Review of Behavioral Finance最新文献

{"title":"How do personality traits affect investors' decision on crypto market including cryptocurrencies and NFTs?","authors":"Ji Luo, Qingning Cao, Shuguang Zhang","doi":"10.1108/rbf-03-2023-0075","DOIUrl":"https://doi.org/10.1108/rbf-03-2023-0075","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>The purpose of the research paper is to investigate the relationship between personality traits and investment decisions in the crypto market, including cryptocurrencies and NFTs. The study aims to explore the effect of dark personalities and the big five personalities on investment decisions in the crypto market.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>The research was conducted through two online questionnaire studies. In Study 1, data were collected from the general public, while in Study 2, data were collected from crypto investors. The researchers analyzed the effect of dark personalities and the big five personalities on investment decisions in the crypto market.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>The present research found that Machiavellianism, narcissism, psychopath, sadism and extraversion have positive effects on having crypto investments. In addition, focusing on actual crypto investors, the present paper showed that personalities including Machiavellianism, narcissism, psychopath, consciousness and extraversion have statistically significant effect on investment decisions such as making investments in Bitcoin.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>The study is original in exploring the relationship between personality traits and investment decisions in the newly emerging crypto market, including cryptocurrencies and NFTs. The research provides insights into how different personality traits affect investment decisions in the crypto market, which can be valuable for investors in making informed decisions.</p><!--/ Abstract__block -->","PeriodicalId":44559,"journal":{"name":"Review of Behavioral Finance","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138493841","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}

{"title":"Familiarity bias in direct stock investment by individual investors","authors":"Shan Lei, Ani Manakyan Mathers","doi":"10.1108/rbf-03-2023-0074","DOIUrl":"https://doi.org/10.1108/rbf-03-2023-0074","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>This study examines the relationship between investors' familiarity bias, including the home bias and endowment bias, and their financial situations, expectations and personal characteristics.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>Using the 2019 Survey of Consumer Finances, the authors utilize an ordinary least squares regression to identify the presence of endowment bias and home bias in individual investors' direct stock holdings and use a Heckman selection model to examine determinants of the extent of endowment bias and home bias.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>This study finds that investors with higher income and more education, men, non-white investors and people with greater risk tolerance are actually at a greater risk of endowment bias. This study also identifies a profile of investors that are more likely to have a home bias: with less financial sophistication, lower net worth, older, female, more risk-averse, with a positive expectation about the domestic economy and a relatively shorter investment horizon.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>This paper is among the first to use US investors' directly reported stock holdings to examine the individual characteristics that are correlated with greater familiarity bias, providing financial professionals with information about how to allocate their limited time in providing education to a variety of clients.</p><!--/ Abstract__block -->","PeriodicalId":44559,"journal":{"name":"Review of Behavioral Finance","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138516697","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}

{"title":"Risk preference, payday loans and other alternative financial services","authors":"Song Wang","doi":"10.1108/rbf-04-2023-0099","DOIUrl":"https://doi.org/10.1108/rbf-04-2023-0099","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>The purpose of this paper is to examine how individual risk preference influences the borrowing of payday loans – a prevalent type of cash loan in the USA with exorbitantly high-interest rates. Additionally, this paper tests how risk preference determines other alternative financial services (AFS), including pawn shops, rent-to-own purchases, title loans, etc.</p><!--/ Abstract__block -->\u0000<h3>Design/methodology/approach</h3>\u0000<p>The author applies Probit and Tobit regressions to test the relationship between individual risk preference and payday borrowing, based on the state-by-state survey data from National Financial Capability Study (NFCS) sponsored by Financial Industry Regulatory Authority (FINRA) Investor Education Foundation.</p><!--/ Abstract__block -->\u0000<h3>Findings</h3>\u0000<p>Individuals with higher risk tolerance are more likely to borrow payday loans and other AFS, after controlling for financial situation, financial literacy, overconfidence and demographic features.</p><!--/ Abstract__block -->\u0000<h3>Originality/value</h3>\u0000<p>This paper is the first to study risk preference as an explanation to the high cost and widely used payday loan services in the United States of America. This study provides evidence that these cash loans are determined by inherent human characteristics. The finding provides new insight for the policymakers and regulators in the consumer debt market.</p><!--/ Abstract__block -->","PeriodicalId":44559,"journal":{"name":"Review of Behavioral Finance","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138516710","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}

