Chasing noise

We present a simple model in which rational but uninformed traders occasionally chase noise as if it were information, thereby amplifying sentiment shocks and moving prices away from fundamental values. In the model, noise traders can have an impact on market equilibrium disproportionate to their size in the market. The model offers a partial explanation for the surprisingly low market price of financial risk in the spring of 2007.     

Applying deep learning models to twitter data to detect airport service quality

Measuring airport service quality (ASQ) is an important process for identifying shortages and suggesting improvements that guide management decisions. This research, introduces a general framework for measuring ASQ using passengers’ tweets about airports. The proposed framework considers tweets in any language, not just in English, to support ASQ evaluation in non-speaking English countries where passengers communicate with other languages. Accordingly, this work uses a large dataset that includes tweets in two languages (English and Arabic) and from four airports. Additionally, to extract passenger evaluations from tweets, our framework applies two different deep learning models (CNN and LSTM) and compares their results. The two models are trained with both general data and data from the aviation domain in order to clarify the effect of data type on model performance. Results show that better performance is achieved with the LSTM model when trained with domain specific data. This study has clear implications for researchers and airport managers aiming to use alternative methods to measure ASQ.     

Drivers of helpfulness of online hotel reviews: A sentiment and emotion mining approach

Although online hotel reviews (OHR) help consumers in better decision–making, and service providers in better service design and delivery, they are hard to manage due to their high volume, velocity, and veracity. This paper focuses on the drivers of helpfulness of textual OHR, for which we have used text-mining techniques to find the sentiment content, polarity, and emotions; we have also used econometric and machine learning techniques to explain and predict its helpfulness. We found that content and title polarity lead to OHRs being less helpful, whereby this negative relationship gets accentuated with higher sentiment content. On the other hand, while negative emotion with low arousal makes OHR helpful, high arousal makes it less helpful. It has also been noted that after controlling for polarity, sentiment, and emotions, longer reviews are less helpful. Higher quantitative rating, recency of OHR and a reviewer’s past expertise make a review more helpful. Additionally, machine-learning techniques have been found to predict ‘review’ helpfulness marginally better than econometric techniques. This study contributes to OHR literature in terms of its performance, and would also help decision makers in OHR management strategy.     

Excess volatility and the cross-section of stock returns

We document a reliable positive relation between excess volatility and the cross-section of stock returns over the sample period of 1963 to 2010. Significantly positive differentials have been found between the two decile portfolios with the largest and the least excess volatility, under all the situations we have examined. Size, value, and momentum effects cannot explain our empirical results. Likewise they cannot be explained by liquidity, bid-ask bounce, and risk-aversion-related inventory effects.     

Exploring the impact of chatbots on consumer sentiment and expectations in retail

Retailers are increasingly using conversational AI (chatbots) for customer service due to the perceived benefits and reduced operational costs of this emerging technology. Yet our understanding of how consumers perceive interactions with chatbots, and how these interactions may influence other consumer service programs, remains limited. This paper investigates the differences in consumers' sentiments towards chatbots across retail sectors, and the influence chatbots have on consumers’ sentiments and expectations towards other service interactions with online human agents. Using a hybrid automated sentiment analysis approach, we identify that (1) overall sentiment towards bots are less negative than sentiment towards online human agents; (2) these sentiments differ across fashion and telecommunications sectors, and finally (3) sentiments towards online human agents in both sectors become more negative after a retailer implements a chatbot.     

The future of service: The power of emotion in human-robot interaction

Astoundingly, recent technological advancements have enabled robots to display emotions. Yet, while emotional expression is valued in the field of service, understanding emotions in human-robot interaction remains underexplored. Since emotions are contagious/transmittable, this study utilised Instagram data to uncover how emotional robots influence potential consumers’ affective feelings. By employing machine learning algorithms and sentiment analysis, the findings suggest that the expressions of surprise and happiness are key to creating positive impacts on potential consumers. The cross-disciplinary nature of this study lays the groundwork for next-level social, design, and creative experiences in artificial intelligence research regarding consumer service and experience contexts.     

Investor climate sentiment and financial markets

We propose to measure investor climate sentiment by performing sentiment analysis on StockTwits posts on climate change and global warming. In financial markets, stocks of emission (carbon-intensive) firms underperform clean (low-emission) stocks when investor climate sentiment is more positive. We document investors overreaction to climate change risk and reversal in longer horizons. Salient but uninformative climate change events, such as the release of a report on climate change and abnormal weather events, facilitate the investor learning process and correction of the mispricing.     

The discrete and differential impact of monetary policy

This study exploits a unique feature of the Australian monetary policy environment to determine whether economic recovery can be stimulated via central bank communications. This study finds that unexpected monetary policy announcements and communications have a significant and comparable impact on the value and volatility of the Australian foreign exchange market, suggesting that they can be used interchangeably to stimulate economic recovery. However, further analysis reveals that the state of the economy influences this impact. Specifically, during poor economic states, monetary policy actions speak louder than words, an adage that in this context provides actionable information for central bank regulators.