| In recent years,more and more scholars have applied machine learning theory to various aspects of the financial field,and generative adversarial networks,which have received great attention in recent years,have also been applied to the financial field.Most scholars currently use generative adversarial networks and their improved models in data generation or simulating the path of asset price movements,as well as in price prediction and fraud detection,etc.Some scholars have also used generative adversarial networks in asset management,but they tend to generate asset price movement paths or distributions first,and then use the generated results to optimize according to an objective function,which leads to asset portfolio strategies.However,in this process,the generators of generative adversarial networks still only play the role of data generation and do not directly get the asset portfolio.Unlike other scholars,this paper uses the generator of the Portfolio-GAN model to directly generate the weight vector of the assets,combine the returns of the assets to obtain the corresponding portfolio returns,and then proceed to adversarial training.Also,to enable multi-period adjustment and intelligent stock selection,we apply a sliding window on the input dataset and a ReLU activation function on the output layer of the generator.For better implementation,we use LSTM and 1D convolutional neural networks in the hidden layers of the generator and discriminator,respectively.To obtain a better performance of asset portfolio,we select multiple funds in the same type,divide the data with a period of every 5 trading days,select the best return performance in each same time period and recombine the data by time to obtain a new portfolio return series,and call it the target fund.We put this target fund into the model as a real distribution for generation and do comparative analysis,and compare the generated portfolio strategy with the equal-weighted allocation model and the risk parity model.The final empirical results show that the return distribution of the generated asset portfolio strategies is close to that of the target fund,indicating that Portfolio-GAN has a superior learning ability.Overall,the evaluation metrics of the portfolios generated by Portfolio-GAN outperform the performance of the funds,and the cumulative return curves are better than or close to the cumulative return curves of the real funds,indicating that the Portfolio-GAN model generates portfolios that outperform the real funds and meet our expected objectives.In comparison with the equal-weighted allocation model and the risk parity model,the portfolio generated by Portfolio-GAN has performed well,with essentially the highest Sharpe ratio and Calmar ratio.Therefore,we believe that the asset portfolio generated by Portfolio-GAN is practically meaningful and can be a new approach to smart investment,and can also provide practical reference value to investors. |
