1 Implementation Shortfall One Objective, Many Algorithms VWAP (Volume Weighted Average Price) has ruled the algorithmic trading world for a long time, but there has been a significant move over the past year toward using decision price, or implementation shortfall, algorithms. ITG s Hitesh Mittal explains why it is high time for this change in focus. Perold (1988) 1 defines implementation shortfall as the difference in return between a theoretical portfolio and the implemented portfolio. When deciding to buy or sell stocks during portfolio construction, a portfolio manager looks at the prevailing prices (decision prices). However, due to a number of factors, the execution prices may be different from the decision prices. This can result in returns that differ from the portfolio manager s expectations. Given the availability of such a simple measure as implementation shortfall, why has VWAP (Volume Weighted Average Price) remained the primary benchmark in algorithmic trading for so many years? What are the properties of algorithms such as Target Percentage of Volume that make them desirable for portfolio managers and traders? This article explores the intuition behind these algorithms, and then examines the following aspects of implementation shortfall benchmarking in algorithmic trading: Why implementation shortfall is the optimal benchmark. Components of an implementation shortfall algorithm. How some popular algorithms (VWAP, Target Percentage of Volume) can be used to reduce implementation shortfall, and the shortcomings of these algorithms. How risk control helps implementation shortfall algorithms in reducing market impact and opportunity cost. Automatic and explicit risk control techniques in Algorithmic trading. Negotiating the conflict between anonymous liquidity and risk control. VWAP: Going with the flow VWAP s enduring appeal lies in its ease of attainability -- it is a moving target, and hence a more forgiving benchmark than arrival prices. With VWAP benchmarks, a trader or an algorithm models the volume distribution and then slices and dices the trades within a certain time interval on that distribution. As long as an algorithm does that, it is likely to achieve the VWAP over a given time horizon. This is true even if there are significant stock price moves during the day, either due to market impacts of the trading, or due to the stock s volatility. 1 Perold, Andre F. (1988), The implementation shortfall: paper vs. reality, Journal of Portfolio Management, 14 (Spring), 4 9.
2 But, with the arrival price benchmarking, if a trader or an algorithm executes trades in size quickly, the ensuing market impact is likely to result in average execution prices that are worse than the arrival price. On the other hand, if the algorithm/trader executes the trades slowly (to reduce market impact), the volatility of the stocks may still result in execution prices that are far from the arrival price. In short, a trader/algorithm s ability to achieve execution prices close to arrival prices depends on the size of the order, the liquidity available for the stock, and the volatility of the stock. 2 It is then appropriate to develop an algorithm that anticipates execution costs by looking at all these factors (order size, volume, volatility, and correlation). In fact, there are many such models that try to anticipate the cost of trading by factoring in these properties of trade-lists, including the ITG ACE (Agency Cost Estimator) model. However, the appeal of the VWAP benchmark in part lies in the fact that it does not require such cost predictions. VWAP vs. Arrival Price So how does arrival price based algorithms stack up against VWAP algorithms in execution? The following charts plot profit and loss (P&L) for VWAP benchmark (in VWAP algorithm-chart 1). It is apparent from the chart that the available liquidity (trade sizes/total volume) do not seem to have much impact on the algorithm s ability to meet VWAP The effect of liquidity on P&L Vs VWAP in VWAP algorithm A) 0-1% B) 1-5% C) 5-10% D) 10-25% E) 25-50% Trade size in % of volume P&L Vs VWAP in VWAP Algorithm Chart 1: VWAP- P&L vs. percentage of volume (Source: ITG) 2 In addition, stock correlations also have an impact on the difficulty of achieving arrival price, as later explained in the article.
