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on Market Microstructure |
| By: | Lijian Wei (Business School, Sun Yat-Sen University); Xiong Xiong (College of Management and Economics, Tianjin University); Wei Zhang (College of Management and Economics, Tianjin University); Xue-Zhong He (Finance Discipline Group, University of Technology Sydney); Yongjie Zhang (College of Management and Economics, Tianjin University) |
| Abstract: | By introducing a genetic algorithm with a classifier system as a learning mechanism for uninformed traders into a dynamic limit order market with asymmetric information, this paper examines the effect of the learning on traders’ trading behavior, market liquidity and efficiency. We show that the learning is effective and valuable with respect to information acquisition, forecasting, buy–sell order choice accuracies, and profit opportunity for uninformed traders. It improves information dissemination efficiency and reduces the information advantage of informed traders and hence the value of the private information. In particular, the learning and information become more valuable with higher volatility, less informed traders, and longer information lag. Furthermore, the learning makes not only uninformed but also informed traders submit more limit orders and hence increases market liquidity supply. |
| Keywords: | Limit order book; Asymmetric information; Genetic algorithm learning; Classifier system; Order submission |
| JEL: | G14 C63 D82 |
| Date: | 2017–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:uts:ppaper:2017-3&r=mst |
| By: | Taiga Saito (CIRJE, Faculty of Economics, The University of Tokyo); Akihiko Takahashi (CIRJE, Faculty of Economics, The University of Tokyo) |
| Abstract: | This paper presents an application of a linear quadratic stochastic differential game to a model in finance, which describes trading behaviors of different types of players in a high frequency stock market. Stability of the high frequency market is a central issue for financial markets. Building a model that expresses the trading behaviors of the different types of players and the price actions in turmoil is important to set regulations to maintain fair markets. Firstly, we represent trading behaviors of the three types of players, algorithmic traders, general traders, and market makers as well as the mid-price process of a risky asset by a linear quadratic stochastic differential game. Secondly, we obtain a Nash equilibrium for open loop admissible strategies by solving a forward-backward stochastic differential equation (FBSDE) derived from the stochastic maximum principle. Finally, we present numerical examples of the Nash equilibrium for open loop admissible strategies and the corresponding price action of the risky asset, which agree with the empirical findings on trading behaviors of players in high frequency markets. This model can be used to investigate the impact of regulation changes on the market stability as well as trading strategies of the players. |
| Date: | 2018–05 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2018cf1087&r=mst |
| By: | Tom Auld (Institute for Fiscal Studies); Oliver Linton (Institute for Fiscal Studies and University of Cambridge) |
| Abstract: | We study the behaviour of the Betfair betting market and the sterling/dollar exchange rate (futures price) during 24 June 2016, the night of the EU referendum. We investigate how the two markets responded to the announcement of the voting results. We employ a Bayesian updating methodology to update prior opinion about the likelihood of the final outcome of the vote. We then relate the voting model to the real time evolution of the market determined prices. We find that although both markets appear to be inefficient in absorbing the new information contained in vote outcomes, the betting market is apparently less inefficient than the FX market. The different rates of convergence to fundamental value between the two markets leads to highly profitable arbitrage opportunities. |
| Keywords: | EU Referendum, Prediction Markets, Machine Learning, Efficient Markets Hypothesis, Pairs Trading, Cointegration, Bayesian Methods, Exchange Rates |
| Date: | 2018–01–10 |
| URL: | http://d.repec.org/n?u=RePEc:ifs:cemmap:01/18&r=mst |
| By: | Oomen, Roel |
| Abstract: | In over-the-counter markets, a trader typically sources indicative quotes from a number of competing liquidity providers, and then sends a deal request on the best available price for consideration by the originating liquidity provider. Due to the communication and processing latencies involved in this negotiation, and in a continuously evolving market, the price may have moved by the time the liquidity provider considers the trader’s request. At what point has the price moved too far away from the quote originally shown for the liquidity provider to reject the deal request? Or perhaps the request can still be accepted but only on a revised rate? ‘Last look’ is the process that makes this decision, i.e. it determines whether to accept—and if so at what rate—or reject a trader’s deal request subject to the constraints of an agreed trading protocol. In this paper, I study how the execution risk and transaction costs faced by the trader are influenced by the last look logic and choice of trading protocol. I distinguish between various ‘symmetric’ and ‘asymmetric’ last look designs and consider trading protocols that differ on whether, and if so to what extent, price improvements and slippage can be passed on to the trader. All this is done within a unified framework that allows for a detailed comparative analysis. I present two main findings. Firstly, the choice of last look design and trading protocol determines the degree of execution risk inherent in the process, but the effective transaction costs borne by the trader need not be affected by it. Secondly, when a trader adversely selects the iquidity provider she chooses to deal with, the distinction between the different symmetric and asymmetric last look designs fades and the primary driver of execution risk is the choice of trading protocol. |
| Keywords: | last look; trading protocol; execution risk; transaction costs; over-the-counter markets |
| JEL: | L81 F3 G3 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:68811&r=mst |
| By: | Oliver Linton (Institute for Fiscal Studies and University of Cambridge); Soheil Mahmoodzadeh (Institute for Fiscal Studies) |
| Abstract: | High frequency trading (HFT) has grown substantially in recent years, due to fast-paced technological developments and their rapid uptake, particularly in equity markets. This paper investigates how HFT could evolve and, by developing a robust understanding of its effects, to identify potential risks and opportunities that it could present in terms of financial stability and other market outcomes such as volatility, liquidity, price efficiency and price discovery. Despite commonly held negative perceptions, the available evidence indicates that HFT and algorithmic trading (AT) may have several beneficial effects on markets. However, they may cause instabilities in financial markets in specific circumstances. Carefully chosen regulatory measures are needed to address concerns in the shorter term. However, further work is needed to inform policies in the longer term, particularly in view of likely uncertainties and lack of data. This will be vital to support evidence-based regulation in this controversial and rapidly evolving field. |
| Date: | 2018–01–10 |
| URL: | http://d.repec.org/n?u=RePEc:ifs:cemmap:06/18&r=mst |
| By: | Apesteguia, Jose; Oechssler, Jörg; Weidenholzer, Simon |
| Abstract: | Copy trading allows traders in social networks to receive information on the success of other agents in financial markets and to directly copy their trades. Internet platforms like eToro, ZuluTrade, and Tradeo have attracted millions of users in recent years. The present paper studies the implications of copy trading for the risk taking of investors. Implementing an experimental financial asset market, we show that providing information on the success of others leads to a significant increase in risk taking of subjects. This increase in risk taking is even larger when subjects are provided with the option to directly copy others. We conclude that copy trading reduces ex-ante welfare, and leads to excessive risk taking. |
| Keywords: | Copy trading; Financial markets; Social networks; Imitation; Experiment |
| Date: | 2018–06–29 |
| URL: | http://d.repec.org/n?u=RePEc:awi:wpaper:0649&r=mst |