We examine whether the social media reaction to an important firm disclosure provides a signal of the quality of that disclosure and whether capital market participants’ reactions to the disclosure are consistent with the social media reaction. Specifically, we examine the sentiment of posts on StockTwits immediately following management forecasts issued between 2010 to 2017 and offer three main findings. First, we document that the relation between StockTwits sentiment and forecast news is stronger when the forecast is later revealed to be more accurate and less biased, suggesting that StockTwits provides an early signal of forecast quality. Second, we find a positive association between the extent to which social media sentiment agrees with the forecast news and stock price reaction to the management forecast. This suggests that when social media sentiment agrees with management forecast news, investors do too. Finally, we find a positive association between the extent to which social media sentiment agrees with the forecast news and subsequent analyst forecast revisions, particularly when forecast news is positive. This suggests that when social media sentiment agrees with positive management forecast news, analysts do too. Additional analysis suggests that investors appear to underreact to the signal provided by social media sentiment, while analysts appear to overreact to the signal. Overall, our results suggest that the social media reaction to management forecasts provides a timely and accurate reflection of not only the forecast’s quality, but also of how the forecast will be received by important capital market participants.
{"title":"Everyone Has an Opinion: The Informativeness of Social Media’s Response to Management Guidance","authors":"John L. Campbell, Jenna D'Adduzio, James R. Moon","doi":"10.2139/ssrn.3689185","DOIUrl":"https://doi.org/10.2139/ssrn.3689185","url":null,"abstract":"We examine whether the social media reaction to an important firm disclosure provides a signal of the quality of that disclosure and whether capital market participants’ reactions to the disclosure are consistent with the social media reaction. Specifically, we examine the sentiment of posts on StockTwits immediately following management forecasts issued between 2010 to 2017 and offer three main findings. First, we document that the relation between StockTwits sentiment and forecast news is stronger when the forecast is later revealed to be more accurate and less biased, suggesting that StockTwits provides an early signal of forecast quality. Second, we find a positive association between the extent to which social media sentiment agrees with the forecast news and stock price reaction to the management forecast. This suggests that when social media sentiment agrees with management forecast news, investors do too. Finally, we find a positive association between the extent to which social media sentiment agrees with the forecast news and subsequent analyst forecast revisions, particularly when forecast news is positive. This suggests that when social media sentiment agrees with positive management forecast news, analysts do too. Additional analysis suggests that investors appear to underreact to the signal provided by social media sentiment, while analysts appear to overreact to the signal. Overall, our results suggest that the social media reaction to management forecasts provides a timely and accurate reflection of not only the forecast’s quality, but also of how the forecast will be received by important capital market participants.","PeriodicalId":18611,"journal":{"name":"Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets eJournal","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88510550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This note contains the supplements to Li and Linton (2020).
本注包含李和林顿(2020)的补编。
{"title":"Supplementary Material for 'A ReMeDI for Microstructure Noise''","authors":"Z. Li, O. Linton","doi":"10.2139/ssrn.3688787","DOIUrl":"https://doi.org/10.2139/ssrn.3688787","url":null,"abstract":"This note contains the supplements to Li and Linton (2020).","PeriodicalId":18611,"journal":{"name":"Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets eJournal","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84858164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-30DOI: 10.1016/J.JBANKFIN.2020.105966
Adam Zaremba, Mehmet Umutlu, Alina Maydybura
{"title":"Where Have the Profits Gone? Market Efficiency and the Disappearing Equity Anomalies in Country and Industry Returns","authors":"Adam Zaremba, Mehmet Umutlu, Alina Maydybura","doi":"10.1016/J.JBANKFIN.2020.105966","DOIUrl":"https://doi.org/10.1016/J.JBANKFIN.2020.105966","url":null,"abstract":"","PeriodicalId":18611,"journal":{"name":"Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets eJournal","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77368282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alessandro Melone, Otto Randl, Leopold Sögner, J. Zechner
We investigate the sources of time-variation in the stock-oil correlation over the period 1986-2018. We first derive an oil futures return news decomposition following Campbell and Shiller (1988) and Campbell (1991). Then, for both stock and oil, we split unexpected returns into cash-flow news (which can be related to asset fundamentals because of its link to production) and discount-rate news (which can be driven by shocks to investors holding both assets) using a vector autoregressive (VAR) model. We find that about 75% of the time-varying correlation is related to the comovement of cash-flow news between the two assets. This result is robust to different specifications of the VAR model used to decompose returns. We provide supportive evidence that underlying changes in the structure of the real economy, such as the increased oil production in the U.S., are key drivers for the changing stock-oil comovement beyond the financialization of commodity market.
