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on Market Microstructure |
| By: | Laura Leal; Mathieu Lauri\`ere; Charles-Albert Lehalle |
| Abstract: | We use a deep neural network to generate controllers for optimal trading on high frequency data. For the first time, a neural network learns the mapping between the preferences of the trader, i.e. risk aversion parameters, and the optimal controls. An important challenge in learning this mapping is that in intraday trading, trader's actions influence price dynamics in closed loop via the market impact. The exploration--exploitation tradeoff generated by the efficient execution is addressed by tuning the trader's preferences to ensure long enough trajectories are produced during the learning phase. The issue of scarcity of financial data is solved by transfer learning: the neural network is first trained on trajectories generated thanks to a Monte-Carlo scheme, leading to a good initialization before training on historical trajectories. Moreover, to answer to genuine requests of financial regulators on the explainability of machine learning generated controls, we project the obtained "blackbox controls" on the space usually spanned by the closed-form solution of the stylized optimal trading problem, leading to a transparent structure. For more realistic loss functions that have no closed-form solution, we show that the average distance between the generated controls and their explainable version remains small. This opens the door to the acceptance of ML-generated controls by financial regulators. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2006.09611&r=all |
| By: | Francois Cocquemas; Ibrahim Ekren; Abraham Lioui |
| Abstract: | We develop a flexible approach to solve a continuous-time, multi-asset/multi-option Kyle-Back model of informed trading under very general assumptions, including on the distribution of the belief about the fundamental, and the noise process. The main insight is to postulate the pricing rule of the market maker at maturity as an optimal transport map. The optimal control of the informed trader reduces to the computation of a conjugate convex function, explicit in some cases, and otherwise easily obtainable using fast numerical algorithms. To illustrate the power of our method, we apply it to a long-standing problem: how are informed investors splitting trades between a spot asset and its options? Our method allows to i) prove the existence of an equilibrium and characterize the informed trader's trading strategy in the spot and the option markets, even for non-Gaussian price priors (e.g., lognormal); ii) show there can be cross-market price impact between the spot market and multiple options even when their noise trading is independent; and iii) compare our pricing results to a simple Black-Scholes model and quantify the price distortion of the option due to strategic trading. In particular, we show that a Black-Scholes implied volatility (IV) smile/smirk can emerge because of the market marker's adaptation to asymmetric information. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2006.09518&r=all |
| By: | Yuan Hu; Abootaleb Shirvani; Stoyan Stoyanov; Young Shin Kim; Frank J. Fabozzi; Svetlazor T. Rachev |
| Abstract: | The objective of this paper is to introduce the theory of option pricing for markets with informed traders within the framework of dynamic asset pricing theory. We introduce new models for option pricing for informed traders in complete markets where we consider traders with information on the stock price direction and stock return mean. The Black-Scholes-Merton option pricing theory is extended for markets with informed traders, where price processes are following continuous-diffusions. By doing so, the discontinuity puzzle in option pricing is resolved. Using market option data, we estimate the implied surface of the probability for a stock upturn, the implied mean stock return surface, and implied trader information intensity surface. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2006.02596&r=all |
| By: | Oomen, Roel |
| Abstract: | Price signatures are statistical measurements that aim to detect systematic patterns in price dynamics localised around the point of trade execution. They are particularly useful in electronic trading because they uncovermarket dynamics, strategy characteristics, implicit execution costs, or counter-party trading behaviours that are often hard to identify, in part due to the vast amounts of data involved and the typically low signal to noise ratio.Because the signature summarises price dynamics over a specified time interval, it constitutes a curve (rather than a point estimate) and because of potential overlap in the price paths it has a non-trivial dependence structure which complicates statistical inference. In this paper, I show how recent advances in functional data analysis can be applied to study the properties of these signatures. To account for data dependence, I analyse and develop resampling-based bootstrap methodologies that enable reliable statistical inference and hypothesis testing. I illustrate the power of this approach using a number of case studies taken from a live trading environment in the over-the-counter currency market. I demonstrate that functional data analysis of price signatures can be used to distinguish between internalising and externalising liquidity providers in a highly effective data driven manner. This in turn can help traders to selectively engage with liquidity providers whose risk management style best aligns with their execution objectives. |
| JEL: | F3 G3 |
| Date: | 2018–11–13 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:90481&r=all |
| By: | David Byrd; Sruthi Palaparthi; Maria Hybinette; Tucker Hybinette Balch |
| Abstract: | There is a pervasive assumption that low latency access to an exchange is a key factor in the profitability of many high-frequency trading strategies. This belief is evidenced by the "arms race" undertaken by certain financial firms to co-locate with exchange servers. To the best of our knowledge, our study is the first to validate and quantify this assumption in a continuous double auction market with a single exchange similar to the New York Stock Exchange. It is not feasible to conduct this exploration with historical data in which trader identity and location are not reported. Accordingly, we investigate the relationship between latency of access to order book information and profitability of trading strategies exploiting that information with an agent-based interactive discrete event simulation in which thousands of agents pursue archetypal trading strategies. We introduce experimental traders pursuing a low-latency order book imbalance (OBI) strategy in a controlled manner across thousands of simulated trading days, and analyze OBI trader profit while varying distance (latency) from the exchange. Our experiments support that latency is inversely related to profit for the OBI traders, but more interestingly show that latency rank, rather than absolute magnitude, is the key factor in allocating returns among agents pursuing a similar strategy. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2006.08682&r=all |
| By: | Stefano Giglio; Matteo Maggiori; Johannes Stroebel; Stephen Utkus |
| Abstract: | We analyze how investor expectations about economic growth and stock returns changed during the February-March 2020 stock market crash induced by the COVID-19 pandemic, as well as during the subsequent partial stock market recovery. We surveyed retail investors who are clients of Vanguard at three points in time: (i) on February 11-12, around the all-time stock market high, (ii) on March 11-12, after the stock market had collapsed by over 20%, and (iii) on April 16-17, after the market had rallied 25% from its lowest point. Following the crash, the average investor turned more pessimistic about the short-run performance of both the stock market and the real economy. Investors also perceived higher probabilities of both further extreme stock market declines and large declines in short-run real economic activity. In contrast, investor expectations about long-run (10-year) economic and stock market outcomes remained largely unchanged, and, if anything, improved. Disagreement among investors about economic and stock market outcomes also increased substantially following the stock market crash, with the disagreement persisting through the partial market recovery. Those respondents who were the most optimistic in February saw the largest decline in expectations, and sold the most equity. Those respondents who were the most pessimistic in February largely left their portfolios unchanged during and after the crash. |
| JEL: | G0 G00 G11 G12 R20 |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27272&r=all |
| By: | Croce, Mariano Massimiliano; Farroni, Paolo; Wolfskeil, Isabella |
| Abstract: | We quantify the exposure of major financial markets to news shocks about global contagion risk accounting for local epidemic conditions. For a wide cross section of countries, we construct a novel data set comprising (i) announcements related to COVID19, and (ii) high-frequency data on epidemic news diffused through Twitter. Across several classes of financial assets, we provide novel empirical evidence about {financial dynamics (i) around epidemic announcements, (ii) at a daily frequency, and (iii) at an intra-daily frequency.} Formal estimations based on both contagion data and social media activity about COVID19 confirm that the market price of contagion risk is very significant. We conclude that prudential policies aimed at mitigating either global contagion or local diffusion may be extremely valuable. |
| Keywords: | asset prices; contagion; Epidemic |
| JEL: | G01 G1 I1 |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14674&r=all |