Abstract
Algorithms that are previously difficult to implement have been successfully applied in different fields because of hardware development. Quantitative investment has the characteristics of rationality and efficiency and has obvious advantages over traditional methods. Based on the SP500 index data for 4936 trading days, 10 characteristics such as PSY, MACD, STOCHK and STOCHD were generated. Based on those features, quantitative investment strategies for the GBDT and LightGBM models were constructed. Validation showed that the annualized returns of the two strategies exceeded the direct purchase and holding of the SP500 index, with the annualized returns of 43.4% and 50.7%. The performance of risk control of the two models was also better than the benchmark strategy. The GBDT model had less risk than the LightGBM model when the same benefits were obtained. The accuracy of the LightGBM model was higher than that of the GBDT, and its F1 score was 0.814, while the GBDT model was 0.805. For the different selected components, the results of the principal component analysis showed that the PSY feature weight in the GBDT model was much higher than other features, and a single feature can be applied for straightforward prediction. In the LightGBM model, the seven feature weights such as STOCHK were relatively balanced, and more features can be balanced at the same time to obtain more accurate results. The article designs investment strategies based on the LightGBM model for the first time and provides new ideas for providing a framework for index investment.
Highlights
Quantitative investment refers to a transaction method that uses quantitative methods and computerized programmatic orders to obtain stable returns [1]
This data set was derived from Yahoo SP500 index data since 2000, a total of 4936 days
The total investment return, average daily return and annualized return of the two models are significantly higher than the SP500 index
Summary
Quantitative investment refers to a transaction method that uses quantitative methods and computerized programmatic orders to obtain stable returns [1]. In accordance with the efficient market hypothesis, the fluctuation of stocks is caused by the random spread of information [3]. The machine learning method was used to fit the non-linear relationship between the moving average index and the stock return, and a better solution than the traditional method was obtained [6]. The use of big data, machine learning and artificial intelligence methods showed that technology-driven investment solutions were of great significance to the development of finance [7]. If deep learning was further applied to portfolio investment and risk management, more innovative results could be obtained compared to traditional standard analysis [8].
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