{"title":"Stock-Oil Comovement: Fundamentals or Financialization?","authors":"Alessandro Melone, Otto Randl, Leopold Sögner, J. Zechner","doi":"10.2139/ssrn.3668239","DOIUrl":"https://doi.org/10.2139/ssrn.3668239","url":null,"abstract":"We investigate the sources of time-variation in the stock-oil correlation over the period 1986-2018. We first derive an oil futures return news decomposition following Campbell and Shiller (1988) and Campbell (1991). Then, for both stock and oil, we split unexpected returns into cash-flow news (which can be related to asset fundamentals because of its link to production) and discount-rate news (which can be driven by shocks to investors holding both assets) using a vector autoregressive (VAR) model. We find that about 75% of the time-varying correlation is related to the comovement of cash-flow news between the two assets. This result is robust to different specifications of the VAR model used to decompose returns. We provide supportive evidence that underlying changes in the structure of the real economy, such as the increased oil production in the U.S., are key drivers for the changing stock-oil comovement beyond the financialization of commodity market.","PeriodicalId":18611,"journal":{"name":"Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets eJournal","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85456962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peter M. Clarkson, Alexander Nekrasov, Andreas Simon, I. Tutticci
This paper reveals that in addition to fundamental factors, the 52-week high price and recent investor sentiment play an important role in analysts’ target price formation. Analysts’ forecasts of short-term earnings and long-term earnings growth are shown to be important explanatory variables for target prices; equally, the 52-week high price and recent investor sentiment are also shown to explain target price levels and especially target price biases. Our analysis additionally reveals that analysts place greater weight on these two non-fundamental factors in settings with greater task complexity and to some extent in those with greater resource constraints. Conversely, on balance, the results suggest that this increased reliance does not translate into an increased impact per unit of each non-fundamental factor on forecast bias. Finally, our results show that target prices are useful in predicting future stock returns beyond earnings forecasts and commonly used risk proxies. However, in an internally consistent fashion, the informativeness of target prices for future returns is significantly reduced when greater weight is placed on either the 52-week high or recent investor sentiment in the target price formation process.
{"title":"Target price forecasts: The roles of the 52-week high price and recent investor sentiment","authors":"Peter M. Clarkson, Alexander Nekrasov, Andreas Simon, I. Tutticci","doi":"10.2139/ssrn.2104433","DOIUrl":"https://doi.org/10.2139/ssrn.2104433","url":null,"abstract":"This paper reveals that in addition to fundamental factors, the 52-week high price and recent investor sentiment play an important role in analysts’ target price formation. Analysts’ forecasts of short-term earnings and long-term earnings growth are shown to be important explanatory variables for target prices; equally, the 52-week high price and recent investor sentiment are also shown to explain target price levels and especially target price biases. Our analysis additionally reveals that analysts place greater weight on these two non-fundamental factors in settings with greater task complexity and to some extent in those with greater resource constraints. Conversely, on balance, the results suggest that this increased reliance does not translate into an increased impact per unit of each non-fundamental factor on forecast bias. Finally, our results show that target prices are useful in predicting future stock returns beyond earnings forecasts and commonly used risk proxies. However, in an internally consistent fashion, the informativeness of target prices for future returns is significantly reduced when greater weight is placed on either the 52-week high or recent investor sentiment in the target price formation process.","PeriodicalId":18611,"journal":{"name":"Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets eJournal","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78031951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We provide empirical evidence that CAPM-betas positively predict asset returns when market returns are predicted to be high, which occurs about every other month. Consequently, the product of beta and the predicted market return (CAPM) predicts asset returns by combining the out-of-sample forecasting power of both beta and the market return predictor. Monthly out-of-sample R2s are substantial for both portfolios and individual stocks and translate into large trading gains. Indeed, trading strategies exploiting the forecasting power of the CAPM have Sharpe ratios up to 100% larger than the corresponding buy-and-hold strategies, and their average returns increase with their CAPM-betas.
