How To Model Volume On A Stock With A Trading Model
|
|
- Peregrine Scott
- 3 years ago
- Views:
Transcription
1 AUTHORS Benjamin Polidore Managing Director Head of Algorithmic Trading Lin Jiang Assistant Vice President ITG Algorithms Yichu Li Analyst ITG Algorithms CONTACT Asia Pacific Canada EMEA United States A Better Way to Trade Small Caps The Power of Volume Volatility in Algorithm Design ABSTRACT The goal of this research was to study methods of altering the standard approach to such that it respects stock-specific volume volatility. The early returns are promising, and we think this concept can be applied to other algorithms where inappropriately tight constraints create excess cost. In this paper, we review the state of the art for volume forecasting and how these efforts are rewarded. We show the results of a random trial of orders that use a static tolerance around the target schedule vs. orders that use a tolerance that is set by the volume volatility of the stock. The results show less aggressive trading. We also argue that traders shouldn t choose algorithms based on stock characteristics. Instead, algorithm choice should focus on the tradeoff between cost and timing risk. INTRODUCTION Volume forecasts are an important input to institutional trading, and although forecasts are still improving, there is a perception that this is a settled science. The industry does a reasonable job at forecasting the forecastable : the level and shape of volume for most stocks. Methods as simple as a moving average (or median) do an acceptable job forecasting both the level and shape of volume throughout a trading day, but most firms (including ITG) have invested in more complex approaches to establish incrementally better fit. Intra-day, some level of blended extrapolation works well to forecast today s volume given new information. So, we have these forecasts, and we use them! We have algorithms, lookaheads on Volume Participation algorithms, risk checks and more. Perhaps we use them too confidently? Isn t there some uncertainty here that should be addressed in trading strategies, especially algorithms? In this paper, we will review volume profiles and the science of forecasting them. We will discuss how little fit even a very sophisticated method can achieve out of sample and the implications of this challenge to trading performance. Our findings are surprising: we maintain that the second moment the variability of the estimate of the volume profile matters more to trading performance than the first moment the estimate itself. Given this insight, we make recommendations for algorithm design that improve strategies especially which are dependent on volume profile forecasts.
2 2 THE FIRST MOMENT Multi-day moving averages/medians create robust estimates of a volume profile for the most liquid stocks. The data gets sparse for illiquid stocks, but we can cluster similar stocks to improve robustness of the estimate. This is the approach ITG takes: use k-means clustering to find similar stocks and use the moving median of those clustered stocks to generate a volume profile. In our experience, this method works better than regression and other factor-based estimates of volume instead of intuiting the factors that influence the volume profile, let the data speak for itself. Here are the results: FIGURE MSE K-Means Beat Hist. Avg Hist. Avg K-Means Overall 3.2% 2.4% 89.8% Micro Cap 3.6% 28.7% 94.% Small Cap 8.4% 8.% 9.% Mid Cap 3.3% 3.2% 88.% Large Cap.4%.3% 8.5% Figure compares the mean squared error of a simple moving average with the k-mean clustering method on out of sample data. In the furthest right column, we tally the number of wins / losses for the k-means method. K-means clearly dominates a moving average approach to estimating the volume profile. It s intuitive that an improved volume profile estimate would improve performance of algorithms, in particular,. But performance defined how? One of the interesting findings in our research is that incremental improvements to volume profile estimates only improve (reduce) the variability of performance, not the average performance. We measure this with ITG s decomposition methodology in which we attribute performance to profile misfit, algorithm discretion (i.e., trading differently than profile) and price improvement. Over a large sample, profile misfit and discretion average out to near zero, which suggests performance is a spread capture and adverse selection game. Here are the results of our decomposition on our strategy. This algorithm uses a very poor volume profile by design, and it doesn t really matter. Time Weighted Average Price. This algorithm uses a flat trading schedule to ensure nearly equal trading in each time period from start to end. Since volume profiles are almost universally curved with a bias toward the morning and afternoon in the U.S., this is a bad estimate of volume. Of course, algorithms are not trying to fit the volume, but this suits the purpose of our experiment by giving us a large sample of orders with a schedule that didn t fit the volume to see how this affects performance.
