Pub Date : 2021-09-06DOI: 10.1080/15427560.2021.1971983
Zhilu Lin, Wentao Wu, Haoran Zhang
Abstract This study investigates the potential explanations to the momentum effect on the equity market. We primarily discuss the underreaction hypothesis, the overreaction hypothesis, and the impact of herding behavior. We find that the momentum effect disappeared after decimalization in all size deciles, which does not support the underreaction hypothesis. We also find that momentum profits do not exist in any intangible assets or R&D expenses deciles, which is not consistent with the continuous overreaction hypothesis. We further investigate the impact of herding behavior on the momentum effect. Using a new firm-level herding measurement, we find that investors require higher returns in high herding stocks and they require even higher returns in high herding stocks among previous losers, indicating that investors herd against the previous losers while they herd toward the winners.
{"title":"Momentum, Information, and Herding","authors":"Zhilu Lin, Wentao Wu, Haoran Zhang","doi":"10.1080/15427560.2021.1971983","DOIUrl":"https://doi.org/10.1080/15427560.2021.1971983","url":null,"abstract":"Abstract This study investigates the potential explanations to the momentum effect on the equity market. We primarily discuss the underreaction hypothesis, the overreaction hypothesis, and the impact of herding behavior. We find that the momentum effect disappeared after decimalization in all size deciles, which does not support the underreaction hypothesis. We also find that momentum profits do not exist in any intangible assets or R&D expenses deciles, which is not consistent with the continuous overreaction hypothesis. We further investigate the impact of herding behavior on the momentum effect. Using a new firm-level herding measurement, we find that investors require higher returns in high herding stocks and they require even higher returns in high herding stocks among previous losers, indicating that investors herd against the previous losers while they herd toward the winners.","PeriodicalId":47016,"journal":{"name":"Journal of Behavioral Finance","volume":"35 1","pages":"219 - 237"},"PeriodicalIF":1.9,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80007402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-02DOI: 10.1080/15427560.2021.1973006
Hana Dvořáčková, T. Tichý, Marek Jochec
Abstract We examine the effect of selected limit order tools (stop loss, take profit, and trailing stop) on the disposition effect, a well-known behavioral bias, by using experimental trading data. Our presumption is that the limit orders should significantly eliminate this behavioral bias, which may lead to higher losses than feasible for a trader. The traders of our data sample can be considered as a sample of beginners or less informed traders. Based on our analysis it is possible to conclude that limit orders have a significant impact on the disposition effect. Traders using these tools were able not only to avoid this behavioral bias, but even reverse it, which is, as far as we know, a unique result within the existing literature. Moreover, we found out that the impact of eliminating of the disposition effect by limit orders use is positive, as it may lead to significant loss reduction. On the other hand, the effect on profits is insignificant.
{"title":"How Do Limit Orders Affect the Disposition Effect on Highly Liquid Markets – Experimental Finance Evidence","authors":"Hana Dvořáčková, T. Tichý, Marek Jochec","doi":"10.1080/15427560.2021.1973006","DOIUrl":"https://doi.org/10.1080/15427560.2021.1973006","url":null,"abstract":"Abstract We examine the effect of selected limit order tools (stop loss, take profit, and trailing stop) on the disposition effect, a well-known behavioral bias, by using experimental trading data. Our presumption is that the limit orders should significantly eliminate this behavioral bias, which may lead to higher losses than feasible for a trader. The traders of our data sample can be considered as a sample of beginners or less informed traders. Based on our analysis it is possible to conclude that limit orders have a significant impact on the disposition effect. Traders using these tools were able not only to avoid this behavioral bias, but even reverse it, which is, as far as we know, a unique result within the existing literature. Moreover, we found out that the impact of eliminating of the disposition effect by limit orders use is positive, as it may lead to significant loss reduction. On the other hand, the effect on profits is insignificant.","PeriodicalId":47016,"journal":{"name":"Journal of Behavioral Finance","volume":"10 1","pages":"290 - 302"},"PeriodicalIF":1.9,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90377591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-04DOI: 10.1080/15427560.2021.1950723
Anamika, Madhumita Chakraborty, S. Subramaniam
Abstract This study examines the impact of investor sentiment on cryptocurrency returns. We use a direct survey-based measure that captures the investors’ sentiment on Bitcoins. This direct measure of Bitcoin investor sentiment is obtained from the Sentix database. The results of the study found that the Bitcoin prices experience appreciation when investors are optimistic about Bitcoin. Bitcoin sentiment has significant power in predicting the Bitcoin prices after controlling for the relevant factors. There is also evidence that the sentiment of the dominant cryptocurrency, i.e., Bitcoin, influences the price of other cryptocurrencies. Further, we extend our analysis by investigating the impact of equity market sentiment on cryptocurrency returns. We proxy equity market sentiment using two measures viz: Baker-Wurgler sentiment Index and the VIX Index. When the equity market investors’ sentiment is bearish, cryptocurrency prices rise, indicating that cryptocurrency can act as an alternative avenue for investment. Our results remain unaffected after controlling for potential factors that could impact cryptocurrency prices.
