基於量化產業選擇和機器學習方法的投資組合策略研究

Abstract

China's economy and securities market are greatly affected by policies. The rapid development of China's economy in recent decades also benefits from the correct national industrial policies to a certain extent. However, as a barometer of economic development, the development of the securities market is not satisfactory. It does not develop synchronously with the economy. Through securities investment, it does not share the benefits brought by the rapid economic development. A large part of the reason is that investment only pursues the hot securities and plates, and ignores the in-depth research on relevant industries. Industry development has certain trends and regularity. Industry research is a means of securities investment, which is forward-looking and stable. Especially the industry research of financial institutions will have a good grasp of relevant industrial policies and industry development. This paper studies the investment in China's securities market from the perspective of industry research. The existing industry research is basically based on the qualitative research of macroeconomic and industrial policy, lack of quantitative analysis. This dissertation studies the industry investment from the perspective of quantification.

Firstly, this paper reviews and analyzes the current situation of China's capital market and industry development; Then it studies the basic situation of China's securities market and the current situation of industry investment; Then it combs and analyzes the relevant research at home and abroad. On this basis, the following four parts are mainly carried out.

First, based on the industry research of comprehensive scholars, the quantitative analysis method is used to construct the industry development index. The industry development index integrates the internal information of the industry and the social attention of related industries. Including the basic information of the industry, technical indicators, and the research attention of financial institutions, which can reflect the current situation and development trend of the industry. Then use the industry index to select the investment field.

Second, select the corresponding stocks according to the selected investment industry to build the investment portfolio. Use machine learning methods for specific industries, and use the fundamental and technical information of stocks in the industry to build a portfolio. The portfolio is highly representative and can reflect the development trend of the industry. After selection, the portfolio will be optimized. In this paper, a variety of machine learning methods are used to select securities in the industry. Based on the comparison of the disadvantages and advantages of each model, an integrated machine learning method is proposed to further improve the stock selection ability in the industry. The problem of portfolio optimization under industry constraints is studied.

Third, research the risk measurement and control of investment. This paper expands the risk measurement index maximum pullback rate commonly used in the industry, endows the maximum pullback with probability measurement, innovatively puts forward Dar index, uses simulation method to generate the maximum pullback surface, and gives the actual risk management and control operation method.

Fourth, the actual operation method of investment strategy is given. Based on the previous industry development index, the machine learning method is used to select the stocks in the industry, and then the trading timing is selected according to a variety of technical information, followed by portfolio optimization and risk control.

Finally, simulated trading, whether industry development index or using machine learning method to select stocks in the industry, will significantly improve the investment, increase the income and reduce the risk.

Innovation: This dissertation makes innovations in the following aspects. Firstly, build an industry development index integrating internal and external factors of the industry, and select the investment industry according to the industry development index; Then, according to the specific industry, the machine learning method is used to select stocks to build a portfolio to reflect the development trend of the industry; Thirdly, an improved risk measurement index Dar is proposed, and the definition and calculation method of the new index are given. Finally, the practical method of investment strategy is given.

On the basis of solving several key problems such as industry development evaluation, portfolio construction and risk control, this dissertation constructs a more comprehensive industry quantitative investment framework, which has strong practical value for Industry Research and investment practice; At the same time, it also has some theoretical and methodological innovations in quantitative research on industry development and differentiated risk management and control.

Date of Award	2 Mar 2022
Original language	Chinese (Traditional)
Awarding Institution	City University of Hong Kong
Supervisor	Mingjie Rui (External Supervisor) & Yue MA (Supervisor)