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Title: Corporate Insider Holdings and Analyst Recommendations
Creator: Gogolak, William Peter
Date: 2022
Description: I pursued two competing theories about insider stock holding levels and analyst recommendations. The complementary hypothesis states that top...
Show moreI pursued two competing theories about insider stock holding levels and analyst recommendations. The complementary hypothesis states that top management and analysts conduct actions in a comparable manner; the contradicting hypothesis states that insiders and analysts exhibit opposite market actions (Hsieh and Ng, 2019). I examined insider stock holding levels and analyst recommendations. I analyzed a sample of S&P 500 firms from 2011-2020. In this sample, I found that the relationship between insider holding levels and analyst recommendations are opposite in concurrent time periods; thus, supporting the contradictory hypothesis. I also analyzed lagged insider holdings levels in a granger causality test. This test supports the idea that top management stock holdings increase when analysts downgrade stocks, and the opposite effect it true when analysts upgrade stocks. Using a sample of S&P 500 firms from 2011 – 2020, I provided support to my hypothesis that aggregated analyst recommendations forecast future aggregate equity returns. Furthermore, I conducted a test to support my conclusion that changes to insider holding levels should be used to forecast changes in future equity returns, beyond what is already explained by analyst recommendations. I argue two compelling additions that I make to the existing body of work regarding aggregate stock prediction. First, I build upon existing papers by using Bloomberg aggregate analyst recommendations as opposed to the IBES datasets. Second, I expand upon recent index forecasting papers by incorporating both aggregate analyst recommendations and aggregate insider holding levels into aggregate stock return models.
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Title: Sharpen Quality Investing: A PLS-based Approach
Creator: Jiao, Zixuan
Date: 2022
Description: I apply a disciplined dimension reduction technique called Partial Least Square (PLS) to construct a new quality factor by aggregating...
Show moreI apply a disciplined dimension reduction technique called Partial Least Square (PLS) to construct a new quality factor by aggregating information from 16 individual signals. It earns significant risk-adjusted returns and outperforms quality factors constructed by alternative techniques, namely, PCA, Fama-Macbeth regression, a combination of PCA and Fama-Mabeth regression and a Rank-based approach. I show that my quality factor performs even better during rough economic patches and thus appears to hedge periods of market distress. I further show adding our quality factor to an opportunity set consisting of the other classical factors increases the maximum Sharpe ratio.
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Title: INFORMATION EFFICIENCY AND THE EFFECT OF HIGH FREQUENCY TRADING IN THE U.S. FUTURES MARKETS
Creator: CHA, SEUNG YOUN
Date: 2021
Description: The paper gives an empirical analysis with the U.S. futures market data on how High Frequency Trading, HFT can improve the information...
Show moreThe paper gives an empirical analysis with the U.S. futures market data on how High Frequency Trading, HFT can improve the information efficiency of asset prices. Various analyses were conducted to determine the degree of efficiency of information in futures high-frequency trading. The paper tries to explain the effect of high-frequency trading on the efficiency of the market in various ways and tries to propose stepping stones for developing a new market analysis measure.The research builds a coherent framework for analyzing both linear and non-linear market efficiency and applies it to a variety of futures contracts using high- frequency data. The major finding of this paper is that market efficiency levels vary widely over time depending on market characteristics. The paper also finds that HFT activities are higher when the market is inefficient. The paper analyzes the relationship between high frequency trading activities and market efficiency and discovers the mechanism. The story that HFT activity responds to market efficiency needs is especially strong in the E-mini, S&P500 futures contract.
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Title: Hedge Fund Replication With Deep Neural Networks And Generative Adversarial Networks
Creator: Chatterji, Devin Mathew
Date: 2022
Description: Hedge fund replication is a means for allowing investors to achieve hedge fund-like returns, which are usually only available to institutions....
Show moreHedge fund replication is a means for allowing investors to achieve hedge fund-like returns, which are usually only available to institutions. Hedge funds in total have over $3 trillion in assets under management (AUM). More traditional money managers would like to offer hedge fund-like returns to retail investors by replicating their performance. There are two primary challenges with existing hedge fund replication methods, difficulty capturing the nonlinear and dynamic exposures of hedge funds with respect to the factors, and difficulty in identifying the right factors that reflect those exposures. It has been shown in previous research that deep neural networks (DNN) outperform other linear and machine learning models when working with financial applications. This is due to the ability of DNNs to model complex relationships, such as non-linearities and interaction effects, between input features without over-fitting. My research investigates DNNs and generative adversarial networks (GAN) in order to address the challenges of factor-based hedge fund replication. Neither of these methods have been applied to the hedge fund replication problem. My research contributes to the literature by showing that the use of these DNNs and GANs addresses the existing challenges in hedge fund replication and improves on results in the literature.
