Stock Investment and Trading Strategy Model Based on Autoregressive Integrated Moving Average

Gold and Bitcoin have long been popular among investors as traditional and newer assets that can fight inflation and preserve value, respectively, which has led to an increasing number of investors interested in investing in Bitcoin and gold. This paper focuses on a trading decision model based on time series analysis combined with innovative bull and bear market forecasts. By comparing the forecasting effectiveness of Autoregressive Integrated Moving Average (ARIMA) and XGBoost neural network, we decided to choose the more effective ARIMA model for stock price forecasting. ARIMA models combined with bull and bear market forecasting models for conservative stock traders analyze the risk and return of trading and propose trading decisions. In addition to this, we verified the optimality of the developed model and analyzed the sensitivity of the model. Adjustments were made to the gold, bitcoin and cash positions on 23 different dates through the trading decision methodology we provided. Ultimately, the model we built is expected to deliver around 260 times profitability over the dates of the data used.