3 3 FIGURE 2 Decomposition_ Vs / 25/2 25/3 25/4 25/5 25/6 profilebps deviationbps pricingbps Figure 2 shows the components of performance: ) profile: the performance attributed to error in the profile estimate; 2) deviation: the performance attributed to algorithm discretion; 3) pricing: the performance attributed to short-term pricing vs. the market With these results in mind, how do we improve average performance? Since discretion and profile misfit seem to average to zero on a large sample, it seems that we should use those levers to try to secure additional price improvement 2, which should improve average performance. This gets a little confusing, so here it is graphically: FIGURE 3 Improvements to Profile Estimate Moments Lead to Improvements in Moments First (expected value) First (expected value) Second (variability) Second (variability) Figure 3 This chart shows how changes in the profile estimate affect the performance of algorithms. It seems a bit unintuitive, but think of it in terms of a cost/risk tradeoff: lower risk in the expected outcome should cost more; taking higher risk against a benchmark should be rewarded with lower cost (or, higher risk leads to higher returns and the converse in a Markowitz sense). 2 When we say price improvement, we really mean your price vs. the market over a short period of time. This could also be read as spread capture, but adverse selection is important as well as you will see later, so we wanted to use a more general term in price improvement here.
4 4 THE SECOND MOMENT In trading, smooth processes are expensive processes. It s fun to imagine the proud researcher, having reduced the error of his volume profile model by a few digits, configuring algorithms to trade even tighter to the model because, well, it s better now statistically. Economically? This researcher would benefit from Figure 3 above. Here is a chart of the distribution of Bank of America s (BAC) volume profile outcomes: FIGURE 4 Distribution of Bank of America s (BAC) Volume Profile Outcomes Cumulative Perc. of Volume Single Day Average Time in Minutes Figure 4 deciles of cumulative volume for BAC (approximate ADV: 8,,) demonstrate fairly wide deviation even for one of the most liquid stocks in the U.S. This is a very liquid (smooth?) stock, and you can see the challenge in modeling a given day s volume profile with any accuracy. Now, let s look at an illiquid stock: FIGURE 5 Deciles of Cumulative Volume for AAOI Cumulative Perc. of Volume Single Day Average Time in Minutes Figure 5 deciles of cumulative volume for AAOI (approximate ADV: 325,) demonstrate wide variation from any central tendency.
5 5 It looks pretty messy! But it s consistently messy, so you can forecast the expected error with robustness. 3 Once you have this estimate, you can use it in an algorithm. In the below chart we show the amount of aggressive trading required to satisfy orders with different levels of volume volatility. In the sample, the algorithm was configured with the same constraint 4 in all groups. We also plot our recommended constraint for the different volatility groups, which is the median difference of volume profiles from their expected value. FIGURE 6 Aggressive % and Median Deviance 4% 35% 3% 25% 2% 5% % 5% % Aggressive MdnDev as %DV Figure 6 plots deciles of stocks based on their median deviation from their average profile. It overlays the amount of aggressive trading realized using a 2% constraint on ahead/behind schedule. At the 5th percentile, you can see that median deviation is about 6% from the average profile and aggressive trading jumps to %. There is a clear correlation between aggressive trading and higher deviation from the average volume profile. As you can see, when a uniform threshold is used in a algorithm across stocks with varying levels of volume volatility, you have to make up the difference with aggressive trading. Another way to say that is you have to purchase the liquidity services of market makers. 3 We used the stock specific distributions since there was plenty of data available, but we did construct a regression to get a feel for the correlations of various fundamental factors with volume volatility. Volume volatility is positively correlated with spread and negatively correlated with volume, which is intuitive. 4 What we mean by constraint is the amount of discretion the algorithm was able to use to trade faster or slower than the target schedule to secure passive fills. This is explained in greater detail in Figures 7 and 8.
