Statistical Arbitrage in Options Markets by Graph Learning and Synthetic Long Positions

TL;DR

This paper introduces a novel two-stage graph learning approach using tree-based methods and synthetic bonds to identify and exploit statistical arbitrage opportunities in options markets, demonstrating significant profitability on real data.

Contribution

It proposes a new prediction target and a tree-based graph learning architecture, SLSA, for arbitrage detection and exploitation in options markets, filling gaps in existing deep learning methods.

Findings

RNConv outperforms baseline graph learning models.

SLSA positions yield positive, risk-neutral arbitrage profits.

Average P&L-CRI of 0.1627 on KOSPI 200 options.

Abstract

Statistical arbitrages (StatArbs) driven by machine learning has garnered considerable attention in both academia and industry. Nevertheless, deep-learning (DL) approaches to directly exploit StatArbs in options markets remain largely unexplored. Moreover, prior graph learning (GL) -- a methodological basis of this paper -- studies overlooked that features are tabular in many cases and that tree-based methods outperform DL on numerous tabular datasets. To bridge these gaps, we propose a two-stage GL approach for direct identification and exploitation of StatArbs in options markets. In the first stage, we define a novel prediction target isolating pure arbitrages via synthetic bonds. To predict the target, we develop RNConv, a GL architecture incorporating a tree structure. In the second stage, we propose SLSA -- a class of positions comprising pure arbitrage opportunities. It is provably of minimal risk and neutral to all Black-Scholes risk factors under the arbitrage-free assumption. We also present the SLSA projection converting predictions into SLSA positions. Our experiments on KOSPI 200 index options show that RNConv statistically significantly outperforms GL baselines, and that SLSA consistently yields positive returns, achieving an average P&L-contract information ratio of 0.1627. Our approach offers a novel perspective on the prediction target and strategy for exploiting StatArbs in options markets through the lens of DL, in conjunction with a pioneering tree-based GL.