In recent years, the combination of machine learning method and traditional financial investment field has become a hotspot in academic and industry. This paper takes CSI 300 and CSI 500 stocks as the research objects. First, this paper carries out kernel function test and parameter optimization for the kernel support vector machine system, and then predict and optimize the combination of market-neutral stock selection strategy and stock right strategy. The results of the experiment show that the multi-factor model based on SVM has a strong predictive power for the selection of stock, and it has a difference in the predictive power of different nuclear functions.

Commodity futures are futures contracts based on the physical commodities. Unlike commodity stocks, which must be “bought first and then sold”, commodity futures can also be “sold first and then bought”. Therefore, it is not possible to directly use the formula of capital flow in the stock market to characterize the capital flow in futures contracts. In this paper, the principal component analysis method is used to construct the principal component factors based on the K-line basic market data and one based on the K-line index data. Then the factors mentioned above are cross-validated using the Holdout verification form to generate the training set and test of the support vector machine. Then, this paper applies genetic algorithm to optimize the penalty parameters and kernel functions of SVM, and obtains the parameters with the highest accuracy of classification and prediction of capital flow. Finally, this paper uses the traversal algorithm to find the time window with the highest accuracy of the SVM classification to predict the capital flow. The research results of this paper show that the SVM-based classification of capital flow in commodity futures market is highly accurate.

In recent years, the applications of machine learning techniques to perfect traditional financial investment models has gained a widespread attention from the academic circle and the financial industry. This paper takes CSI300 stocks as the object of the research, uses Adaboost to enhance the classification ability of original linear support vector machine, and combines all major factors to build Adaboost-SVM multi-factor stock selection model based on Adaboost enhancement. In the backtesting analysis, the stock selection strategy of original linear support vector machine was compared with the Adaboost-SVM multi-factor stock selection strategy based on Adaboost enhancement. The result shows that the Adaboost-SVM multi-factor stock selection strategy based on Adaboost enhancement possesses stronger profitability and smaller income fluctuation than the original algorithm model.