koster et al. 2006;Seasholes and Wu 2007;Barber and Odean 2008;Yuan 2015; Gödker and Lukas 2019), advertising (Grullon et al. 2004;Lou 2014;Madson and Niessner 2019;Focke et al. 2020), news and headlines (i.e. media coverage) (Barber and Loeffler 1993;Liang 1999;Barber and Odean 2008;Engelberg and Parsons 2011;Yuan 2015), etc. We will review these events separately in the following paragraphs. ...
... Although these recommendations rarely convey fresh information, they can sometimes play a role in bringing stocks into public sights. For example, Barber and Loeffler (1993) find that stocks recommended from 1988 to 2000 in "dartboard column" of the Wall Street Journal garnered attention and experienced a double in trading volume on the two days following the publishment. Liang (1999) also suggests a sharp increase in trading volume by examining the stocks which dartboard expert recommended from 1990 to 1994. ...
... There is ample evidence showing that investor attention plays a crucial role in affecting investor behaviour and therefore has accordingly impacts on financial market dynamics. As we will review in this section, the gathering of investor attention would generate more trading volume (Mayer 2020), realized volatility (Zhang et al. 2021a), higher stock price (with reversal) (eg., Barber and Loeffler 1993;Seasholes and Wu 2007;Chen 2017; Gödker and Lukas 2019; Mayer 2020; Zhang et al. 2021b etc.), net buying behaviour (Odean 1999;Odean 2000, 2008), etc. ...
Investor attention refers to the limited attention that investors can devote to the information which might affect their investment decisions. Given attention is a scarce cognitive resource and individuals have limited capacity to process information, existing studies have shown that the allocation of attention, which is caused by the attention-grabbing events, has impacts on financial market dynamics accordingly. These attention-grabbing (or “stimuli”) events have also been adopted as indirect proxies to measure investor attention in early studies. This study contributes to the literature on investor attention in explaining why and how investor attention matters, especially to investor behaviours and financial market dynamics. It also reviews the measurements of investor attention used in current literature as well as the corresponding limitations. Based on a systematic review of the existing literature, this paper envisages potential future directions for investor attention with a more comprehensive understanding of current studies.
... positive reaction reverses after the issuance, consistent with the price pressure hypothesis that posits that the initial response is based on temporary buying pressure rather than a re-evaluation of firm value (e. g., Barber & Loeffler, 1993). 12 While price pressure offers an alternative explanation for the observed short-term returns, it does not undermine the signaling argument. ...
... Panels C hand information provided to the mass investing public community. Many studies find analysts' recommendation result in short positive price pressure that reverse afterwards (Barber & Loeffler, 1993;Davies & Canes, 1978;Liu et al., 1990). ...
... Beneish (1991), using a large sample of Buy and Sell recommendations reported in the WSJ, found a similar effect on abnormal returns. Barber and Loeffler (1993), also studied recommendations from the WSJ and concluded that abnormal returns accrued on two days succeeding the recommendation with an increase in trading volume. The underlying theme connecting several studies in this period was the temporary effect on abnormal returns which appeared on the date of their publication, or prior to it. ...
... Many studies in the international context find evidence of excess returns especially for Buy recommendations on the event day and a few days prior. In addition, most of these studies report a significant effect on stock prices following the recommendation (Liu, et al., 1990;Beneish, 1991;Barber and Loeffler, 1993). However, a few studies find a post-recommendation drift in stock prices and abnormal returns in the long term, contributing to the debate concerning market efficiency (Womack, 1996). ...
Purpose
This study aims to evaluate the short-term impact of brokerage analysts’ recommendations on abnormal returns using a sample selected from the S&P BSE 100 in the Indian context. The efficient market hypothesis, specifically, its semi-strong form, is tested for “Buy” stock recommendations published in the electronic version of Business Standard. The crucial issue is, are there any abnormal returns that can be earned following a recommendation? If so, how quickly do prices incorporate the information value of these recommendations? It tests the impact of analyst recommendations on average abnormal returns (AARs) and standardized abnormal returns (SRs) to determine their statistical significance.
Design/methodology/approach
Using a sample of stock recommendations published in the e-version of Business Standard, the event study methodology is used to determine whether AARs and SRs are significantly different from zero for the duration of the event window by using several significance tests.
