Market Making with Learned Beta Policies

In market making, a market maker (MM) can concurrently place many buy and sell limit orders at various prices and volumes, re- sulting in a vast action space. To handle this large action space, beta policies were introduced, utilizing a scaled beta distribution to concisely represent the volume distribution of an MM’s orders across different price levels. However, in these policies, the param- eters of the scaled beta distributions are either fixed or adjusted only according to predefined rules based on the MM’s inventory. As we show, this approach potentially limits the effectiveness of market-making policies and overlooks the significance of other market characteristics in a dynamic market. To address this limita- tion, we introduce a general adaptive MM based on beta policies by employing deep reinforcement learning (RL) to dynamically control the scaled beta distribution parameters and generate orders based on current market conditions. A sophisticated market simulator is employed to evaluate a wide range of existing market-making policies and to train the RL policy in markets with varying levels of inventory risk, ensuring a comprehensive assessment of their performance and effectiveness. By carefully designing the reward function and observation features, we demonstrate that our RL beta policy outperforms baseline policies across multiple metrics in dif- ferent market settings. We emphasize the strong adaptability of the learned RL beta policy, underscoring its pivotal role in achieving superior performance compared to other market-making policies

The Effect of Liquidity on the Spoofability of Financial Markets

We investigate the relationship between market liquidity and spoof- ing, a manipulative practice involving the submission of deceptive orders aimed at misleading other traders. Utilizing an agent-based market simulator, we model markets with varying levels of liquidity, adjusting the spread and intervals of a market maker’s orders to control liquidity. Within these simulated markets, we evaluate the effectiveness of two novel spoofing strategies against a benchmark approach. Our experiments show that in high-liquidity markets, spoofing is substantially less profitable and less detrimental to other traders compared to their low-liquidity counterparts. Additionally, we identify two distinct spoofing behavior regimes based on liq- uidity, each of which employ drastically different profit-making strategies. Finally, building on our quantitative findings, we iden- tify and expound upon the mechanisms through which liquidity mitigates market manipulation