6 6 Imagine a stylized stock that trades in units of, shares with a random delay between each execution. Now, imagine an algorithm trading with a very smooth volume profile with tight tolerances. It might look like this: FIGURE 7 Constrained Order Shares Executed :3 : :3 : :3 2: 2:3 3: 3:3 4: 4:3 5: 5:3 6: Time Cumulative Percent Volume and Cumulative Schedule Cumulative Mkt Volume Passive Constrained Aggressive Constrained Cumulative Target Schedule Figure 7 stylized order doesn t allow the algorithm discretion to trade more or less than the targeted schedule for a given 3 minute bin (orange line). The realized volume (grey area) is not nearly as smooth as the target. The algorithm does achieve passive fills (green bars) when volume is available, but not enough to sustain it over dry spells where the difference is made up with aggressive trading (magenta bars). You can see the problem: the algo doesn t fill enough when volume is available and is too impatient when things are quiet. Each mistake, under trading and over trading, comes at a cost. In fact, multiple costs:. Spread 2. Information leakage 5 3. Impact 5 Information leakage is the risk that the market will detect institutional liquidity demand in a name and move away from that order ahead of its execution. The difference between information leakage and impact is a matter of timing. Information leakage happens before much of the order is executed and impact happens as the order trades. They are difficult to untie, but information leakage is considered worse than impact because it is not a tradeoff between fulfilled demand and higher prices; it is someone trading ahead of your order. In our opinion, the best way to create information leakage is to pay the spread to market makers in a consistent, predictable manner, which is the behavior you see in Figure 7.
7 7 Would any volume profile model improve the outcome for such an order? We argue that the only way to improve the performance outcome for such a stock is to increase the tolerances to achieve an outcome more like this stylized unconstrained order: FIGURE 8 Unconstrained Order Shares Executed :3 : :3 : :3 2: 2:3 3: 3:3 4: 4:3 5: 5:3 6: Time Cumulative Mkt Volume Passive Unconstrained Cumulative Target Schedule Cumulative Percent Volume and Cumulative Schedule Figure 8 stylized order has no constraints except that the order must finish by end time. It uses the same realized market volume and target schedule as in Figure 7, but it is able to fulfill all of its liquidity needs with passive trading by trading more than scheduled when volume is available and less when volume isn t available. That said, widening tolerances increases the risk of adverse selection. Let s try to better understand this tradeoff. ADVERSE SELECTION Since most algorithms are price takers, passive trading behavior leads to some degree of adverse selection. We find that adverse selection has a fairly flat curve for a given stock. That is, unlike a cost model which has logarithmic growth based on order size, an adverse selection model has almost no slope. 6 In general, this means that reducing impact dominates risk of an increase in adverse selection, but there is no general rule for trading! Liquid stocks have very low transaction costs and fairly high relative adverse selection risk. In other words, for very liquid stocks, it s best to avoid deviating much from the volume profile because of the risk of adverse selection. 6 This might not be an entirely fair way to describe the true economic cost of adverse selection since AS is measured at the execution level and implementation shortfall is measured at the parent order level. The sum of adverse selection should directly affect implementation shortfall, but trying to come up with the precise cumulative cost of all of the executions in an order is beyond the scope of this research paper. For this reason, we only represent adverse selection and implementation shortfall as a ratio rather than some kind of economic equilibrium.
8 8 FIGURE 9 ACE Vs. advsel 8% 7% 6% 5% 4% 3% 2% % % A) - 2K B) 2-5K C) 5 - K D) - K E) K - M A) - 2K B) 2-5K C) 5 - K D) - K E) K - M A) - 2K B) 2-5K C) 5 - K D) - K E) K - M A) - 2K B) 2-5K C) 5 - K D) - K E) K - M A) > 4M B) - 4M C).5 - M D) <.5M AS/ACE% Figure 9 shows the ratio of adverse selection to ITG s ACE pre-trade cost estimate. Higher ratios represent a low payoff for discretionary trading. Larger orders or orders for less liquid stocks have lower ratios, which should encourage one to use discretionary trading on such orders. As you can see, for illiquid stocks, the cost savings of reducing impact in this case through trading more opportunistically dominate increased adverse selection costs. The picture is not so clear for liquid stocks. Luckily, volume patterns align with the adverse selection / cost tradeoff. That is, liquid stocks have much lower volume volatility, so it s not necessary or prudent to use too much discretion on liquid stocks. For illiquid stocks, it makes sense to use more discretion due to increased volume volatility and higher expected cost relative to adverse selection. PERFORMANCE: MUST YOU TRADE RIGHT THIS SECOND? Using the median difference 7 from the expected profile (a measure of volume volatility) as the tolerance for our algorithm instead of a fixed tolerance has reduced cost and increased passivity for stocks with higher volume volatility. In the below chart, we demonstrate this through a random trial of statically constrained (control group) orders and orders constrained by the median difference (experimental group). 7 We used the median difference from the expected value instead of something like standard deviation because the population of volume profile outcomes is not normally distributed. If you use a standard deviation, you end up with very large, impractical values.
