Trends and Applications of Machine Learning in QuantitativeFinance

ArXiv ID: ssrn-3397005 “View on arXiv”

Authors: Unknown

Abstract

Recent advances in machine learning are finding commercial applications across many industries, not least the finance industry. This paper focuses on applicatio

Keywords: machine learning, algorithmic trading, predictive analytics, quantitative finance, Multi-Asset

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Philosophers
• Why: The paper is a broad literature review of ML applications in finance, focusing on conceptual categorization rather than novel mathematical derivations or empirical backtesting. It outlines common algorithms and use cases but lacks implementation details, statistical metrics, or specific experimental results.

  flowchart TD
    G["Research Goal: Evaluate ML in Quant Finance"] --> D["Data Sources"]
    D --> M["Key Methodology"]
    D --> C["Computational Processes"]
    M --> F["Key Findings/Outcomes"]
    C --> F
    
    subgraph D ["Data/Inputs"]
        D1["Multi-Asset Market Data"]
        D2["Historical Price & Volatility"]
    end
    
    subgraph M ["Methodology Steps"]
        M1["Algorithmic Trading Strategies"]
        M2["Predictive Analytics"]
    end
    
    subgraph C ["Computational Processes"]
        C1["Deep Learning Models"]
        C2["Reinforcement Learning"]
    end
    
    subgraph F ["Outcomes"]
        F1["Enhanced Portfolio Optimization"]
        F2["Improved Risk Management"]
        F3["Commercial Applications in Finance"]
    end