3 Implementation Shortfall algorithms do not have the same luxury. For obvious reasons there is a clear impact of liquidity on the performance Vs Arrival Price on Implementation Shortfall algorithms,i.e., the performance degrades as the trade sizes grow relative to the available liquidity ( average trading volume). VWAP as Implementation Shortfall This article has discussed two important properties of VWAP algorithms so far--they are easily achievable and they are simple to measure. VWAP algorithms can also serve as good tools for arrival price or implementation shortfall (IS) algorithms, as long as the traders involved are not risk averse, as explained below. One of the objectives of implementation shortfall algorithms is to reduce the market impact of the trade. This requires the algorithm to minimize the demand and supply imbalance at any given point during the trading time horizon. For example, assume that one has a period of three hours in which to trade. During that time horizon, the volume distribution is one million shares, as follows: Average volume per hour: Hour 1: 300,000 Hour 2: 400,000 Hour 3: 300,000 Suppose that one has a trade for 100,000 shares. To reduce the market impact, one intuitively wants to minimize the demand/supply impact at any point. To do that we will have to trade as follows: Hour 1: 30,000 Demand/Supply: 10% Hour 2: 40,000 Demand/Supply: 10% Hour 3: 30,000 Demand/Supply: 10% In addition to being intuitive, this is exactly how a VWAP algorithm will trade during this period. Thus, if a portfolio manager asks a trader to use the VWAP benchmark, not only can he or she measure the trader s performance easily (by checking VWAP), he or she can also make sure that the trades have very low market impact. Below is a chart that plots the performance of VWAP and IS algorithms (in basis points away from arrival price) for trades with different liquidity requirements:
4 VWAP Algorithm performance vs. IS Algorithm performance for Arrival Price benchmark 2 P&L in VWAP - P&L in IS Algorithm A) 0-1% B) 1-5% C) 5-10% D) 10-25% E) 25-50% Trade size in % of volume Chart 3: Comparison of P&L (vs. Arrival Price) in VWAP algorithm & IS Algorithm plotted against percentage of volume. (Source: ITG) The chart explains that the performance in VWAP Algorithm is similar to the performance in Implementation Shortfall algorithm. One should also note that the demanded liquidity impacts both algorithms in a similar fashion. This explains in part why VWAP algorithms have remained so popular: they are easy to measure, easy to execute, and can produce similar results on average. However, this does not take into account the potential hidden costs of using a VWAP algorithm for implementation shortfall, as discussed below. Problems with VWAP algorithms for Implementation Shortfall 1) Potentially high opportunity costs Traders and Portfolio Managers care about average costs vs. arrival price, and they also care about the consistency of the cost. A passive algorithm such as VWAP can ensure a good average price vs. arrival price, but it cannot ensure consistency. To illustrate that, one can plot the standard deviation of the P&L vs. arrival price of trades against the percent of volume for VWAP and implementation shortfall algorithms.
5 Opportunity Cost -VWAP algorithm vs. IS algorithm A) 0-1% B) 1-5% C) 5-10% D) 10-25% E) 25-50% Trade size in % of volume Chart 4: Comparison of Standard deviation of cost (vs. Arrival Price) of VWAP algorithm & IS Algorithm plotted against percentage of volume. (Source: ITG) One can clearly see that VWAP does a poor job compared to implementation shortfall algorithms in terms of consistency of performance vs. arrival price as the trade size/volume goes down. As the trade size/volume rises above roughly 20% of ADV, the differences are not very significant. Example 1: One can understand this behavior by way of an example: suppose MSFT trades 30 million shares on an average day. If a trader has three million MSFT shares to trade, a VWAP algorithm may be appropriate. However, if the trader gets 30,000 shares of MSFT to trade, then the savings of market impact (by spreading the trade over the whole day) is not significant compared against the opportunity cost the trader could save by trading the stock within the next few minutes.
6 2) VWAP can be influenced It is possible to beat a VWAP benchmark, by conducting trades in a manner that may actually lead to increasing the trading impact. In general, any benchmark that has future price as a component can be influenced. Closing price and VWAP are examples of such benchmarks. VWAP in its simplest form can be written as: VWAP = Pt1.Vt1 + Pt2.Vt2 +.Pt(n).Vt(n) / Total volume Where Pt(n) is the volume weighted average price of interval n and Vt(n) is the volume in interval n. Average Price = APt1.St1 + APt2.St2 Apt(n).St(n) / Total shares If a trader overloads (buys/sells more than the volume distribution) in the first few bins (for example, t=1 and t=2), this will result in market impact and can affect prices in the later bins. Since the trader is frontloading the distribution, St1/Total shares and St2/Total shares will be higher than Vt1/Total shares and Vt2/Total shares. Similarly, Pt1 and Pt2 would be better than Pt3.Pt(n), and the average execution price would be better than the VWAP price. One can clearly see that even though a trader can beat the VWAP benchmark by influencing the VWAP itself, this will have a larger impact overall and will result in higher cost versus the arrival price. In our opinion, it is not sufficient to examine performance vs. VWAP alone. In evaluating a VWAP algorithm s performance, a portfolio manager or trader should also compare the algorithm s execution price to the arrival price. 3) Risk aversion can focus on VWAP rather than on Arrival Price Traders/VWAP algorithms may become risk averse to the VWAP benchmark and thus may not seize upon opportunities that could lead to a more optimal outcome, i.e. a reduction in implementation shortfall. For example, alternative trading systems such as POSIT allow a trader to execute shares with no market impact. But by doing so, an algorithm might deviate from the VWAP price, and it may not take that chance, even if it would be the optimal thing to do from an arrival price perspective. Thus, one can see that VWAP is often not an optimal strategy from an average performance perspective, even if a portfolio manager or a trader has very low risk aversion with regard to the arrival price.
