Revisiting the ICAPM under the distortion of risk–return tradeoff in short-horizon stock returns

We suggest that the distortion of the positive risk–return relation in the ICAPM is a consequence of trading by informed investors to exploit mispricing. We hypothesize and demonstrate that a non-positive (strongly positive) risk–return relation following positive (negative) market returns is attributed to short-selling (purchasing) of overpriced (underpriced) stocks along with optimistic (pessimistic) expectations conditional on good (bad) market news. We verify this asymmetry in the risk–return relation through the indirect risk–return relation conditional on good (bad) market news. We also find that the attenuation (reinforcement) of the positive risk–return relation is more profound in high- (low-) sentiment periods.     

Collective mental accounting: an integrated behavioural portfolio selection model for multiple mental accounts

We propose a behavioural portfolio selection model called collective mental accounting (CMA), which integrates all mental sub-portfolios (mental accounts) in one mathematical model. Moreover, this study contributes to the literature of behavioural portfolio selection in three further ways: first, the CMA model can determine the proportions of wealth allocated to each mental sub-portfolio with and without input from the investor. Second, unlike other mental accounting models (MA), in CMA it is possible to define constraints on total asset holdings such as short-selling, and cardinality constraints. Third, in order to make CMA more tractable and mathematically elegant, we obtain a semi-definite programming representation of the model. We also present a numerical example to investigate the effects of short-selling constraints as well as to compare the portfolio recommendations, utility functions, feasibility, and optimality of the CMA and MA models. The results reveal that although both models’ solutions are mean-variance efficient, CMA outperforms MA in terms of behavioural efficient frontier and utility functions.     

Positive Feedback Trading and Investor Sentiment

This article examines how investor sentiment affects positive feedback trading behavior. By analyzing the daily closing total return of CSI 300 index and its individual returns of stocks, we find that relatively high or low sentiment induces active positive feedback trading. With a specific indicator of sentiment, we explain the microstructure setting of the relationship between positive feedback trading and sentiment. We adopt the classical feedback model from Sentana and Wadhwani (1992) to measure positive feedback trading behavior. By adding sentiment factor to the model, we successfully explain how sentiment influences the behavior of both feedback traders and rational investors. The empirical findings suggest that positive feedback traders are more likely to trade when the prices of most securities move forward together. When the sentiment of feedback traders is at an intermediate level, the feedback trading behavior is insignificant.     

Aggregate insider trading and market returns: The role of transparency

Using a sample of countries that require timely disclosures of insider trades, I investigate the effect of country‐level institutions that promote transparency on the extent to which aggregate insider trades predict market returns. I find that financial information transparency mitigates the predictive content of aggregate insider trades when markets are more likely to deviate from fundamentals (i.e., during market fads), and when there is greater co‐movement in stock prices. In contrast, there is some evidence that governance and investor protection mitigate the association between aggregate insider trades and future earnings surprises. Hence, holding constant the timely disclosures of insider trades, other capital market institutions play complementary roles in mitigating the informational frictions that give rise to the predictive content of aggregate insider trades.     

Comparing consumer-produced product reviews across multiple websites with sentiment classification

ABSTRACT

Online consumer reviews have been extensively studied. However, existing literature analyzing online consumer review data mostly relies on a single data source, resulting in potentially biased analytics conclusions. Many websites encourage consumers to post reviews of their purchased products, so that new consumers can evaluate these reviews for the same product across different websites to help them make purchasing decisions. Confusions often arise in this process, because there often exist substantial discrepancies in customer reviews across different retailers on the same product. Clarifying such confusions can help consumers reduce concerns to make up their mind for their purchases, therefore benefiting both consumers and retailers. Through text analytics and sentiment analysis, we comparatively examine the underlying patterns of online consumer reviews of three large retailers including Sears, Home Depot, and Best Buy for a same product. Afterward, we combine online consumer reviews from these large retailers and conduct an overall text analytics and sentiment analysis. The overall results are further compared with the results from individual retailers. The findings show that the sentiment of the online consumer reviews could vary substantially so relying on a single data source to make purchase decision is not a wise idea. Based on the results, we further devise a framework to comparatively examine and integrate multiple data sources for social media analytics of online consumer reviews. This study offers important managerial implications and identifies several new research directions for social media analytics.     

