Research on the investment ratio allocation of gold and bitcoin based on price prediction model and Markowitz model
DOI:
https://doi.org/10.54691/bcpbm.v26i.2075Keywords:
STL-ARIMA; LSTM; Volatility aggregation; Momentum effect.Abstract
With the development of modern society and economy, people are more willing to trade volatile assets to maximize their returns. People often want to use the historical data they have to predict the future price trend of investment products. This article uses the price data of gold and bitcoin as an example to make predictions. Gold has significant seasonality, so this paper combines the Seasonality and Trend decomposition using Loess (STL), and the time series model ARIMA to build the STL-ARIMA model for forecasting, and the RMSE of STL-ARIMA (5,1,5) was determined to be 17.0634. Bitcoin is affected by volatility aggregation and momentum effects. The LSTM model combined with GRACH (1,1) and the momentum effects equation achieves 91% accuracy in forecasting results.
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