Two Essays on Investor Emotions and Their Effects in Financial Markets

This dissertation provides empirical evidences on media-based investor emotions in predicting stock return, conditional volatility, and stock and bond return comovements. We first studied the interaction between US media content and the US stock market returns and volatility. We utilize propriety investor sentiment measures developed by Thompson Reuters MarketPsych. We select four measures of investor sentiment that reflect both pessimism and optimism of small investors. Our objective is two-fold. First, we examine the ability of these sentiment measures to predict market returns. For this purpose, we use dynamic Vector Auto-Regressive models. Second, we are interested in exploring the effects of these sentiment measures on the market returns and volatility. For this purpose, we utilize a Threshold-GARCH model. Next, we investigated the effect of investor emotions (fear, gloom, joy and optimism) in financial futures markets by using Thompson Reuters MarketPsych Indices. The purpose of this study is three fold. First, we investigate the extent of usefulness of informational content of our sentiment measures in predicting stock futures and treasures futures returns using daily data for different measures of emotional sentiments. Second, we investigate whether emotion sentiments affect financial futures returns and volatilities. Third, we explore the role of emotion sentiment factors in volatility transmission in financial futures markets. To the best of our knowledge, this is the first study that extensively explores the role of investors’ sentiment in the most liquid contracts (S&P 500 futures and 10-year Treasury notes) in futures markets

Global geoid model GGM2022

We provide an updated 5 $^\prime$ $\times$ 5 $^\prime$ global geoid model GGM2022, which is determined based on the shallow layer method (Shen method). First, we choose an inner surface $\Gamma$ below the EGM2008 global geoid by 15 m, and the layer bounded by the inner surface $\Gamma$ and the Earth's geographical surface $S$ is referred to as the shallow layer. The Earth's geographical surface $S$ is determined by the digital topographic model DTM2006.0 combining with the DNSC2008 mean sea surface. Second, we formulate the 3D shallow mass layer model using the refined 5 $^\prime$ $\times$ 5 $^\prime$ crust density model CRUST$_-$re , which { is an improved 5 $^\prime$ $\times$ 5 $^\prime$ density model of the CRUST2.0 or CRUST1.0 with taking into account the corrections of the areas covered by ice sheets and the land-ocean crossing regions. Third, based on the shallow mass layer model and the gravity field EGM2008 that is defined in the region outside the Earth's geographical surface $S$, we determine the gravity field model EGM2008s that is defined in the whole region outside the inner surface $\Gamma$, where the definition domain of the gravity field is extended from the domain outside $S$ to the domain outside $\Gamma$. Fourth, based on the gravity field EGM2008s and the geodetic equation $W(P)=W_0$ (where $W_0$ is the geopotential constant on the geoid and $P$ is the point on the geoid $G$), we determine a 5 $^\prime$ $\times$ 5 $^\prime$ global geoid, which is referred to as GGM2022. Comparisons show that the GGM2022 fits the globally available GPS/leveling data better than EGM2008 global geoid in the USA, Europe and the western part of China.Comment: 17 pages, 25 figure

Does the Market Believe White Knights and Hostile Bidders are acting in Their Shareholders\u27 Interest?

This study examines why white knights suffer significant losses while their rival hostile bidders experience significant abnormal gains. We address two research questions: 1) Does the market believe that white knights and hostile bidders are acting in their shareholders\u27 interest? 2) Does Tobin\u27s q explain why white knights suffer significant losses and hostile bidders experience significant gains upon the announcement of their bids? The results show that hostile bidders are value-maximizing investors and white knights are not acting in their shareholders\u27 interest. Instead, white knights suffered significant reductions in value and historically have not maximized the wealth of investor

Wetland mapping in the Balqash Lake Basin Using Multi-source Remote Sensing Data and Topographic features Synergic Retrieval

