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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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Optimization methods and machine learning model for improved projection of energy market dynamics
Title
Optimization methods and machine learning model for improved projection of energy market dynamics
Creator
Saafi, Mohamed Ali
Date
2023
Description
Since signing the legally binding Paris agreement, governments have been striving to fulfill the decarbonization mission. To reduce carbon...
Show moreSince signing the legally binding Paris agreement, governments have been striving to fulfill the decarbonization mission. To reduce carbon emissions from the transportation sector, countries around the world have created a well-defined new energy vehicle development strategy that is further expanding into hydrogen vehicle technologies. In this study, we develop the Transportation Energy Analysis Model (TEAM) to investigate the impact of the CO2 emissions policies on the future of the automotive industries. On the demand side, TEAM models the consumer choice considering the impacts of technology cost, energy cost, refueling/charging availability, consumer travel pattern. On the supply side, the module simulates the technology supply by the auto-industry with the objective of maximizing industry profit under the constraints of government policies. Therefore, we apply different optimization methods to guarantee reaching the optimal automotive industry response each year up to 2050. From developing an upgraded differential evolution algorithm, to applying response surface methodology to simply the objective function, the goal is to enhance the optimization performance and efficiency compared to adopting the standard genetic algorithm. Moreover, we investigate TEAM’s robustness by applying a sensitivity analysis to find the key parameters of the model. Finally based on the key sensitive parameters that drive the automotive industry, we develop a neural network to learn the market penetration model and predict the market shares in a competitive time by bypassing the total cost of ownership analysis and profit optimization. The central motivating hypothesis of this thesis is that modern optimization and modeling methods can be applied to obtain a computationally-efficient, industry-relevant model to predict optimal market sales shares for light-duty vehicle technologies. In fact, developing a robust market penetration model that is optimized using sophisticated methods is a crucial tool to automotive companies, as it quantifies consumer’s behavior and delivers the optimal way to maximize their profits by highlighting the vehicles technologies that they could invest in. In this work, we prove that TEAM reaches the global solution to optimize not only the industry profits but also the alternative fuels optimized blends such as synthetic fuels. The time complexity of the model has been substantially improved to decrease from hours using the genetic algorithm, to minutes using differential evolution, to milliseconds using neural network.
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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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