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on Market Microstructure |
| By: | Matthew Brigida |
| Abstract: | Previous research has found that high-frequency traders will vary the bid or offer price rapidly over periods of milliseconds. This is a benefit to fast traders who can time their trades with microsecond precision, however it is a cost to the average market participant due to increased trade execution price uncertainty. In this analysis we attempt to construct real-time methods for determining whether the liquidity of a security is being altered rapidly. We find a four-state Markov switching model identifies a state where liquidity is being rapidly varied about a mean value. This state can be used to generate a signal to delay market participant orders until the price volatility subsides. Over our sample, the signal would delay orders, in aggregate, over 0 to 10% of the trading day. Each individual delay would only last tens of milliseconds, and so would not be noticeable by the average market participant. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.10930&r=all |
| By: | Sandrine Jacob Leal (Groupe de Recherche en Droit, Economie et Gestion); Mauro Napoletano (Observatoire français des conjonctures économiques) |
| Abstract: | We investigate the effects of a set of regulatory policies directed towards high-frequency trading (HFT) through an agent-based model of a limit order book able to generate flash crashes as the result of the interactions between low- and high-frequency traders. In particular, we study the impact of the imposition of minimum resting times, of circuit breakers, of cancellation fees and of transaction taxes on asset price volatility and on the occurrence and the duration of flash crashes. Monte-Carlo simulations reveal that HFT-targeted policies imply a trade-off between market stability and resilience. Indeed, we find that policies able to tackle volatility and flash crashes also hinder the market from quickly recovering after a crash. This result is mainly due to the dual role of HFT, as both a cause of flash crashes and a key player in the post-crash recovery. |
| Keywords: | High-frequency trading; Flash crashes; Regulatory policies; Agent-based models; Limit order book; Market volatility |
| JEL: | G12 G1 C63 |
| Date: | 2019–01 |
| URL: | http://d.repec.org/n?u=RePEc:spo:wpmain:info:hdl:2441/6ummnc8nko827b2luohnctekk7&r=all |
| By: | Xiao-Yang Liu; Hongyang Yang; Qian Chen; Runjia Zhang; Liuqing Yang; Bowen Xiao; Christina Dan Wang |
| Abstract: | As deep reinforcement learning (DRL) has been recognized as an effective approach in quantitative finance, getting hands-on experiences is attractive to beginners. However, to train a practical DRL trading agent that decides where to trade, at what price, and what quantity involves error-prone and arduous development and debugging. In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies. Along with easily-reproducible tutorials, FinRL library allows users to streamline their own developments and to compare with existing schemes easily. Within FinRL, virtual environments are configured with stock market datasets, trading agents are trained with neural networks, and extensive backtesting is analyzed via trading performance. Moreover, it incorporates important trading constraints such as transaction cost, market liquidity and the investor's degree of risk-aversion. FinRL is featured with completeness, hands-on tutorial and reproducibility that favors beginners: (i) at multiple levels of time granularity, FinRL simulates trading environments across various stock markets, including NASDAQ-100, DJIA, S&P 500, HSI, SSE 50, and CSI 300; (ii) organized in a layered architecture with modular structure, FinRL provides fine-tuned state-of-the-art DRL algorithms (DQN, DDPG, PPO, SAC, A2C, TD3, etc.), commonly-used reward functions and standard evaluation baselines to alleviate the debugging workloads and promote the reproducibility, and (iii) being highly extendable, FinRL reserves a complete set of user-import interfaces. Furthermore, we incorporated three application demonstrations, namely single stock trading, multiple stock trading, and portfolio allocation. The FinRL library will be available on Github at link https://github.com/AI4Finance-LLC/FinRL- Library. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.09607&r=all |
| By: | Michael J. Fleming; Or Shachar; Peter Van Tassel |
| Abstract: | When the U.S. Treasury sells a new security, the security is announced to the public, auctioned a number of days later, and then issued sometime after that. When-issued (WI) trading refers to trading of the new security after the announcement but before issuance. Such trading promotes price discovery, which may reduce uncertainty at auction, potentially lowering government borrowing costs. Despite the importance of WI trading, and the advent of Treasury trading volume statistics from the Financial Industry Regulatory Authority (FINRA), little is known publicly about the level of WI activity. In this post, we address this gap by analyzing WI transactions recorded in FINRA’s Trade Reporting and Compliance Engine (TRACE) database. |
| Keywords: | Treasury market; trading volume; when issued; TRACE |
| JEL: | G1 |
| Date: | 2020–11–30 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednls:89094&r=all |