{"title":"Does Sentiment Impact Cryptocurrency?","authors":"Anamika, Madhumita Chakraborty, S. Subramaniam","doi":"10.1080/15427560.2021.1950723","DOIUrl":"https://doi.org/10.1080/15427560.2021.1950723","url":null,"abstract":"Abstract This study examines the impact of investor sentiment on cryptocurrency returns. We use a direct survey-based measure that captures the investors’ sentiment on Bitcoins. This direct measure of Bitcoin investor sentiment is obtained from the Sentix database. The results of the study found that the Bitcoin prices experience appreciation when investors are optimistic about Bitcoin. Bitcoin sentiment has significant power in predicting the Bitcoin prices after controlling for the relevant factors. There is also evidence that the sentiment of the dominant cryptocurrency, i.e., Bitcoin, influences the price of other cryptocurrencies. Further, we extend our analysis by investigating the impact of equity market sentiment on cryptocurrency returns. We proxy equity market sentiment using two measures viz: Baker-Wurgler sentiment Index and the VIX Index. When the equity market investors’ sentiment is bearish, cryptocurrency prices rise, indicating that cryptocurrency can act as an alternative avenue for investment. Our results remain unaffected after controlling for potential factors that could impact cryptocurrency prices.","PeriodicalId":47016,"journal":{"name":"Journal of Behavioral Finance","volume":"67 1","pages":"202 - 218"},"PeriodicalIF":1.9,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76861444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-28DOI: 10.1080/15427560.2021.1948853
Ahmed Bouteska, Mehdi Mili
Abstract The authors investigate the empirical relation among investor sentiment, valuation uncertainty, and announcements of changes in analyst recommendation decisions among U.S. firms. Recent behavioral finance evidence shows market sentiment to have predictive content that affects the classical relationship between analyst recommendations and stock return dynamics. Contrary to this evidence, the authors find that degree of valuation uncertainty is associated to the impact of investor sentiment when examining a likelihood of consensus recommendation upgrade or downgrade. While not totally eliminating the significant investor sentiment effect under high valuation uncertainty, the investor sentiment does not powerfully explain the stock market reactions to analyst recommendation changes under low valuation uncertainty. Furthermore, the authors show that analyst recommendations provide significant buy or sell signals if valuation uncertainty is great, referring to the market being highly competitive. However, in less competitive markets, analyst reports become less informative. Overall, the authors demonstrate that magnitude of valuation uncertainty is an important complement to investor sentiment for further understanding analyst recommendations.