7 Beyond VWAP Using Volume Participation Algorithms in Implementation Shortfall As discussed, VWAP algorithms suffer from high opportunity cost (in terms of standard deviation of execution price vs. arrival price) for trades that represent a low percentage of ADV. Use of a participation algorithm can help solve that problem. A participation algorithm is similar to a VWAP algorithm except that it uses a constant participation rate. So, if a trader believes that he or she can live with the impact that will be caused by 10% participation, then he or she can simply use a 10% participation algorithm. Returning to the MSFT example discussed above, if a 10% participation algorithm is used instead of VWAP, trading three million shares of MSFT (with 30 million shares average daily volume) will have results similar to using a VWAP algorithm. But, in contrast, shares of MSFT would be traded in a few minutes, with little deviation likely from the arrival price. Implementation Shortfall Average Cost and Opportunity Cost As discussed, implementation shortfall algorithms are mainly concerned with balancing market impact and opportunity cost. This section will examine the factors that influence market impact and opportunity cost. Average Cost: this article has discussed intuitive ways (Volume participation, VWAP) to reduce average costs. Volume participation is only a first order approximation of a cost-optimal strategy, and there are other factors that play a role in minimizing the demand and supply imbalance. Order type placement is one such factor. For instance, the spread and temporary price impact is higher at the beginning of the day because there is much more uncertainty of the future price of a security. Consequently, traders place more limit orders at the beginning of the day than towards the close. Liquidity suppliers are more careful early in the day, and often charge more or try to get a risk premium. An implementation shortfall algorithm should model these factors along with the volume distribution to determine an optimal trading distribution with minimum market impact. Opportunity Cost: Opportunity cost can be defined as the standard deviation of the trading cost. This is a function of trade distribution, stock volatility, volatility distributions and the correlation among stocks on a trade list over a given time horizon. By assuming normal distribution, traders can use opportunity cost and average cost numbers to determine various if-then scenarios. For example, traders can determine the probability of a strategy costing more than a certain number of basis points, or determine the worst-case scenario 95% of the time for a given strategy.
8 ITG Algorithms and Implementation Shortfall ITG algorithmic trading products ITG SmartServer and ITG HorizonPlus provide Implementation Shortfall algorithms that model all these factors and provide a trader with the solutions that have the least opportunity cost for a given trading cost and the least trading cost for a given opportunity cost. ITG HorizonPlus provides these options to a trader in the form of trading efficient frontier (described in the next section) so that a trader can choose his/her tradeoff easily. These algorithms adjust themselves by looking at real time conditions and thus make the best use of historical and real time information. These algorithms control risk implicitly during the optimization process. ITG HorizonPlus also provides many ways to explicity control the residual risk of the trading list during trading. Risk control becomes an important factor in reducing Implementation Shortfall as this article illustrates in the next few paragraphs. Implementation Shortfall - Trading Efficient Frontier If an algorithm trades on certain distributions over a given time horizon, then the average cost and standard deviation of that cost can be predicted. Many distributions can lead to the same execution cost. Each of these distributions will have a different standard deviation of the cost. For a given execution cost, an optimal distribution is the trading distribution that has the lowest standard deviation. Many such optimal distributions can yield pairs of costs and standard deviations and these can be plotted in a chart, (such as the one below): Chart 6: Efficient Frontier (Source: ITG)
9 Note that the trading efficient frontier is downward sloping -- the inverse of the efficient frontier, which one is accustomed to in modern portfolio theory. That is because this is not plotting risk vs. return, instead this is a plot of opportunity risk vs. average execution cost. The higher the opportunity cost, the lower the average execution cost. Implementation Shortfall and Risk Control In trading, risk control refers to keeping the risk of the unexecuted list low. Risk control has a very useful effect on implementation shortfall algorithms. This article has discussed the different components that affect the cost of trading and opportunity costs. Risk control generally reduces the opportunity cost of trading and thus provides a trader or an algorithm with an extended time horizon in which to trade. The extended time horizon is an opportunity to reduce the market impact of these trades, resulting in smaller overall trading costs. This is illustrated below for a two-sided (both buy and sell order) list as well as for a single-sided list. If one has a two-sided list and the two sides are highly correlated, then each side acts as a hedge for the other. Thus, even though the volatility of each stock will increase the opportunity risk, a strong and negative correlation (positive, but on different sides) will reduce the risk of keeping (not trading) those names. An algorithm can take advantage of this effect to increase the time horizon in which that trade list can be traded. Increasing the time horizon will lead to reduction of the cost. Thus, by controlling the risk, an algorithm can reduce the trading cost as well as the opportunity cost. The higher the correlation between the buy- and sell-side baskets, the lower the risk of the overall list and the longer the time horizon can be extended. To use another example: suppose one has a two-sided list where the buy side represents $30 million and the sell side basket represents $5 million, and both sides have very high correlation with each other. In this case, an implementation shortfall algorithm should front-load the buy-side basket and back load the sell-side basket. Front loading the buy-side basket will reduce the opportunity cost and back loading the sell-side basket will reduce the market impact as well as the opportunity cost (by hedging against the buys). The same concept applies if the list is single-sided -- an algorithm will determine a different optimal distribution if it is aware of the correlations of the stocks. For example, if there are multiple sectors, it may be important to keep the sector imbalance low at any point in time, because the correlation between the stocks from different sectors will be low.
