{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T18:51:48Z","timestamp":1792003908639,"version":"4.4.0"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":0,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.uspto.gov\/ip-policy\/copyright-policy\/copyright-basics"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>This survey reviews selected machine learning methods in asset pricing in two parts. The first part focuses on time-series predictability of the aggregate stock market excess return, covering recent advances such as the \u201cvirtue of complexity,\u201d the \u201cvirtue of noise,\u201d and the single optimal factor of predictors. We also discuss other new methods and novel predictors, including return decomposition, forecast-agnostic portfolios, option-based bounds, macro-financial trends, and large language model (LLM)-based textual signals. The second part examines cross-sectional stock return predictability and efficient portfolio estimation. Starting from the Fama-MacBeth framework and key performance measures, we survey recent developments such as LASSO regularization, forecast combination and encompassing, heterogeneous return response models, constrained deep learning, genetic programming, feasible stochastic discount factor estimation under portfolio constraints, and LLM applications to numerical financial data. Overall, the evidence strongly supports return predictability, with machine learning methods delivering substantial gains vis-\u00e0-vis conventional benchmarks.<\/jats:p>","DOI":"10.2139\/ssrn.7611982","type":"posted-content","created":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T18:39:11Z","timestamp":1792003151000},"source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning in Asset Pricing"],"prefix":"10.2139","author":[{"given":"David","family":"Rapach","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guofu","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T18:39:11Z","timestamp":1792003151000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7611982"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7611982","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}