Paper 2025/1744

Randomness beacons from financial data in the presence of an active attacker

Abstract

Using stock market data as a source of public randomness has deep historical roots and has seen renewed interest with the development of verifiable delay functions. Prior work has estimated that asset prices contain ample entropy to prevent prediction by a passive observer, but has not considered an active attacker making trades in the marketplace. VDFs can make manipulation more difficult, forcing an attacker to precompute beacon results for some number of potential outcomes and then force the market into one of them by price manipulation. To date, there has been no analysis of the difficulty of such an attack. We propose a framework for evaluating this attack using standard finance models for price movement and the price impact of trades. We then estimate from empirical data that, even under generous assumptions, for a basket of large-cap stocks (the S&P 100) an active adversary would require huge capital reserves (on the order of billions of US dollars) and incur major losses to slippage (millions of US dollars).