10 Implementation Shortfall and Hedging With a single-sided list there are other ways to reduce the risk and thus increase the trading time horizon. One common way is through hedging, (if the trader can find a liquid instrument which tracks the trading list). The liquidity of this instrument is crucial, otherwise trading that instrument itself will result in undesirable market impact. For example, if a buy-side trade list has very low tracking error to the S&P 500, the trader can sell S&P 500 futures to hedge that list (provided that trading S&P 500 futures will have much less impact than trading each stock in the list). Once hedged, a trader can extend the time horizon of the list and start closing out the future positions as the algorithm trades the underlying list. In this case, a trader would want to specify the objective of keeping the tracking error against the S&P 500 to a minimum during the whole trading horizon. ITG algorithms provide mechanisms for traders to indicate such explicit risk objectives. Specifically, ITG ResRisk+ SM provides traders with a way to run such optimization scenarios manually. Risk Control: Explicit or Automatic In the section Implementation Shortfall and Risk Control it was observed that an algorithm could control risk automatically with an optimal distribution by looking at the stocks correlations and volatility. It was also observed in the section Implementation Shortfall and Hedging that a trader may want to provide explicit risk objectives when hedging with other instruments. There are other cases in which traders may want to specify explicit risk control objectives for an algorithm. Some common cases are as follows: 1) Instead of relying on the risk models used by an algorithm, some traders prefer more intuitive approaches to risk control. For example, traders might specify in their objectives that the algorithm should keep the entire list dollarneutral. Traders might additionally want to specify that the algorithm should keep the list dollar-neutral for a specific sector or sectors in the list. 2) Traders/PMs might want to control the risk of new holdings as a result of new executions. Tools such as ITG ResRisk+ SM provide traders and portfolio managers with the ability to control the risk of holdings while an algorithm is still executing the list.
11 Anonymous Liquidity and Risk Control: Anonymous block liquidity pools such as POSIT present some very useful options to traders and algorithms looking to reduce market impact. Unlike the liquidity available in the exchanges, POSIT s liquidity can be accessed without information leakage. Thus, it creates zero market impact, freeing the algorithm/trader to deal with trades that are a smaller percentage of daily volume. But if a trader or an algorithm is following one of the risk control methods discussed above, then they may question whether trading a particular stock will increase or reduce the overall risk of their list. A simple rule of thumb is that for a single-sided list, trading a stock will always reduce the overall risk of the list (unless the trader is hedging the list with another instrument). If the list is two-sided, trading a stock may increase or reduce the risk of the overall list. Both of these points are explained in Appendix 1. An algorithm or a trader should be able to determine if the reduction in cost for trading that stock will compensate for the potential increase in risk. For example, POSIT provides an interface that can be used to take advantage of such tradeoffs without letting an algorithm see the liquidity available inside. Note that when increases/decreases in risk are discussed, absolute terms (dollars) are used rather than percentages. Risk could go up percentage-wise and at the same time drop in dollar terms, because of a reduction in the number of shares remaining on the trade list. Risk Control and Number of Stocks: All the risk control techniques defined in this section are useful for a large trade list. As a general rule, one starts seeing the positive effects of risk control as the number of stocks on the list climbs above 20. Conclusion Various algorithms can be used to reduce implementation shortfall, from traditional ones such as VWAP and volume participation, to more sophisticated algorithms such as list-based or risk-based implementation shortfall algorithms. While VWAP and volume participation algorithms provide intuitive solutions, they lack certain properties that can be used to reduce the market impact and the opportunity costs of trading. An ideal implementation shortfall algorithm should model the optimal trade distribution by looking at the liquidity profile, trade sizes, volatility of stocks, volatility distributions of stocks, spread distribution of stocks, and especially the stock correlations. Risk control helps an algorithm increase the trade time
