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ON THE FLOW AND PERFORMANCE OF MUTUAL FUNDS
Title
ON THE FLOW AND PERFORMANCE OF MUTUAL FUNDS
Creator
Zhang, Jingqi
Date
2019
Description
ABSTRACTThis dissertation consists of three essays on mutual funds. I first discuss the flow of active ETFs. And then I focus on the...
Show moreABSTRACTThis dissertation consists of three essays on mutual funds. I first discuss the flow of active ETFs. And then I focus on the performance of mutual funds. Finally, I evaluate the timing ability of mutual fund investors.Using a data set from 2000 to 2016, this thesis first studies the behavior of active ETF investors from the perspective of fund flows. The results show that the investors chase past returns as they do for mutual funds. Furthermore, I find that the return-chasing behavior can be influence by other considerations, such as fee changes. However, the evidence of performance persistence is weak for active ETFs. Therefore, I propose that the return-chasing behavior is not smart, and the flows of active ETFs instead behave more like “dumb money”, which are demonstrated by the data.I continue to study the performance of the mutual funds. To avoid the bias caused by pricing models themselves, I introduce a model-independent method to assess the mutual fund performance relative to the portfolios constructed by ordinary investors, assuming they are following a naive strategy. Using a data set from October 1984 to September 2017, I find that the majority of mutual funds have higher buy-and-hold returns than the T-bill returns as well as the market returns in the long run. And employing the model-independent measure of performance, I find that the mutual fund industry creates value for individual investors for that mutual funds on average exceed the performance of the majority of the portfolios constructed by the investors selecting stocks randomly.To measure the timing ability of mutual fund investors, I use the difference between the internal rate of return realized by investors and the buy-and-hold return of the funds. Different from the existing literature, I modify the cash flows used to generate the internal rate of return, in which way I can capture the realized return of investors more accurately. I find that investors show timing skills in short horizon. And on average, investors of mutual funds have worse timing skills than those of ETFs. And compared with active fund investors, passive fund investors have better timing skills. I also find that investors who simply chase past winners would show worse timing skills.
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High Frequency Trading and Its Impact on Market Quality in U.S. Futures Market
Title
High Frequency Trading and Its Impact on Market Quality in U.S. Futures Market
Creator
Wang, Chao
Date
2020
Description
This research focuses on the effects of high frequency trading (HFT) on market liquidity in US futures market. This research utilizes a unique...
Show moreThis research focuses on the effects of high frequency trading (HFT) on market liquidity in US futures market. This research utilizes a unique data set consisting of all book events for multiple underlying assets and contracts during calendar year 2018, covering all trading days information of E-mini S&P 500, Gold, Eurodollar, Crude Oil, Corn and Soybean futures with their nearby and deferred contract data each day. This study extends findings from existing HFT equity research (e.g. Brocher et al., 2016; Frino et al., 2019, etc.) that HFT promotes market liquidity, into the commodity market. It also addresses HFT’s contributions to price discovery, and find it varies by types of commodities. Furthermore, the research identifies how an HFT phenomenon, the Cancel Cluster, impacts the futures market. Also, this research verifies and extends the models in Frino et al. (2019) to multiple commodities. Finally, a series of promising future analyses are suggested.
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THREE ESSAYS IN ENTREPRENEURIAL FINANCE AND COMMODITY MARKETS
Title
THREE ESSAYS IN ENTREPRENEURIAL FINANCE AND COMMODITY MARKETS
Creator
Jia, Jian
Date
2020
Description
This dissertation includes three essays with a series of empirical investigations in areas of entrepreneurial finance and commodity markets.In...
