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.

Technical Trading Rules in Emerging Markets and the 1997 Asian Currency Crises

Abstract: The ability of simple technical trading rules to forecast future stock market movements is considered for seventeen emerging markets, sampled from January 1986 to September 2003. Some of the trading rules considered generated significant returns; this information could be exploited profitably on occasion. Market conditions and trading volume are found to be important to determining the usefulness of technical trading rules.

Estimating the Probability of Informed Trading

Abstract: Using a new empirical model, I estimate the probability of trades being generated by privately informed traders. Inference is drawn on a trade-by-trade basis using data samples from the New York Stock Exchange (NYSE). The modeling setup facilitates in-depth analysis of the estimated probability of informed trading at the intraday level and for stocks with different levels of trading activity. The most important empirical results are: (a) the intradaily pattern of the inferred probability of informed trading is highly correlated with the intradaily pattern of observed quoted spreads, (b) differences in the magnitude of quoted spreads across volume categories are not exclusively related to differences in the level of informed trading, and (c) private information is incorporated faster in the quotes for high-volume stocks than in the quotes for low-volume stocks.

Algorithmic Trading: Winning Strategies and Their Rationale

Abstract: Praise for Algorithmic Trading"Algorithmic Trading is an insightful book on quantitative trading written by a seasoned practitioner. What sets this book apart from many others in the space is the emphasis on real examples as opposed to just theory. Concepts are not only described, they are brought to life with actual trading strategies, which give the reader insight into how and why each strategy was developed, how it was implemented, and even how it was coded. This book is a valuable resource for anyone looking to create their own systematic trading strategies and those involved in manager selection, where the knowledge contained in this book will lead to a more informed and nuanced conversation with managers."DAREN SMITH, CFA, CAIA, FSA, Managing Director, Manager Selection & Portfolio Construction, University of Toronto Asset Management"Using an excellent selection of mean reversion and momentum strategies, Ernie explains the rationale behind each one, shows how to test it, how to improve it, and discusses implementation issues. His book is a careful, detailed exposition of the scientific method applied to strategy development. For serious retail traders, I know of no other book that provides this range of examples and level of detail. His discussions of how regime changes affect strategies, and of risk management, are invaluable bonuses."Roger Hunter, Mathematician and Algorithmic Trader

Assessing the Impact of Algorithmic Trading on Markets: A Simulation Approach

Abstract: Innovative automated execution strategies like Algorithmic Trading gain significant market share on electronic market venues worldwide, although their impact on market outcome has not been investigated in depth yet. In order to assess the impact of such concepts, e.g. effects on the price formation or the volatility of prices, a simulation environment is presented that provides stylized implementations of algorithmic trading behavior and allows for modeling latency. As simulations allow for reproducing exactly the same basic situation, an assessment of the impact of algorithmic trading models can be conducted by comparing different simulation runs including and excluding a trader constituting an algorithmic trading model in its trading behavior. By this means the impact of Algorithmic Trading on different characteristics of market outcome can be assessed. The results indicate that large volumes to execute by the algorithmic trader have an increasing impact on market prices. On the other hand, lower latency appears to lower market volatility. JEL Classification: G10

Detection of intra-firm matching and response thereto

Abstract: The disclosed systems and methods relate to allowing trading of over the counter ('OTC') foreign exchange ('FX') contracts on a centralized matching and clearing mechanism, such as that of the Chicago Mercantile Exchange's ('CME''s) futures exchange system (the 'Exchange'). The disclosed systems and methods allow for anonymous transactions, centralized clearing, efficient settlement and the provision of risk management/credit screening mechanisms to lower risk, reduce transaction costs and improve the liquidity in the FX market place. In particular, the disclosed embodiments increase speed of execution facilitating growing demand for algorithmic trading, increased price transparency, lower cost of trading, customer to customer trading, and automated asset allocations, recurring trades as well as clearing and settlement efficiencies.