12 horizon and thus reduces the market impact while lowering the opportunity cost. For a risk-controlled list, traders can also hedge with liquid instruments, (as long as the algorithms they use permit them to specify explicit risk control objectives). Traders may also prefer specifying explicit risk control objectives if they favor an intuitive risk control approach over automatic optimizations. Finally, anonymous block liquidity sources such as POSIT can be extremely helpful in reducing implementation shortfall. The use of risk control may be helpful while accessing such liquidity, particularly in the trading of twosided lists. Notes: 1) The data displayed in the above charts are random samples from the database of ITG execution algorithms. Appendix 1. If a trade list is single-sided, then even though a stock may reduce the risk of the overall list in terms of percentage, it will always increase the risk in terms of dollars. We can illustrate this with a two-stock example, which can further be generalized for more than two stocks: Let us assume that in most cases, a correlation between two stocks is positive. Assume we have stocks S1 and S2 that have very low correlation of ρ12. The volatility of S1 and S2 are σ1 and σ2. We have D1 dollars of S1 to buy and D2 dollars of S2 to buy. The total risk of the list would be (D1 σ1) 2 + (D2 σ2) D1 D2 σ1 σ2 ρ12 Even if ρ12 is very low, since it is always positive, reducing D2 or reducing D1 will always reduce the overall risk of the list. If S2 is a stock to be sold then we can write the risk as (D1 σ 1) 2 + (D2 σ 2) 2-2 D1 D2 σ1 σ2 ρ12 In this case reducing D2 can either reduce the risk or increase the risk depending on D1, D2, σ1, σ2 and ρ12. For example if ρ12 is very low then reducing the dollar amount of any stock will always reduce the total risk but if it is very high then reducing the dollar amount of one stock only may actually increase the overall risk.
13 The information contained herein has been taken from trade and statistical services and other sources we deem reliable but we do not represent that such information is accurate or complete and it should not be relied upon as such. Any opinions expressed herein reflect our judgment at this date and are subject to change. All information, terms and pricing set forth here are indicative, based on among other things, market conditions at the time of this writing and are subject to change without notice. This report is for informational purposes and is neither an offer to sell nor a solicitation of an offer to buy any security or other financial instrument in any jurisdiction where such offer or solicitation would be illegal. No part of this report may be reproduced in any manner without permission. Past results are not necessarily indicative of future results. Futures trading is speculative and involves risk of substantial loss. 2005, ITG Inc. Member NASD, SIPC. All rights reserved "S&P 500 " is a registered trademark of McGraw-Hill Companies, Inc.
Measuring and Interpreting the Performance of Broker Algorithms Ian Domowitz Managing Director ITG Inc. 380 Madison Ave. New York, NY 10017 212 588 4000 email@example.com Henry Yegerman Director ITG
CONTRIBUTORS Jeff Bacidore Managing Director, Head of Algorithmic Trading, ITG, Inc. Jeff.Bacidore@itg.com +1.212.588.4327 Kathryn Berkow Quantitative Analyst, Algorithmic Trading, ITG, Inc. Kathryn.Berkow@itg.com
Algorithmic and advanced orders in SaxoTrader Summary This document describes the algorithmic and advanced orders types functionality in the new Trade Ticket in SaxoTrader. This functionality allows the
BECS Pre-Trade Analytics An Overview January 2010 Citi s Pre-Trade Analytical Products and Services Citi has a long history of providing advanced analytical tools to our clients. Significant effort has
Active Versus Passive Low-Volatility Investing Introduction ISSUE 3 October 013 Danny Meidan, Ph.D. (561) 775.1100 Low-volatility equity investing has gained quite a lot of interest and assets over the
The of Algorithmic Trading: A First Look at Comparative Performance Ian Domowitz Managing Director ITG Inc. 380 Madison Ave. New York, NY 10017 212 588 4000 firstname.lastname@example.org Henry Yegerman Director
algorithmic & program trading services Featuring the Abel/Noser Signature Algorithm Series Institutional Agency Execution and Trade Analytics algorithmic & program trading services * Abel/Noser has offered
