{"id":233041,"date":"2025-10-20T12:32:45","date_gmt":"2025-10-20T16:32:45","guid":{"rendered":"https:\/\/ibkrcampus.com\/campus\/?p=233041"},"modified":"2025-10-20T12:48:56","modified_gmt":"2025-10-20T16:48:56","slug":"can-machine-learning-predict-factor-returns","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/can-machine-learning-predict-factor-returns\/","title":{"rendered":"Can Machine Learning Predict Factor Returns?"},"content":{"rendered":"\n<p><em>The article &#8220;Can Machine Learning Predict Factor Returns?&#8221; was originally published on <a href=\"https:\/\/alphaarchitect.com\/predict-factor-returns\/\">Alpha Architect<\/a> blog.<\/em><\/p>\n\n\n\n<p>Nusret Cakici, Christian Fieberg, Carlos Osorio, Thorsten Poddig, and Adam Zaremba, authors of the study \u201cPicking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,\u201d published in the April 2025 issue of&nbsp;<a href=\"https:\/\/www.pm-research.com\/content\/iijpormgmt\/51\/6\/96\" target=\"_blank\" rel=\"noreferrer noopener\">The Journal of Portfolio Management<\/a>, set out to answer a critical question: Can machine learning techniques improve the prediction of cross-sectional factor returns in equity markets? They focus on the cross-sectional predictability\u2014that is, whether it\u2019s possible to forecast which factors (like value, momentum, size, etc.) will outperform others in the future using advanced data-driven methods rather than traditional statistical approaches. To do this, they applied a variety of popular machine learning algorithms commonly employed in prominent asset pricing studies on the returns data of 242 factor characteristics, aiming to extract predictive signals that might not be captured by conventional models. Their analysis spanned the period January 1972 to December 2021 and examined the 153 long-short anomaly portfolios in the US market from the 2023&nbsp;<a href=\"https:\/\/url.avanan.click\/v2\/r01\/___https:\/research-api.cbs.dk\/ws\/portalfiles\/portal\/95651880\/theis_ingerslev_jensen_et_al_is_there_a_replication_crisis_in_finance_publishersversion.pdf___.YXAzOnNhcmFncmlsbG86YTpnOjEzZWY5ZGQ3MGQ4MTQ2MGVjMzVkMmJlZTUxNDE3ODhkOjc6YzUwNzo5ZDI3M2M4OWRlOWQ5MTRlM2U4ZTA2YzM3ZmU5NTllYjEzMGZjNDM3NmVlYzQ2M2YwM2YwNGE0YTU3YzE4NTY4OnA6VDpO\" target=\"_blank\" rel=\"noreferrer noopener\">study<\/a>&nbsp;\u201cIs There a Replication Crisis in Finance?\u201d<\/p>\n\n\n\n<p>\u201cGiven the study period of January 1972 to December 2021 (600 months), the first training period is January 1972 to December 1981, the first validation period is January 1982 to December 1986, and the subsequent test period covers data from January to December 1987.\u201d<\/p>\n\n\n\n<p>For each annual reestimation, \u201cwe extend the training window by one year while keeping the validation and test periods fixed. The entire test period spans from January 1987 to December 2021, or 420 months.\u201d<\/p>\n\n\n\n<p><br><strong>Key Findings<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>There was strong cross-sectional predictability of factor returns using machine learning techniques, focusing on past returns, risks, and spreads. The magnitude of return predictability at the factor level compared favorably with the evidence at the US equity level.<\/li>\n\n\n\n<li>Return predictability can be translated into successful factor selection strategies\u2014the decile of factors with the highest returns outperformed the decile with the worst prospects by 0.27% to 1.39% per month, depending on the prediction algorithm. For the forecast combination model, which aggregated the predictions of all the individual models in the study, the monthly return on such a spread portfolio was 1.08%. The alphas were robust to many considerations\u2014although return predictability remained strong and significant, its magnitude declined by about half compared to the earlier years.<\/li>\n\n\n\n<li>Most alphas\u2014excluding&nbsp;<a href=\"https:\/\/url.avanan.click\/v2\/r01\/___https:\/www.xlstat.com\/solutions\/features\/ordinary-least-squares-regression-ols___.YXAzOnNhcmFncmlsbG86YTpnOjEzZWY5ZGQ3MGQ4MTQ2MGVjMzVkMmJlZTUxNDE3ODhkOjc6MWZhYjpjODIyNTM2YTMyZjY1NDZjZGM0NTZjMWVkNDAwNzlkMjNmMjk3Zjk3ZTZkZjM1MmQ1ZDJjYWM4MGUzODA4MmUyOnA6VDpO#:~:text=Ordinary%20Least%20Squares%20regression%20(OLS)%20is%20a%20common%20technique%20for,often%20evaluated%20using%20r%2Dsquared.