{"title":"Sentiment investor, exchange rates, geopolitical risk and developing stock market: evidence of co-movements in the time-frequency domain during RussiaUkraine war","authors":"Fatma Hachicha","doi":"10.1108/rbf-04-2023-0119","DOIUrl":"https://doi.org/10.1108/rbf-04-2023-0119","url":null,"abstract":"Purpose The aim of this paper is threefold: (1) to develop a new measure of investor sentiment rational (ISR) of developing countries by applying principal component analysis (PCA), (2) to investigate co-movements between the ten developing stock markets, the sentiment investor's, exchange rates and geopolitical risk (GPR) during Russian invasion of Ukraine in 2022, (3) to explore the key factors that might affect exchange market and capital market before and mainly during Russia–Ukraine war period. Design/methodology/approach The wavelet approach and the multivariate wavelet coherence (MWC) are applied to detect the co-movements on daily data from August 2019 to December 2022. Value-at-risk (VaR) and conditional value-at-risk (CVaR) are used to assess the systemic risks of exchange rate market and stock market return in the developing market. Findings Results of this study reveal (1) strong interdependence between GPR, investor sentiment rational (ISR), stock market index and exchange rate in short- and long-terms in most countries, as inferred from (WTC) analysis. (2) There is evidence of strong short-term co-movements between ISR and exchange rates, with ISR leading. (3) Multivariate coherency shows strong contributions of ISR and GPR index to stock market index and exchange rate returns. The findings signal the attractiveness of the Vietnamese dong, Malaysian ringgits and Tunisian dinar as a hedge for currency portfolios against GPR. The authors detect a positive connectedness in the short term between all pairs of the variables analyzed in most countries. (4) Both foreign exchange and equity markets are exposed to higher levels of systemic risk in the period of the Russian invasion of Ukraine. Originality/value This study provides information that supports investors, regulators and executive managers in developing countries. The impact of sentiment investor with GPR intensified the co-movements of stocks market and exchange market during 2021–2022, which overlaps with period of the Russian invasion of Ukraine.","PeriodicalId":44559,"journal":{"name":"Review of Behavioral Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136227739","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}

{"title":"Investor sentiments revisited: negligence of stock-level sentiments may be a mistake","authors":"Te-Kuan Lee, Askar Koshoev","doi":"10.1108/rbf-02-2023-0037","DOIUrl":"https://doi.org/10.1108/rbf-02-2023-0037","url":null,"abstract":"Purpose The primary objective of this research is to provide evidence that there are two distinct layers of investor sentiments that can affect asset valuation models. The first is general market-wide sentiments, while the second is biased approaches toward specific assets. Design/methodology/approach To achieve the goal, the authors conducted a multi-step analysis of stock returns and constructed complex sentiment indices that reflect the optimism or pessimism of stock market participants. The authors used panel regression with fixed effects and a sample of the US stock market to improve the explanatory power of the three-factor models. Findings The analysis showed that both market-level and stock-level sentiments have significant contributions, although they are not equal. The impact of stock-level sentiments is more profound than market-level sentiments, suggesting that neglecting the stock-level sentiment proxies in asset valuation models may lead to severe deficiencies. Originality/value In contrast to previous studies, the authors propose that investor sentiments should be measured using a multi-level factor approach rather than a single-factor approach. The authors identified two distinct levels of investor sentiment: general market-wide sentiments and individual stock-specific sentiments.","PeriodicalId":44559,"journal":{"name":"Review of Behavioral Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135431099","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}

