Paper: arXiv 2506.05565

Authors: Feliks Bańka, Jarosław A. Chudziak

Abstract

Accurate option pricing is essential for effective trading and risk management in financial markets, yet it remains challenging due to market volatility and the limitations of traditional models like Black-Scholes. In this paper, we investigate the application of the Informer neural network for option pricing, leveraging its ability to capture long-term dependencies and dynamically adjust to market fluctuations. This research contributes to the field of financial forecasting by introducing Informer’s efficient architecture to enhance prediction accuracy and provide a more adaptable and resilient framework compared to existing methods. Our results demonstrate that Informer outperforms traditional approaches in option pricing, advancing the capabilities of data-driven financial forecasting in this domain.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 5.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper involves advanced deep learning architecture (Informer with ProbSparse attention, O(L log L) complexity) and necessary financial data preprocessing/normalization, indicating moderate-to-high mathematical density. Empirical rigor is moderate due to explicit benchmarking against traditional models and profit analysis on historical data, though it lacks code or dataset specifics, suggesting it’s backtest-ready but not fully implemented.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Option Pricing with Informer"] --> B["Data & Input<br>Options Data & Market Volatility"]
  B --> C["Methodology<br>Informer Transformer Architecture"]
  C --> D["Process<br>Long-term Dependency Modeling"]
  D --> E["Outcome<br>Outperforms Black-Scholes & Traditional Models"]
  E --> F["Result<br>Enhanced Prediction Accuracy & Adaptability"]

Cited References (15)

  1. The Study of Option Pricing Problems based on Transformer Model (2022) — link
  2. Applied Option Pricing using Transformers (2024) — link
  3. The Volatility Surface: A Practitioner’s Guide (2006)
  4. Elements of Financial Risk Management (2009)
  5. Toward Predictive Stock Trading with Hidformer Integrated into Reinforcement Learning Strategy (2024)
  6. Transformer-Style Neural Network in Stock Price Forecasting (2024)
  7. ElliottAgents: A Natural Language-Driven Multi-Agent System for Stock Market Analysis and Prediction (2024)
  8. Predictability of stock returns using neural networks: Elusive in the long term (2023)
and 7 more
  1. Long-Term Rolling Window for Stock Market Predictions (2019)
  2. A deep learning framework for financial time series using stacked autoencoders and long-short term memory (2017) — link
  3. Machine learning approaches in stock market prediction: A systematic literature review (2023) — link
  4. A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options (1993)
  5. Pricing and hedging long-term options (2000) — link
  6. Neural networks for option pricing and hedging: a literature review (2020) — link
  7. FRACTAL MARKETS HYPOTHESIS AND THE GLOBAL FINANCIAL CRISIS: SCALING, INVESTMENT HORIZONS AND LIQUIDITY (2012) — link

0 of 15 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.