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Expert System for Decision-Making on Stock Markets Using Investor Sentiment
Janková, Zuzana ; Lenort, Radim (referee) ; Zinecker, Marek (referee) ; Chramcov, Bronislav (referee) ; Dostál, Petr (advisor)
The presented dissertation examines the potential of using the sentiment score extracted from textual data with historical stock index data to improve the performance of stock market prediction through the created model of the expert system. Given the large number of financial-related text documents published by both professional and amateur investors, not only on online social networks that could have an impact on real stock markets, but it is also crucial to analyze and in particular extract financial texts published by different users. investor sentiment. In this work, investor sentiment is obtained from online financial reports and contributions published on the financial social platform StockTwits. Sentiment scores are determined using a hybrid approach combining machine learning models with the teacher and neural networks, with multiple lexicons of positive and negative words used to classify sentiment polarity. The influence of sentiment score on the stock market through causality, cointegration and coherence is analyzed. The dissertation proposes a model of an expert system based on fuzzy logic methods. Fuzzy logic provides remarkable features when working with vague, inaccurate or unclear data and is able to deal with the chaotic environment of stock markets. In recent scientific studies, it has gained in popularity a higher level of fuzzy logic, which is referred to as type-2 fuzzy logic. Unlike the classic type-1 fuzzy logic, this higher type is able to integrate a certain level of uncertainty between the dual membership functions. However, this type of expert system is considerably neglected in the subject issue of stock market prediction using the extracted investor sentiment. For this reason, the dissertation examines the potential to use and the performance of type-2 fuzzy logic. Specifically, several type-2 fuzzy models are created. which are trained on historical stock index data and sentiment scores extracted from text data for the period 2018-2020. The created models are assessed to measure the prediction performance without sentiment and with the integration of investor sentiment. Subsequently, based on the created expert model, the investment strategy is determined, and its profitability is monitored. The prediction performance of fuzzy models is compared with the performance of several comparison models, including SVM, KNN, naive Bayes and others. It has been observed from experiments that fuzzy logic models are able to improve prediction by appropriate setting of membership and uncertainty functions contained in them and are able to compete with classical expert prediction models, which are standardly used in research studies. The created model should serve as a tool to support investment decisions for individual investors.
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IPO underpricing and sentiment of investors
Scheerová, Lucie ; Dědek, Oldřich (advisor) ; Lupusor, Adrian (referee)
The thesis investigates investor sentiment, proxied by grey market prices, being a common source for IPO underpricing, long-term underperformance of IPOs, and cycles in IPO volume. The paper contributes to the field of research by an updated German dataset from 2000 to 2010, and by investigating all main IPO market anomalies together with their common trigger. The results show evidence of a positive relationship between the investor sentiment and IPO underpricing, indicating the investor sentiment being an explanation for it. Moreover, the study shows investor sentiment being positively linked to offer prices - an evidence of issuers exploiting that sentiment. However, the long-term underperformance relative to the aftermarket price of IPOs from high underpricing periods - another evidence of investor sentiment being a source for IPO underpricing - has not been confirmed. Other hypotheses have also not been verified. They include higher IPO volume following high underpricing periods and long-term underperformance relative to the offer price of IPOs from high underpricing periods. Both these hypotheses would represent another confirmation of firms exploiting the investor sentiment. The statistically significant results are consistent with other papers. The insignificance might have been caused by the method...