Findings
The findings indicate a marginal opportunity for profit in the short term, restricted to the event day. However, the effect does not persist, i.e. the market is efficient in its semi-strong form implying that investors cannot consistently earn abnormal returns by following analysts’ recommendations. Post the event date, the market reaction to analyst recommendations becomes positive, however, insignificant until the ninth day after the recommendation providing support to the underreaction hypothesis given by Shliefer (2000) and post-recommendation price drift documented by Womack (1996). The study contributes by using different statistical tests to determine the significance of returns.
Practical implications
There are important implications for traders, investors and portfolio managers. The speed with which market prices incorporate publicly available information is useful in formulating trading strategies. However, stock characteristics such as market capitalization, volatility and level of analyst coverage need to be incorporated while making investment decisions.
Originality/value
The study contributes by using different statistical tests to determine the significance of returns.
... Existing literature has documented two hypotheses to explain the market reaction to recommendations and media coverage. Focusing on the analysts' recommendations published in the "Dartboard" column of the Wall Street Journal, Barber and Loeffler (1993) show that the positive abnormal return on the announcement is partially reversed within 25 trading days and then conclude that positive abnormal return on the announcement of the recommendations is a result of both buying pressure and information diffusion. With the post-event estimation methodology, Albert and Smaby (1996) further show that the positive abnormal return on announcement displays no significant reversal and conclude that the abnormal return is driven by information diffusion. ...
... We estimate the CAPM model using individual stock returns in the SAH stocks group and GO stocks group as the dependent variable and the CSI 300 market index return as the independent variable. In line with Barber and Loeffler (1993) and Zhang et al. (2016b), we choose the trading days of [ − 125, − 26] as the estimation period. Specially, we estimate the following regression equation: ...
This paper investigates the distinct market reactions to the COVID-19 outbreak by focusing on two groups of stocks in the Chinese stock market, i.e., the stay-at-home (SAH) stocks, and the go-outsides (GO) stocks. The empirical results mainly reveal that: (1) for the GO stocks, there exists a significantly negative return on the event date and the cumulative abnormal return reveals reversal pattern; (2) for the SAH stocks, no significantly negative return is observed on the event date and the cumulative abnormal return continues to increase; and (3) generally speaking, the reaction of the GO stocks supports the price pressure hypothesis, while the reaction of the SAH stocks supports the information diffusion hypothesis. Our results suggest that investors in the Chinese stock market could moderately interpret the good news but underestimate the bad news.
... Researchers collect stock recommendations released in several media formats. These include the ranking in subscription-based newsletters (e.g., Copeland & Mayers, 1982), news story in business magazines (e.g., Desai & Jain, 1995), news articles and recommendations in columns of newspapers (e.g., Barber & Loeffler, 1993;Tetlock, Saar-Tsechansky, & Macskassy, 2008), segments or shows on television stations (e.g., Engelberg, Sasseville, & Williams, 2012;Busse & Green, 2002), analyst forecasts in financial databases (e.g., Womack, 1996;Loh, 2010), and posts on internet social media (e.g., Antweiler & Frank, 2004;Chen et al., 2014). There are also studies that provide evidence on Taiwan stock markets (e.g., Lin, Lin, & Wang, 2009) for newspaper; Chiao, Lin, and Lee (2017) for TV show; Wu and Lin (2017) for mass media). ...
... Stickel (1985) report that the initial price effect is permanent in response to a ranking change in Value Line, while Engelberg et al. (2012) document that the initial reaction is accompanied by a reversal for recommendations of Jim Cramer, host of the CNBC show Mad Money. Barber and Loeffler (1993) provide evidence of a partially mean-reverting price pattern, implying the existence of both effects. ...
This study examines the cross-sectional determinants of the price reaction to analysts’ recommendations disseminated through various type of media and for firms listed in Taiwan stock markets. We measure abnormal returns using the market model of event study. Based on the type of media (traditional media/social media) and the type of exchange (Taiwan Stock Exchange (TWSE)/Taipei Exchange (TPEx)), we classify the combined sample observations into four samples and run quantile regressions to investigate whether the relation will be uniform across various quantile levels. Our results show that the relation between firm characteristics and cumulative abnormal returns is not homogeneous across various quantiles of abnormal returns. Our evidence indicates that in general the relation tends to be stronger for firms at higher performance quantile levels and tends to be more pronounced for TWSE firms. The strongest relation is found for the Traditional/TWSE sample, where the abnormal returns are positively related to insider ownership and prior-period earnings, and negatively related to institutional shareholding and price-to-book ratio for firms in the highest abnormal performance quantile.
