Stock Selection Based On Deep Model And Spectrum Method

Quantitative investment has many advantages,such as discipline,systematization and decentralization.It has attracted more and more attention from academia and investment circles.In recent years,with the development of artificial intelligence and deep learning technology,scholars and investment practitioners have applied more machine learning algorithms to various aspects of the financial field and achieved good investment returns.Compared with mature overseas markets,quantitative investment has a broader development space and unlimited research value in the domestic market.In this paper,the application of data mining and deep learning in quantitative stock selection and statistical arbitrage is studied,and a combination stock selection model based on deep model and spectral method is proposed.The main work of this paper is divided into the following three parts:1.Deep learning is applied to the problem of quantitative stock selection.Using IRGAN(Information Retrieval in Generative Adversarial Networks)as the stock selection model,a convolutional network structure is designed and embedded in the generator and discriminator components of IRGAN.The stock selection results are obtained according to the output of the model.The experimental results show that the accuracy of this method in choosing high yield stocks is better than that of other comparative methods.2.Spectral method is applied to the problem of selecting similar stock portfolios for statistical arbitrage.The Euclidean distance is replaced by the dynamic time warping distance as the distance measure of stocks.Similar stock portfolios are generated by clustering using spectral clustering algorithm.The experimental results show that on the medium and low frequency time scale,the similarity degree of the similar stock portfolio mined by this method is higher than that of other comparative algorithms.3.Combining the above two models,a combination stock selection model based on deep model and spectral method is proposed.The stock selected by the deep model is taken as the core stock set,and the similar stock model based on spectral method is used to get the stock set similar to the price trend of the core stock.The core stock set and the similar stock set are taken as the result of the combination stock selection model.The experimental results show that the combined model proposed in this paper can greatly improve the accuracy of stock selection results and the return on simulated investment of the model is better than other comparative algorithms.