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on Market Microstructure |
| By: | Adamantios Ntakaris; Gbenga Ibikunle |
| Abstract: | High-frequency trading (HFT) has transformed modern financial markets, making reliable short-term price forecasting models essential. In this study, we present a novel approach to mid-price forecasting using Level 1 limit order book (LOB) data from NASDAQ, focusing on 100 U.S. stocks from the S&P 500 index during the period from September to November 2022. Expanding on our previous work with Radial Basis Function Neural Networks (RBFNN), which leveraged automated feature importance techniques based on mean decrease impurity (MDI) and gradient descent (GD), we introduce the Adaptive Learning Policy Engine (ALPE) - a reinforcement learning (RL)-based agent designed for batch-free, immediate mid-price forecasting. ALPE incorporates adaptive epsilon decay to dynamically balance exploration and exploitation, outperforming a diverse range of highly effective machine learning (ML) and deep learning (DL) models in forecasting performance. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.19372 |
| By: | Alif Aqsha; Fay\c{c}al Drissi; Leandro S\'anchez-Betancourt |
| Abstract: | We study strategic interactions in a broker-mediated market. A broker provides liquidity to an informed trader and to noise traders while managing inventory in the lit market. The broker and the informed trader maximise their trading performance while filtering each other's private information; the trader estimates the broker's trading activity in the lit market while the broker estimates the informed trader's private signal. Brokers hold a strategic advantage over traders who rely solely on prices to filter information. We find that information leakage in the client's trading flow yields an economic value to the broker that is comparable to transaction costs; she speculates profitably and mitigates risk effectively, which, in turn, adversely impacts the informed trader's performance. In contrast, low signal-to-noise sources, such as prices, result in the broker's trading performance being indistinguishable from that of a naive strategy that internalises noise flow, externalises informed flow, and offloads inventory at a constant rate. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.20847 |
| By: | Amit Goyal (University of Lausanne; Swiss Finance Institute); Adam V. Reed (University of North Carolina Kenan-Flagler Business School); Esad Smajlbegovic (Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE); Erasmus Research Institute of Management (ERIM); Tinbergen Institute); Amar Soebhag (Erasmus University Rotterdam (EUR) - Department of Business Economics; Robeco Asset Management) |
| Abstract: | Short sellers are widely known to be informed, which would typically suggest that they demand liquidity. We obtain comprehensive transaction-level data to decompose daily short volume into liquidity-demanding and liquidity-supplying components. Contrary to conventional wisdom, we show that the most informed short sellers are actually liquidity suppliers, not liquidity demanders. They are particularly informative about future returns on news days and trade on prominent cross-sectional return anomalies. Our analysis suggests that market making and opportunistic risk-bearing are unlikely to explain these findings. Instead, our results align with recent market microstructure theory, pointing to strategic liquidity provision by informed traders. |
| Keywords: | Short sales, liquidity supply, informed trading, asset pricing |
| JEL: | G10 G12 G14 G23 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2475 |
| By: | Neil A. Chriss |
| Abstract: | This paper examines strategic trading under incomplete information, where firms lack full knowledge of key aspects of their competitors' trading strategies such as target sizes and market impact models. We extend previous work on competitive trading equilibria by incorporating uncertainty through the framework of Bayesian games. This allows us to analyze scenarios where firms have diverse beliefs about market conditions and each other's strategies. We derive optimal trading strategies in this setting and demonstrate how uncertainty significantly impacts these strategies compared to the complete information case. Furthermore, we introduce a novel approach to model the presence of non-strategic traders, even when strategic firms disagree on their characteristics. Our analysis reveals the complex interplay of beliefs and strategic adjustments required in such an environment. Finally, we discuss limitations of the current model, including the reliance on linear market impact and the lack of dynamic strategy adjustments, outlining directions for future research. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.01241 |