... Cowles (1933) noted that stock market forecasters fail to generate abnormal returns. However, abnormal returns were documented by Beneish (1991) and B. M. Barber and Loeffler (1993). Womack (1996) noted that post-recommendation excess returns are not mean reverting. ...
Target prices forecasted by sell-side equity research analysts play a crucial role in market participants’ investment decisions. We, using a large sample for Indian markets, determine during the period and end of the period 12-month ahead target price achievements, examine the effectiveness of valuation methods for determining target prices, evaluate target price accuracy using prediction error metrics, and investigate the factors influencing target price accuracy. Our findings indicate that sell-side analysts have reasonable forecasting abilities, achieving 63% of their target prices over a 12-month forecasting horizon. The level of achievement decreased with increasing optimism in predictions. Analysts generally prefer holistic and multiple-based valuation approaches to determine the target prices. The DCF methodology was less effective than the SOTP hybrid and multiple-based approaches in predicting target prices. We find that more optimistic target prices and higher beta contribute to increased prediction errors, whereas better market returns reduce errors. Analysts struggle to predict prices for loss-making enterprises, and have difficulty forecasting target prices in capital-intensive sectors. These findings contribute to the existing body of knowledge and have significant implications for stakeholders in financial markets.
... The literature on the information content of analysts' data and stock returns is extensive and can be split into two sub-streams: quantitative-based (recommendation) and qualitative-based (textual content). Earlier studies by Dimson and Marsh (1984); Barber and Loeffler (1993); Stickel (1995); Womack (1996); Michaely and Womack (1999) evaluate the recommendation-based data in terms of stock return prediction power. Such predictability typically comes from shifts in analysts' opinions about the covered firm. ...
Our paper studies the role of analysts' research outputs in predicting the cross-section of stock returns when a large number of firm characteristics provide competing information. Utilizing a 25-year dataset with 2,515 stocks and 1,309,335 analyst reports, we find that applying machine learning (ML) algorithms to 94 factors from Gu et al. (2020) yields 10-20% annual alphas. Analyst-generated information adds value only when the representative ML agent fails to process these baseline characteristics efficiently. With comprehensive factors and complex ML, analyst incremental value is minimal. Post-2003 Global Research Settlement analysis confirms this, showing a near-zero value of analysts' contributions.
... Since the introduction of investor attention to the financial market, numerous studies have adopted several indicators to represent this variable. For example, Gervais et al. and Barber [8,9,[34][35][36][37][38][39][40][41][42][43][44][45]. All the previous investigations prove that investor attention surely affects asset characteristics. ...
This study performed comprehensive investigations of the complex interconnections between investor attention and cotton futures price volatility under different term structures. In this paper, in-sample analysis, out-of-sample forecast, influencing mechanisms, as well as nonlinear connections are fully explored using several linear model specifications. The results can be summarized as follows: first, investor attention is the Granger causality of the cotton futures volatility and shows significant linear impacts on cotton volatility; second, models incorporated with investor attention significantly improve the prediction accuracy of cotton volatility in the long term compared with the commonly used AR benchmark model; third, the influence of investor attention on cotton volatility may occur through open interest; and fourth, investor attention presents nonlinear impacts on cotton volatility as well. Overall, the results of this article can provide strong supporting evidence for the important roles of investor attention in asset pricing applications.
... In a technical analysis, indices such as World Bank reports on the GDP, analyzed by human experts, and also the technical analysis of market indicators are used [7][8][9], while in a fundamental analysis, text mining methods are applied to identify important events that influence investors and cause market fluctuations. To understand fundamental subjects that affect the market, one needs to represent text according to the contextual information in a document as well as the proximity of the information in news streams that report various aspects of events; however, most works that were published before 2006 only analyzed the market response to simple parameters, such as news counts. ...
Abstract: News dissemination in social media causes fluctuations in financial markets. (Scope) Recent advanced methods in deep learning-based natural language processing have shown promising results in financial market analysis. However, understanding how to leverage large amounts of textual data alongside financial market information is important for the investors’ behavior analysis. In this study, we review over 150 publications in the field of behavioral finance that jointly investigated natural language processing (NLP) approaches and a market data analysis for financial decision support.
