Machine Learning for Trading
Machine Learning for Trading ArXiv ID: ssrn-3015609 “View on arXiv” Authors: Unknown Abstract In multi-period trading with realistic market impact, determining the dynamic trading strategy that optimizes expected utility of final wealth is a hard problem Keywords: Market Impact, Optimal Execution, Dynamic Trading, Utility Maximization, Algorithmic Trading, Equities / Quantitative Trading Complexity vs Empirical Score Math Complexity: 8.5/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The paper uses advanced multi-period optimal control theory, utility theory, and Hamilton-Jacobi-Bellman equations, indicating high mathematical complexity, but focuses on theoretical proof-of-concept in a simulated market with no real-world data, backtests, or implementation details, resulting in low empirical rigor. flowchart TD Start(["Research Goal"]) --> Method["Dynamic Trading Strategy<br/>Optimization with Market Impact"] Start --> Input["Realistic Market Data<br/>& Historical Prices"] Method --> Process["Computational Process:<br/>Multi-Period Optimization<br/>Maximizing Expected Utility"] Input --> Process Process --> Outcome1["Novel Optimal<br/>Execution Algorithms"] Process --> Outcome2["Quantified Market<br/>Impact Costs"] Process --> Outcome3["Dynamic Strategy<br/>Constraints Analysis"] Outcome1 --> End(["Key Findings"]) Outcome2 --> End Outcome3 --> End