Algorithmic Trading: What Should You Be Doing?
Peter Bergan and Colleen Devine
Bergan and Devine consider the advantages and disadvantages that algorithmic trading offers the investment industry. The authors outline the evolution of the market, examine the effects of best execution and trade cost analysis, and assess the key ingredients for a successful modern trading desk.
Key Considerations in Selecting an Algorithmic Trading Provider
Derek Morris and Lenore Kantor-Hendrick
Algorithmic trading has become a part of the mainstream in response to buy-side traders' need to move large blocks of shares with minimum market impact in today's complex institutional trading environment. With myriad service providers in the marketplace, the decision about which algorithmic trading service provider to select can be challenging for many buy-side firms and is dependent upon several key factors. The authors present an overview of the key providers in the marketplace and the variables that affect the decision to buy or build. While algorithmic trading relies on technology, in the end, working with a committed and experienced team, coupled with a "white glove" service, appears to be the right combination for those firms trying to avoid being left behind.
The Cost of Algorithmic Trading: A First Look at Comparative Performance
Ian Domowitz and Henry Yegerman
The authors examine transaction costs associated with algorithmic trading, based on a sample of 2.5 million orders, of which one million are executed via algorithmic means. The data permit a comparison of algorithmic executions with a broader universe of trades, as well as across multiple providers of model-based trading services. Algorithmic trading is found to be a cost-effective technique, based on a measure of implementation shortfall. The superiority of algorithm performance applies only for order sizes up to 10 % of average daily volume, however. Algorithmic trading performance relative to a commonly used volume participation benchmark also is quite good, although certainty of outcome declines sharply with the size of the order. A clear link between performance and variability in performance relative to both benchmarks appears to be lacking. Although rough equality across providers is observed on average, this equality of performance breaks down quickly as order size grows.
Understanding the Profit and Loss Distribution of Trading Algorithms
Robert Kissell and Roberto Malamut
With the advent of algorithmic trading, it is essential that investors become more proactive in the decision-making process to ensure selection of the most appropriate algorithm. Investors need to specify benchmark price, implementation goal, and preferred deviation strategy (i.e., how the optimally prescribed algorithm is to react to changing market conditions or prices). In this article the authors describe an analytical process to assess the impact of these decisions on the profit and loss distribution of the algorithm.
Institutions, Brokers, and Algorithmic Trading: An Uneasy New Dependence upon Vendors
Leigh Henson and Meredith Moss
The rise of algorithmic trading has had profound implications for vendors and users of information and trading products. Understanding these implications enables customers on the buyside and sellside to be more thoughtful consumers of algorithmic trading partnerships. Solution providers offering algorithmic trading solutions through their products are facing significant changes in their business models, including: relationships with sellside customers, relationships with buyside customers, the importance of broker-neutrality and competition, time to market, the role of direct market access, and the relationship with prime brokers. Institutions ignore these changes at their peril.
Algorithmic Trading: The Black Box Trading Market
Gavin Little-Gill
2005 appears to be the year of the algorithm. Brokers are rabidly building, integrating, and pushing their algorithmic offerings to the buy side, and algorithms are promising to fundamentally alter the equity trading. Despite the buzz, widespread confusion exists around algorithmic trading. What is it? Is it all hype? How do we separate fact from fiction? In this piece the author answers these questions and provides:
• Definitions and delineation between Algorithmic, Arbitrage/Strategy Trading, Program Trading and Smart Order Routing
• Data on the current and projected use of quantitative trading solutions by asset managers, hedge funds, and institutional brokers
• A discussion of what impact algorithmic trading will have on the broader trading landscape
• Perspective on emerging trends and the future of algorithmic trading.
A Review of Trading Cost Models: Reducing Transaction Costs
Andrew Freyre-Sanders, Renate Guobuzaite, and Kevin Byrne
Over the last few years, transaction cost analysis has been one of the biggest areas of investment for both the buy and sell side of the equity industry. This increased focus has led to intensification of research in this field. At the same time, there has been an enormous leap in the provision of tools to aid measuring and predicting costs. As a precursor to modeling transaction costs, the authors felt it would be useful to the reader to compile a literature review that takes the reader from the very early attempts of modeling market microstructure through to some techniques used today. Although many of the early (classical) studies may seem inappropriate to today's electronic order books, many of the concepts developed are still relevant at present. Until recently, the focus of the investment community has been on commissions, taxes, and spreads. In this article, the authors have not gone into any detail regarding these fixed costs, but have focused on the so-called "hidden" costs of trading.
Transaction Costs and Best Execution: Compliance and Measurement
Wayne H. Wagner
This article presents a framework for thinking about trading and its costs upon which to base an effective investment-manager compliance program. An efficient and effective transaction process is vital to the satisfactory implementation of an investment strategy. The author shows why controlling transaction costs is an important component of successful investment management. He discusses the history of regulation and voluntary compliance standards, the surprising complexity of institutional trading, and concludes with some practical suggestions to reduce transaction costs and enhance performance.
OMSs Get Up to Algorithmic Speed
Michael Hayes
In an effort to reduce spreads, reduce execution costs, and increase fair competition, regulatory bodies have introduced market structure changes. Asset managers have determined the need for their traders to help increase overall fund performance through better executions. Order management systems (OMS) have emerged as logical, front-end means for providing traders with access centralized order management, pre-trade compliance, and a seamless trading workflow. This commentary discusses the need for brokers and OMS vendors to work together on the development of these systems and the benefits that they will produce.
Spread Charts
2004 All-America Execution & Sales-Trading Report
Glossary
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