This work differs from other reviews by focusing on applied publications in computer science and artificial intelligence that contributed to a heterogeneous information fusion for the investors’ behavior analysis. (Goal) We study various text representation methods, sentiment analysis, and information retrieval methods from heterogeneous data sources. (Findings) We present current and future research directions in text mining and deep learning for correlation analysis, forecasting, and recommendation systems in financial markets, such as stocks, cryptocurrencies, and Forex (Foreign Exchange Market).
... Huth et al. found through media news that rumors have more impact on large-scale enterprises [15]. Barber et al. filter rumors by manually reading the "rumors" section of Business Week [16]. Kiymaz et al. detect rumors by analyzing stock market rumors in the Turkish media one by one, and found that rumors in the categories of "earnings" and "foreign takeover" had a more significant impact on stock market volatility by collating media information [17]. ...
The Stock Market is a typical complex network composed of investors, stocks, and market information. The abnormal fluctuation of the Stock Market has a strong effect on the economy of a country and even that of the world. Fueled by the herd effect of the increasingly abundant social media, Internet rumors, as an important source of market information and an exogenous financial risk, can lead to the collapse of investor confidence and the further propagation of financial risks, which can damage the financial system and even lead to social unrest. With additional availability of computing techniques, we attempt to uncover the media information effects in the stock market and seek to provide researchers with 1) a theoretical reference for a comprehensive understanding of such a complex network, 2) accurate prediction of future data, and 3) design of efficient and reliable risk intervention models. Based on the data of China’s Stock Market, this study uses machine learning to investigate social media rumors to reveal the interplay of social media rumors and stock market volatility. In this work, we find patterns from social media rumors from financial forums using machine learning, quantify social media rumors based on statistics, and analyze the mechanism of propagation and influence of social media rumors on stock market volatility using econometric models. The empirical results show that rumors play an important information transmission effect on stock market volatility and the constructed Internet Financial Forum Rumor Index is helpful to sense the potential impact of rumors, i.e., a significant lagged negative effect. These findings are of guidance for the optimization of the information environment, and can serve to promote the healthy and stable development of the stock market.
... Given attention is a scarce cognitive resource and individuals have limited capacity to process information (Kahneman, 1973;Pashler and Johnston, 1998), existing studies have shown that the allocation of attention, which is caused by the attention-grabbing events, would affect investors' decision making and asset pricing with empirical observations in stock markets (eg., Barber and Loeffler 1993;Seasholes and Wu 2007;Chen 2017;Mayer 2021, etc.). These attention-grabbing (or "stimuli") events have also been adopted as traditional indirect proxies to measure investor attention. ...
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.
... Analysts' judgment could affect investors' evaluation of enterprise value. Barber and Loeffler (1993) found that the stock recommended by the "dartboard" column in the Wall Street Journal produced an average abnormal short-term return of 2% per day. Womack (1996) demonstrated that on the day before and after the release of the rating report, the abnormal return rate was 4% for stocks whose ratings were upgraded to buy, and the abnormal return rate was −-3.87% for stocks whose ratings were downgraded to sell. ...
The unspecified impact of green innovation on corporate financial performance has made some enterprises delay green innovation investment plans, and even abandon green innovation. Mitigating the economic concerns faced in the process of green innovation decision-making is of great significance to accelerate the process of enterprises’ green transformation. Using an unbalanced panel data of Chinese heavy pollution listed companies from 2008-2017, this paper investigates the impact of green innovation on firm value. We further test the likely channels through which green innovation can affect firm value, including the financial flexibility channel and analyst coverage channel. The study finds that: 1) increasing the proportion of green patent applications leads to the devaluation of firm value, but this devaluation effect only occurs in the short term; 2) both financial flexibility and analyst coverage partially mediate the impact of green innovation on firm value; 3) heterogeneity analysis indicates that enterprises can reduce the negative impact of green innovation on firm value by increasing the executive equity incentive and the management-employee pay gap. In addition, as economic policy uncertainty increases from low to high, the negative impact becomes smaller. Our research helps to broaden the cognitive boundaries of the economic impact of green innovation, and assists policymakers and researchers to better grasp the characteristics of green innovation behavior of enterprises in emerging economies. Finally, we provide useful enlightenments for policymakers and business managers to stimulate green innovation in enterprises.