{"title":"The Role of Investor Sentiment and Valuation Uncertainty in the Changes around Analyst Recommendations: Evidence from U.S. Firms","authors":"Ahmed Bouteska, Mehdi Mili","doi":"10.1080/15427560.2021.1948853","DOIUrl":"https://doi.org/10.1080/15427560.2021.1948853","url":null,"abstract":"Abstract The authors investigate the empirical relation among investor sentiment, valuation uncertainty, and announcements of changes in analyst recommendation decisions among U.S. firms. Recent behavioral finance evidence shows market sentiment to have predictive content that affects the classical relationship between analyst recommendations and stock return dynamics. Contrary to this evidence, the authors find that degree of valuation uncertainty is associated to the impact of investor sentiment when examining a likelihood of consensus recommendation upgrade or downgrade. While not totally eliminating the significant investor sentiment effect under high valuation uncertainty, the investor sentiment does not powerfully explain the stock market reactions to analyst recommendation changes under low valuation uncertainty. Furthermore, the authors show that analyst recommendations provide significant buy or sell signals if valuation uncertainty is great, referring to the market being highly competitive. However, in less competitive markets, analyst reports become less informative. Overall, the authors demonstrate that magnitude of valuation uncertainty is an important complement to investor sentiment for further understanding analyst recommendations.","PeriodicalId":47016,"journal":{"name":"Journal of Behavioral Finance","volume":"3 1","pages":"73 - 96"},"PeriodicalIF":1.9,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89966757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-24DOI: 10.1080/15427560.2021.1949718
Qiang Bu
Abstract The author examines whether the direct and indirect sentiment measures are distinct from each other. The author finds that the 2 types of sentiment measures have a relatively low correlation between them. The direct sentiment measures have significant explanatory power on contemporaneous stock returns, whereas the indirect sentiment measures have a lagging effect in such explanatory power. If both sentiment measures are used in a model, one can observe a strong synergistic effect in adjusted R 2. One can find that the indirect measures’ predictive power on future stock return is remarkably higher than that of the direct measures. Also, the indirect measures are mainly driven by short-term interest rate, whereas stock returns most drive the direct measures.
{"title":"Are All the Sentiment Measures the Same?","authors":"Qiang Bu","doi":"10.1080/15427560.2021.1949718","DOIUrl":"https://doi.org/10.1080/15427560.2021.1949718","url":null,"abstract":"Abstract The author examines whether the direct and indirect sentiment measures are distinct from each other. The author finds that the 2 types of sentiment measures have a relatively low correlation between them. The direct sentiment measures have significant explanatory power on contemporaneous stock returns, whereas the indirect sentiment measures have a lagging effect in such explanatory power. If both sentiment measures are used in a model, one can observe a strong synergistic effect in adjusted R 2. One can find that the indirect measures’ predictive power on future stock return is remarkably higher than that of the direct measures. Also, the indirect measures are mainly driven by short-term interest rate, whereas stock returns most drive the direct measures.","PeriodicalId":47016,"journal":{"name":"Journal of Behavioral Finance","volume":"36 1","pages":"161 - 170"},"PeriodicalIF":1.9,"publicationDate":"2021-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89602224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-15DOI: 10.1080/15427560.2021.1949715
Ran Barniv, Wei Li, Timothy C. Miller
Abstract Motivated by the mixed findings in prior archival studies, this study conducts three experiments to examine the relationships among analysts’ intrinsic valuation estimates (V), stock recommendations (REC) and stock returns. Experiment 1, built on implications from prospect theory, provides direct observations on analysts’ asymmetric use of the valuation to price (V/P) ratio in making their REC. Experiments 2 and 3 indicate differences and similarities between professional and nonprofessional investors in using analysts’ V and REC in making their investment-related judgments. Our results provide implications for both research and practice.
{"title":"Independence of Intrinsic Valuations and Stock Recommendations – Experimental Evidence from Equity Research Analysts and Investors","authors":"Ran Barniv, Wei Li, Timothy C. Miller","doi":"10.1080/15427560.2021.1949715","DOIUrl":"https://doi.org/10.1080/15427560.2021.1949715","url":null,"abstract":"Abstract Motivated by the mixed findings in prior archival studies, this study conducts three experiments to examine the relationships among analysts’ intrinsic valuation estimates (V), stock recommendations (REC) and stock returns. Experiment 1, built on implications from prospect theory, provides direct observations on analysts’ asymmetric use of the valuation to price (V/P) ratio in making their REC. Experiments 2 and 3 indicate differences and similarities between professional and nonprofessional investors in using analysts’ V and REC in making their investment-related judgments. Our results provide implications for both research and practice.","PeriodicalId":47016,"journal":{"name":"Journal of Behavioral Finance","volume":"1 1","pages":"147 - 160"},"PeriodicalIF":1.9,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89878507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-12DOI: 10.1080/15427560.2021.1949717
Aditya Ganesh, S. Iyer
ABSTRACT Recent SEC guidelines enabled many Fortune 500 companies to actively adopt social media, such as Twitter, to disseminate information. In this paper, we analyze the relationship between tweets by corporations and stock returns. Our study used over 1.2 million corporate tweets made by thirty companies in the Dow Jones Industrial Average between April 2013 and July 2020. The shocks from the frequency of corporate tweets can positively impact stock returns and trading volume. We, therefore, examine causality and impulse response between frequency of corporate tweets, stock returns, and changes in trading volume using a vector autoregression model. Our findings indicate that 43 percent of stocks exhibit Granger causality between firm-initiated tweets and changes in trading volume. We find evidence consistent with the attention-induced price pressure hypothesis proposed by Barber and Odean. We observe that a shock in corporate tweeting behavior translates into a positive effect on changes in trading volume and returns in 73 percent and 60 percent of stocks, respectively. These results are significant for developing appropriate social media communication strategies. The findings are also valuable for investors and traders who can deploy forecasting models utilizing corporate tweets to earn superior returns.
