Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

An Agent-Based Approach to Artificial Stock Market Modeling

Consumer stock markets have long been a target of modeling efforts for the economic gains anticipatorily enabled by well-performing models. Aimed at identifying strategies capable of achieving desired returns, many modeling approaches have attempted to capture the innumerable and intricate complexities present within these adaptive socio-technical systems. Decreasingly constrained by available computation power, contemporary models have grown in sophistication to include several of the features present in de facto market systems. However, these models require extensive effort to dictate the variety of states, behaviors, and adaptations that entities of the system may exhibit. Mandating the development of complex formulas and an incredible number of situational considerations, traditional approaches to stock market modeling are intensive to architect and applicable to a limited range of scenarios. Further, these models commonly fail to incorporate external influences on the actions of investing parties. Employing an agent-based approach, independent and externally influenced entities are modeled to simulate market activity. Under the jurisdiction of assigned simple rules, agents of the system interact in complex and emergent ways without requiring macroscopic guiding equations. Successive trails are conducted using varying initialization values, enabling the determination of robust investment strategies performing well across a range of market scenarios

Two Essays on Investor Sentiment

The body of literature on investor sentiment underlines its impact on future stock returns, with general consensus that investor sentiments and future returns are negatively correlated (Baker and Wurgler, 2006; Brown and Cliff, 2004). This extends to the notion that a bullish investor would expect returns to be above average, while a bearish investor anticipates below-average returns (Brown and Cliff, 2004). The first essay proposes a model to examine the influence of unexpected volatility of investor sentiment on the equity risk premium. Assumptions underpinning the model include risk-averse investors, homogeneous expectations regarding asset returns and price changes, and sentiment-influenced expectations of asset returns. The model also presumes continuous-time stochastic (Weiner) processes for asset returns and sentiment. The developed model is rooted in several principles, including the Efficient Market Hypothesis, Martingale theory, and the impact of uncertain sentiment change on stock returns. Utilizing Thomson Reuters MarketPsych Indices for data analysis, the model tests sentiment metrics against the performance of the S&P 500. The results provide insights into the dynamics of investor sentiment and its impact on equity risk premium, laying the groundwork for further empirical investigation. In the first essay, we evaluate the link between industry tournament incentives and investment inefficiency. We find that firms with higher tournament incentives exhibit higher investment inefficiency. Additionally, cross-sectional tests suggest that these effects operate at least in part through both a financing channel and a monitoring channel. Taken together, our results suggest that industry tournament incentives place pressure on CEOs and affect the efficiency of firm investments. In the second essay, we examines the phenomenon of sentiment transmission across stock markets, focusing on the influence of U.S. investors\u27 sentiment on G7 countries. The study utilizes data from the Global Finance database, including stock indices for G7 countries and two measures of sentiment for the U.S. market: news sentiment and social media sentiment. News sentiment captures the impact of positive and negative news articles on market sentiment, while social media sentiment reflects the influence of social media posts on market sentiment. The analysis employs a vector autoregression (VAR) model and Multivariate GARCH model to understand the interdependence of these variables and how changes in U.S. investors\u27 sentiment affect other markets. The study highlights the increasing prevalence and significant impact of sentiment transmission due to the global interconnectedness of markets, amplified by financial innovations like ETFs. The findings contribute to a better understanding of sentiment transmission and its implications for global financial markets, providing insights for policymakers and market participants

How did the discussion go: Discourse act classification in social media conversations

We propose a novel attention based hierarchical LSTM model to classify discourse act sequences in social media conversations, aimed at mining data from online discussion using textual meanings beyond sentence level. The very uniqueness of the task is the complete categorization of possible pragmatic roles in informal textual discussions, contrary to extraction of question-answers, stance detection or sarcasm identification which are very much role specific tasks. Early attempt was made on a Reddit discussion dataset. We train our model on the same data, and present test results on two different datasets, one from Reddit and one from Facebook. Our proposed model outperformed the previous one in terms of domain independence; without using platform-dependent structural features, our hierarchical LSTM with word relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively to predict discourse roles of comments in Reddit and Facebook discussions. Efficiency of recurrent and convolutional architectures in order to learn discursive representation on the same task has been presented and analyzed, with different word and comment embedding schemes. Our attention mechanism enables us to inquire into relevance ordering of text segments according to their roles in discourse. We present a human annotator experiment to unveil important observations about modeling and data annotation. Equipped with our text-based discourse identification model, we inquire into how heterogeneous non-textual features like location, time, leaning of information etc. play their roles in charaterizing online discussions on Facebook

News-based sentiment and bitcoin volatility

In this work, I studied whether news media sentiments have an impact on Bitcoin volatility. In doing so, I applied three different range-based volatility estimates along with two different sentiments, namely psychological sentiments and financial sentiments, incorporating four various sentiment dictionaries. By analyzing 17,490 news coverages by 91 major English-language newspapers listed in the LexisNexis database from around the globe from January 2012 until August 2021, I found news media sentiments to play a significant role in Bitcoin volatility. Following the heterogeneous autoregressive model for realized volatility (HAR-RV)—which uses the heterogeneous market idea to create a simple additive volatility model at different scales to learn which factor is influencing the time series—along with news sentiments as explanatory variables, showed a better fit and higher forecasting accuracy. Furthermore, I also found that psychological sentiments have medium-term and financial sentiments have long-term effects on Bitcoin volatility. Moreover, the National Research Council Emotion Lexicon showed the main emotional drivers of Bitcoin volatility to be anticipation and trust.© 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

