Paper: SSRN 3015609

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

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: 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.

Research Flowchart

  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