Ten Financial Applications of Machine Learning (Seminar Slides)

ArXiv ID: ssrn-3197726 “View on arXiv”

Authors: Unknown

Abstract

Financial ML offers the opportunity to gain insight from data:* Modelling non-linear relationships in a high-dimensional space* Analyzing unstructured d

Keywords: Financial ML, machine learning, non-linear modeling, high-dimensional data, unstructured data analysis, General Financial Markets

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 4.0/10
• Quadrant: Philosophers
• Why: The content is conceptual, emphasizing high-level ML applications and data insights (e.g., non-linear relationships, meta-labeling) without presenting specific equations, derivations, or implementation details. It lacks backtest metrics, code, or datasets, focusing more on theoretical justification and conceptual frameworks than on hands-on empirical validation.

  flowchart TD
    A["Research Goal<br>Apply ML to Finance"] --> B["Key Methodology<br>Non-linear & High-dimensional Modeling"]
    B --> C{"Data Inputs"}
    C --> D["Unstructured &<br>Market Data"]
    C --> E["Structured<br>Financial Data"]
    D & E --> F["Computational Processes<br>ML Algorithms"]
    F --> G["Key Outcomes<br>Insight Generation"]
    G --> H{"General Financial<br>Markets Application"}
    H --> I["Improved Prediction"]
    H --> J["Risk Management"]