Abstract:
This paper is discusses the problems of the short-term forecasting of
financial time series using supervised machine learning (ML) approach. For this
goal, we applied several the most powerful methods including Support Vector
Machine (SVM), Multilayer Perceptron (MLP), Random Forests (RF) and
Stochastic Gradient Boosting Machine (SGBM). As dataset were selected the
daily close prices of two stock index: SP 500 and NASDAQ, two the most
capitalized cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), and exchange rate
of EUR-USD. As features we used only the past price information. To check the
efficiency of these models we made out-of-sample forecast for selected time
series by using one step ahead technique. The accuracy rates of the forecasted
prices by using ML models were calculated. The results verify the applicability
of the ML approach for the forecasting of financial time series. The best out of
sample accuracy of short-term prediction daily close prices for selected time
series obtained by SGBM and MLP in terms of Mean Absolute Percentage Error
(MAPE) was within 0.46-3.71 %. Our results are comparable with accuracy
obtained by Deep learning approaches.