9 9 The results are promising: FIGURE Agg% for Different Volume Volatility Bucket % 9% 8% 7% 6% 5% 4% 3% 2% % % A) <3% B) >=3% A) <3% B) >=3% Avg of Control Groups Experimental Group Figure shows the proportion of aggressive executions required to fill orders in various groups. The blue box and whisker represents the control group which used a fixed constraint of 2% vs. the schedule. The magenta diamonds represent the realized average for the experimental group that used median deviation from profile as the discretionary constraint. The results are broken out by algorithm ( and ) and by the median deviation for stocks. Those stocks that have low median deviations (i.e. median deviations smaller than 3% as group A) from the expected profile don t show much of a performance improvement because the current fixed 2% was close enough to the modeled value, but those stocks with a median deviation of greater than 3% (group B) show a material reduction in the proportion of aggressive fills required to fill an order. This small change to our algorithm is representative of a general source of cost for institutions: many algorithms are configured to trade very tightly to smoothly modeled inputs like volume profiles or risk optimizing schedules. We see this blithe assumption of smoothness as the primary revenue source for a segment of market makers. This isn t always bad: market makers spread out risk from a wide cross section of market participants when they re at their most useful. But if your trading patterns allow you to internalize (economically, not the dark pool kind!) this risk, you can internalize a share of the profits earned by market makers as well.
10 CONCLUSION When people talk about small cap algorithms, what they re really talking about is algorithms that handle volume volatility properly. We don t like the idea of traders choosing algorithms based on stock characteristics; in a perfect world, algorithm choice should be solely about the tradeoff between impact and timing risk. Unfortunately, is usually very expensive for small cap stocks because it trades a profile that is not representative of any single day of a small cap stock s life. The goal of this research was to study methods of altering the standard approach to such that it respects stock-specific volume volatility. The early returns are promising, and this concept can be applied to other algorithms where inappropriately tight constraints create excess cost. 25 Investment Technology Group, Inc. All rights reserved. Not to be reproduced or retransmitted without permission These materials are for informational purposes only, and are not intended to be used for trading or investment purposes or as an offer to sell or the solicitation of an offer to buy any security or financial product. 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. No guarantee or warranty is made as to the reasonableness of the assumptions or the accuracy of the models or market data used by ITG or the actual results that may be achieved. These materials do not provide any form of advice (investment, tax or legal). ITG Inc. is not a registered investment adviser and does not provide investment advice or recommendations to buy or sell securities, to hire any investment adviser or to pursue any investment or trading strategy. All functionality described herein is subject to change without notice. Broker-dealer products and services are offered by: in the U.S., ITG Inc., member FINRA, SIPC; in Canada, ITG Canada Corp., member Canadian Investor Protection Fund ( CIPF ) and Investment Industry Regulatory Organization of Canada ( IIROC ); in Europe, Investment Technology Group Limited, registered in Ireland No ( ITGL ) and/or Investment Technology Group Europe Limited, registered in Ireland No ( ITGEL ) (the registered office of ITGL and ITGEL is Block A, Georges Quay, Dublin 2, Ireland). ITGL and ITGEL are authorized and regulated by the Central Bank of Ireland; in Asia, ITG Hong Kong Limited (SFC License No. AHD8), ITG Singapore Pte Limited (CMS License No. 38-), and ITG Australia Limited (AFS License No ). All of the above entities are subsidiaries of Investment Technology Group, Inc. MATCH NowSM is a product offering of TriAct Canada Marketplace LP ( TriAct ), member CIPF and IIROC. TriAct is a wholly owned subsidiary of ITG Canada Corp.