{"title":"Impact of Firm-Initiated Tweets on Stock Return and Trading Volume","authors":"Aditya Ganesh, S. Iyer","doi":"10.1080/15427560.2021.1949717","DOIUrl":"https://doi.org/10.1080/15427560.2021.1949717","url":null,"abstract":"ABSTRACT Recent SEC guidelines enabled many Fortune 500 companies to actively adopt social media, such as Twitter, to disseminate information. In this paper, we analyze the relationship between tweets by corporations and stock returns. Our study used over 1.2 million corporate tweets made by thirty companies in the Dow Jones Industrial Average between April 2013 and July 2020. The shocks from the frequency of corporate tweets can positively impact stock returns and trading volume. We, therefore, examine causality and impulse response between frequency of corporate tweets, stock returns, and changes in trading volume using a vector autoregression model. Our findings indicate that 43 percent of stocks exhibit Granger causality between firm-initiated tweets and changes in trading volume. We find evidence consistent with the attention-induced price pressure hypothesis proposed by Barber and Odean. We observe that a shock in corporate tweeting behavior translates into a positive effect on changes in trading volume and returns in 73 percent and 60 percent of stocks, respectively. These results are significant for developing appropriate social media communication strategies. The findings are also valuable for investors and traders who can deploy forecasting models utilizing corporate tweets to earn superior returns.","PeriodicalId":47016,"journal":{"name":"Journal of Behavioral Finance","volume":"2008 1","pages":"171 - 182"},"PeriodicalIF":1.9,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86236337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-12DOI: 10.1080/15427560.2021.1949716
Mohammad Hashemi Joo, Edward R. Lawrence, Yuka Nishikawa
Abstract This study investigates the relationship between founder-led firms and non-securities (operational) litigation risk. We postulate lower operational litigation risk for founder-led firms than for nonfounder-led firms based on founder-CEOs’ limited agency conflicts and stronger emotional attachment to the firms they establish. Our empirical results suggest that having a founder as CEO mitigates the risk of being involved in operational lawsuits that could result in substantial financial losses and long-lasting negative consequences.
{"title":"Founder-Led Firms and Operational Litigation Risk","authors":"Mohammad Hashemi Joo, Edward R. Lawrence, Yuka Nishikawa","doi":"10.1080/15427560.2021.1949716","DOIUrl":"https://doi.org/10.1080/15427560.2021.1949716","url":null,"abstract":"Abstract This study investigates the relationship between founder-led firms and non-securities (operational) litigation risk. We postulate lower operational litigation risk for founder-led firms than for nonfounder-led firms based on founder-CEOs’ limited agency conflicts and stronger emotional attachment to the firms they establish. Our empirical results suggest that having a founder as CEO mitigates the risk of being involved in operational lawsuits that could result in substantial financial losses and long-lasting negative consequences.","PeriodicalId":47016,"journal":{"name":"Journal of Behavioral Finance","volume":"22 1","pages":"183 - 201"},"PeriodicalIF":1.9,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85114097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-10DOI: 10.1080/15427560.2021.1949719
Rangan Gupta, Jacobus Nel, Christian Pierdzioch
Abstract Using a machine-learning technique known as random forests, we analyze the role of investor confidence in forecasting monthly aggregate realized stock-market volatility of the United States (US), over and above a wide-array of macroeconomic and financial variables. We estimate random forests on data for a period from 2001 to 2020, and study horizons up to one year by computing forecasts for recursive and a rolling estimation window. We find that investor confidence, and especially investor confidence uncertainty has out-of-sample predictive value for overall realized volatility, as well as its “good” and “bad” variants. Our results have important implications for investors and policymakers.