AbstractWetland plays a major role in the hydrological cycle, the carbon sink (carbon sequestration), nitrogen absorption, geochemical cycle, water conservation, biological diversity. Traditional field surveys for mapping wetlands distribution in large areas are very difficult to undertake. Remote sensing techniques offer promising solutions to this problem. But spectral confusion with other land cover classes and different types of wetlands, it is difficult to extract wetland information automatically. The overarching goal of this study was to develop a hybrid method for lake wetlands automated delineation by integrated using multi-source remote sensing data and DEM data. Firstly, it is to do radiance correction and convert image DN value to reflectance or radiance. Secondly, spectral index calculation and topographic indices derive, such as NDVI, NDWI, TVDI, slope and others topographic feature indices and etc. Thirdly, water bodies extraction through the NDWI iterative computation. Finally, it is to retrieve marsh land from image via comprehensive information of soil moisture character, topographic factors and spatial analysis. By the above steps, we got the ultimate wetlands distribution information. The methodology was evaluated by the balqash lake basin wetland extraction in Kazakhstan. Experiments result shows that the hybrid method performs well in lake wetlands delineation. The overall accuracies of wetland classes exceed 85%, which can meet the application requirements

DeepPsych: Harnessing Market Psychology with Deep Learning

Investor psychology provides an important avenue for modeling non-fundamental behaviors in financial analysis. Yet, whether market psychological information has a practical application in predicting asset returns is still under debate. Thus, a burgeoning number of machine learning algorithms have been developed to test the effectiveness of investor psychology in financial predictions. With all the merits of machine learning approach, the drawbacks are prediction biases, data overfitting issues and poor performance. To address these issues, we developed a DeepPsych system to harness the power of high frequency TRMI psychology data for market prediction. In a “hybridization–generalization–dual-channel-fusion” three-stage experiment, we evaluate each proposed module and the entire framework against the state-of-art machine learning benchmarks on investor psychology and trading data of the SPY (SP500 ETF). Results demonstrate that our deep learning framework can automatically identify features that are more effective than fundamental factors and support profitable trading

News and Social Media Emotions in the Commodity Market

Purpose--Emotion plays a significant role in both institutional and individual investors\u27 decision-making process. Emotions affect the perception of risk and the assessment of monetary value. However, there is a lack of empirical evidence available that addresses how investors\u27 emotions affect commodity market returns. The purpose of this paper is to investigate whether media-based emotions can be used to predict future commodity returns. Design/methodology/approach--The authors examine the short-term predictive power of media-based emotion indices on the following five days\u27 commodity returns. The research adopts a proprietary data set of commodity-specific market emotions, which is computed based on a comprehensive textual analysis of sources from newswires, internet news sources and social media. Time series econometrics models (threshold generalized autoregressive conditional heteroskedasticity and vector autoregressive) are employed to analyze 14 years (January 1998-December 2011) of daily observations of the CRB commodity market index, crude oil and gold returns, and the market-level sentiments and emotions (optimism, fear and joy). Findings--The empirical results suggest that the commodity-specific emotions (optimism, fear and joy) have significant influence on individual commodity returns, but not on commodity market index returns. Additionally, the research findings support the short-term predictability of the commodity-specific emotions on the following five days\u27 individual commodity returns. Compared to the previous studies of news sentiment on commodity returns (Borovkova, 2011; Borovkova and Mahakena, 2015; Smales, 2014), this research provides further evidence of the effects of news and social media-based emotions (optimism, fear and joy) in the commodity market. Additionally, this work proposes that market emotion incorporates both a sentimental effect and appraisal effect on commodity returns. Empirical results are shown to support both the sentimental effect and appraisal effect when market sentiment is controlled in crude oil and gold spot markets. Originality/value - This paper adopts the valence-arousal approach and cognitive appraisal approach to explain financial anomalies caused by investors\u27 emotions. Additionally, this is the first paper to explore the predictive power of investors\u27 emotions (optimism, fear and joy) on commodity returns

Understanding Mobile Banking Applications’ Security risks through Blog Mining and the Workflow Technology

This paper provides a review of the security aspect of mobile banking applications. We employed blog mining as a research method to analyze blog discussion on security of mobile banking applications. Furthermore, we used the workflow technology to simulate real-life scenarios related to attacks on mobile banking applications. Insights are summarized to help banks and consumers mitigate the security risks of mobile banking applications