Volatility Series Trading Halts
AUTHORS Ben Polidore Managing Director Head of ITG Algorithms ben.polidore@itg.com Philip Pearson, CFA Director ITG Algorithms philip.pearson@itg.com CONTACT Asia Pacific +852.2846.3500 Canada +1.416.874.0900
More informationPairs Trading Algorithms in Equities Markets 1
AUTHORs Di Wu Vice President, Algorithmic Trading di.wu@itg.com Kenny Doerr Director, Electronic Trading Desk kenny.doerr@itg.com Cindy Y. Yang Quantitative Analyst, Algorithmic Trading yu.yang@itg.com
More informationTrading Around the Close
AUTHORs Jeff Bacidore Managing Director, Head of Algorithmic Trading jeff.bacidore@itg.com Ben Polidore Director, Algorithmic Trading ben.polidore@itg.com Wenjie Xu Quantitative Analyst, Algorithmic Trading
More informationBig Data, Big Decisions The coming sea change in technology investments
25 March 2014 Volume 5 Issue 2 The Blotter presents ITG s insights on complex global market structure, technology, and policy issues. Big Data, Big Decisions The coming sea change in technology investments
More informationThe Scope of the Market for ETFs
15 June 212 Volume 3 Issue 14 The Blotter presents ITG s insights on complex global market structure, technology, and policy issues. CONTRIBUTORS Ian Domowitz Ian.Domowitz@itg.com 1.212.444.6279 Kumar
More informationCluster Analysis for Evaluating Trading Strategies 1
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
More informationMeasuring the Trading Activity at the Open and Close Auctions Around the Globe
3 June 2014 Volume 5 Issue 6 The Blotter presents ITG s insights on complex global market structure, technology, and policy issues. Measuring the Trading Activity at the Open and Close Auctions Around
More informationGarbage In, Garbage Out: An Optical Tour of the Role of Strategy in Venue Analysis
AUTHORS Ian Domowitz Managing Director Head of Analytics Ian.Domowitz@itg.com Kristi Reitnauer, Assistant Vice President Analytics Research Kristi.Reitnauer@itg.com Colleen Ruane Director Analytics Research
More informationSubtitle. Doug Clark, Managing Director Liquidity Research ITG Canada Bank of Canada Market Structure Conference 10/25/2012
Doug Clark, Managing Director Liquidity Research ITG Canada Bank of Canada Market Structure Conference 10/25/2012 1 Let s Remove the Emotion from the Debate Historic Canadian Highway Stats 4000 250000
More informationThe Need for Speed: It s Important, Even for VWAP Strategies
Market Insights The Need for Speed: It s Important, Even for VWAP Strategies November 201 by Phil Mackintosh CONTENTS Speed benefits passive investors too 2 Speed helps a market maker 3 Speed improves
More informationITG Smart Limit Algorithm Low latency algorithm for passive trading
uthors Jeff acidore Managing Director, Head of lgorithmic Trading Rana Goyal Vice President, lgorithmic Trading en Polidore Managing Director, lgorithmic Trading lak Vasa Director, lgorithmic Trading indy
More informationAn introduction to measuring trading costs - TCA
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
More informationAlgorithmic and advanced orders in SaxoTrader
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
More informationMeasuring and Interpreting the Performance of Broker Algorithms
Measuring and Interpreting the Performance of Broker Algorithms Ian Domowitz Managing Director ITG Inc. 380 Madison Ave. New York, NY 10017 212 588 4000 idomowitz@itginc.com Henry Yegerman Director ITG
More informationImplementation Shortfall One Objective, Many Algorithms
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
More informationAdjusting for Size Liquidity and Risk Effects in Foreign Exchange Trading
AUTHORS Jim Cochrane Director ITG TCA for FX jim.cochrane@itg.com Ian Domowitz Managing Director Head of ITG Analytics ian.domowitz@itg.com Milan Borkovec Managing Director Head of Financial Engineering
More informationWhat Can Multi-Asset TCA Learn from the Equity Experience?