... Similarly (Mitchell and Mulherin 1994) did not find a strong relation between the frequency of news announcements and market moves, since patterns in news announcements did not explain day-of-theweek seasonalities in market activity. On the other hand, Brad and Douglas (1993) found that during the two days after the publication of stock recommendations positive abnormal returns of 4% and an average volume double the normal values can be observed. Along the same lines (Schumaker and Chen 2009) obtained a directional accuracy of 57% with return of simulated portfolio of 2.06%. ...
In this work we study how company co-occurrence in news events can be used to discover business links between them. We develop a methodology that is able to process raw textual data, embed it into a numerical form, and extract a meaningful network of connections. Each news event is considered as a node on the graph and we define the similarity between the two events as the cosine similarity between their vectors in the embedded space. Using this procedure, we contribute to the literature by successfully reconstructing business links between companies, which is usually a difficult task since the data on this topic is either outdated, incomplete or not widely available. We then demonstrate possible uses of this network in two forecasting applications. First, we show how the network can be used as an exogenous feature vector, which improves the prediction of the correlation between companies in the network. This correlation is determined from their realized variance as well as using a wide set of machine learning models for prediction. Second, we demonstrate the use of network for predicting future events with point processes. Our methodology can be applied on any series of events, where we have demonstrated and evaluated its applicability on news events and large market moves. For most of the tested algorithms the experimental results show an improvement in performance when including information from our graphs. More specifically, in certain sectors using Neural Networks shows improved performance by up to 50%.
... A few studies have examined the effect of news media on stock markets. For example, Barber and Loeffler (1993) analyse the Wall Street Journal column and observe average positive abnormal returns of 4 percent for the two days following the publication of the recommendation. Huberman and Regev (2001) study a Sunday New York Times article on a possible improvement of new cancer-curing drugs, which give rise to biotechnology stocks on the following Monday and in the three following weeks. ...
Investors seeking quality information rely on market experts on financial news platforms such as Google Finance or Bloomberg. However, in recent years, stock discussion forums hosted on social media platforms are competing with financial news platforms and vying to become an important and credible source of information in this knowledge driven economy. Stock discussion forums are likely to attract retail investors who seek and share their opinions at no cost, and are competing with financial news platforms. This research compares the effect of information available on these two knowledge sources on stock returns. We use text mining methods to capture the sentiments revealed on a popular stock discussion forum and a news media platform and compare their ability to predict market returns. We find that sentiments from both social media and news media platforms predict future stock returns but the effect of social media appears to be stronger and more long lasting compared to news media.
... Nonetheless, the significance of financial analysts" recommendations has been well established in other empirical and experimental studies as well. For example, Barber and Loeffler (1993) study recommendations published in another column of the Wall Street Journal and find positive abnormal returns of about 4% following the publication. Also, Barber et al. (2001) investigate the performance of consensus forecasts (mean or median of all the analysts following a specific firm) and conclude that highly recommended shares generate 4.2% of excess returns if transaction costs are ignored. ...
This PhD dissertation comprises of a detailed theoretical study and two empirical studies on financial analysts’ earnings forecasts when firms manage earnings. The first study explains the gap in the literature – what do analysts forecast when earnings are managed – which the subsequent studies aim to fulfill. The second study finds that analysts generally tend to be informative around seasoned equity offerings (SEO), especially after the adoption of the Market Abuse Directive. The third study confirms that in the long-term informative analyst forecasts are more value relevant than accurate analyst forecasts as well as reported earnings around SEOs. These findings contribute to the literature on analyst forecasts by showing that some analysts may deliberately forego accuracy for informativeness.
... Given the importance of the financial press to the functioning of public markets (see Barber and Loeffler 1993;Tetlock 2007;Fang and Peress 2009;Tetlock, Saar-Tsechansky, and Macskassy 2008;Tetlock 2011;and Peress 2014), we were concerned that widely publicized accounts of fund sponsor fee wars do not accurately describe the severity of competition within the ETF industry. Although tremendous deals may be found in some of the most popular investment categories, fee competition among fund providers has not lowered expenses for all ETFs across the industry. ...
Despite widely publicized fee reductions, average expense ratios of ETFs have remained relatively steady. Thousands of new funds have not led to lower fees. Investors should examine all available opportunities before choosing specific funds.
