本研究旨在探討投資人情緒是否影響到分析師的報導決策，以及分析師發佈預測和推薦時是否會注意到投資人情緒，亦即當投資人情緒較樂觀時，分析師是否會發佈較長期的預測以及較有利的股票推薦。本文以中央大學台灣經濟研究中心所編制之消費者信心指數作為投資人情緒的替代變數。研究結果與本文預期相符，當投資人情緒較高昂時，分析師會發佈較長期之盈餘預測以及較有利之推薦評等；同時也發現當投資人情緒上升，分析師之推薦評等亦向上修正。顯示分析師雖為專精且較為理性之投資人，然其行為仍受到消費者信心所影響。 / This study investigates the relation between investor sentiment and analysts' coverage decisions. Secondly, we also examine whether analysts who pay attention to investor sentiment issue longer-horizon earnings forecasts and more favorable stock recommendations during high-sentiment periods. We use the Consumer Confidence Index (CCI) survey from the National Central University to measure sentiment. We find that analysts tend to issue longer-horizon earnings forecasts and favorable stock recommendations when investor sentiment is more optimistic. Moreover, analysts tend to revise upward their stock recommendations during investor sentiment raise period. Taken together, these findings suggest that analysts are affected by investor sentiment even though they are more rational investors.

本文旨在探討投資人情緒與法人說明會之關聯性，即公司如何經由召開法人說明會，策略性地回應投資人情緒反應，以企圖影響情緒所導致的預期偏差。實證發現：管理當局策略性地改變其自願性揭露政策，以反映投資人情緒。當投資人情緒愈低落時，公司將傾向於召開法人說明會，且公司召開法人說明會之頻率亦會增加。相反的，當投資人情緒高昂時，公司則愈不會召開法人說明會。再者，當投資人情緒愈低落時，法人說明會所揭露之公司資訊將愈樂觀。此研究亦顯示公司自願性揭露政策的選擇，反映管理當局渴望維持樂觀之評價。 / In this paper we explore the association between investor sentiment and the likelihood of holding conference calls. In other words, this paper investigates how firms react strategically to investor sentiment via their conference calls in an attempt to influence the sentiment-induced biases in expectations. We show that managers strategically vary their voluntary disclosure policies in response to prevailing sentiment. We find that during low-sentiment periods, the firms are more likely to conduct conference calls and conduct them more frequently; while during periods of high sentiment they decrease the frequency of conference calls. In addition, during low-sentiment periods, the conference calls disclose more optimistic information. Overall, this study provides evidence that company’s voluntary disclosure choices reflect managers desire to maintain optimistic valuations.