{"title":"Analyst coverage and the probability of stock price crash and jump","authors":"Mohammed Bouaddi, Omar Farooq, Catalina Hurwitz","doi":"10.1108/rbf-06-2022-0156","DOIUrl":"https://doi.org/10.1108/rbf-06-2022-0156","url":null,"abstract":"Purpose The aim of this paper is to document the effect of analyst coverage on the ex ante probability of stock price crash and the ex ante probability stock price jump. Design/methodology/approach This paper uses the data of non-financial firms from France to test the arguments presented in this paper during the period between 1997 and 2019. The paper also uses flexible quadrants copulas to compute the ex ante probabilities of crashes and jumps. Findings The results show that the extent of analyst coverage is positively associated with the ex ante probability of crash and negatively associated with the ex ante probability of jump. The results remain qualitatively the same after several sensitivity checks. The results also show that the relationship between the extent of analyst coverage and the probability of cash and the probability of jump holds when ex post probability of stock price crash and stock price jump is used. Originality/value Unlike most of the earlier papers on this topic, this paper uses the ex ante probability of crash and jump. This proxy is better suited than the ones used in the prior literature because it is a forward-looking measure.","PeriodicalId":44559,"journal":{"name":"Review of Behavioral Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135431102","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}

{"title":"Investigation of herding behavior using machine learning models","authors":"Muhammad Asim, Muhammad Yar Khan, Khuram Shafi","doi":"10.1108/rbf-05-2023-0121","DOIUrl":"https://doi.org/10.1108/rbf-05-2023-0121","url":null,"abstract":"Purpose The study aims to investigate the presence of herding behavior in the stock market of UK with a special emphasis on news sentiment regarding the economy. The authors focus on the news sentiment because in the current digital era, investors take their decision making on the basis of current trends projected by news and media platforms. Design/methodology/approach For empirical modeling, the authors use machine learning models to investigate the presence of herding behavior in UK stock market for the period starting from 2006 to 2021. The authors use support vector regression, single layer neural network and multilayer neural network models to predict the herding behavior in the stock market of the UK. The authors estimate the herding coefficients using all the models and compare the findings with the linear regression model. Findings The results show a strong evidence of herding behavior in the stock market of the UK during different time regimes. Furthermore, when the authors incorporate the economic uncertainty news sentiment in the model, the results show a significant improvement. The results of support vector regression, single layer perceptron and multilayer perceptron model show the evidence of herding behavior in UK stock market during global financial crises of 2007–08 and COVID’19 period. In addition, the authors compare the findings with the linear regression which provides no evidence of herding behavior in all the regimes except COVID’19. The results also provide deep insights for both individual investors and policy makers to construct efficient portfolios and avoid market crashes, respectively. Originality/value In the existing literature of herding behavior, news sentiment regarding economic uncertainty has not been used before. However, in the present era this parameter is quite critical in context of market anomalies hence and needs to be investigated. In addition, the literature exhibits varying results about the existence of herding behavior when different methodologies are used. In this context, the use of machine learning models is quite rare in the herding literature. The machine learning models are quite robust and provide accurate results. Therefore, this research study uses three different models, i.e. single layer perceptron model, multilayer perceptron model and support vector regression model to investigate the herding behavior in the stock market of the UK. A comparative analysis is also presented among the results of all the models. The study sheds light on the importance of economic uncertainty news sentiment to predict the herding behavior.","PeriodicalId":44559,"journal":{"name":"Review of Behavioral Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135217538","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}

{"title":"The value and growth effect in the Vietnamese stock market: a mispricing explanation","authors":"Le Quy Duong","doi":"10.1108/rbf-04-2023-0090","DOIUrl":"https://doi.org/10.1108/rbf-04-2023-0090","url":null,"abstract":"Purpose Although the value effect is comprehensively investigated in developed markets, the number of studies examining the Vietnamese stock market is limited. Hence, the first aim of this research is to provide empirical evidence regarding returns on value and growth stocks in Vietnam. The second aim is to explain abnormal returns on Vietnamese growth and value stocks using both risk-based and behavioral points of view. Design/methodology/approach From the risk-based explanation, the Capital Asset Pricing Model (CAPM), Fama–French three- and five-factor models are estimated. From the behavioral explanation, to construct the mispricing factor, this paper relies on the method of Rhodes-Kropf et al. (2005), one of the most popular mispricing estimations in the financial literature with numerous citations (Jaffe et al ., 2020). Findings While the CAPM and Fama–French multifactor models cannot capture returns on growth and value stocks, a three-factor model with the mispricing factor has done an excellent job in explaining their returns. Three out of four Fama–French mimic factors do not contain additional information on expected returns. Their risk premiums are also statistically insignificant according to the Fama–MacBeth second-stage regression. By contrast, both robustness tests prove the explanatory power of a three-factor model with mispricing. Taken together, mispricing plays an essential role in explaining returns on Vietnamese growth and value stocks, consistent with the behavioral point of view. Originality/value There are several value-enhancing aspects in the field of market finance. First, this paper contributes to the literature of value effect in emerging markets. While the evidence of value effect is obvious in numerous developed as well as international markets, both growth and value effects are discovered in Vietnam. Second, the explanatory power of Fama–French multifactor models is evaluated in the Vietnamese context. Finally, to the best of the author's knowledge, this is the first paper that incorporates the mispricing estimation of Rhodes-Kropf et al. (2005) into the asset pricing model in Vietnam.","PeriodicalId":44559,"journal":{"name":"Review of Behavioral Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135220569","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}