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Title: TWO ESSAYS IN SUSTAINABILITY AND ASSET RETURN PREDICTABILITY
Creator: Nguyen, Lanh Vu Thuc
Date: 2021
Description: Our paper consists of two chapters in Financial Modeling for Sustainability and Asset Return Predictability. Recent developments in data...
Show moreOur paper consists of two chapters in Financial Modeling for Sustainability and Asset Return Predictability. Recent developments in data scraping and analytical methods have enhanced the possibility to construct the data and modeling required to examine the topics in each chapter. Chapter 1 proposes a simple yet strategic model involving a personal financial system to achieve a sustainable and prosperous future. The proposed model emphasizes the optimization of carbon footprints of one person at a time through the decentralization of the electricity use. While describing steps to develop a decentralized system considering electricity as a credit product, the model also underlines the importance of geographic economic dimensions and energy market prices due to their anticipated impact on the effectiveness of designing strategies for optimizing individuals’ energy use habits. Geographical conditions as well as market electricity prices can be used to signal individual energy use scores over time, therefore could also be instrumental in customizing energy use habits as the users realize variations in their energy use scores resulting from hourly electricity price changes at their locations. In other words, not only the changes in the individual’s behavior, but also the changes in the geographical conditions and community of users will affect the improvement of energy use behaviors of an individual over time using our model. We believe that the proposed model can be efficiently adopted to take on challenges threatening the future sustainability. While describing the basic characteristics of the model, we also open the possibility for future studies its capabilities to reduce carbon footprints from other societal choices, for example, using water, managing waste, or designing sustainable transportation systems. In Chapter 2, we examine asset return predictability, which is an important topic in finance with rich literature. Much of the current literature considers dividend yield as the main predictor for expected returns, and the main discussion centers around confirming or rejecting the predictive power of dividend yield with mixed evidence. However, dividend payments have been consistently declining and public firms have been increasingly using stock repurchase as the alternative to return values to shareholders. We aim to contribute to the literature by investigating a panel data of total equity payout, which takes into account not only dividend payout but also other forms of payment such as stock repurchase, as the main predictor for expected returns. In the asset return predictability literature, existing studies gather stock repurchase data from financial statements. In this paper, we manually construct our database of returns and payouts of public companies from various sources to create precise firm-level total equity payout dataset without relying on approximations from annual financial statements. This study adds to understanding of total equity payout and stock returns by analyzing a finer granularity than an annum and cross section of stock returns.
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Title: Two Essays on Cryptocurrency Markets
Creator: Fan, Lei
Date: 2022
Description: Understanding the dependence relationships among cryptocurrencies and equity markets is of interest to both academics and researchers. This...
Show moreUnderstanding the dependence relationships among cryptocurrencies and equity markets is of interest to both academics and researchers. This dissertation is comprised of two essays to add to this understanding. In the first essay, I investigate the interdependencies among the level of informational efficiency of four cryptocurrencies. I examine the correlations between the market efficiencies of cryptocurrencies using the rolling window method. I find that the correlations between those levels of market efficiencies are time-varying and influenced by the market condition and external events. I extend the study by employing Granger causality tests to analyze the causal relationships among these levels of market efficiency. I find that the Granger causalities among the levels of the cryptocurrency market efficiencies are time-varying and impacted by the level of the market efficiencies. In the second essay, I investigate the pairwise dependencies and causalities between the returns of the cryptocurrencies and six equity market indices. I examine the pairwise dependencies between the returns of cryptocurrencies and those of the equity indices by using the DCC-GARCH framework. I find the dynamic conditional correlations between the cryptocurrencies and equity indices are time-varying and generally weak. Furthermore, I study the causal relationship between cryptocurrencies and equity indices by employing the rolling Granger causality test. I find that the Granger causalities between cryptocurrencies and equity indices are time-varying, and more unidirectional Granger causalities are found from cryptocurrencies to equity indices. In addition, I examine the impact of cryptocurrency returns on the correlations between the equity market indices, and likewise, the impact of equity market returns on the correlations between the cryptocurrencies. I find that the cryptocurrency price fluctuations have minimal impact on the correlations between equity indices. Moreover, the dynamic conditional correlation between cryptocurrencies is unaffected by equity price innovations except for some extreme events. These findings could have implications for understanding the relationships among cryptocurrencies and equity markets and for investors wishing to incorporate these relationships in their portfolio choices.
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Title: DEFAULT RISK AND MOMENTUM PREMIUM
Creator: Zhang, Yi
Date: 2022
Description: Birge and Zhang (2018) reported that combining common factors models with functions of the default risk improves models' performance to...