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Expert System for Decision-Making on Stock Markets Using Investor Sentiment
Janková, Zuzana ; Lenort, Radim (referee) ; Zinecker, Marek (referee) ; Chramcov, Bronislav (referee) ; Dostál, Petr (advisor)
The presented dissertation examines the potential of using the sentiment score extracted from textual data with historical stock index data to improve the performance of stock market prediction through the created model of the expert system. Given the large number of financial-related text documents published by both professional and amateur investors, not only on online social networks that could have an impact on real stock markets, but it is also crucial to analyze and in particular extract financial texts published by different users. investor sentiment. In this work, investor sentiment is obtained from online financial reports and contributions published on the financial social platform StockTwits. Sentiment scores are determined using a hybrid approach combining machine learning models with the teacher and neural networks, with multiple lexicons of positive and negative words used to classify sentiment polarity. The influence of sentiment score on the stock market through causality, cointegration and coherence is analyzed. The dissertation proposes a model of an expert system based on fuzzy logic methods. Fuzzy logic provides remarkable features when working with vague, inaccurate or unclear data and is able to deal with the chaotic environment of stock markets. In recent scientific studies, it has gained in popularity a higher level of fuzzy logic, which is referred to as type-2 fuzzy logic. Unlike the classic type-1 fuzzy logic, this higher type is able to integrate a certain level of uncertainty between the dual membership functions. However, this type of expert system is considerably neglected in the subject issue of stock market prediction using the extracted investor sentiment. For this reason, the dissertation examines the potential to use and the performance of type-2 fuzzy logic. Specifically, several type-2 fuzzy models are created. which are trained on historical stock index data and sentiment scores extracted from text data for the period 2018-2020. The created models are assessed to measure the prediction performance without sentiment and with the integration of investor sentiment. Subsequently, based on the created expert model, the investment strategy is determined, and its profitability is monitored. The prediction performance of fuzzy models is compared with the performance of several comparison models, including SVM, KNN, naive Bayes and others. It has been observed from experiments that fuzzy logic models are able to improve prediction by appropriate setting of membership and uncertainty functions contained in them and are able to compete with classical expert prediction models, which are standardly used in research studies. The created model should serve as a tool to support investment decisions for individual investors.
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Predicting stock market crises using investor sentiment indicators
Havelková, Kateřina ; Kukačka, Jiří (advisor) ; Kočenda, Evžen (referee)
Using an early warning system (EWS) methodology, this thesis analyses the predictability of stock market crises from the perspective of behavioural fnance. Specifcally, in our EWS based on the multinomial logit model, we consider in- vestor sentiment as one of the potential crisis indicators. Identifcation of the relevant crisis indicators is based on Bayesian model averaging. The empir- ical results reveal that price-earnings ratio, short-term interest rate, current account, credit growth, as well as investor sentiment proxies are the most rele- vant indicators for anticipating stock market crises within a one-year horizon. Our thesis hence provides evidence that investor sentiment proxies should be a part of the routinely considered variables in the EWS literature. In general, the predictive power of our EWS model as evaluated by both in-sample and out-of-sample performance is promising. JEL Classifcation G01, G02, G17, G41 Keywords Stock market crises, Early warning system, In- vestor sentiment, Crisis prediction, Bayesian model averaging Title Predicting stock market crises using investor sentiment indicators
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IPO underpricing and sentiment of investors
Scheerová, Lucie ; Dědek, Oldřich (advisor) ; Lupusor, Adrian (referee)
The thesis investigates investor sentiment, proxied by grey market prices, being a common source for IPO underpricing, long-term underperformance of IPOs, and cycles in IPO volume. The paper contributes to the field of research by an updated German dataset from 2000 to 2010, and by investigating all main IPO market anomalies together with their common trigger. The results show evidence of a positive relationship between the investor sentiment and IPO underpricing, indicating the investor sentiment being an explanation for it. Moreover, the study shows investor sentiment being positively linked to offer prices - an evidence of issuers exploiting that sentiment. However, the long-term underperformance relative to the aftermarket price of IPOs from high underpricing periods - another evidence of investor sentiment being a source for IPO underpricing - has not been confirmed. Other hypotheses have also not been verified. They include higher IPO volume following high underpricing periods and long-term underperformance relative to the offer price of IPOs from high underpricing periods. Both these hypotheses would represent another confirmation of firms exploiting the investor sentiment. The statistically significant results are consistent with other papers. The insignificance might have been caused by the method...
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