Stocks

My first study proposes that stock price manipulation erodes trust, damages corporate reputation, reorients management towards short-termism, harms entrepreneurial innovation culture, and increases the cost of capital. I tested these ideas by linking stock manipulation data to corporate venture capital data for firms listed on NASDAQ and NYSE. The data indicate CVC investments in entrepreneurial firms are followed by a rise in market manipulation in the short run [-3 months, +3 months], but a decline thereafter. The data further indicates that stock manipulation harms the ability of CVCs to form investment syndicates and reduces the likelihood of successful IPO and acquisition exits. The hazard rate to IPO is 0.54 for CVC-backed firms that face market manipulation. Overall, the theory and evidence provide insights into how firm's manipulation can damage the effectiveness of their venture capital endeavors, ultimately contributing to sustainable growth and innovation.

I examine the importance of corporate scientific research. It is crucial to understand the role of corporate scientific research because such a knowledge could form an appropriate response to the current decline of corporate scientific research amidst the evolving innovation ecosystem featured with growing university research and tech companies’ research. R&D is often treated as a single construct in accounting and finance research for firm innovation. However, corporate scientific research (“R”) has different implications for firm innovations, “R” creates new knowledge, and reduced investment in "R" may lead to a loss of internal research capability, disrupting the speed and quality of innovation. As such, it is necessary and meaningful to examine "R" separately from "R&D."
Historically, corporate scientific research has played an important role in driving breakthrough innovations. Beginning in the 1980s, there has been a decline in corporate scientific research in favor of university research and tech companies’ research. Consequently, this raises a question: if corporate scientific research was important, is it still important? This is a fundamental question because if corporate scientific research is still important, declining or even stagnant corporate scientific research would present an issue of concern for firms and the economy.

Financial time-series data are noisy, volatile, and nonlinear. The classic statistical linear models may not capture those underlying structures of the data. The rapid advancement in artificial intelligence and machine learning techniques, availability of large-scale data, and increased computational capabilities of a machine opens the door to developing sophisticated deep learning models to capture the nonlinearity and hidden information in the data. Creating a robust model by unlocking the power of a deep neural network and using real-time data is essential in this tech era. This study constructs a new computational framework to uncover the information in the financial time-series data and better inform the related parties. It carries out the comparative analysis of the performance of the deep learning models on stock price prediction with a well-balanced set of factors from fundamental data, macroeconomic data, and technical indicators responsible for stock price movement. We further build a novel computational framework through a merger of recurrent neural networks and random compression for the time-series analysis. The performance of the model is tested on a benchmark anomaly time-series dataset. This new computational framework in a compressed paradigm leads to improved computational efficiency and data privacy. Finally, this study develops a custom trading simulator and an agent-based hybrid model by combining gradient and gradient-free optimization methods. In particular, we explore the use of simulated annealing with stochastic gradient descent. The model trains a population of agents to predict appropriate trading behaviors such as buy, hold, or sell by optimizing the portfolio returns. Experimental results on S&P 500 index show that the proposed model outperforms the baseline models.

Information leakage before full acquisitions has been widely documented. The information leakage, and the resulting pre-bid runup in the target's stock, generally increases the total cost of the acquisition. That is, information leakage and the ensuing pre-bid runup is a gain to the target and loss to the acquirer. Herein, I first ascertain the characteristics of full acquisitions that affect the amount of information leakage. I find that if the acquirer borrows to finance the acquisition then information leakage is greater. Further if the acquirer is foreign, if the target is a high-tech firm, and if the target has options on its stock all increase information leakage. I find hostile deals are effective in reducing information leakage. Lastly, information leakage increases in the percentage of managerial ownership. I next hypothesize that the identity and intent of partial acquirers is known to market participants before the announcement of a partial acquisition. I find that the market can anticipate whether a partial acquirer intends to fully-acquire or take an active role in the management of the target. Also, the market anticipates whether the acquirer is a private investment find or a non-financial corporation. Further, the acquirer's identity or intent is fully reflected in the target's stock price before the announcement of the partial acquisition. These results help explain why there are few partial acquisitions as precursors to full acquisitions.