Show moreThis dissertation includes three essays with a series of empirical investigations in areas of entrepreneurial finance and commodity markets.In the first essay, I study the impact of General Data Protection Regulation (GDPR) on investment in new and emerging technology firms. My findings indicate negative post-GDPR effect after its 2018 rollout on EU ventures, relative to their US counterparts, but no such effects following its 2016 enactment.In the second essay, I examine how investors’ tendency to prefer investing in local ventures interacts with the effects of the GDPR on venture investment in EU. I demonstrate that GDPR’s enactment and rollout differentially affect investors as a function of their proximity to ventures. Specifically, I show that GDPR’s rollout in 2018 has a negative effect on EU venture investment and the effects are higher when ventures and lead investors are not in the same country or union. The relationship manifests in the number of deals per month and in the amount invested per deal, and is particularly pronounced for newer and data-related ventures.In the third essay, I formulate two claims about spot and futures return prediction in industrial metal futures market. These claims lead to testable hypotheses, and provide theory-based restrictions for the coefficients of spot and futures return regression. I investigate six industrial metals and find empirical support for my hypotheses. The in-sample and out-of-sample evidence shows that financial variables, proxies for global economic activities, and the basis predict futures and spot price returns consistently with my hypotheses. Furthermore, my out-of-sample trading experiments document economic significance of the restrictions.
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Socially Responsible Investing and Style Investing
Title
Socially Responsible Investing and Style Investing
Creator
He, Di
Date
2020
Description
This study focuses on two popular investment strategies. The first one is a combination of socially responsible investing and factor investing...
Show moreThis study focuses on two popular investment strategies. The first one is a combination of socially responsible investing and factor investing (SRIF), it is therefore a comparison between factor investing portfolios and their corresponding ESG screened factor investing portfolios, aiming at indicating whether there is an opportunity costs or benefits of being responsible in factor investing. Opportunity cost is regarded if the ESG screened factor investing portfolios have lower raw return, Sharpe ratio, and risk-adjusted return than their respective factor investing portfolios. In addition to simply comparison, I also build an empirical SRI strategy, achieving real outperformance of SRI. For the second strategy, investing in R&D intensity (high technology) stocks results in significant positive alpha over 40 years. However, the alphas decrease significantly after the “Tech Bubble”, because investors nowadays prefer those technology firms who can produce true profits. I provide empirical evidence to investor sentiment, proving both risk bearing and investor sentiment play important roles in the positive association between R&D-intensive and excess return.In the first SRIF strategy, five widely-accepted factors in academic: value, size, profit, investment, and momentum are used to construct original single factor investing portfolio as benchmarks, which can naturally solve the benchmark bias, factor bias in previous literature at some extent. In addition to fulfill empirical industry’s generalities and constraints, this study also covers multi-factor framework and constructs different long-short positions for investment processing. Following considerations of ESG measurement (ESG_net and ESG_Industry, the latter one for calibration of industry bias), sample period (whole period and sub period), portfolio weighting methods (equally weighted and capitalization weighted), and after excluding undiversified portfolio, there are total 192 comparisons between factor investing portfolios and ESG screened factor investing portfolios for each measures of performance. Results suggest that most investors (80% - 90%) have to bear non-statistically significant opportunity costs if they want to be socially responsible in factor investing. In addition, the opportunity costs in sub period (2004-2017) is remarkably less in scale than those in whole period (1992-2017), indicating an obvious “time effect” that investors will have less opportunity costs recently with more and more ESG information is disclosed. For empirical consideration of industry, I build a double sorting factor portfolio on profit and value, and its ESG screened portfolio outperform the single factor portfolio.For the second research, R&D expense is a key component of investment. There is long history literature claim that there is a positive relationship between R&D and stock returns. There are two main explanations of the positive association, which are mispricing and risk bearing. This study separates whole sample into two periods: before “Tech Bubble” and after “Tech Bubble”, indicating that the mispricing is weaker after “Tech Bubble” than that in before “Tech Bubble”, while risk bearing is persistent. In addition, this study finds that the excess returns are relatively high for those highly subjective and difficult to arbitrage technology securities, which are small stocks, high volatility stocks, unprofitable stocks, non-dividend-paying stocks before the “Tech Bubble”, but almost vanish after the “Tech Bubble”. Therefore, investor sentiment does exist. While for those true earning technology securities, their excess returns are persistent, indicating compensation of risk bearing.
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Corporate Insider Holdings and Analyst Recommendations
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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TWO ESSAYS IN SUSTAINABILITY AND ASSET RETURN PREDICTABILITY
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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Integrating Deep Learning And Innovative Feature Selection For Improved Short-Term Price Prediction In Futures Markets
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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Financialization in the Structured Products Market
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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Two Essays on Mergers and Acquisitions
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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