AUTHOR Yossi Brandes Managing Director yossi.brandes@itg.com Ian Domowitz Managing Director ian.domowitz@itg.com contact Asia Pacific +852.2846.3500 Canada +1.416.874.0900 EMEA +44.20.7670.4000 United
More informationTransaction Cost Analysis and Best Execution
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
More informationTransaction Cost Analysis to Optimize Trading Strategies
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
More informationDoes trading at the Fix fix FX?
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
More informationDoes Trading at the Fix fix FX?
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
More informationPRE- AND POST- MARKET TRADING FOR US STOCKS: Are You Missing Out on Liquidity?
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
More informationG100 VIEWS HIGH FREQUENCY TRADING. Group of 100
G100 VIEWS ON HIGH FREQUENCY TRADING DECEMBER 2012 -1- Over the last few years there has been a marked increase in media and regulatory scrutiny of high frequency trading ("HFT") in Australia. HFT, a subset
More informationPortfolio Strategy. Trading Less Competitive Markets is Costly
(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
More informationPerformance of pairs trading on the S&P 500 index
Performance of pairs trading on the S&P 500 index By: Emiel Verhaert, studentnr: 348122 Supervisor: Dick van Dijk Abstract Until now, nearly all papers focused on pairs trading have just implemented the
More informationInvesting in Projected Russell 2000 Stock Additions: A Viable Investment Strategy or a Loser s Game?
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
More informationGoldman Sachs Electronic Trading India: Algorithmic Trading. FIXGlobal Face2Face Electronic Trading Forum - India
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
More informationWhy Are Institutional Investors Missing the International Small Cap Opportunity?
Why Are Institutional Investors Missing the International Small Cap Opportunity? October 2011 Chris Tessin, CFA ctessin@acuitasinvestments.com Dennis Jensen, CFA djensen@acuitasinvestments.com Brian Stoner,
More informationFxPro Education. Automated trading
FxPro Education More than a quarter of trades with FxPro are executed automatically by computer programs. These types of trades are known by many terms, including expert advisors, algos and robots. Many
More informationRESP Investment Strategies
RESP Investment Strategies Registered Education Savings Plans (RESP): Must Try Harder Graham Westmacott CFA Portfolio Manager PWL CAPITAL INC. Waterloo, Ontario August 2014 This report was written by Graham
More informationAlgorithmic Trading Session 1 Introduction. Oliver Steinki, CFA, FRM
Algorithmic Trading Session 1 Introduction Oliver Steinki, CFA, FRM Outline An Introduction to Algorithmic Trading Definition, Research Areas, Relevance and Applications General Trading Overview Goals
More informationSeparately managed accounts
FOR INSTITUTIONAL/WHOLESALE/PROFESSIONAL CLIENTS AND QUALIFIED INVESTORS ONLY - NOT FOR RETAIL USE OR DISTRIBUTION Separately managed accounts A J.P. Morgan Global Liquidity solution Separately managed
More informationalgorithmic & program trading services
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
More informationEXPLORING SEPARATE ACCOUNTS
EXPLORING SEPARATE ACCOUNTS FOR INSTITUTIONAL AND FINANCIAL PROFESSIONAL USE ONLY SEPARATE ACCOUNTS In today s investment environment, where risks remain and meaningful returns on cash are hard to come
More informationFUNDAMENTALS. an introduction to ALTERNATIVES [1]
ALTEGRIS ACADEMY FUNDAMENTALS an introduction to ALTERNATIVES [1] Important Risk Disclosure Alternative investments involve a high degree of risk and can be illiquid due to restrictions on transfer and
More informationMixed Results During a Challenging Year
6 January 2009 Short-Term Technical FX Global FX Strategy George Davis, CMT Chief Technical Analyst Dominion Securities Inc. +1 416 842 6633 george.davis@rbccm.com Mixed Results During a Challenging Year
More informationOpportunities for Optimism? A New Vision for Value in Asset Management
Opportunities for Optimism? A New Vision for Value in Asset Management Featuring the findings of the 2015 State Street Asset Manager Survey Opportunities for Optimism? A New Vision for Value in Asset Management
