Machine Learning for Price Prediction and Risk-Adjusted Portfolio Optimization in Cryptocurrencies

Abstract

Accurately forecasting price swings is nowadays essential to investors looking to maximize their portfolios as the cryptocurrency markets continue to develop and fluctuate rapidly. The intricate, non-linear patterns in these markets are sometimes difficult for traditional financial models to depict. In response, this paper presents two machine learning techniques for predicting bitcoin prices: Extreme Gradient Boosting and Long Short-Term Memory. The study first evaluates how well these models forecast Bitcoin prices, assessing their accuracy with measures like Mean Absolute Error and Root Mean Squared Error. Four significant cryptocurrencies are then predicted by LSTM. In order to allocate assets in a way that optimizes returns while reducing risk, the forecasted prices are then incorporated into portfolio optimization algorithms utilizing Monte Carlo simulation and the efficient frontier. The results of the study show how machine learning approaches may be used to improve investing strategies through optimal portfolio allocation, in addition to projecting cryptocurrency values.