Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 3rd ACM International Conference on AI in Finance |
| Subtitle of host publication | ICAIF 2022 |
| Pages | 214-222 |
| Number of pages | 9 |
| ISBN (electronic) | 9781450393768 |
| Publication status | Published - Nov 2022 |
| Event | 3rd ACM International Conference on AI in Finance, ICAIF 2022 - New York, United States Duration: 2 Nov 2022 → 4 Nov 2022 |
Abstract
This paper introduces a new representation for the actions of a market maker in an order-driven market. This representation uses scaled beta distributions, and generalises three approaches taken in the artificial intelligence for market making literature: single price-level selection, ladder strategies, and "market making at the touch". Ladder strategies place uniform volume across an interval of contiguous prices. Scaled beta distribution based policies generalise these, allowing volume to be skewed across the price interval. We demonstrate that this flexibility is useful for inventory management, one of the key challenges faced by a market maker. We conduct three main experiments: first, we compare our more flexible beta-based actions with the special case of ladder strategies; then, we investigate the performance of simple fixed distributions; and finally, we devise and evaluate a simple and intuitive dynamic control policy that adjusts actions in a continuous manner depending on the signed inventory that the market maker has acquired. All empirical evaluations use a high-fidelity limit order book simulator based on historical data with 50 levels on each side.
Keywords
- inventory risk, limit order book, liquidity provision, market making
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Economics, Econometrics and Finance(all)
- Finance
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Proceedings of the 3rd ACM International Conference on AI in Finance: ICAIF 2022. 2022. p. 214-222.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Market Making with Scaled Beta Policies
AU - Jerome, Joseph
AU - Palmer, Gregory
AU - Savani, Rahul
N1 - Funding Information: Jerome and Savani would like to acknowledge the support of a 2021 J.P.Morgan Chase AI Research Faculty Research Award.
PY - 2022/11
Y1 - 2022/11
N2 - This paper introduces a new representation for the actions of a market maker in an order-driven market. This representation uses scaled beta distributions, and generalises three approaches taken in the artificial intelligence for market making literature: single price-level selection, ladder strategies, and "market making at the touch". Ladder strategies place uniform volume across an interval of contiguous prices. Scaled beta distribution based policies generalise these, allowing volume to be skewed across the price interval. We demonstrate that this flexibility is useful for inventory management, one of the key challenges faced by a market maker. We conduct three main experiments: first, we compare our more flexible beta-based actions with the special case of ladder strategies; then, we investigate the performance of simple fixed distributions; and finally, we devise and evaluate a simple and intuitive dynamic control policy that adjusts actions in a continuous manner depending on the signed inventory that the market maker has acquired. All empirical evaluations use a high-fidelity limit order book simulator based on historical data with 50 levels on each side.
AB - This paper introduces a new representation for the actions of a market maker in an order-driven market. This representation uses scaled beta distributions, and generalises three approaches taken in the artificial intelligence for market making literature: single price-level selection, ladder strategies, and "market making at the touch". Ladder strategies place uniform volume across an interval of contiguous prices. Scaled beta distribution based policies generalise these, allowing volume to be skewed across the price interval. We demonstrate that this flexibility is useful for inventory management, one of the key challenges faced by a market maker. We conduct three main experiments: first, we compare our more flexible beta-based actions with the special case of ladder strategies; then, we investigate the performance of simple fixed distributions; and finally, we devise and evaluate a simple and intuitive dynamic control policy that adjusts actions in a continuous manner depending on the signed inventory that the market maker has acquired. All empirical evaluations use a high-fidelity limit order book simulator based on historical data with 50 levels on each side.
KW - inventory risk
KW - limit order book
KW - liquidity provision
KW - market making
UR - http://www.scopus.com/inward/record.url?scp=85142516186&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2207.03352
DO - 10.48550/arXiv.2207.03352
M3 - Conference contribution
AN - SCOPUS:85142516186
SP - 214
EP - 222
BT - Proceedings of the 3rd ACM International Conference on AI in Finance
T2 - 3rd ACM International Conference on AI in Finance, ICAIF 2022
Y2 - 2 November 2022 through 4 November 2022
ER -