Adaptive Arrival Price Julian Lorenz (ETH Zurich, Switzerland) Robert Almgren (Adjunct Professor, New York University) Algorithmic Trading 2008, London 07. 04. 2008 Outline Evolution of algorithmic trading
An introduction to measuring trading costs - TCA Ofir Gefen, Head of Research & Execution Consulting ITG Asia Pacific 2011 Investment Technology Group, Inc. All rights reserved. Not to be reproduced or
By: Michael DuCharme, CFA, Head of Foreign Exchange JUNE 2013 Does trading at the Fix fix FX? Foreign exchange transactions are significant components of millions of daily financial transactions, yet most
FTS Real Time System Project: Portfolio Diversification Note: this project requires use of Excel s Solver Question: How do you create a diversified stock portfolio? Advice given by most financial advisors
Answers to Concepts in Review 1. A portfolio is simply a collection of investments assembled to meet a common investment goal. An efficient portfolio is a portfolio offering the highest expected return
Concentrated Stock Overlay INCREMENTAL INCOME FROM CONCENTRATED WEALTH INTRODUCING RAMPART CONCENTRATED STOCK OVERLAY STRATEGY When a portfolio includes a concentrated equity position, the risk created
By: Michael DuCharme, CFA MAY 2013 Does Trading at the Fix fix FX? Introduction Foreign exchange transactions are significant components of millions of daily financial transactions, yet most currency transactions
May 2011 Investing in Projected Russell 2000 Stock Additions: Abstract Existing studies on the annual rebalancing of the Russell indexes utilize the actual additions and deletions of these indexes to draw
Pairs Trading Pairs trading refers to opposite positions in two different stocks or indices, that is, a long (bullish) position in one stock and another short (bearish) position in another stock. The objective
AMERICAN REGISTRY OF INTERNET NUMBERS INVESTMENT POLICY STATEMENT January 2014 Introduction This statement of investment policy has been adopted by the Board of Trustees of the American Registry of Internet
A Pared-Down Approach to Stock Picking That is Market-Neutral and Results-Driven Q1 2011 A white paper discussing how this strategy of market-neutral approaches delivers on its promise 1 A Pared-Down Approach
How to Win the Stock Market Game 1 Developing Short-Term Stock Trading Strategies by Vladimir Daragan PART 1 Table of Contents 1. Introduction 2. Comparison of trading strategies 3. Return per trade 4.
PURE ALGORITHMIC TRADING SOLUTIONS WHEN TACTICS OVERWHELM STRATEGY Technology and sophisticated trading tools help traders find the best trading venues. In a fragmented environment, however, traders risk
XVII. SECURITY PRICING AND SECURITY ANALYSIS IN AN EFFICIENT MARKET Consider the following somewhat simplified description of a typical analyst-investor's actions in making an investment decision. First,
ATMonitor Commentary July 2011 Issue Transaction Cost Analysis and Best Execution 10 things you wanted to know about TCA but hesitated to ask Foreword This is not an academic paper on theoretical discussions
HIGH FREQUENCY TRADING, ALGORITHMIC BUY-SIDE EXECUTION AND LINGUISTIC SYNTAX Dan dibartolomeo Northfield Information Services 2011 GOALS FOR THIS TALK Assert that buy-side investment organizations engage
AUTHORS Benjamin Polidore Managing Director Head of Algorithmic Trading email@example.com Lin Jiang Assistant Vice President ITG Algorithms firstname.lastname@example.org Yichu Li Analyst ITG Algorithms email@example.com
(212 ( Portfolio Strategy Trading Less Competitive Markets is Costly TRADING STRATEGY Market Commentary 13 August 2013 Key Points Despite complaints about US market structure, it remains among the cheapest
Investing on hope? Small Cap and Growth Investing! Aswath Damodaran Aswath Damodaran! 1! Who is a growth investor?! The Conventional definition: An investor who buys high price earnings ratio stocks or
SUMMARY CURRENCY-HEDGED INTERNATIONAL FIXED INCOME INVESTMENT In recent years, the management of risk in internationally diversified bond portfolios held by U.S. investors has been guided by the following
September 2011 Dual Listing of the SGX EURO STOXX 50 Index Futures in Singapore Mr Tobias Hekster Managing Director, True Partner Education Ltd Senior Strategist, Algorithmic Training Group of Hong Kong
Effective downside risk management Aymeric Forest, Fund Manager, Multi-Asset Investments November 2012 Since 2008, the desire to avoid significant portfolio losses has, more than ever, been at the front
OPTION PRICING Options are contingency contracts that specify payoffs if stock prices reach specified levels. A call option is the right to buy a stock at a specified price, X, called the strike price.