Despite widely publicized fee reductions, average expense ratios of exchange-traded funds (ETFs) remained relatively steady between 2004 and 2018. Even though thousands of new funds entered the market during this period, the arrival of most ETF sponsors into a narrowly defined area has not generally led to lower fees for competing funds. Given the impact of fees on long-term investment returns, investors should carefully examine all available opportunities before choosing specific funds. Furthermore, as objectives for newer ETFs become increasingly specialized, investors must also consider whether the benefits of targeted strategies justify their higher prices.
Regarding social media in the new era of marketing of the companies, there are a vast number of channels through which the investors can gain the information they may concern. Investors can collect information on newspapers, TV programs, online news sources, online meetings and councils and so forth. They can also directly affect the companies' stock efficiency to increase or decrease by producing their own content through these social media. This study investigates the transmission of oscillation of users' production content and efficiency rate of the shares of Iran Khodro Company in Tehran Stock Exchange. The results of the data in this study are analyzed based on the correlation coefficient. Variables of stock efficiency rate for each product, positive and negative comments and google searches about the product have been used for the stocks of Iran Khodro. Data on the stocks of Iran Khodro during 1397 to 1400 were collected on the Stock Exchange Website. Simple random sampling method was applied in this study. The results showed meaningful relations between the efficiency rate and positive comments, between the efficiency rate and negative comments and between the efficiency rate and google searches.
This study is aimed specifically at persons who have savings but avoid investing in the financial market. The application of our funds somewhere with the goal of gaining some more as we are applyingfor make an investment. The researcher attempted to identify the factors that restrict the transfer of funds from savers to demanders. The seamless flow of funds from both sides is critical to the success of the financial market. It was discovered that there is a schism between borrowers and lenders. To close this gap, it is vital to encourage ordinary people to invest. In developing countries such as India, just 5% of the population (1.2 billion) is involved in the investing process. The behavioral characteristics of investors are influenced by socioeconomic, cultural, and psychological factors. The findings indicate that instances of persons achieving financial security through share investment had the greatest influence on investors. Market considerations, hedging variables, and economic indicators all have a stronger impact on investment decisions. This result also demonstrates that the use of business annual reports containing financial ratios influences investors' decisions in the stock market. Purpose: The purpose of this literature review is to examine the literature on the factors that influence investors' decision to invest in stocks. This article provides a comprehensive overview of the factors influencing investors' decisions to invest in stocks in financial markets around the world and provides an overview of the factors influencing investors' decisions to invest in stocks. Stock market performance and risk, returns and volatility. , fundamental analysis and technical analysis using systematic review techniques. Various research articles are evaluated to illustrate underlying data and concepts. Available literature on the factors that affect the investor's decision to invest in stocks, financial performance, stock market risk, returns, volatility, fundamental and technical analysis and other areas related to stock analysis, attempts to Stock search, analysis and classification is done. Design/Methodology/Approach: For the current study, a systematic review of literature (SLR) method is used to identify the research gap and set the research agenda. Hundreds of peer-reviewed research articles published over two decades are analyzed & also examined in light of the theoretical prospect underpinning the study. The examination of literature focuses on essential topics such as factors influencing investors' decisions to invest in shares Finally, the research deficit and research agenda are examined for future studies implementing the ABCD frameworks.
We investigate retail investors' movement towards/from securities with different environmental, social and governance scores during COVID-19 pandemic using data from Robinhood. We find that COVID reduces the number of retail investors holding securities with low environmental scores, but not those with high scores. We also find heterogeneity in investors' reactions to different subcategory scores. The equal-weighted buy-and-hold portfolio of high-score securities does not outperform that of low-score securities in terms of volatility or return, suggesting retail investors' preference for high environmental score securities is not driven by financial return or risk, and such 'voting' is independent from pecuniary indicators.
This paper estimates the effect of news positioning on the speed of price discovery, using exogenous variation in prominent (“front‐page”) positioning of news articles on the Bloomberg terminal. Front‐page articles see 240% higher trading volume and 176% larger absolute excess returns during the first 10 minutes after publication than equally important non‐front‐page articles. Overall, the information in front‐page articles is fully incorporated into prices within an hour of publication. The response to non‐front‐page information of similar importance eventually converges but takes more than two days to be fully reflected in prices.
