Intraday trading strategy based on time series and machine learning for Chinese stock market
“The empirical results reveal the profitability of Markowitz portfolio optimization and validate the intraday stock price prediction using MLP.”
AssetEquities
StrategyPortfolio optimizationmachine learning
TechniqueFeedforward network
GeographyChina
Also stated: Intraday, Profit
Missing from abstract: benchmark, out-of-sample test, 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 neighbourhoodPortfolio optimization 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 | |
|---|---|---|---|---|---|
| On the Limits of Low-Frequency OHLCV Signals in Machine Learning-Driven Portfolio Optimization A Cost-Aware, Leak-Free Benchmark on Large-Cap US Equities by Pardo | 2026 | ||||
| Comparative Evaluation of Modern Deep Learning Methodologies for Portfolio Optimization by Ozechi et al. | 2026 | ||||
| A Sustainable Portfolio Construction Model Based on ESG and Deep Learning Algorithms: Evidence from the U.S. Market by Sajadi et al. | 2025 | ||||
| Machine Learning-Driven Virtual Bidding with Electricity Market Efficiency Analysis by Li et al. | 2021 | ||||
| AI-Driven Strategic Asset Allocation vs Traditional Mean-Variance Optimization: Evidence from Global Institutional Portfolios by Ngewa | 2026 |