The effects of twitter sentiment on renewable energy stock's returns : a Portuguese study about EDP renováveis stocks

Investors’ rationality in the decision-making process has been topic of discussion in the last decades due to conflicts between schools of thought. Several anomalies in the Efficient Market Hypothesis (EMH) led to a new line of thought in the matter of rationality called behavior finance. Sentiment analysis is one branch of this new school of thought who studies investors’ emotions influence on economic variables. There is no consensus between academics if these emotions can make the investment decision biased or not. The aim of this paper is to observe if the prevailing sentiment in tweets can predict the stock returns for a renewable energy company of the Portuguese market. This study looks at the second biggest company by capitalizations of the Portuguese market, EDP Renováveis (EDPR), in the period from the June 1st 2021, to June 1st 2022, and finds no significant evidence of a relationship between Twitter mood and EDP Renováveis stock returns. The reasons for this result might be explained by EDPR belonging to a very small and concentrated market, corroborating the existing theory, as well as the stakeholder composition of the company only having a very small percentage of individual investors, being this kind of investors the most influenced by biases and heuristics present in the tweets. These findings have implications for the development of the sentiment analysis theory, giving more details of the influence of sentiment in smaller and concentrated market, in the renewable energy branch, and in the period of the beginning of the war between Ukraine and Russia and the worldwide economic recovery from the Covid-19 pandemic.A racionalidade dos investidores no processo de decisão de investimento tem sido tópico de discussão nas últimas décadas devido ao conflito entre duas linhas de pensamento diferentes. Várias anomalias que não iam de encontro com a hipótese do mercado eficiente deram origem a uma nova escola de pensamento em relação à racionalidade dos investidores chamada de finanças comportamentais. Análise de sentimentos é um dos ramos desta nova linha de pensamento que estuda a influência das emoções dos investidores em diferentes variáveis económicas. Não existe consenso entre académicos se estas emoções conseguem enviesar as decisões de investimento ou não. O objetivo desta tese é observar se o sentimento presente em tweets consegue fazer prever os retornos das ações de uma empresa de energias renováveis do mercado português. Este estudo analisa a segunda maior empresa portuguesa por capitalizações, a EDP Renováveis (EDPR), no período temporal entre o dia 1 de junho de 2021 e o dia 1 de julho de 2022, e não encontrou evidência com significância de uma relação entre o estado de espírito do Twitter e os retornos das ações da EDP Renováveis. As razões que justificam estes resultados podem ser o facto da EDPR pertencer a um mercado muito pequeno e concentrado como o português, indo de encontro com a evidência empírica, assim como a composição dos proprietários das ações da empresa ter uma percentagem muito reduzida de investidores individuais, que são o tipo de investidor mais facilmente influenciado por heurísticas presentes nos tweets. Este resultado tem implicações para o desenvolvimento da teoria de análise do sentimento, dando mais detalhes da influência deste em mercados mais pequenos e concentrados, no ramo das energias Renováveis, no período de tempo do início da guerra entre a Ucrânia e a Rússia e a recuperação financeira mundial pós-Covid-19

Style investing: International evidence

This dissertation studies the impact of investor sentiment on a portfolio formed of sin stocks—publicly traded companies in the alcohol, tobacco, and gaming industries. It also investigates the returns of a new type of sin stock in the UK—online gambling. Chapter 3 first uses a vector autogressive model to study the impact of both rational and irrational investor sentiments on pure sin returns. Next, making use of a variety of sentiments-augmented asset pricing models, this research examines whether investor sentiment is a risk factor for sin stock returns and if the abnormal returns of sin stocks persist after controlling for investor sentiment. Finally, the possible relationship between investor sentiment and the conditional volatility of the sin portfolio is studied by utilizing a generalized autoregressive conditional heteroscedasticity-inmean model. The results indicate that rational-based sentiments shocks illicit a larger positive response in pure sin returns, than do irrational-based sentiments shocks. After controlling for the role of investor sentiment, the asset-pricing results suggest that the abnormal returns for sin stocks found in previous studies disappear. Furthermore, findings show that both individual and institutional investor sentiment are priced factors in sin stock returns. Additionally, results indicate that investor sentiment has a significant impact on sin stocks’ formation of volatility. Chapter 4 of this dissertation examines the financial performance, time-varying betas, and time-varying correlations of an internet gambling portfolio relative to both the market and socially responsible portfolios. Findings indicate that the online gambling portfolio underperforms relative to both the market and socially responsible portfolios. The evidence also suggests that beta is time-varying for the online gambling portfolio. Furthermore, market betas and correlations for the online gambling portfolio increase considerably around the passage of the Gambling Act 2005