{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T06:32:08Z","timestamp":1793341928956,"version":"4.5.0"},"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>Finite wealth records make long-run portfolio rankings uncertain. We develop one-path inference for fixed feedbacks in diffusion-factor markets with shared factor and asset noise. A Poisson corrector links long-run growth variance to finite-block bias, while a weighted semigroup gradient estimate ensures convergence of the bias integral under additional local drift conditions. Batch means is mean-square consistent as block length and count diverge, with an explicit initial-state bias bound and wealth-based asymptotic intervals when variance is positive. Under dissipativity, uniform ellipticity and stated regularity assumptions, a linear-Gaussian benchmark shows that shared noise changes variance by a factor of 2.3 without changing growth, while weaksignal detection horizons for known Kelly-Merton rules scale as the inverse square of predictive slope. Simulations examine how persistence and factor estimation affect finite-horizon calibration and use training likelihoods to localize conservative cutoffs with other market and rule coefficients held fixed. Historical applications compare U.S. training-selected exposure and drawdown budgets alongside equity\/cash allocations and wealth-adaptive budgets in Europe and Japan, separating evidence for a growth ranking from choices of equity exposure and risk budget.<\/jats:p>","DOI":"10.2139\/ssrn.7666678","type":"posted-content","created":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T06:06:24Z","timestamp":1793340384000},"source":"Crossref","is-referenced-by-count":0,"title":["One-Path Inference for Long-Run Portfolio Growth in Stochastic Factor Markets"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3531-6017","authenticated-orcid":true,"given":"Xinrui","family":"Dong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T06:06:24Z","timestamp":1793340384000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7666678"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7666678","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}