Relying on the heuristic of trust: a case study

This study examines the reliance on trust as a heuristic by individuals when making personal financial decisions, using a qualitative case study. We show that in the face of complexity and choice, individual investors predominantly resorted to the heuristic of trust to make financial decisions. This demonstrates the need for industry and public policy‐makers to be aware that individuals can and will resort to simplified heuristics as a basis for financial decision‐making, particularly within an environment where substantial complexity and choice exist.     

Exploring sentiment-driven trading behaviour of different types of investors in the London office market

We investigates the sentiment-driven trading behaviour of the four types of investors in the London office market, i.e. UK institutional investors, UK private investors, UK listed real estate companies/Real Estate Investment Trust (REIT)s and overseas investors. In addition, we examine the relationship between investor sentiment and property performance. Related indices are calculated to examine the existence of herding behaviour of different investors. We find that UK private investors follow a contrarian strategy to UK institutional investors and listed real estate companies/REITs and enter/exit the market at different points of time. UK institutional investors tend to follow the sentiment of UK listed real estate companies/REITs and overseas investors with lags. There is no evidence that overseas investors rely upon the sentiment of UK specialised property investors in their decision-making. We find the sentiment of different investors is influenced differently by market fundamentals. Yield and rental growth rate have significant impact on trading activity of overseas investors, but not on other investors. The stock market return and securitised real estate return have significant impact on the trading activity of UK institutional investor and overseas investor, but have no significant influence on the trading behaviour of UK private investor and listed real estate company/REIT.     

Media-Based Sentiment Indices as an Alternative Measure of Consumer Confidence

In this paper, we consider the feasibility of constructing online sentiment indices, using large amounts of media data, as an alternative to the conventional survey method used to create the consumer confidence index in South Africa. A clustering framework is adopted to provide an indication of possible candidate sentiment indices constructed from a combination of different text sources and dictionaries that best mimic the traditional survey-based consumer confidence index from the South African Bureau for Economic Research (BER). The results conclude that it is possible to create an index using sentiment analysis using online editorial data that does resemble the BER’s consumer confidence index. The different media-based sentiment indices (MSI) show a significant level of correlation and co-movement with the BER’s CCI. Impulse responses and cross-correlation functions indicate that the MSI could potentially lead the survey-based method up to two quarters. Furthermore, Granger-causality tests show that the media-based indices are good predictors of future consumer confidence index values. The results provide motivation for further study on the use of sentiment-based techniques and online media data sources to track consumer confidence within an emerging market such as South Africa.     

禽流感危机下肉鸡产业市场状态分析*——基于互联网大数据和MS-VAR模型

[目的]为了有效应对禽流感冲击、维持肉鸡产业稳定健康发展。[方法]基于互联网大数据构建了禽流感舆情指数,选取2012年1月至2017年3月周度数据,通过MS-VAR模型分析了禽流感危机下肉鸡市场状态的转换特征以及不同市场状态下产业链价格传导关系。[结果]2012年以来可将肉鸡产业市场状态划分为危机状态和正常状态,肉鸡产业市场在两个市场状态下转换频繁;肉鸡产业危机市场状态和正常市场状态平均持续期分别为4.65周和9.05周;肉鸡市场在区制转移概率上存在明显的非对称特征;肉雏鸡、活鸡和白条鸡价格之间在危机状态下相关性小于正常市场状态下相关性;不同市场状态下肉鸡产业链价格传导存在差异;肉鸡产业市场在危机状态下不稳定程度明显增加。[结论]提出了加大对禽流感疫情的监测以及疫情处理能力,密切关注公众禽流感舆情动态变动情况,做好舆情引导,加强禽流感疫情科学知识的宣传,增强公众对禽流感的认知等对策建议。     

The impact of COVID-19 on the stock market crash risk in China

This study investigates the impact of the COVID-19 pandemic on the stock market crash risk in China. For this purpose, we first estimated the conditional skewness of the return distribution from a GARCH with skewness (GARCH-S) model as the proxy for the equity market crash risk of the Shanghai Stock Exchange. We then constructed a fear index for COVID-19 using data from the Baidu Index. Based on the findings, conditional skewness reacts negatively to daily growth in total confirmed cases, indicating that the pandemic increases stock market crash risk. Moreover, the fear sentiment exacerbates such risk, especially with regard to the impact of COVID-19. In other words, when the fear sentiment is high, the stock market crash risk is more strongly affected by the pandemic. Our evidence is robust for the number of daily deaths and global cases.