# Efficient Market Dynamics: Unraveling Informational Efficiency in UK Horse Racing Betting Markets Through Betfair's Time Series Analysis

arXiv: https://arxiv.org/abs/2402.02623 · HTML: https://mlsanity.com/paper/2402.02623/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 4 Feb 2024.

## Builds on (most cited first, 9 of 9)

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- [The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review](https://mlsanity.com/paper/1912.11762/) (2019)
- [Transformational application of Artificial Intelligence and Machine learning in Financial Technologies and Financial services: A bibliometric review](https://mlsanity.com/paper/2401.15710/) (2024)
- [AI-based Personalization and Trust in Digital Finance](https://mlsanity.com/paper/2401.15700/) (2024)
- [Exploring the Impact of Blockchain, AI, and ML on Financial Accounting Efficiency and Transformation](https://mlsanity.com/paper/2401.15715/) (2024)
- [BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modelling](https://mlsanity.com/paper/2105.08310/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2410.21484/) (2024)

## Abstract (arXiv)

Using Betfair's time series data, an analysis of the United Kingdom (UK) horse racing market reveals an interesting paradox: a market with short tails, rapidly decaying autocorrelations, and no long-term memory. There seems to be a remarkably high level of informational efficiency in betting exchange returns, in contrast to financial assets that are characterized by heavy tails and volatility clustering. The generalized Gaussian unconditional distribution with a light tail point to a market where knowledge is quickly assimilated and reflected in prices. This is further supported by the extremely quick fading of autocorrelations and the absence of gain-loss asymmetry. Therefore, in addition to measuring long-range memory, the Hurst exponent also shows mean reversion, a sign that markets respond quickly to fresh information.