Abstract
With the continuous development of the stock market, designing a reasonable risk identification tool will help to solve the irrational problem of investors. This paper first selects the stocks with the most valuable investment value in the future through the random forest algorithm in the nine-factor model and then analyzes them by using the higher-order moment model to find that different investors’ preferences will make the weight of the portfolio change accordingly, which will eventually make the optimal return and risk set of the composition of the portfolio change. The risk identification system designed in this paper can provide an effective risk identification tool for investors and help them make rational judgments.
Highlights
As China’s stock market continues to develop, how to identify the risk of many financial assets and investor preferences and reduce the bias of investors’ investment behavior due to speculative and subjective nature has become a growing concern for investors
With the development of data analysis technology, quantitative analysis has become the mainstream research approach; and from the perspective of strategic stock selection, the value-growth investment strategy is a perfect blend of traditional valuebased and active growth investment strategies, which considers assets that are somehow undervalued at the current stage but at the same time have a better potential for sustained growth, to have room for investment. erefore, stock selection using the value-growth investment strategy can yield more stable investment returns; in terms of indicator stock selection, stock selection is the study of stock impact factors, and stock prices are determined by a multidimensional spatial system of factor composition. erefore, stock selection explores the problem of optimal classification in a multidimensional space
With the continuous development in recent years, many scholars have applied machine learning methods to stock selection strategies; Fan and Palaniswami were the first to apply support vector machine algorithms to stock selection [1]; Kim et al used vector machines for stock price prediction with good results [2]; Yu et al used support vector machines for stock market prediction by combining the selection properties of genetic algorithms to improve the efficiency of the model [3]; Genuer et al, Bin Li et al, and Ladyzynski et al applied the random forest algorithm to the classification prediction problem with relatively good experimental results [4,5,6]; and Heaton et al studied the application on reasset pricing and financial risk control based on deep learning [7]
Summary
As China’s stock market continues to develop, how to identify the risk of many financial assets and investor preferences and reduce the bias of investors’ investment behavior due to speculative and subjective nature has become a growing concern for investors. With the continuous development in recent years, many scholars have applied machine learning methods to stock selection strategies; Fan and Palaniswami were the first to apply support vector machine algorithms to stock selection [1]; Kim et al used vector machines for stock price prediction with good results [2]; Yu et al used support vector machines for stock market prediction by combining the selection properties of genetic algorithms to improve the efficiency of the model [3]; Genuer et al, Bin Li et al, and Ladyzynski et al applied the random forest algorithm to the classification prediction problem with relatively good experimental results [4,5,6]; and Heaton et al studied the application on reasset pricing and financial risk control based on deep learning [7]. Those with quarterly returns greater than 0 are classified as 1 class and those with quarterly returns less than 0 are classified as 0 class; in the fifth classification, those with quarterly returns greater than 0.05 are classified as 2 classes, those with quarterly returns less than 0.05 but greater than 0.01 are classified as 1 class, those with quarterly returns greater than less than 0.01 but greater than −0.01 are classified as 0 class the quarterly return less than −0.01 but greater than −0.05 is classified as −1, and the quarterly return less than −0.05 is classified as −2
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