\" target=\"_blank\" rel=\"noreferrer noopener\">OLS<\/a>\u2014showed t-values exceeding 3 and, in some cases, even surpassing 4\u2014the abnormal returns are unlikely to be mere statistical artifacts.<\/li>\n\n\n\n<li>The cross-sectional patterns in anomaly returns were remarkably robust and could not be explained by common asset pricing factors. They also outperformed a naive benchmark that equally weighted all anomalies in the sample.<\/li>\n\n\n\n<li>Factor momentum was the main driver of cross-sectional variation in anomaly returns, capturing most of the predictability the machine learning strategy\u2014 returns comoved with factor momentum. Other characteristics, such as characteristic (or value) spreads or risk measures, played minor roles. Specifically, once factor momentum was controlled for, no long\u2013short machine learning portfolio generated significant alpha.<\/li>\n\n\n\n<li>The machine learning strategies required substantial portfolio rotation\u2014each month, an investor needs to replace 37% to 66% of all factors, depending on the strategy.<\/li>\n<\/ul>\n\n\n\n<p>Their findings led the authors to conclude: \u201cMachine learning models capture a significant amount of return predictability, allowing them to pick winners and avoid losers among factor strategies.\u201d They added: \u201cFactor characteristics\u2014including factor momentum in particular\u2014 contain valuable information about their future returns, allowing one to separate the wheat from the chaff. As a result, one can potentially pick the future winners and avoid the losers in the factor space.\u201d<\/p>\n\n\n\n<p>These findings on the predictive power of factor momentum provide further support to prior empirical research on the predictive power of factor momentum.<\/p>\n\n\n\n<p><strong>Factor Momentum Research<\/strong><\/p>\n\n\n\n<p>Prior empirical research on factor momentum, including the 2019 studies \u201c<a href=\"https:\/\/url.avanan.click\/v2\/___https:\/www.aqr.com\/Insights\/Research\/Working-Paper\/Factor-Momentum-Everywhere___.YXAzOnNhcmFncmlsbG86YTpnOjZlMTExNzIzNzI0NzI0MWM2ZWQyNGVjZGMyNGQ3ZGUyOjY6OTdjODpiMjgzYzQ3N2M5OGQyN2RkZGJjOWQ0ZDc0NTdjNGY2MTAzYzNmNjNjYTg5YWE1ZWM1YWU4NmI5Y2Y5NzIxZGIxOnA6VA\" target=\"_blank\" rel=\"noreferrer noopener\">Factor Momentum Everywhere<\/a>\u201d and \u201c<a href=\"https:\/\/url.avanan.click\/v2\/___https:\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3332927___.YXAzOnNhcmFncmlsbG86YTpnOjZlMTExNzIzNzI0NzI0MWM2ZWQyNGVjZGMyNGQ3ZGUyOjY6OWFkMzpmY2M2YmJlNTU1ZDIzM2ZlYWMzZGNjZWZkNDU5MmI4Njc3ODJhOTIwNjA5MzJlZDJmNGY1YTc2YTQ3YWQ3ODkwOnA6VA\" target=\"_blank\" rel=\"noreferrer noopener\">Is there Momentum in Factor Premia? Evidence from International Equity Markets<\/a>,\u201d the 2020 study \u201c<a href=\"https:\/\/url.avanan.click\/v2\/___https:\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3014521___.YXAzOnNhcmFncmlsbG86YTpnOjZlMTExNzIzNzI0NzI0MWM2ZWQyNGVjZGMyNGQ3ZGUyOjY6ZjQ0Nzo0NjdhNzg3NjViYzMyMTY0OTcyZjU1ZDNiNjliYjhjOTY0NTE0ZjIwYjYxMzk0MmFjNGVhNjQ3MDU0OWIwN2YxOnA6VA\" target=\"_blank\" rel=\"noreferrer noopener\">Factor Momentum and the Momentum Factor<\/a>,\u201d and the 2021 studies \u201c<a href=\"https:\/\/url.avanan.click\/v2\/___https:\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3116974___.YXAzOnNhcmFncmlsbG86YTpnOjZlMTExNzIzNzI0NzI0MWM2ZWQyNGVjZGMyNGQ3ZGUyOjY6ZGRlMTo4MGNkNGIwZDIxMjcxNDZkNTI5ZmE4NDFmNTU3YmU0NDA3MTk0ZDBlMmJlMDYzODQxOTNmYzI3NzViY2UxZDhlOnA6VA\" target=\"_blank\" rel=\"noreferrer noopener\">Factor Momentum<\/a>,\u201d&nbsp;<a href=\"https:\/\/url.avanan.click\/v2\/___https:\/www.risk.net\/journal-of-investment-strategies\/7936101\/is-factor-momentum-greater-than-stock-momentum___.YXAzOnNhcmFncmlsbG86YTpnOjZlMTExNzIzNzI0NzI0MWM2ZWQyNGVjZGMyNGQ3ZGUyOjY6YzQxYzo4Y2MxOTNjNzJmN2YwZmIzMjliNGQxODQ2MmJmYTIzOWIyNjgzNTlmZjI4MzM4NGIxN2E1MzU1MzA2NzMzYWVhOnA6VA\" target=\"_blank\" rel=\"noreferrer noopener\">\u201cIs Factor Momentum More than Stock Momentum<\/a>?\u201d and \u201c<a href=\"https:\/\/url.avanan.click\/v2\/___https:\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3423287___.YXAzOnNhcmFncmlsbG86YTpnOjZlMTExNzIzNzI0NzI0MWM2ZWQyNGVjZGMyNGQ3ZGUyOjY6ZDlmOToxOWVhYmJmNmI4ODU2M2M0MjdkYjEwYTYzN2M2N2Q4NGIwZjZiNDBiYjU5MzU2ZDJlMzcxZWQwNzlkYWJkNjQyOnA6VA\" target=\"_blank\" rel=\"noreferrer noopener\">Momentum-Managed Equity Factors<\/a>,\u201d has examined whether momentum can be found in factors as well and found:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Time-series (trend) factor momentum has been a pervasive property of factors\u2014a strategy that buys the recent top-performing factors and sells poor-performing factors achieved significant investment performance above and beyond traditional stock momentum.