Option-Implied Factors and Deep Learning in Equity Index Return Prediction
A 2026 paper by Zhou on machine learning return prediction in volatility and options, using a recurrent neural network on a 2014–2023 sample: the abstract mentions a benchmark and an out-of-sample test and leaves out transaction costs, multiple testing and the size of the universe.
“This study investigates whether option-implied information can improve next-day S&P 500 return forecasts.”
AssetVolatility and optionsoptions, indices
StrategyMachine learningmomentum
TechniqueRecurrent neural networkfeedforward network
GeographyUS
Also stated: Daily, 2014–2023, Long-short, Options-implied, benchmark, out-of-sample test
Missing from abstract: 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
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
| Similar papers | citation | same method | same asset | same authors | |
|---|---|---|---|---|---|
| Binocular Directional Forecasting: A Cross-Attention Fusion Approach by Yuan and Zhao | 2026 | ||||
| Return Predictability and Market Sentiment: Evidence from Deep Learning by Khan | 2022 | ||||
| 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 Khatu | 2026 | ||||
| Empirical Asset Pricing via Machine Learning: Evidence from Indian Equity Markets by Sharma | 2026 |