Show moreBirge and Zhang (2018) reported that combining common factors models with functions of the default risk improves models' performance to explain stock returns. Default risk contains firm specific information and may help to explain momentum premium that compensates investors for the firm specific risk exposures. In this paper, we confirmed that the forward-looking measure of default risk, as proposed by Birge and Zhang (2018), seems to capture some pricing information in the momentum premium. This provides an alternative to explain the underlying risks associated with the momentum strategy.
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Title: Essays on Clean Energy Finance and Cryptocurrency Market
Creator: Xie, Yao
Date: 2021
Description: This dissertation includes four essays with several empirical investigations in the areas of clean energy finance and cryptocurrencies.In the...
Show moreThis dissertation includes four essays with several empirical investigations in the areas of clean energy finance and cryptocurrencies.In the first essay, I investigate the heterogeneous relationship between various determinants of the clean energy market across all subsectors of the clean energy stock market. My findings reveal that VIX is the most significant predictor of all clean energy subsectors conditional volatility. During the COVID-19 stress period, economic uncertainty measures become more significant measures. The heterogeneity of clean energy market persists in the out-of-sample results. These results suggest that portfolio diversification for different clean energy subsector is necessary. In the second essay, I study the safe haven property of several volatility indexes on clean energy subsectors. I compare the current COVID-19 stress period and the time before. The results show that market volatility and commodity volatility are good safe haven assets during the COVID-19 period. But they are not safe haven assets against the clean energy subsector before the pandemic period. Among all volatility indexes, gold volatility index is the most effective safe haven assets. In the third essay, I investigate the characteristics of Bitcoin as a financial asset. A comprehensive set of information variables under five categories: macroeconomics, blockchain technology, other markets, stress level, and investor sentiment. The empirical results show that blockchain technology, stress level and investor sentiment have strong predicting power on Bitcoin returns. In the fourth essay, I aim to study how extreme sentiment measures from Google Trend and Wikipedia Pageviews affect both traditional cryptocurrency, such as Bitcoin and stablecoin, like Tether. Our results show that Tether’s return is not affected by the extreme sentiment measures during the COVID-19 stress period which suggests that stablecoin can offer price stability.
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Title: Integrating Deep Learning And Innovative Feature Selection For Improved Short-Term Price Prediction In Futures Markets
Creator: Tian, Tian
Date: 2024
Description: This study presents a novel approach for predicting short-term price movements in futures markets using advanced deep-learning models, namely...
Show moreThis study presents a novel approach for predicting short-term price movements in futures markets using advanced deep-learning models, namely LSTM, CNN_LSTM, and GRU_LSTM. By incorporating cophenetic correlation in feature preparation, the study addresses the challenges posed by sudden fluctuations and price spikes while maintaining diversification and utilizing a limited number of variables derived from daily public data. However, the effectiveness of adding features relies on appropriate feature selection, even when employing powerful deep-learning models. To overcome this limitation, an innovative feature selection method is proposed, which combines cophenetic correlation-based hierarchical linkage clustering with the XGBoost importance listing function. This method efficiently identifies and integrates the most relevant features, significantly improving price prediction accuracy. The empirical findings contribute valuable insights into price prediction accuracy and the potential integration of algorithmic and intuitive approaches in futures markets. Moreover, the developed feature preparation method enhances the performance of all deep learning models, including LSTM, CNN_LSTM, and GRU_LSTM. This study contributes to the advancement of price prediction techniques by demonstrating the potential of integrating deep learning models with innovative feature selection methods. Traders and investors can leverage this approach to enhance their decision-making processes and optimize trading strategies in dynamic and complex futures markets.
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Title: Financialization in the Structured Products Market
Creator: Zhu, Lizi
Date: 2023
Description: This dissertation aims to study financialization in the structured products market. The structured products market has been undergoing a major...
Show moreThis dissertation aims to study financialization in the structured products market. The structured products market has been undergoing a major transformation in recent years. The market used to mainly serve institutional investors. However, as a few trading platforms powered by fintech companies emerged on the horizon, more and more banks are starting to compete in this market. The average trade size has also been declining significantly, thereby making the market increasingly accessible to retail investors. What are the factors that facilitate the development of this market? What are the economic incentives of issuers and investors? How do issuers compete? What does the future hold for this market? The main finding of this dissertation is that structured products provide utility to retail investors; As the level of risk aversion increases, an investor increasingly prefers structured products to other traditional asset classes; issuers develop three sources of competitive advantage to be a satisficer; the rise of fintech and improvement of financial education are the key to opening this market to retail investors.
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Title: Two Essays on Mergers and Acquisitions
Creator: Xu, Yang
Date: 2024
Description: This dissertation is composed of two self-contained chapters that both relate to mergers and acquisitions (M&A). In the first essay, we...
