Algorithmic trading

About: Algorithmic trading is a research topic. Over the lifetime, 6718 publications have been published within this topic receiving 162209 citations. The topic is also known as: algotrading & Algorithmic trading.

Margin Rules, Informed Trading in Derivatives and Price Dynamics

Abstract: We analyze the impact of option trading and margin rules on the behavior of informed traders and on the microstructure of stock and option markets. In the absence of binding margin requirements, the introduction of an options market causes informed traders to exhibit a relative trading bias towards the stock because of its greater information sensitivity. In turn, this widens the stock's bid-ask spread. But when informed traders are subject to margin requirements, their bias towards the stock is enhanced or mitigated depending on the leverage provided by the option relative to the stock, leading to wider or narrower stock bid-ask spreads. The introduction of option trading, with or without margin requirements, unambiguously improves the informational efficiency of stock prices. Margin rules improve market efficiency when stock and option margins are sufficiently large or small but not when they are of moderate size.

Bayesian Adaptive Trading with a Daily Cycle

Abstract: Standard models of algorithmic trading neglect the presence of a daily cycle. We construct a model in which the trader uses information from observations of price evolution during the day to continuously update his estimate of other traders9 target sizes and directions. He uses this information to determine an optimal trade schedule to minimize total expected cost of trading, subject to sign constraints (never buy as part of a sell program). We argue that although these strategies are determined using very simple dynamic reasoning—at each moment they assume that current conditions will last until the end of trading—they are in fact the globally optimal strategies as would be determined by dynamic programming.

Equity Trading Practices and Market Structure: Assessing Asset Managers’ Demand for Immediacy

Abstract: Practitioners and students of the securities markets widely assume that traders demand immediate execution of their orders. However, little hard evidence exists about the demand for immediacy. This monograph analyzes the issue and presents the results of the responses to a questionnaire that we have sent to equity traders through TraderForum of the Institutional Investor. The respondents manage in total a very significant percentage of equity assets under management in the United States. The focus of the questions was the extent of the demand for immediate execution of orders. We found that the majority of traders are willing to trade patiently if this reduces execution costs. Many traders indicate that they frequently delay trades to obtain better prices. Most respondents indicate that they are typically given more than a day to implement a large order, that they typically break up more than 20% of their large orders for execution over time, and that they regularly take more than a day for a large order that has been broken into lost to be executed completely. There is a generally positive view of alternative electronic trading systems, such as Instinet and Investment Technology Group’s POSIT. The key motives for trading on these systems are reduced market impact, lower spreads, better liquidity, and anonymity. Changes that would make alternative electronic systems more attractive are an increase in execution rates and more convenient times of trading. Also, alternative electronic systems would be used more if the traders did not have soft dollar arrangements.

Mean-Field Game Strategies for Optimal Execution

Abstract: We introduce, for the first time, a mean-field game framework for a multiple agent optimal execution problem with continuous trading. This modeling generalizes the classical single agent optimal liquidation problem to a setting with (i) a major agent who is liquidating a large portion of shares, and (ii) a number of minor agents (high-frequency traders (HFTs)) who detect and trade along with the liquidator. As in the classical framework, all agents are exposed to temporary price impact and attempt to balance their impact against price uncertainty. Unlike most other works, we account for the permanent price impact that order-flow from all agents have on the midprice and this induces a distinct cross interaction between major and minor agents. This formulation falls into the realm of stochastic dynamic game problems with mean-field couplings in the dynamics, and we analyze the problem using a mean-field game approach. We obtain a set of decentralized feedback trading strategies for the major and minor agents, and express the solution explicitly in terms of a deterministic fixed point problem. For a finite N population of HFTs, the set of major-minor agent mean-field game strategies is shown to have an epsilon-Nash equilibrium property where epsilon→0 as N→∞.

A reality check on technical trading rule profits in the U.S. futures markets

Abstract: This article investigates the profitability of technical trading rules in U.S. futures markets during the years 1985–2004. Statistical significance of performance across the trading rules is evaluated using White's Bootstrap Reality Check and Hansen's Superior Predictive Ability tests, which can directly measure the effect of data snooping by testing the performance of the best rule in the context of the full universe of technical trading rules. Results show that the best rules generate statistically significant economic profits for only two of 17 futures markets after correcting for data snooping biases. This evidence suggests that technical trading rules generally have not been profitable in the U.S. futures markets. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:633–659, 2010