Feature selection with annealing for forecasting financial time series  

作　　者：Hakan Pabuccu Adrian Barbu 

机构地区：[1]Department of Business,Bayburt University,69000 Bayburt,Turkey [2]Statistics Department,Florida State University,Tallahassee,FL 32306,USA

出　　处：《Financial Innovation》2024年第1期201-226,共26页金融创新(英文)

基　　金：supported by THE SCIENTIFIC AND TECHNOLOGICAL RESEARCH COUNCIL OF TURKIYE.

摘　　要：Stock market and cryptocurrency forecasting is very important to investors as they aspire to achieve even the slightest improvement to their buy-or-hold strategies so that they may increase profitability.However,obtaining accurate and reliable predictions is challenging,noting that accuracy does not equate to reliability,especially when financial time-series forecasting is applied owing to its complex and chaotic tendencies.To mitigate this complexity,this study provides a comprehensive method for forecasting financial time series based on tactical input–output feature mapping techniques using machine learning(ML)models.During the prediction process,selecting the relevant indicators is vital to obtaining the desired results.In the financial field,limited attention has been paid to this problem with ML solutions.We investigate the use of feature selection with annealing(FSA)for the first time in this field,and we apply the least absolute shrinkage and selection operator(Lasso)method to select the features from more than 1000 candidates obtained from 26 technical classifiers with different periods and lags.Boruta(BOR)feature selection,a wrapper method,is used as a baseline for comparison.Logistic regression(LR),extreme gradient boosting(XGBoost),and long short-term memory are then applied to the selected features for forecasting purposes using 10 different financial datasets containing cryptocurrencies and stocks.The dependent variables consisted of daily logarithmic returns and trends.The mean-squared error for regression,area under the receiver operating characteristic curve,and classification accuracy were used to evaluate model performance,and the statistical significance of the forecasting results was tested using paired t-tests.Experiments indicate that the FSA algorithm increased the performance of ML models,regardless of problem type.The FSA hybrid models showed better performance and outperformed the other BOR models on seven of the 10 datasets for regression and classification.FSA-based models also outp

关 键 词：Financial time-series forecasting Feature selection Machine learning Cryptocurrency Stock market Return forecasting 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]