<\/li>\n\n\n\n<li>Factor momentum explained all forms of individual stock momentum\u2014<a href=\"https:\/\/alphaarchitect.com\/absolute-momentum-and-stock-momentum-strategies-friends-not-enemies\/\" target=\"_blank\" rel=\"noreferrer noopener\">stock momentum<\/a>&nbsp;strategies indirectly timed factors; they profited when the factors remained autocorrelated and crashed when those autocorrelations broke down.<\/li>\n\n\n\n<li>Demonstrating pervasiveness, factor momentum has been a global phenomenon. And cross-country factor momentum exists (if the value factor in Germany recently outpaced the U.S. value factor, it is likely that this trend will continue).<\/li>\n\n\n\n<li>Factor momentum could have been captured by trading almost any set of factors.<\/li>\n\n\n\n<li>Industry momentum stemmed from factor momentum.<\/li>\n\n\n\n<li>The value-added induced by factor management via short-term momentum was a robust empirical phenomenon that survived transaction costs and carried over to multifactor portfolios\u2014while managing factors based on last month\u2019s momentum increased turnover, the increase in turnover induced by timing did not outweigh the benefits of timing. In addition, turnover could have been reduced using a smoothed version of the timing signal, and timing still would have yielded significant benefits.<\/li>\n<\/ul>\n\n\n\n<p><strong>Summary<\/strong><\/p>\n\n\n\n<p>The convergence of machine learning capabilities with factor momentum research represents an advancement in quantitative investment management. However, while the Cakici et al. study demonstrates that sophisticated algorithms can indeed predict factor returns with economically meaningful magnitudes, the underlying driver remained surprisingly straightforward: factors that have performed well recently tend to continue performing well in the near term. This finding validates decades of factor momentum research while highlighting both the opportunities and challenges facing modern portfolio managers.<\/p>\n\n\n\n<p>For practitioners, these results suggest that factor selection strategies based on momentum signals can generate alpha, though success comes at the cost of high turnover and associated transaction costs. The robustness of factor momentum across different machine learning models, time periods, and international markets indicates this is not merely a statistical artifact but a persistent market phenomenon worthy of serious consideration in factor allocation decisions.<\/p>\n\n\n\n<p>Perhaps most importantly, this research underscores that in an era of increasingly complex quantitative methods, some of the most powerful investment insights may still stem from relatively simple behavioral patterns\u2014in this case, the tendency for winning factors to keep winning, at least in the short run.<\/p>\n\n\n\n<p><em>Larry Swedroe is the author or co-author of 18 books on investing, including his latest&nbsp;<\/em><a href=\"https:\/\/url.avanan.click\/v2\/___https:\/www.amazon.com\/Enrich-Your-Future-Successful-Investing\/dp\/1394245440\/___.YXAzOnNhcmFncmlsbG86YTpnOmM4MzhlMWE2MGMwMzRlMjUyOTg4YmEwNTExZDI2OWIwOjY6ODMxMjo0YzA5ZDM0ZGJlZGNiNTQ2NDdjNDFhZWUzZmY0MTI4Nzc0NmFhMjNmNTc3N2M4N2E1ZjZmMzM5MmVlODdiMzQzOnA6VDpO\" target=\"_blank\" rel=\"noreferrer noopener\"><em>Enrich Your Future<\/em><\/a><em>. He is also a consultant to RIAs as an educator on investment strategies.<\/em><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Can machine learning techniques improve the prediction of cross-sectional factor returns in equity markets?<\/p>\n","protected":false},"author":298,"featured_media":182748,"comment_status":"open","ping_status":"closed","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,338,341],"tags":[7168,852],"contributors-categories":[13651],"class_list":{"0":"post-233041","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-ibkr-quant-news","9":"category-quant-development","10":"tag-factor-momentum","11":"tag-machine-learning","12":"contributors-categories-alpha-architect"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.9 (Yoast SEO v27.4) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Can Machine Learning Predict Factor Returns? | IBKR Quant<\/title>\n<meta name=\"description\" content=\"Can machine learning techniques improve the prediction of cross-sectional factor returns in equity markets?