The invention relates to a stock index prediction method based on adaptive
feature extraction, and the method comprises the steps: S1, obtaining stock index data, and obtaining the daily opening price, lowest price, highest price, closing price and transaction volume; s2, calculating an artificial index value proposed by a financial and economic expert; s3, constructing sample features and samplelabels, and dividing all samples into a
training set, a
verification set and a
test set; s4, performing adaptive
feature extraction on the sample; and S5, inputting self-adaptive extraction features and the artificial indexes calculated in the S2 into a neural network prediction model based on a factor
machine, and outputting a prediction result. According to the method, the characteristics of thestock indexes are extracted in a self-adaptive manner, and the extraction method is simple and high in
interpretability; the neural network based on the factor
machine is used as a prediction model,interaction between features can be learned, the nonlinear expression capability is achieved, and the
linear complexity is achieved; the accuracy of the stock index prediction technology can be effectively improved.