Asset Forecasting Analysis Based on ARIMA Model and BP Neural Network

Abstract

This paper forecasts the trend of the asset based on historical prices. First, through the establishment of exponential smoothing method, ????????????????????, BP neural network and other models, the trader invest in three assets: gold, bitcoin and cash in USB. The thesis is based on historical price to predict the trend of assets, determine whether traders should purchase, hold or sell and what percentage of the asset, and evaluate its future value. This paper first predicts the future returns and volatility of the two assets. In the initial forecast, the exponential smoothing method and ???????????????????? model are used to predict premiums and future returns. BP neural network is used to predict the future earnings in the middle and late forecast. First, for the first 60 days, we sit tight and wait for the data to accumulate. After 60 days, by looking back at historical data and setting appropriate technical indicators, the secondary trend curve and risk exposure curve of gold and Bitcoin can be obtained respectively. Once we have the curves, the commission, expected rate of return, and volatility of the two markets are combined, we will set up a scoring system to score the daily trading feasibility. Finally, we simulate the transaction, allocate the investment share, get the asset accumulation curve, and complete the decision.