Life Cycle Asset Allocation A Suitable Approach for Defined Contribution Pension Plans Challenges for defined contribution plans While Eastern Europe is a prominent example of the importance of defined
Exchange-Traded Funds Exchange Traded Funds (ETF s) are becoming popular investment vehicles for many investors. Most ETF s are cost effective, broad market funds. We have put together a layman s explanation
Chapter 6 Market Risk for Single Trading Positions Market risk is the risk that the market value of trading positions will be adversely influenced by changes in prices and/or interest rates. For banks,
Hedging Strategies Using Futures Chapter 3 Fundamentals of Futures and Options Markets, 8th Ed, Ch3, Copyright John C. Hull 2013 1 The Nature of Derivatives A derivative is an instrument whose value depends
VWAP Strategies ANANTH MADHAVAN ANANTH MADHAVAN is managing director of research at ITG Inc., in New York City. It is common to evaluate the performance of traders by their ability to execute orders at
Goldman Sachs Electronic Trading India: Algorithmic Trading FIXGlobal Face2Face Electronic Trading Forum - India 11 Agenda Goldman Sachs Algorithmic Trading solutions available in India How to access the
GLOBAL AGENCY BROKERAGE PRE- AND POST- MARKET TRADING FOR US STOCKS: Are You Missing Out on Liquidity? By Kapil Phadnis and Shawn Chen Summary For U.S.-listed stocks, a significant percentage of the day
AUTHORS Jim Cochrane Director ITG TCA for FX firstname.lastname@example.org Ian Domowitz Managing Director Head of ITG Analytics email@example.com Milan Borkovec Managing Director Head of Financial Engineering
In this chapter, we present the theory of consumer preferences on risky outcomes. The theory is then applied to study the demand for insurance. Consider the following story. John wants to mail a package
Empirical Analysis of an Online Algorithm for Multiple Trading Problems Esther Mohr 1) Günter Schmidt 1+2) 1) Saarland University 2) University of Liechtenstein firstname.lastname@example.org email@example.com Abstract:
An Unconventional View of Trading Volume With all the talk about the decline in trading volume (over 30% on a year- over- year basis for most of the third quarter) you would think that no one is playing
Trade Execution Analysis Generated by Markit Global Liquidity Partners Execution Peer Review 2nd Quarter 2015 Contents S VT POV Report Summary Summarizes the trade execution document and illustrates the
Triton All the trading power of ITG, now on your desktop. Client-Site Trading Products Triton ITG is known as the leader in quantitative trading, with more extensive capabilities and expertise than any
The Benefits of Making Systematic Trade-Offs Between Risk and Return August 2015 By Jeremy Berman Justin Frankel Co-Portfolio Managers of the RiverPark Structural Alpha Fund The Benefits of Making Systematic
Section 1 - Overview; trading and leverage defined Download this in PDF format. In this lesson I am going to shift the focus from an investment orientation to trading. Although "option traders" receive
PRICE PERSPECTIVE In-depth analysis and insights to inform your decision-making. Target Date Glide Paths: BALANCING PLAN SPONSOR GOALS 1 EXECUTIVE SUMMARY We believe that target date portfolios are well
MPRA Munich Personal RePEc Archive Overlapping ETF: Pair trading between two gold stocks Peter N Bell and Brian Lui and Alex Brekke University of Victoria 1. April 2012 Online at http://mpra.ub.uni-muenchen.de/39534/
IIJ-130-STAUB.qxp 4/17/08 4:45 PM Page 1 RENATO STAUB is a senior assest allocation and risk analyst at UBS Global Asset Management in Zurich. firstname.lastname@example.org Deploying Alpha: A Strategy to Capture
Setting the Scene FIX the Enabler & Electronic Trading Topics Overview of FIX and connectivity Direct Market Access Algorithmic Trading Dark Pools and Smart Order Routing 2 10,000+ firms use FIX globally
Course #: Title Module 2 Option pricing in detail Topic 1: Influences on option prices - recap... 3 Which stock to buy?... 3 Intrinsic value and time value... 3 Influences on option premiums... 4 Option
Market Linked Certificates of Deposit This material was prepared by Wells Fargo Securities, LLC, a registered brokerdealer and separate non-bank affiliate of Wells Fargo & Company. This material is not
Concentration of Trading in S&P 500 Stocks Recent studies and news reports have noted an increase in the average correlation between stock returns in the U.S. markets over the last decade, especially for
STRATEGIES FOR USING ETFS Tax Loss Harvesting Investors can sell individual stock positions currently trading below purchase price, realize the loss and maintain similar exposure by purchasing the appropriate
ASSET ALLOCATION TO ISRAEL: STRENGTHENING YOUR PORTFOLIO BY INCLUDING ISRAELI GLOBAL EQUITIES May 2014 FOREWORD BlueStar Global Investors develops investment solutions and research on Israeli and Middle
Investment Research Index Volatility Futures in Asset Allocation: A Hedging Framework Jai Jacob, Portfolio Manager/Analyst, Lazard Asset Management Emma Rasiel, PhD, Assistant Professor of the Practice
The Role of Alternative Investments in a Diversified Investment Portfolio By Baird Private Wealth Management Introduction Traditional Investments Domestic Equity International Equity Taxable Fixed Income
