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on Market Microstructure |
| By: | Maochun Xu; Zixun Lan; Zheng Tao; Jiawei Du; Zongao Ye |
| Abstract: | Artificial Intelligence (AI) and Machine Learning (ML) are transforming the domain of Quantitative Trading (QT) through the deployment of advanced algorithms capable of sifting through extensive financial datasets to pinpoint lucrative investment openings. AI-driven models, particularly those employing ML techniques such as deep learning and reinforcement learning, have shown great prowess in predicting market trends and executing trades at a speed and accuracy that far surpass human capabilities. Its capacity to automate critical tasks, such as discerning market conditions and executing trading strategies, has been pivotal. However, persistent challenges exist in current QT methods, especially in effectively handling noisy and high-frequency financial data. Striking a balance between exploration and exploitation poses another challenge for AI-driven trading agents. To surmount these hurdles, our proposed solution, QTNet, introduces an adaptive trading model that autonomously formulates QT strategies through an intelligent trading agent. Incorporating deep reinforcement learning (DRL) with imitative learning methodologies, we bolster the proficiency of our model. To tackle the challenges posed by volatile financial datasets, we conceptualize the QT mechanism within the framework of a Partially Observable Markov Decision Process (POMDP). Moreover, by embedding imitative learning, the model can capitalize on traditional trading tactics, nurturing a balanced synergy between discovery and utilization. For a more realistic simulation, our trading agent undergoes training using minute-frequency data sourced from the live financial market. Experimental findings underscore the model's proficiency in extracting robust market features and its adaptability to diverse market conditions. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.15730&r=mst |
| By: | Sergei Glebkin (INSEAD); Semyon Malamud (Ecole Polytechnique Federale de Lausanne; Centre for Economic Policy Research (CEPR); Swiss Finance Institute); Alberto Teguia (University of British Columbia) |
| Abstract: | We analyze asset prices and liquidity in an economy with large investors and many risky assets. The model allows for general investors' preferences and distributions of asset payoffs. We propose a constructive solution approach: solving for equilibrium reduces to solving nonlinear first-order ODE. We show that the equilibrium is unique under mild restrictions on payoffs and preferences. Liquidity risk is priced in equilibrium, leading to deviations from the consumption-CAPM. In stark contrast to a constant absolute risk aversion (CARA) benchmark, in a model with wealth effects, we obtain (1) illiquidity of risk-free assets (such as, e.g., Treasuries); (2) illiquidity contagion (a sell-off in one asset may have a price impact on assets with unrelated fundamentals) and asymmetry in cross-asset price impacts; (3) market liquidity may decrease in the number of traders and their wealth; and (4) in the presence of liquidity shortage, price impact may become negative giving rise to an illiquidity premium in asset prices; (5) safe assets are more illiquid because they have a larger price impact. In the presence of wealth heterogeneity, large traders trade more but also reduce their demands more. As a group, they account for a smaller fraction of orders compared to small investors. Fatter-tailed wealth distribution makes markets less liquid. |
| Keywords: | Market Liquidity, Funding Liquidity, Price Impact, Strategic Trading, Wealth Effects |
| JEL: | D21 G31 G32 G35 L11 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp23116&r=mst |
| By: | Christoph K\"uhn; Christopher Lorenz |
| Abstract: | We present a discrete time version of Kyle's (1985) classic model of insider trading. The model has three kinds of traders: an insider, random noise traders, and a market maker. The insider aims to exploit her informational advantage and maximise expected profits while the market maker observes the total order flow and sets prices accordingly. First, we show how the multi-period model with finitely many pure strategies can be reduced to a (static) social system in the sense of Debreu (1952) and prove the existence of a sequential Kyle equilibrium, following Kreps and Wilson (1982). This requires no probabilistic restrictions on the true value, the insider's dynamic information, and the noise trader's actions. In the single-period model we establish bounds for the insider's strategy in equilibrium. Finally, we prove the existence of an equilibrium for the game with a continuum of actions, by considering an approximating sequence of games with finitely many actions. Because of the lack of compactness of the set of measurable price functions, standard infinite-dimensional fixed point theorems are not applicable. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.00904&r=mst |
| By: | Wladimir Ostrovsky |
| Abstract: | This paper explores the utility of agent-based simulations in realistically modelling market structures and sheds light on the nuances of optimal dealer strategies. It underscores the contrast between conclusions drawn from probabilistic modelling and agent-based simulations, but also highlights the importance of employing a realistic test bed to analyse intricate dynamics. This is achieved by extending the agent-based model for auction markets by \cite{Chiarella.2008} to include liquidity providers. By constantly and passively quoting, the dealers influence their own wealth but also have ramifications on the market as a whole and the other participating agents. Through synthetic market simulations, the optimal behaviour of different dealer strategies and their consequences on market dynamics are examined. The analysis reveals that dealers exhibiting greater risk aversion tend to yield better performance outcomes. The choice of quote sizes by dealers is strategy-dependent: one strategy demonstrates enhanced performance with larger quote sizes, whereas the other strategy show a better results with smaller ones. Increasing quote size shows positive influence on the market in terms of volatility and kurtosis with both dealer strategies. However, the impact stemming from larger risk aversion is mixed. While one of the dealer strategies shows no discernible effect, the other strategy results in mixed outcomes, encompassing both positive and negative effects. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.05943&r=mst |