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L’augmentation massive du volume de données générées chaque jour par les individus sur Internet offre aux chercheurs la possibilité d’aborder la question de la prédictibilité des marchés financiers sous un nouvel angle. Sans prétendre apporter une réponse définitive au débat entre les partisans de l’efficience des marchés et les chercheurs en finance comportementale, cette thèse vise à améliorer notre compréhension du processus de formation des prix sur les marchés financiers grâce à une approche Big Data. Plus précisément, cette thèse porte sur (1) la mesure du sentiment des investisseurs à fréquence intra-journalière, et le lien entre le sentiment des investisseurs et les rendements agrégés du marché,(2) la mesure de l’attention des investisseurs aux informations économiques et financières en temps réel, et la relation entre l’attention des investisseurs et la dynamique des prix des actions des sociétés à forte capitalisation, et enfin, (3) la détection des comportements suspicieux pouvant amoindrir le rôle informationnel des marchés financiers, et le lien entre le volume d’activité sur les réseaux sociaux et le prix des actions des entreprises de petite capitalisation. Le premier essai propose une méthodologie permettant de construire un nouvel indicateur du sentiment des investisseurs en analysant le contenu des messages publiés sur le réseau social Stock-Twits. En examinant les caractéristiques propres à chaque utilisateur (niveau d’expérience, approche d’investissement, période de détention), cet essai fournit des preuves empiriques montrant que le comportement des investisseurs naïfs, sujets à des périodes d’excès d’optimisme ou de pessimisme, a un impact sur la valorisation du marché action, et ce en accord avec les théories de la finance comportementale. Le deuxième essai propose une méthodologie permettant de mesurer l’attention des investisseurs aux informations en temps réel, en combinant les données des médias traditionnels avec le contenu des messages envoyés par une liste d’experts sur la plateforme Twitter. Cet essai démontre que lorsqu’une information attire l’attention des investisseurs, les mouvements de marchés sont caractérisés par une forte hausse des volumes échangés, une hausse de la volatilité et des sauts de prix. Cet essai démontre également qu’il n’y a pas de fuite d’information significative lorsque les sources d’informations sont combinées pour corriger un potentiel problème d’horodatage. Le troisième essai étudie le risque de manipulation informationnelle en examinant un nouveau jeu de données de messages publiés sur Twitter à propos des entreprises de petite capitalisation. Cet essai propose une nouvelle méthodologie permettant d’identifier les comportements anormaux de manière automatisée en analysant les interactions entre les utilisateurs. Étant donné le grand nombre de recommandations suspicieuses d’achat envoyées par certains groupes d’utilisateurs, l’analyse empirique et les conclusions de cet essai soulignent la nécessité d’un plus grand contrôle par les régulateurs de l’information publiée sur les réseaux sociaux ainsi que l’utilité d’une meilleure éducation des investisseurs individuels. / The massive increase in the availability of data generated everyday by individuals on the Internet has made it possible to address the predictability of financial markets from a different perspective. Without making the claim of offering a definitive answer to a debate that has persisted for forty years between partisans of the efficient market hypothesis and behavioral finance academics, this dissertation aims to improve our understanding of the price formation process in financial markets through the use of Big Data analytics. More precisely, it analyzes: (1) how to measure intraday investor sentiment and determine the relation between investor sentiment and aggregate market returns, (2) how to measure investor attention to news in real time, and identify the relation between investor attention and the price dynamics of large capitalization stocks, and (3) how to detect suspicious behaviors that could undermine the in-formational role of financial markets, and determine the relation between the level of posting activity on social media and small-capitalization stock returns. The first essay proposes a methodology to construct a novel indicator of investor sentiment by analyzing an extensive dataset of user-generated content published on the social media platform Stock-Twits. Examining users’ self-reported trading characteristics, the essay provides empirical evidence of sentiment-driven noise trading at the intraday level, consistent with behavioral finance theories. The second essay proposes a methodology to measure investor attention to news in real-time by combining data from traditional newswires with the content published by experts on the social media platform Twitter. The essay demonstrates that news that garners high attention leads to large and persistent change in trading activity, volatility, and price jumps. It also demonstrates that the pre-announcement effect is reduced when corrected newswire timestamps are considered. The third essay provides new insights into the empirical literature on small capitalization stocks market manipulation by examining a novel dataset of messages published on the social media plat-form Twitter. The essay proposes a novel methodology to identify suspicious behaviors by analyzing interactions between users and provide empirical evidence of suspicious stock recommendations on social media that could be related to market manipulation. The conclusion of the essay should rein-force regulators’ efforts to better control social media and highlights the need for a better education of individual investors.

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.

