Multi-Period Portfolio Optimization Using Predictive Machine Learning Models

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Optimization Using Predictive Machine Learning Models</title> <meta name="description" content="Multi-Period Portfolio Optimization Using Predictive Machine Learning Models"> <meta name="keywords" content="multi-period portfolio optimization, look-ahead constrained optimization, machine learning, sequential decision making"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> 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publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Multi-Period Portfolio Optimization Using Predictive Machine Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peng%20Liu">Peng Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Chyng%20Wen%20Tee"> Chyng Wen Tee</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaofei%20Xu"> Xiaofei Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper integrates machine learning forecasting techniques into the multi-period portfolio optimization framework, enabling dynamic asset allocation based on multiple future periods. We explore both theoretical foundations and practical applications, employing diverse machine learning models for return forecasting. This comprehensive guide demonstrates the superiority of multi-period optimization over single-period approaches, particularly in risk mitigation through strategic rebalancing and enhanced market trend forecasting. Our goal is to promote wider adoption of multi-period optimization, providing insights that can significantly enhance the decision-making capabilities of practitioners and researchers alike. <iframe src="https://publications.waset.org/abstracts/186542.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-period%20portfolio%20optimization" title="multi-period portfolio optimization">multi-period portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=look-ahead%20constrained%20optimization" title=" look-ahead constrained optimization"> look-ahead constrained optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=sequential%20decision%20making" title=" sequential decision making"> sequential decision making</a> </p> <a href="https://publications.waset.org/abstracts/186542/multi-period-portfolio-optimization-using-predictive-machine-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186542.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">48</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational 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Multi-Period Portfolio Optimization Using Predictive Machine Learning Models