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.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

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.     

Mobile Commerce Website Success: Antecedents of Consumer Satisfaction and Purchase Intention

With the rapid development of apparel mobile commerce in the United States, more companies view mobile commerce as a new source of competitive advantage. Despite the importance of apparel mobile website quality and its effect on consumer satisfaction and future purchase stimulus, extant research has paid little attention to these topics. This study proposes a website quality–consumer satisfaction–purchase intention research model based on the self-regulatory process theory. Six dimensions of apparel mobile website quality—website visual appeal, apparel visual appeal, brand trust, website information quality, website response time, and website security—were investigated. In all, 293 eligible responses were collected via an online survey. Multiple regression analysis was utilized to test the proposed relationships. Results reveal that website information quality, website visual appeal, apparel visual appeal, and website security positively affect consumer satisfaction toward apparel mobile commerce websites, while website response time and brand trust show insignificant impacts on consumer satisfaction. With higher satisfaction on an apparel mobile commerce website, consumers are more likely to purchase apparel through the website.     

The impact of German-speaking online media on tourist arrivals in popular tourist destinations for Europeans

This empirical study analyzes the relationship between the sentiments in online media with regard to travel destinations and corresponding tourist arrivals. We expect the media reports on political and economic instability and turmoil to enhance tourist arrival nowcasts and forecasts, as they can probably complement them with information on disruptions and shocks. Therefore, we believe this research will help to build better models for tourism demand nowcasting and forecasting. We use the sentiment in the German-speaking online media because the German-speaking region is the most populated in Europe and has the largest group of travelers visiting destinations in and around Europe.

An artificial neural network is used to analyze the mood of the media. The software classifies news items regarding potential tourist destinations with either positive or negative labels. The number of positive and negative news items is used to build sentiment indices for popular tourist destinations for Europeans.

Our results show strong correlations between the mood concerning tourist destinations and tourist arrivals in these countries. Indeed, disruptions and shocks prevalent in the news are reflected in similar ratios in both tourist arrivals and sentiment indices. These results can be used as a new explanatory variable for tourism demand modelling.     

Does local political support influence financial markets? A study on the impact of job approval ratings of political representatives on local stock returns

Using data on job approval ratings of governors, U.S. senators, and the president, we find that firms located in states with high approval ratings outperform firms located in states with low approval ratings by .64% per month. Furthermore, this relationship is stronger when investors are actively involved in politics, when local politicians are closer to the center of political power, for small firms that have a larger proportion of local investors, and for financially strong areas where investors are ready to execute investments in local stocks. Overall, our study shows that investors’ political sentiment is important in determining stock returns.     

Islamic and conventional portfolios optimization under investor sentiment states: Bayesian vs Markowitz portfolio analysis

This paper investigates the portfolio optimization under investor’s sentiment states of Hidden Markov model and over a different time horizon during the period 2004–2016. To compare the efficient portfolios of the Islamic and the conventional stock indexes, we have employed two approaches: the Bayesian and Markowitz mean-variance. Our findings reveal that the Bayesian efficient frontier of Islamic and conventional stock portfolios is affected by the investor’s sentiment state and the time horizon. Our findings also indicate that the investor’s sentiment regimes change the Islamic and the conventional optimal diversified portfolios.Moreover, the results show that the potential diversification benefits seem to be more important when using the Bayesian approach than when applying the Markowitz approach. This finding is valid for the bearish, depressed, bullish and calm states in Islamic stock markets. However, the diversification of potential portfolios is significant only for the bullish and the bubble states in the conventional financial markets.The findings of the study provided additional evidence for investors to exploit googling investor sentiment states to evaluate the portfolio performance and make an optimal portfolio allocation.     

Online marketing: When to offer a refund for advanced sales

Advance selling is a marketing strategy commonly used by online retailers to increase sales by exploiting consumer valuation uncertainty. Recently, some online retailers have started to allow refunds on products sold in advance. On the one hand this reduces the net advance sales, but on the other hand it allows a higher advance sales price. This research is the first to explore the overall effect of allowing a refund on profits from advance sales, identifying conditions where advance selling with or without refunds (or no advance selling at all) is best. We analytically compare the profits of three advance selling strategies: none, without refund, and with refund. We show that selling in advance and allowing a refund is optimal for products with a relatively small profit margin and small strategic market size, and that the added profit can be considerable. Our results guide managers in selecting the right advance selling strategy. To facilitate this, we graphically display, based on the two dimensions of regular profit margin and strategic market size, under what conditions the different strategies are optimal.     

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.     

The effect of activity identity fusion on negative consumer behavior

Negative consumer behavior is an important research topic as it explores consumer behaviors that threaten a brand's image and financial stability. However, prior research offers conflicting findings on whether a strong consumer‐brand relationship hurts or protects a brand after a market disruption or a brand transgression. To provide clarity on this issue, this work argues that disrupting the consumer‐activity relationship motivates consumers to reaffirm and protect their identity, thereby leading to negative consumer behavior. The data reveal that, after a brand‐initiated market disruption, consumers with high activity identity fusion are more likely to spread negative word‐of‐mouth, boycott the brand, and avoid repurchasing the brand in the future. Moreover, the data suggest that high brand identity fusion protects the brand during market disruptions; therefore, prior conflicting results may be due to the fact that the consumer‐activity relationship was not accounted for. Theoretically, this work establishes that consumers' relationship with their consumption activities has significant impacts on consumer behavior. Brand managers and marketers are urged to develop strategies that focus on strengthening the consumer‐brand relationship and not the consumer‐activity relationship.