This paper investigates the investor trading behavior and the relationship between investor sentiment and stock returns. First we explore whether individual investors behave as the Disposition Effect stated — hold their losers too long while realize their winners too soon. Second, we apply four sentiment indicators — number of recommended stocks, margin purchase value, net fund redemption and odd-lot trade value — to examine relationship between investor sentiment and stock returns. We would like to see if past returns have anything to do with current sentiment, and if sentiment provides predictive power to future returns. First of all, from our analysis to over eight hundreds cash accounts trading records in two research periods, January to March and September to December in 2000, we found the Disposition Effect holds in average but not statistically. Second, the number of recommended stocks, weighted number of recommended stocks, margin purchase value, change in margin purchase value, net fund redemption and odd-lot trade value as proxies of investor sentiment are good at measuring the effect of past 4-week and 26-week returns on sentiment. Third, the margin purchase value, net fund redemption and odd-lot trade value provide predictive power to future 26-week returns in our study, which also implies there is likely underlying mean-reversion within half year during the research period. 　Finally, exploiting the change in margin purchase value as proxy of investor sentiment, we found the past 4-week returns volatility is inversely related with the indicator. That is, investors are scared on facing with high returns volatility.

This thesis consists of four self-contained essays on the various topics in finance.  The first essay, The Information Content of The Systematic Risk Structure of Corporate Yields for Future Real Activity: An Exploratory Empirical Investigation, constructs a proxy for the systematic component of the risk structure of corporate yields (or systematic risk structure), and tests how well it predicts real economic activity in the United States. It finds that the systematic risk structure predicts the growth rate of industrial production 3 to 18 months into the future even when other leading indicators are controlled for, outperforming other models. A regime-switching estimation also shows that the systematic risk structure is very successful in identifying and capturing different growth regimes of industrial production.  The second essay, How Much Leverage is Too Much, or Does Corporate Risk Determine the Severity of a Recession? investigates whether financial conditions of the U.S. corporate sector  can explain the probability and severity of recessions. It proposes a measure of corporate vulnerability, the Corporate Vulnerability Index (CVI) constructed as the default probability for the entire corporate sector. It finds that the CVI is a significant predictor of the probability of a recession 4 to 6 quarters ahead, even controlling for other leading indicators, and that an increase in the CVI is also associated with a rise in the probability of a more severe and lengthy recession 3 to 6 quarters ahead.  The third essay, Asian Flu or Wall Street Virus? Tech and Non-Tech Spillovers in the United States and Asia (with Jorge A. Chan-Lau), using TGARCH models, finds that U.S. stock markets have been the major source of price and volatility spillovers to stock markets in the Asia-Pacific region during three different periods: the pre-LTCM crisis period, the “tech bubble” period, and the “stock market correction” period. Hong Kong SAR, Japan, and Singapore were sources of spillovers within the region and affected the United States during the latter period. There is also evidence of structural breaks in the stock price and volatility dynamics induced during the “tech bubble” period.  The fourth essay, Coping with Financial Spillovers from the United States: The Effect of U. S. Corporate Scandals on Canadian Stock Prices, investigates the effect of U.S. corporate scandals on stock prices of Canadian firms interlisted  in the United States. It finds that firms interlisted during the pre-Enron period enjoyed increases in post-listing equilibrium prices, while firms interlisted during the post-Enron period experienced declines in post-listing equilibrium prices, relative to a model-based benchmark. Analyzing the entire universe of Canadian firms, it finds that interlisted firms, regardless of their listing time, were perceived as increasingly risky by Canadian investors after the Enron’s bankruptcy. / Diss. Stockholm : Handelshögskolan, 2003

In this paper, market participants’ tendency to form investor herds in the stocks listed on Nasdaq First North Growth Market of Sweden is examined for the period from 2018 to 2023. The models used in this study to detect herd behavior in stocks consist of two measures of dispersions, Cross-Sectional Standard Deviation of returns (CSSD) and Cross-Sectional Absolute Deviation of returns (CSAD), which were proposed by Christie and Huang (1995) and Chang, et al. (2000), respectively. An equally-weighted index consisting of all of the stocks that have traded on this market during the period is created and a quantitative analysis is conducted. Evidence showed absence of herd behavior when using both models, as well as when accounting for robustness tests consisting of small, mid-and large cap portfolios. Our results also support the prediction of rational asset pricing models, which suggest that stock return dispersions around the market returns increase during periods of market stress.