Stock Prediction with Random Forests and Long Short-term Memory

Machine learning as a popular computer science area has been promoted and developed for more than two decades. It has been applied in many fields in our life, like domestic products such as Alexa from Amazon, photographic products such as Mavic from Dji and so many other areas. This report represents an interesting way to apply machine learning and deep learning technologies on the stock market. We explore multiple approaches, including Long Short-Term Memory (LSTM), a type of Artificial Recurrent Neural Networks (RNN) architectures, and Random Forests (RF), a type of ensemble learning methods. The goal of this report is to use real historical data from the stock market to train our models, and to show reports about the prediction of future returns for picked stocks

A Critique of Chinese Dual-Class Structure Listing Regulation: Lessons Learned from Overseas Experiences

The concept of the dual-class structure listing (DCS listing) indicates the corporate financing and governance practice under which a particular listing firm issues two or more classes of common shares with different voting shares per class. The advantage of DCS concentratedly lies in the sufficiently safeguarding the founder’s idiosyncratic visions and protecting the long-term benefit maximization goal from the short-termism. Simultaneously, the latent defects of DCS consist of the volatility of the superiors voting rights holders’ personal attributes and the weighted voting power abuse risks. To a large degree, the DCS regulation within China’s institutional context can be a new issue. In comparison with the US history for over a century of DCS listing practice and regulation, China did not lift its ban on the domestic DCS listing until 2019. Among these jurisdictions in the Asian-Pacific region, the US, Hong Kong SAR, and Singapore might provide remarkable experiences. Chapter 1 will portray a general tendency of the rise of DCS listing worldwide and briefly comb the practical issues regarding DCS listing within the Chinese institutional context. Chapter 2 will try to clarify the conceptual boundary of DCS listing in terms of history and discourse evolution, this chapter will briefly retrospect China’s overseas and domestic DCS listing practices and the current DCS listing regulation framework as well. Chapter 3 aims at systematically delineate and retrospect China’s institutional environment involving corporate governance. Chapter 4 aims at examining both the empirical and on one hand, this chapter tries to review the existing empirical studies to evaluate the empirical evidence’s support for the question raised above. On the other hand, this section will come back to examine the theoretical corporate governance discussion on long-termism v. short-termism. Comprehensively, this part will try to retrospect the shortcomings of the short-termism rhetoric and simultaneously construct the legitimacy of long-termism discourse in terms of improving corporate governance patterns. Chapter 5 focuses on how to supply specific measures to improve both DCS listing practices and regulation. First, it will discuss the feasibility of mandating a time-based sunset provision. Also, it will evaluate the whether it is possible to use the time-phased voting to mitigate the latent defects of sunset provisions utilisation. Following, a brief conclusion is given

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Ph.DDOCTOR OF PHILOSOPH