Paper: arXiv 2403.02523

Abstract

The transformer models have been extensively used with good results in a wide area of machine learning applications including Large Language Models and image generation. Here, we inquire on the applicability of this approach to financial time series. We first describe the dataset construction for two prototypical situations: a mean reverting synthetic Ornstein-Uhlenbeck process on one hand and real S&P500 data on the other hand. Then, we present in detail the proposed Transformer architecture and finally we discuss some encouraging results. For the synthetic data we predict rather accurately the next move, and for the S&P500 we get some interesting results related to quadratic variation and volatility prediction.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced mathematical concepts like Ornstein-Uhlenbeck processes and transformer architecture with positional encoding properties, while also detailing dataset construction, specific model architecture, and preliminary backtesting on synthetic and real S&P500 data.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Applicability of Transformers to Financial Time Series?"] --> B["Data Preparation"]
  B --> B1["Synthetic Data<br>Ornstein-Uhlenbeck Process"]
  B --> B2["Real Data<br>S&P 500 Historical Data"]
  
  B1 & B2 --> C["Transformer Architecture<br>Multi-Head Self-Attention & Feed-Forward Networks"]
  
  C --> D{"Model Training & Prediction"}
  
  D --> E["Synthetic Data Results<br>Accurate Prediction of Next Move"]
  D --> F["S&P 500 Results<br>Volatility Prediction<br>Quadratic Variation Analysis"]
  
  E & F --> G["Conclusion<br>Encouraging Results for Financial TS"]