\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.interactivebrokers.com\/campus\/wp-json\/wp\/v2\/posts\/233041\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Can Machine Learning Predict Factor Returns?\" \/>\n<meta property=\"og:description\" content=\"Can machine learning techniques improve the prediction of cross-sectional factor returns in equity markets?\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/can-machine-learning-predict-factor-returns\/\" \/>\n<meta property=\"og:site_name\" content=\"IBKR Campus US\" \/>\n<meta property=\"article:published_time\" content=\"2025-10-20T16:32:45+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-10-20T16:48:56+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2023\/02\/robot-space-hand-code.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"900\" \/>\n\t<meta property=\"og:image:height\" content=\"550\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Larry Swedroe\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Larry Swedroe\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\n\t    \"@context\": \"https:\\\/\\\/schema.org\",\n\t    \"@graph\": [\n\t        {\n\t            \"@type\": \"NewsArticle\",\n\t            \"@id\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/ibkr-quant-news\\\/can-machine-learning-predict-factor-returns\\\/#article\",\n\t            \"isPartOf\": {\n\t                \"@id\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/ibkr-quant-news\\\/can-machine-learning-predict-factor-returns\\\/\"\n\t            },\n\t            \"author\": {\n\t                \"name\": \"Larry Swedroe\",\n\t                \"@id\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/#\\\/schema\\\/person\\\/75544487e0633f170eb31cb59c37e64f\"\n\t            },\n\t            \"headline\": \"Can Machine Learning Predict Factor Returns?\",\n\t            \"datePublished\": \"2025-10-20T16:32:45+00:00\",\n\t            \"dateModified\": \"2025-10-20T16:48:56+00:00\",\n\t            \"mainEntityOfPage\": {\n\t                \"@id\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/ibkr-quant-news\\\/can-machine-learning-predict-factor-returns\\\/\"\n\t            },\n\t            \"wordCount\": 1083,\n\t            \"commentCount\": 0,\n\t            \"publisher\": {\n\t                \"@id\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/#organization\"\n\t            },\n\t            \"image\": {\n\t                \"@id\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/ibkr-quant-news\\\/can-machine-learning-predict-factor-returns\\\/#primaryimage\"\n\t            },\n\t            \"thumbnailUrl\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/wp-content\\\/uploads\\\/sites\\\/2\\\/2023\\\/02\\\/robot-space-hand-code.jpg\",\n\t            \"keywords\": [\n\t                \"Factor Momentum\",\n\t                \"Machine Learning\"\n\t            ],\n\t            \"articleSection\": [\n\t                \"Data Science\",\n\t                \"Quant\",\n\t                \"Quant Development\"\n\t            ],\n\t            \"inLanguage\": \"en-US\",\n\t            \"potentialAction\": [\n\t                {\n\t                    \"@type\": \"CommentAction\",\n\t                    \"name\": \"Comment\",\n\t                    \"target\": [\n\t                        \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/ibkr-quant-news\\\/can-machine-learning-predict-factor-returns\\\/#respond\"\n\t                    ]\n\t                }\n\t            ]\n\t        },\n\t        {\n\t            \"@type\": \"WebPage\",\n\t            \"@id\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/ibkr-quant-news\\\/can-machine-learning-predict-factor-returns\\\/\",\n\t            \"url\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/ibkr-quant-news\\\/can-machine-learning-predict-factor-returns\\\/\",\n\t            \"name\": \"Can Machine Learning Predict Factor Returns? 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