Answers to Concepts in Review 1. Puts and calls are negotiable options issued in bearer form that allow the holder to sell (put) or buy (call) a stipulated amount of a specific security/financial asset,
EVOLUTION OF CANADIAN EQUITY MARKETS This paper is the first in a series aimed at examining the long-term impact of changes in Canada s equity market structure. Our hope is that this series can help inform
Best of Breed in Global Algorithmic Trading by Gary Ardell & William Capuzzi Published by THE TRADE Algorithmic trading & smart order routing - 3rd edition Best of breed in global algorithmic trading William
Foreign Exchange Investments Discover the World of Currencies Credit Suisse Securities (USA) llc Private Banking USA 2 Foreign exchange: There s no ignoring the largest market in the world. Introduction
APPENDIX 3 Investing in International Financial Markets http:// Visit http://money.cnn.com for current national and international market data and analyses. The trading of financial assets (such as stocks
FTS Real Time System Project: Using Options to Manage Price Risk Question: How can you manage price risk using options? Introduction The option Greeks provide measures of sensitivity to price and volatility
Stifel CONQUEST Portfolios PORTFOLIO STRATEGY The Washington Crossing Advisors Stifel CONQUEST Portfolios seek to add value by actively allocating assets among U.S. equities, bonds, commodities, and foreign
The Risk/Reward Properties of Bull Spreads ThE RiSk/REWaRD PRoPERTiES of BUll SPREaDS Bull Spreads are versatile instruments. They can be used to trade direction, to trade volatility or, in conjunction
Internet Appendix to Momentum, Reversal, and Uninformed Traders in Laboratory Markets * 1 I. Instructions for Experiment One Overview During this session, you will trade shares in 12 different securities
The Hidden Costs of Changing Indices Terrence Hendershott Haas School of Business, UC Berkeley Summary If a large amount of capital is linked to an index, changes to the index impact realized fund returns
Fixed Income Liquidity in a Rising Rate Environment 2 Executive Summary Ò Fixed income market liquidity has declined, causing greater concern about prospective liquidity in a potential broad market sell-off
CONTRIBUTORS J.R. Rieger Global Head Fixed Income Indices email@example.com Tyler Cling Senior Manager Fixed Income Indices firstname.lastname@example.org Bond laddering is a strategy that calls for maturity
How credit analysts view and use the financial statements Introduction Traditionally it is viewed that equity investment is high risk and bond investment low risk. Bondholders look at companies for creditworthiness,
Stock market simulation with ambient variables and multiple agents Paolo Giani Cei 0. General purposes The aim is representing a realistic scenario as a background of a complete and consistent stock market.
Corporate Risk Corporate Portfolio Management Capital allocation from a risk-return perspective Premise Aligning the right information with the right people to make effective corporate decisions is one
Closed-End Funds Understanding Leverage in Closed-End Funds The concept of leverage seems simple: borrowing money at a low cost and using it to seek higher returns on an investment. Leverage as it applies
Wealth Management Solutions Invest in the Future Life has significant moments. Making sure you re prepared for them is important. But what can you do when the pace of your life leaves you little time to
Protecting Your Investment Portfolio -an with Gold Bullion WORLD GOLD COUNCIL gold is an EFFECTIVE diversifier Gold s ability to serve as a diversifier is principally due to its negative correlation with
W W W. I I J O T. C O M OT F A L L 2 0 1 0 V O L U M E 5 N U M B E R 4 Transaction Cost Analysis to Optimize Trading Strategies CARLA GOMES AND HENRI WAELBROECK Sponsored by: Goldman Sachs Morgan Stanley
Finance 400 A. Penati - G. Pennacchi Market Micro-Structure: Notes on the Kyle Model These notes consider the single-period model in Kyle (1985) Continuous Auctions and Insider Trading, Econometrica 15,
Yield Curve Basics The yield curve, a graph that depicts the relationship between bond yields and maturities, is an important tool in fixed-income investing. Investors use the yield curve as a reference
Journal of Financial s 12 (2009) 418 437 www.elsevier.com/locate/finmar Measures of implicit trading costs and buy sell asymmetry Gang Hu Babson College, 121 Tomasso Hall, Babson Park, MA 02457, USA Available
Ta t i a n a L o z o v a i a a n d H e l e n H i z h n i a k o v a HOW TO EXTEND MODERN PORTFOLIO THEORY TO MAKE MONEY FROM TRADING EQUITY OPTIONS HOW TO READ DISPERSION NUMBERS, OR MARKET IS THE BIGGEST
Fall 11 The Bond Market: Where the Customers Still Have No Yachts Quantifying the markup paid by retail investors in the bond market. Darrin DeCosta Robert W. Del Vicario Matthew J. Patterson www.bulletshares.com
Challenges to Target Funds James Breen, President, Horizon Fiduciary Services Since their invention in the early 1990 s Target Date Funds have become a very popular investment vehicle, particularly with
Using Currency Futures to Hedge Currency Risk By Sayee Srinivasan & Steven Youngren Product Research & Development Chicago Mercantile Exchange Inc. Introduction Investment professionals face a tough climate.