Market making and mean reversion

Abstract

Market making refers broadly to trading strategies that seek to profit by providing liquidity to other traders, while avoiding accumulating a large net position in a stock. In this paper, we study the profitability of market making strategies in a variety of time series models for the evolution of a stock's price. We first provide a precise theoretical characterization of the profitability of a simple and natural market making algorithm in the absence of any stochastic assumptions on price evolution. This characterization exhibits a trade-off between the positive effect of local price fluctuations and the negative effect of net price change. We then use this general characterization to prove that market making is generally profitable on mean reverting time seriestime series with a tendency to revert to a long-term average. Mean reversion has been empirically observed in many markets, especially foreign exchange and commodities. We show that the slightest mean reversion yields positive expected profit, and also obtain stronger profit guarantees for a canonical stochastic mean reverting process, known as the Ornstein-Uhlenbeck (OU) process, as well as other stochastic mean reverting series studied in the finance literature. We also show that market making remains profitable in expectation for the OU process even if some realistic restrictions on trading frequency are placed on the market maker.

Welfare Effects of Market Making in Continuous Double Auctions

“…In this last category, Chakraborty and Kearns (2011) demonstrate the profitability of market making, given a mean-reverting price series. They propose a simple MM algorithm to submit a ladder of prices; the market makers we investigate can be viewed as variations on this strategy.…”

“…For background traders, we consider parameterized strategies based on Zero Intelligence agents (Gode & Sunder, 1993). For the MM, we consider heuristic strategies loosely based on that defined by Chakraborty and Kearns (2011). From extensive simulation over thousands of strategy profiles, we estimate game models for various instances of the target scenario.…”

Predictive Market Making via Machine Learning

“…The market spread is computed using the formula (lowest ask price -highest bid price). In our formulation, the state is continuous in nature, similar to RMM-Spooner and [31], and denoted by 8 variables, namely volatility, volume imbalance, relative strength index, market spread, mid-price move, inventory, ask distance, bid distance. The inventory denotes the volume of asset(s), either positive (long position) or negative (short position), of the PMM agent.…”

A strategy based on mean reverting property of markets and applications to foreign exchange trading with trailing stops

“…On a more applied side, mean reversion on stock markets was used for various investment strategies of different levels up to market makers: and others. For instance, Spierdijk and Bikker concluded that mean reversion in stock price implies also mean reversion in stock returns.…”

“…For instance, Spierdijk and Bikker concluded that mean reversion in stock price implies also mean reversion in stock returns. Chakraborty and Kearns discussed that ‘the slightest mean reversion yields positive expected profit’ and the presence of mean reversion ‘implies stronger profit guarantees for the OU process, as well as other stochastic mean reverting series studied in the finance literature’. In , Zhuang gave a detailed review of evidence of mean reversion, including long‐term and shorter‐term evidences, and developed a consistent systematic numerical procedure for trading a mean‐reverting asset.…”