{"title":"Herd behavior in cryptocurrency market: evidence of network effect","authors":"Phasin Wanidwaranan, Santi Termprasertsakul","doi":"10.1108/rbf-03-2023-0079","DOIUrl":"https://doi.org/10.1108/rbf-03-2023-0079","url":null,"abstract":"Purpose This study examines herd behavior in the cryptocurrency market at the aggregate level and the determinants of herd behavior, such as asymmetric market returns, the coronavirus disease 2019 (COVID-19) pandemic, 2021 cryptocurrency's bear market and the network effect. Design/methodology/approach The authors applied the Google Search Volume Index (GSVI) as a proxy for the network effect. Since investors who are interested in a particular issue have a common interest, they tend to perform searches using the same keywords in Google and are on the same network. The authors also investigated the daily returns of cryptocurrencies, which are in the top 100 market capitalizations from 2017 to 2022. The authors also examine the association between return dispersion and portfolio return based on aggregate market herding model and employ interactions between herding determinants such as, market direction, market trend, COVID-19 and network effect. Findings The empirical results indicate that herding behavior in the cryptocurrency market is significantly captured when the market returns of cryptocurrency tend to decline and when the network effect of investors tends to expand (e.g. such as during the COVID-19 pandemic or 2021 Bitcoin crash). However, the results confirm anti-herd behavior in cryptocurrency during the COVID-19 pandemic or 2021 Bitcoin crash, regardless of the network effect. Practical implications These findings help investors in the cryptocurrency market make more rational decisions based on their determinants since cryptocurrency is an alternative investment for investors' asset allocation. As imitating trades lead to return comovement, herd behavior in the cryptocurrency has a direct impact on the effectiveness of portfolio diversification. Hence, market participants or investors should consider herd behavior and its underlying factors to fully maximize the benefits of asset allocation, especially during the period of market uncertainty. Originality/value Most previous studies have focused on herd behavior in the stock market. Although some researchers have recently begun studying herd behavior in the cryptocurrency market, the empirical results are inconclusive due to an incorrectly specified model or unclear determinants.","PeriodicalId":44559,"journal":{"name":"Review of Behavioral Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136293218","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}

{"title":"Herding behavior by socially responsible investors during the COVID-19 pandemic","authors":"Manuel Lobato, Javier Rodríguez, Herminio Romero-Perez","doi":"10.1108/rbf-04-2023-0101","DOIUrl":"https://doi.org/10.1108/rbf-04-2023-0101","url":null,"abstract":"Purpose This study aims to examine the herding behavior of socially responsible exchange traded funds (SR ETFs) in comparison to conventional ETFs during the COVID-19 pandemic. Design/methodology/approach To test for herding behavior, the authors use the cross-sectional absolute deviation and a quadratic market model. Findings During the pandemic, investments in socially responsible financial products grew rapidly. And investors in the popular SR ETFs herd during this special period, while holders of conventional ETFs did not. Practical implications Investors in socially responsible investments must do their own research and make their own financial decisions, rather than follow the crowd, especially during extreme events like the COVID-19 pandemic. Originality/value The evidence shows that, during the pandemic, socially responsible ETFs behaved in line with theoretical predictions of herding, that is, herding is more significant during extreme market conditions.","PeriodicalId":44559,"journal":{"name":"Review of Behavioral Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134885443","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}