Portfolio Decision Model Based on NIWPSO-LSTM

Abstract

As a cross product between computer science and financial science, the primary purpose of quantitative investment is to explain the formation principle of financial asset prices and to predict the future price of financial assets. With gold and bitcoin daily price data from London Bullion Market Association and NASDAQ, we develop a model that uses only the past stream of daily prices to date to determine each day if the trader should buy, hold, or sell their assets in their portfolio. Firstly, we choose the Long Short-Term Memory neural network based on the improved particle swarm algorithm (NIWPSO-LSTM) to predict the price. We use metabolic grey model (MGM(1,1)) to correct the price in the initial period, to make up for the shortcomings of LSTM which needs a large number of training sets to achieve better prediction results. Secondly, we quantify the return and risk of portfolio investment, establish a nonlinear programming model, and use Monte Carlo simulation method to solve the initial solution. Last, we use indicators to verify the accuracy of our model. All the results show that our model is able to provide extraordinary decision support in a real investment environment.