Journal of Behavioral Finance最新文献_第10页

Momentum, Information, and Herding 动量、信息和羊群

IF 1.9 3区经济学

Journal of Behavioral Finance Pub Date : 2021-09-06 DOI: 10.1080/15427560.2021.1971983

Zhilu Lin, Wentao Wu, Haoran Zhang

引用次数: 0

How Do Limit Orders Affect the Disposition Effect on Highly Liquid Markets – Experimental Finance Evidence 在高流动性市场上，限价单如何影响处置效应——实验性金融证据

IF 1.9 3区经济学

Journal of Behavioral Finance Pub Date : 2021-09-02 DOI: 10.1080/15427560.2021.1973006

Hana Dvořáčková, T. Tichý, Marek Jochec

引用次数: 1

Does Sentiment Impact Cryptocurrency? 市场情绪会影响加密货币吗?

IF 1.9 3区经济学

Journal of Behavioral Finance Pub Date : 2021-08-04 DOI: 10.1080/15427560.2021.1950723

Anamika, Madhumita Chakraborty, S. Subramaniam

引用次数: 39

The Role of Investor Sentiment and Valuation Uncertainty in the Changes around Analyst Recommendations: Evidence from U.S. Firms 投资者情绪和估值不确定性在分析师建议变化中的作用:来自美国公司的证据

IF 1.9 3区经济学

Journal of Behavioral Finance Pub Date : 2021-07-28 DOI: 10.1080/15427560.2021.1948853

Ahmed Bouteska, Mehdi Mili

{"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}

引用次数: 3

Are All the Sentiment Measures the Same? 所有的情绪指标都一样吗?

IF 1.9 3区经济学

Journal of Behavioral Finance Pub Date : 2021-07-24 DOI: 10.1080/15427560.2021.1949718

Qiang Bu

引用次数: 2

Independence of Intrinsic Valuations and Stock Recommendations – Experimental Evidence from Equity Research Analysts and Investors 内在估值和股票推荐的独立性——来自股票研究分析师和投资者的实验证据

IF 1.9 3区经济学

Journal of Behavioral Finance Pub Date : 2021-07-15 DOI: 10.1080/15427560.2021.1949715

Ran Barniv, Wei Li, Timothy C. Miller

引用次数: 1

Impact of Firm-Initiated Tweets on Stock Return and Trading Volume 公司发起的推文对股票收益和交易量的影响

IF 1.9 3区经济学

Journal of Behavioral Finance Pub Date : 2021-07-12 DOI: 10.1080/15427560.2021.1949717

Aditya Ganesh, S. Iyer

{"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}

引用次数: 4

Founder-Led Firms and Operational Litigation Risk 创始人领导的公司和运营诉讼风险

IF 1.9 3区经济学

Journal of Behavioral Finance Pub Date : 2021-07-12 DOI: 10.1080/15427560.2021.1949716

Mohammad Hashemi Joo, Edward R. Lawrence, Yuka Nishikawa

引用次数: 0

Investor Confidence and Forecastability of US Stock Market Realized Volatility: Evidence from Machine Learning 美国股市已实现波动的投资者信心和可预测性:来自机器学习的证据

IF 1.9 3区经济学

Journal of Behavioral Finance Pub Date : 2021-07-10 DOI: 10.1080/15427560.2021.1949719

Rangan Gupta, Jacobus Nel, Christian Pierdzioch

引用次数: 12

Employing Google Trends and Deep Learning in Forecasting Financial Market Turbulence 运用谷歌趋势和深度学习预测金融市场动荡

IF 1.9 3区经济学

Journal of Behavioral Finance Pub Date : 2021-05-25 DOI: 10.1080/15427560.2021.1913160

Anastasios Petropoulos, Vasileios G. Siakoulis, Evangelos Stavroulakis, Panagiotis Lazaris, Nikolaos E. Vlachogiannakis

{"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}

引用次数: 9