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Purpose
This study aims to examine the behaviour of institutional and retail investors in response to news about industry leaders (peer firms) and to determine its impact on the stock prices of other firms (focal firms) within the same industry.
Design/methodology/approach
The study investigates the impact of peer news on investor behaviour of Chinese A-shares listed on the Shanghai and Shenzhen Stock Exchanges from 2010 to 2019. The media coverage of industry leaders is sourced from prominent Chinese online financial outlets and the Chinese Financial Press. Support vector machine is applied to identify the positive, neutral and negative news within the articles. The study uses event study and logistic regression to examine the effects of peer news on focal firms’ investor behaviour.
Findings
The results show that both good and bad news about leaders cause peers’ stock prices to increase initially, but then reverse within one quarter. Further analysis reveals that when leaders’ shares receive positive news coverage, institutional investors tend to exert excessive abnormal buying pressure on peers’ shares, resulting in overreactions. Conversely, retail investors do not actively trade on peers on leaders’ news day due to limited attention. In addition, the study shows that short-selling constraint inhibits bad news from reflecting in the stock prices.
Originality/value
The study highlights differences in investor behaviour. The finding that institutional investors tend to overreact more to peer firms’ news when focal firms are smaller and have a lower frequency of information disclosure supports the salient theory. This is consistent with the previous framework that suggests overreaction is more pronounced when it is difficult to combine external sources of information to evaluate the focal firms. In contrast, retail investors do not engage in active trading on peers on leaders’ news day due to the limited attention theory.
This study discusses the stock market reaction to the firm’s carbon neutrality commitments. By hand-collecting firm-level news and stock data, we conduct event studies as well as regression modelling studies. The results show that firms experience losses in market value from committing to being carbon neutral, and the decline in cumulative abnormal returns ranges from −2.09% to −1.21% across different event windows. However, we find better previous ESG performance and a higher level of carbon disclosure could mitigate adverse market reactions. This study innovatively links the ‘trade-off theory’ and ‘resource-based view’ to the discussion of CSR/ESG on firm value from the lens of carbon neutrality commitments.
We study the influence of financial innovation by fintech brokerages on individual investors’ trading and stock prices. Using data from Robinhood, we find that Robinhood investors engage in more attention‐induced trading than other retail investors. For example, Robinhood outages disproportionately reduce trading in high‐attention stocks. While this evidence is consistent with Robinhood attracting relatively inexperienced investors, we show that it is also driven in part by the app's unique features. Consistent with models of attention‐induced trading, intense buying by Robinhood users forecasts negative returns. Average 20‐day abnormal returns are ‐4.7% for the top stocks purchased each day. This article is protected by copyright. All rights reserved
Using the brokerage analyst reports released from 2014 to 2019, this paper examines the effects of the information role of brokerage firms and the information transmission role of the media on investor trading activity in the Taiwan stock market. Trading activity in the market can be categorized by investor types, including retail investors and institutional investors (foreign institutions, mutual funds, and broker dealers). This paper reviews the “buy the rumor, sell the news” trading strategy, which refers to institutional investors acting as early-informed traders and exhibiting short-term profit-taking characteristics. Notably, mutual funds have the best informational advantage among institutional investors, whose trading activities are significantly and positively related to the abnormal returns around the release of brokerage reports. On the other hand, retail investors may follow the trading activity of institutional investors without making a significant profit. The empirical findings are robust to include several firm and market characteristics. Overall, the empirical results provide in-depth insights into the regulation of brokerage analyst report disclosures.
Purpose
This study explores the price and trading volume effects around the quarterly Dow Jones Islamic Market-GCC index (DJIM-GCC) revisions and investigates whether these reactions are driven by firms' fundamentals or by investors' perception of ethical screening.
Design/methodology/approach
The authors adopt an event study methodology to analyze the price and volume effects of Islamic indices redefinitions.
Findings
The results exhibit a positive (negative) price reaction for added (deleted) stocks. The authors also document an asymmetric volume response for index additions and deletions. The multivariate analysis of the cumulative abnormal returns reveals that the documented market reaction around Islamic index revisions is mainly related to the compliance attribution (withdrawal).