{"title":"Does the CAPM Predict Returns?","authors":"M. Hasler, Charles Martineau","doi":"10.2139/ssrn.3368264","DOIUrl":"https://doi.org/10.2139/ssrn.3368264","url":null,"abstract":"We provide empirical evidence that CAPM-betas positively predict asset returns when market returns are predicted to be high, which occurs about every other month. Consequently, the product of beta and the predicted market return (CAPM) predicts asset returns by combining the out-of-sample forecasting power of both beta and the market return predictor. Monthly out-of-sample R2s are substantial for both portfolios and individual stocks and translate into large trading gains. Indeed, trading strategies exploiting the forecasting power of the CAPM have Sharpe ratios up to 100% larger than the corresponding buy-and-hold strategies, and their average returns increase with their CAPM-betas.","PeriodicalId":18611,"journal":{"name":"Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets eJournal","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91389037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
W. Brooke Elliott, Jessen L. Hobson, Ben W. Van Landuyt, Brian J. White
We test whether individuals with incentivized directional preferences akin to investors taking a long position in a stock are more prone to forming biased beliefs than individuals with incentives akin to those of short investors. Extending motivated reasoning theory with insights from psychology research on goal pursuit suggests that motivated reasoning is likely muted when the typical preference implied by a specific setting—what we refer to as a “conventional contextual preference”—is directionally inconsistent with an individual’s incentivized preference. Experiment 1 tests for such asymmetric motivated reasoning in an investing setting in which participants take either long or short positions. Results indicate that compared to a rational benchmark, long traders’ estimates of future stock price exhibit upward bias while short traders’ estimates are unbiased. Moreover, consistent with motivated reasoning as the process underlying these results, the magnitude of long traders’ bias becomes less pronounced as the amount of uncertainty in the information environment decreases. We examine the robustness and generality of these findings in supplemental analyses and a second experiment. Overall, our paper contributes new insights regarding the role of motivated reasoning in shaping investors’ judgments, and also speaks to the broader literature on bias in judgment and decision-making that spans multiple fields.
{"title":"Asymmetric Motivated Reasoning in Investor Judgment","authors":"W. Brooke Elliott, Jessen L. Hobson, Ben W. Van Landuyt, Brian J. White","doi":"10.2139/ssrn.2950329","DOIUrl":"https://doi.org/10.2139/ssrn.2950329","url":null,"abstract":"We test whether individuals with incentivized directional preferences akin to investors taking a long position in a stock are more prone to forming biased beliefs than individuals with incentives akin to those of short investors. Extending motivated reasoning theory with insights from psychology research on goal pursuit suggests that motivated reasoning is likely muted when the typical preference implied by a specific setting—what we refer to as a “conventional contextual preference”—is directionally inconsistent with an individual’s incentivized preference. Experiment 1 tests for such asymmetric motivated reasoning in an investing setting in which participants take either long or short positions. Results indicate that compared to a rational benchmark, long traders’ estimates of future stock price exhibit upward bias while short traders’ estimates are unbiased. Moreover, consistent with motivated reasoning as the process underlying these results, the magnitude of long traders’ bias becomes less pronounced as the amount of uncertainty in the information environment decreases. We examine the robustness and generality of these findings in supplemental analyses and a second experiment. Overall, our paper contributes new insights regarding the role of motivated reasoning in shaping investors’ judgments, and also speaks to the broader literature on bias in judgment and decision-making that spans multiple fields.","PeriodicalId":18611,"journal":{"name":"Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets eJournal","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83308143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A unique data set from NASDAQ OMX Nordic allows a deep analysis of trader types’ activity and provides evidence on the roles played in the trading ecosystem. We specifically investigate the impact of algorithmic traders on market quality relative to the activities of other market participants under various conditions. We find that relative to other traders, algorithmic traders contribute to lower spreads, especially during highly volatile markets, and provide more shares traded at the NBBO. We also identify the main determinants of algorithmic traders’ liquidity provisions and order cancellation patterns.