{"title":"Investor Confidence and Forecastability of US Stock Market Realized Volatility: Evidence from Machine Learning","authors":"Rangan Gupta, Jacobus Nel, Christian Pierdzioch","doi":"10.1080/15427560.2021.1949719","DOIUrl":"https://doi.org/10.1080/15427560.2021.1949719","url":null,"abstract":"Abstract Using a machine-learning technique known as random forests, we analyze the role of investor confidence in forecasting monthly aggregate realized stock-market volatility of the United States (US), over and above a wide-array of macroeconomic and financial variables. We estimate random forests on data for a period from 2001 to 2020, and study horizons up to one year by computing forecasts for recursive and a rolling estimation window. We find that investor confidence, and especially investor confidence uncertainty has out-of-sample predictive value for overall realized volatility, as well as its “good” and “bad” variants. Our results have important implications for investors and policymakers.","PeriodicalId":47016,"journal":{"name":"Journal of Behavioral Finance","volume":"39 1","pages":"111 - 122"},"PeriodicalIF":1.9,"publicationDate":"2021-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80578930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-25DOI: 10.1080/15427560.2021.1913160
Anastasios Petropoulos, Vasileios G. Siakoulis, Evangelos Stavroulakis, Panagiotis Lazaris, Nikolaos E. Vlachogiannakis
Abstract In this paper we apply text mining methodologies on a set of 10,000 Central Bank speeches to construct a financial dictionary, based on which we use Google Trends indices to measure people’s interest in financial news. Particularly, we investigate the relationship between these indices and financial market turbulence leveraging on Deep Learning techniques, which are benchmarked against a variety of Machine Learning algorithms and traditional statistical techniques. Our main finding is that Google queries convey information able to predict future market turbulence in a short time period (one month), and that Deep Learning algorithms clearly outperform over benchmark techniques. Google Trends can provide useful input in the creation of crisis Early Warning Systems, as social data are more responsive compared to official financial indicators, which are usually available with a lag of several weeks or months. Thus, such an Early Warning System (EWS) that is continuously updated with current social data can be a valuable tool for policymakers, as it can immediately identify signs of whether a crisis is imminent or not.
{"title":"Employing Google Trends and Deep Learning in Forecasting Financial Market Turbulence","authors":"Anastasios Petropoulos, Vasileios G. Siakoulis, Evangelos Stavroulakis, Panagiotis Lazaris, Nikolaos E. Vlachogiannakis","doi":"10.1080/15427560.2021.1913160","DOIUrl":"https://doi.org/10.1080/15427560.2021.1913160","url":null,"abstract":"Abstract In this paper we apply text mining methodologies on a set of 10,000 Central Bank speeches to construct a financial dictionary, based on which we use Google Trends indices to measure people’s interest in financial news. Particularly, we investigate the relationship between these indices and financial market turbulence leveraging on Deep Learning techniques, which are benchmarked against a variety of Machine Learning algorithms and traditional statistical techniques. Our main finding is that Google queries convey information able to predict future market turbulence in a short time period (one month), and that Deep Learning algorithms clearly outperform over benchmark techniques. Google Trends can provide useful input in the creation of crisis Early Warning Systems, as social data are more responsive compared to official financial indicators, which are usually available with a lag of several weeks or months. Thus, such an Early Warning System (EWS) that is continuously updated with current social data can be a valuable tool for policymakers, as it can immediately identify signs of whether a crisis is imminent or not.","PeriodicalId":47016,"journal":{"name":"Journal of Behavioral Finance","volume":"33 1","pages":"353 - 365"},"PeriodicalIF":1.9,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84672904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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