MGPraxis6,400 papers identified by name

The Geometry of Alpha Manifold Learning, Topological Data Analysis, and Non-Linear Factor Structures in Global Equity Markets

Tushar Kholia·2026
SSRN 6393860 ↗

A 2026 paper by Kholia on machine learning return prediction in equities, using a feedforward neural network on a 2010–2024 sample: the abstract mentions a Sharpe ratio, alpha, drawdowns and an out-of-sample test and leaves out a benchmark, transaction costs, multiple testing and the size of the universe.

“We further develop a Hidden Markov Model (HMM) regime detection system that conditions factor exposures on latent market states, reducing maximum drawdown by 67% relative to regime-naive approaches.”

AssetEquities
StrategyMachine learning
TechniqueFeedforward networkgraph neural network
GeographyGlobalEurope

Also stated: 2010–2024, Sharpe ratio, Alpha, Drawdown, out-of-sample test

Missing from abstract: benchmark, costs, multiple testing, universe

Institutional-grade audit of this strategy.
£5 — Request the audit
  • Audit of the paper claims accounting for costs, luck, crises, originality, geography, etc.
  • Reproduction code integratable into Bloomberg, IBKR, etc.
  • Bias-free datasets for backtesting included
  • Delivered in 2 working days
The paper's neighbourhoodMachine learning return prediction by year
■ equities■ crypto■ commodities■ bonds and credit■ other assets2010201520201862This paper2026■ equities■ crypto■ commodities■ bonds and credit■ other assets20102015202062This paper2026
FamilyMachine learning return prediction153Momentum and trend following289Factor investing240News sentiment and text signals14818 more · 854
AssetEquities106Crypto11
MethodState-space models3Neural networks88Linear factor models26Tree ensembles264 more · 29
AuthorsKholia1Bryan T. Kelly4Turan G. Bali3David E. Rapach31 more · 3
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
Similar paperscitationsame methodsame assetsame authors
Spatiotemporal Signals in Residuals: A Two-Stage Boosting Graph Neural Network for Stock Return Prediction by Liu et al.2026
Machine Learning for Cross-Sectional Return Prediction: A Comparative Study of US and Indian Equities by Khatu2026
Empirical Asset Pricing via Machine Learning: Evidence from Indian Equity Markets by Sharma2026
Stock Return Prediction Guided by a Revised Random Walk Hypothesis: Adaptive Information Smoothing and Signed Correlation-Aware TimeMixing by Seo and Yang2026
Visualizing Income Statements by Garfinkel et al.2026
Not yet audited by MarketGraphs Praxis. Tags from the abstract. Counts from the universe.
RSS · Contacts