{"title":"Algorithmic Trading and Market Quality","authors":"J. Broussard, Andrei Nikiforov, S. Osmekhin","doi":"10.2139/ssrn.3673881","DOIUrl":"https://doi.org/10.2139/ssrn.3673881","url":null,"abstract":"A unique data set from NASDAQ OMX Nordic allows a deep analysis of trader types’ activity and provides evidence on the roles played in the trading ecosystem. We specifically investigate the impact of algorithmic traders on market quality relative to the activities of other market participants under various conditions. We find that relative to other traders, algorithmic traders contribute to lower spreads, especially during highly volatile markets, and provide more shares traded at the NBBO. We also identify the main determinants of algorithmic traders’ liquidity provisions and order cancellation patterns.","PeriodicalId":18611,"journal":{"name":"Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets eJournal","volume":"163 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76758842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Does collecting regulatory financial information with the help of automated computer algorithms (robots) affect the stock market? Using the EDGAR Server Log data set, I construct firm-level measures of information acquisition by robots and non-robots and show that robots are extensively used for information acquisition when new information becomes available. The SEC's mandateregarding interactive data leads to a notable increase in information demand, consistent with decreased information acquisition costs for standardised regulatory financial information in XBRL-format. A higher relative importance of robots acquiring information about a firm combined with the XBRL adoption is associated with a consequent increase in trading volume, smaller bid-ask spreads, lower volatility, positive cumulative abnormal return and increased volume coefficient of variation. The findings are consistent with the idea that automation and standardisation bene fits informed investors disproportionately more than uninformed traders.
在自动计算机算法(机器人)的帮助下收集监管 金融信息会影响股市吗?使用EDGAR Server Log数据集，我构建了 机器人和非机器人获取信息的公司级别度量，并表明当新信息可用时，机器人被广泛用于获取信息。美国证券交易委员会关于交互式数据的规定导致信息需求的显著增加，与xbrl格式的标准化监管金融信息的信息获取成本降低相一致。机器人获取 公司信息的相对重要性越高，结合XBRL的采用，随之而来的交易量增加，买卖价差越小，波动性越低，累积异常收益为正，量变系数增加。 的研究结果与自动化和标准化对 知情的投资者比不知情的交易者更有利的观点是一致的。
{"title":"The Impact of Automated Information Acquisition on the Stock Market","authors":"Ivika Jäger","doi":"10.2139/ssrn.3671538","DOIUrl":"https://doi.org/10.2139/ssrn.3671538","url":null,"abstract":"Does collecting regulatory \u001cfinancial information with the help of automated computer algorithms (robots) affect the stock market? Using the EDGAR Server Log data set, I construct \u001cfirm-level measures of information acquisition by robots and non-robots and show that robots are extensively used for information acquisition when new information becomes available. The SEC's mandateregarding interactive data leads to a notable increase in information demand, consistent with decreased information acquisition costs for standardised regulatory financial information in XBRL-format. A higher relative importance of robots acquiring information about a \u001cfirm combined with the XBRL adoption is associated with a consequent increase in trading volume, smaller bid-ask spreads, lower volatility, positive cumulative abnormal return and increased volume coefficient of variation. The \u001cfindings are consistent with the idea that automation and standardisation bene\u001cfits informed investors disproportionately more than uninformed traders.","PeriodicalId":18611,"journal":{"name":"Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets eJournal","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73440997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I argue that one rationale for central clearing counterparties (CCPs) is to mitigate inefficiencies associated with distressed asset sales. First, I build a simple model where asset sales give rise to multiple equilibria, and show that a contract resembling a CCP ensures coordination on the Pareto-dominating equilibrium. Second, I empirically study the first event in economic history during which a CCP successfully eliminated inefficient asset sales: the global wool crisis of 1900.
{"title":"Mitigating Fire Sales with a Central Clearing Counterparty","authors":"Guillaume Vuillemey","doi":"10.2139/ssrn.3355142","DOIUrl":"https://doi.org/10.2139/ssrn.3355142","url":null,"abstract":"I argue that one rationale for central clearing counterparties (CCPs) is to mitigate inefficiencies associated with distressed asset sales. First, I build a simple model where asset sales give rise to multiple equilibria, and show that a contract resembling a CCP ensures coordination on the Pareto-dominating equilibrium. Second, I empirically study the first event in economic history during which a CCP successfully eliminated inefficient asset sales: the global wool crisis of 1900.","PeriodicalId":18611,"journal":{"name":"Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets eJournal","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86853148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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