Originality/value
The approach allows to separate the market reaction arising from changes in firms' fundamentals from that induced by investors' perception of the attribution or withdrawal of a compliance certification. Moreover, the focus on the GCC region, where countries share the same cultural traits and perceive Islamic law identically excludes any social effect that would influence the market reaction due to cultural differences between countries.
This paper examines the market behavior of stocks that are favorably mentioned on official WeChat account (OWA). To the best of our knowledge, we are the first to investigate market reactions to recommendations on WeChat. The empirical results show that there is a significantly positive abnormal return and excess trading volume on the publication day. Moreover, the cumulative average abnormal return for OWA completely reverses in a short time, which supports the price pressure hypothesis. Additional analyses reveal that market reactions in the smaller firms are significantly greater than those in the largest firms on the publication day. Finally, we preclude possibilities that market reactions on the event day are induced by the secondary dissemination of analyst recommendations, firm-specific news releases, media coverage, and previous positive significant abnormal returns.
A formal statistical framework is proposed for synthesis of text information into sentiment indicators. Each text document is treated as an exchangeable collection of stems of words (tokens), and used in conjunction with a multinomial inverse regression approach to efficiently synthesize the information content in text documents. The proposed methodology is illustrated through the buildup of sentiment indicators using Twitter news outlet text information. These synthesizing indicators, quantitative in nature, can be built across disciplines to capture changes in the economic, financial, and social conditions, and also serve to reveal heterogeneity across countries, sectors, or markets. The proposed approach is computationally fast and allows for time variation in the indexes.
Stock prices occasionally move in response to unverified rumors. I propose a cheap talk model in which a rumormonger’s incentives to tell the truth depend on the interaction between her investment horizon and the information acquisition decisions of message-receiving investors. The model’s key prediction is that short investment horizons can facilitate credible information sharing between investors, thereby accelerating the information capitalization into market prices. Analyzing a data set of takeover rumors covered by U.S. newspapers, I find suggestive evidence in support of this prediction.
Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
In this paper, we investigate the relationship between unexpected information from postings and news, and the unexpected information is measured by the residual of regressions of trading volume on numbers of news or postings. We mainly find that (i) There are significant positive contemporaneous correlations between the unexpected information coming from postings and different kinds of news; the correlation between the unexpected information coming from postings and new media news is stronger than that between the unexpected information coming from postings and mass media news; (ii) The unexpected information coming from postings could cause the unexpected information coming from news, but only the unexpected information coming from the mass media news could cause that coming from postings; (iii) There are persistent power-law cross-correlations between the unexpected information coming from postings and that coming from mass media news and new media news. The cross-correlation between the unexpected information coming from postings and new media news is more persistent than the one between the unexpected information coming from postings and mass media news. The cross-correlations are all more stable in long term than in short term. We attribute our findings above to the dissemination speed of the information on the Internet.
I test whether advertising affects stock prices through an investor attention channel. I use corporate sponsorships of college football bowl games as a natural experiment that provides variation in advertising exposure that is unrelated to firm fundamentals. Sponsoring firms' stocks experience large increases in investor attention, abnormally high turnover, and temporary price pressure that is related to bowl games' TV‐ratings and score differentials. Retail investors are net buyers of sponsors' stocks, whereas institutional investors initially remain neutral and then start selling, ultimately driving a reversal toward fundamental values. These findings shed light on who wins/loses when advertising attracts investor attention.
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We investigate the news coverage effect in explaining and predicting the portfolio returns. We find that stocks with more news coverage yield higher abnormal returns. The news coverage effect is still robust even after controlling for firm characteristics and industry sectors. Furthermore, the return premium on news coverage is particularly large in small-cap stocks due to the information dissemination role of news coverage. Then we construct a news coverage factor to explain the abnormal returns. We also confirm the predictability of news coverage. This indicates news coverage has a daily momentum effect. Finally, we propose three investment strategies and verify their profitabilities.
The arrival of a public signal worsens the adverse selection problem if informed investors are risk averse. Precisely, the public signal reduces uncertainty which boosts informed investors’ participation leading to a more toxic order flow. I confirm the model’s empirical predictions by estimating the effect of the publication of the weekly change in oil inventories on liquidity via a difference-in-differences strategy. The bid-ask spread of stocks related to oil doubles after the release and their volume increases by 32% regardless of the report’s surprise. Further, consistent with the model, implied volatility drops and insider’s trading increases after the report’s publication.
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