Mean–Variance Portfolio Optimization Using Ensemble Learning-Based Cryptocurrency Price Prediction

Mean–Variance Portfolio Optimization Using Ensemble Learning-Based Cryptocurrency Price Prediction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mean–Variance Portfolio Optimization Using Ensemble Learning-Based Cryptocurrency Price Prediction Mojtaba Safari, Nawapon Nakharutai, Phisanu Chiawkhun, Parkpoom Phetpradap This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5734118/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The success of portfolio construction largely relies on accurately forecasting future asset performance. Advances in machine learning present significant opportunities to integrate prediction theory into portfolio selection. However, some studies indicate that relying on a single prediction model may lead to biased forecasts or suboptimal portfolio outcomes due to individual algorithms' inherent limitations and assumptions. This study proposes an ensemble learning approach for portfolio optimization by combining advanced predictive analytics with classical mean-variance (MV) models as a two-stage methodology. To do so, seven machine learning models, including LSTM, BLSTM, GRU, DMLP, RF, XGBoost, and SVR, are combined using ensemble approaches such as ridge regression (RR) and principal component regression (PCR). Next, the future insights are integrated into the MV framework to optimize asset allocations, aiming to balance risk and return effectively. An empirical analysis of top cryptocurrencies showed that ensemble learning improves prediction accuracy. Furthermore, the integration of forecasting results with the MV framework outperformed traditional approaches in terms of risk-adjusted returns and portfolio stability. Portfolio optimization machine learning ensemble learning risk management cryptocurrency Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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