Show moreThis dissertation is composed of two self-contained chapters that both relate to mergers and acquisitions (M&A). In the first essay, we examine the Delaware (DE) reincorporation effect on firms’ post-IPO behaviors on mergers and acquisitions. We find that firms’ DE reincorporation decisions enhance the likelihood of engaging in M&A as targets. However, as a tradeoff, DE reincorporated firms get lower takeover valuations compared to stay-at-home-state firms, and the acquisition of reincorporated firms is less likely to be successful. Our second essay aims to explore the role of the options market in price discovery for M&A. We find that the predictive power of the changes in implied volatility of the target firm stock for the takeover outcome is statistically and economically significant. The risk arbitrage portfolios incorporating filters derived from the options on stocks of the target firms generate annualized risk-adjusted abnormal returns between 2.6% and 5%, depending on the portfolio weighting method, the threshold of filters for the implied volatility change, and the asset pricing models applied for abnormal returns. The results are robust to different empirical setups and are not explained by traditional factors.
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Title: Integrating Deep Learning And Innovative Feature Selection For Improved Short-Term Price Prediction In Futures Markets
Creator: Tian, Tian
Date: 2024
Description: This study presents a novel approach for predicting short-term price movements in futures markets using advanced deep-learning models, namely...
Show moreThis study presents a novel approach for predicting short-term price movements in futures markets using advanced deep-learning models, namely LSTM, CNN_LSTM, and GRU_LSTM. By incorporating cophenetic correlation in feature preparation, the study addresses the challenges posed by sudden fluctuations and price spikes while maintaining diversification and utilizing a limited number of variables derived from daily public data. However, the effectiveness of adding features relies on appropriate feature selection, even when employing powerful deep-learning models. To overcome this limitation, an innovative feature selection method is proposed, which combines cophenetic correlation-based hierarchical linkage clustering with the XGBoost importance listing function. This method efficiently identifies and integrates the most relevant features, significantly improving price prediction accuracy. The empirical findings contribute valuable insights into price prediction accuracy and the potential integration of algorithmic and intuitive approaches in futures markets. Moreover, the developed feature preparation method enhances the performance of all deep learning models, including LSTM, CNN_LSTM, and GRU_LSTM. This study contributes to the advancement of price prediction techniques by demonstrating the potential of integrating deep learning models with innovative feature selection methods. Traders and investors can leverage this approach to enhance their decision-making processes and optimize trading strategies in dynamic and complex futures markets.
Show less

Title: Two Essays on Mergers and Acquisitions
Creator: Xu, Yang
Date: 2024
Description: This dissertation is composed of two self-contained chapters that both relate to mergers and acquisitions (M&A). In the first essay, we...
Show moreThis dissertation is composed of two self-contained chapters that both relate to mergers and acquisitions (M&A). In the first essay, we examine the Delaware (DE) reincorporation effect on firms’ post-IPO behaviors on mergers and acquisitions. We find that firms’ DE reincorporation decisions enhance the likelihood of engaging in M&A as targets. However, as a tradeoff, DE reincorporated firms get lower takeover valuations compared to stay-at-home-state firms, and the acquisition of reincorporated firms is less likely to be successful. Our second essay aims to explore the role of the options market in price discovery for M&A. We find that the predictive power of the changes in implied volatility of the target firm stock for the takeover outcome is statistically and economically significant. The risk arbitrage portfolios incorporating filters derived from the options on stocks of the target firms generate annualized risk-adjusted abnormal returns between 2.6% and 5%, depending on the portfolio weighting method, the threshold of filters for the implied volatility change, and the asset pricing models applied for abnormal returns. The results are robust to different empirical setups and are not explained by traditional factors.
Show less

Title: Financialization in the Structured Products Market
Creator: Zhu, Lizi
Date: 2023
Description: This dissertation aims to study financialization in the structured products market. The structured products market has been undergoing a major...
Show moreThis dissertation aims to study financialization in the structured products market. The structured products market has been undergoing a major transformation in recent years. The market used to mainly serve institutional investors. However, as a few trading platforms powered by fintech companies emerged on the horizon, more and more banks are starting to compete in this market. The average trade size has also been declining significantly, thereby making the market increasingly accessible to retail investors. What are the factors that facilitate the development of this market? What are the economic incentives of issuers and investors? How do issuers compete? What does the future hold for this market? The main finding of this dissertation is that structured products provide utility to retail investors; As the level of risk aversion increases, an investor increasingly prefers structured products to other traditional asset classes; issuers develop three sources of competitive advantage to be a satisficer; the rise of fintech and improvement of financial education are the key to opening this market to retail investors.
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