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on Market Microstructure |
| By: | Craig Burnside; Mario Cerrato; Zhekai Zhang |
| Abstract: | We propose a novel pricing factor for currency returns motivated by the marketmicrostructure literature. Our factor aggregates order flow data to provide a measure of buying and selling pressure related to conventional currency trading strategies. It successfully prices the cross-section of currency returns sorted on the basis of interest rates and momentum. The association between our factor and currency returns differs according to the customer segment of the foreign exchange market. In particular, it appears that financial customers are risk takers in the market, while non-financial customers serve as liquidity providers. |
| Keywords: | exchange rates, market microstructure, order flow, carry trade, currency momentum, crash risk, stochastic discount factor |
| JEL: | F31 G15 |
| Date: | 2023–01 |
| URL: | https://d.repec.org/n?u=RePEc:gla:glaewp:2023_03 |
| By: | João A. Bastos; Fernando Cascão |
| Abstract: | We examine the factors influencing equity market liquidity through explainable machine learning techniques. Unlike previous studies, our approach is entirely nonparametric. By studying daily placement orders for equity securities managed by a European asset management institution, we uncover multiple nonlinear relationships between market liquidity and placement characteristics typically not captured by a traditional parametric model. As expected, the results show that liquidity tends to increase in highly active markets. However, we also note that liquidity remains relatively stable within certain trading volume ranges. Price volatility, broker efficiency, and the market impact of the trade are important predictors of liquidity. Price volatility shows a linear relationship with bid-ask spreads, whereas broker efficiency and market impact have non-symmetric convex effects. Large bid-ask spreads are linked to increased uncertainty and weak economic activity. |
| Keywords: | Market liquidity; Equity markets; Bid-ask spreads, Nonparametric models; Machine learning, Explainable AI. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:ise:remwps:wp03322024 |
| By: | Xi Cheng; Jinghao Zhang; Yunan Zeng; Wenfang Xue |
| Abstract: | Algorithmic trading refers to executing buy and sell orders for specific assets based on automatically identified trading opportunities. Strategies based on reinforcement learning (RL) have demonstrated remarkable capabilities in addressing algorithmic trading problems. However, the trading patterns differ among market conditions due to shifted distribution data. Ignoring multiple patterns in the data will undermine the performance of RL. In this paper, we propose MOT, which designs multiple actors with disentangled representation learning to model the different patterns of the market. Furthermore, we incorporate the Optimal Transport (OT) algorithm to allocate samples to the appropriate actor by introducing a regularization loss term. Additionally, we propose Pretrain Module to facilitate imitation learning by aligning the outputs of actors with expert strategy and better balance the exploration and exploitation of RL. Experimental results on real futures market data demonstrate that MOT exhibits excellent profit capabilities while balancing risks. Ablation studies validate the effectiveness of the components of MOT. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.01577 |
| By: | Jabir Sandhu; Rishi Vala |
| Abstract: | We find that on any given day, nearly half of Government of Canada bond transactions by clients of dealers can be offset with other clients, including during the turmoil in March 2020. Our results show that under certain conditions clients could potentially trade directly with each other and are a step towards understanding the relevance of broader all-to-all trading in the Government of Canada bond market. |
| Keywords: | Coronavirus disease (COVID-19); Financial institutions; Financial markets; Financial stability; Market structure and pricing |
| JEL: | D4 D47 D5 D53 G0 G01 G1 G12 G13 G14 G2 G21 G23 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocsan:24-17 |