More informationI.e., the return per dollar from investing in the shares from time 0 to time 1,
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,
More informationEVOLUTION OF CANADIAN EQUITY MARKETS
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
More informationFrom Particles To Electronic Trading. Simon Bevan
From Particles To Electronic Trading Simon Bevan May 13th, 2015 Introduction For the first few slides we will aim to give you a feeling of what high frequency trading means and the arguments for and against
More informationInvestment Philosophy
Investment Philosophy Our unique approach to investment management puts you at the heart of everything we do Wealth and Investment Management Discover a new side to your personality: your investment self
More informationDELTA Dashboards Visualise, Analyse and Monitor kdb+ Datasets with Delta Dashboards
Delta Dashboards is a powerful, real-time presentation layer for the market-leading kdb+ database technology. They provide rich visualisation of both real-time streaming data and highly optimised polled
More informationThe Hidden Costs of Changing Indices
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
More informationPricing and Strategy for Muni BMA Swaps
J.P. Morgan Management Municipal Strategy Note BMA Basis Swaps: Can be used to trade the relative value of Libor against short maturity tax exempt bonds. Imply future tax rates and can be used to take
More informationA More Informed Picture of Market Liquidity In the U.S. Corporate Bond Market
MARKETAXESS ON MARKETAXESS BID-ASK SPREAD INDEX (BASI) A More Informed Picture of Market Liquidity In the U.S. Corporate Bond Market MARKETAXESS ON MARKETAXESS ON is a forum for thought leadership and
More informationFinancial Markets. Itay Goldstein. Wharton School, University of Pennsylvania
Financial Markets Itay Goldstein Wharton School, University of Pennsylvania 1 Trading and Price Formation This line of the literature analyzes the formation of prices in financial markets in a setting
More informationITG ACE Agency Cost Estimator: A Model Description
ITG ACE Agency Cost Estimator: A Model Description Investment Technology Group, Inc. Financial Engineering Group October 31, 2007 2007 Investment Technology Group, Inc. All rights reserved. Not to be reproduced
More informationFigure 1: Lower volatility does not necessarily mean better returns. Asset A Asset B. Return
November 21 For professional investors and advisers only. Is volatility risk? After a long period without significant market upheavals, investors might be forgiven for pushing volatility down their list
More informationInternational Securities Exchange Whitepaper on. Dividend Trade Strategies in the U.S. Options Industry
International Securities Exchange Whitepaper on Dividend Trade Strategies in the U.S. Options Industry March 2010 Dividend Trade Strategies in the U.S. Options Industry Key Terms Assignment Notification
More informationFasano Associates 7 th Annual Conference. Longevity Risk Protection. Cormac Treanor Vice President Wilton Re. October 18, 2010
Fasano Associates 7 th Annual Conference Longevity Risk Protection October 18, 2010 Cormac Treanor Vice President Wilton Re Focus of Presentation Extension Risk Protection in the Senior Life Settlements
More informationUnderstanding Fixed Income
Understanding Fixed Income 2014 AMP Capital Investors Limited ABN 59 001 777 591 AFSL 232497 Understanding Fixed Income About fixed income at AMP Capital Our global presence helps us deliver outstanding
More informationAsia-Pacific Market Hours Changes: A Distraction?
March 22, 2011 Asia-Pacific Market Hours Changes: A Distraction? Key Takeaways While the increase in total trading hours in Hong Kong, Japan and Singapore is being touted as a means to respond globalization,
More informationFINANCIAL ECONOMICS OPTION PRICING
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.
More informationExecution Costs. Post-trade reporting. December 17, 2008 Robert Almgren / Encyclopedia of Quantitative Finance Execution Costs 1
December 17, 2008 Robert Almgren / Encyclopedia of Quantitative Finance Execution Costs 1 Execution Costs Execution costs are the difference in value between an ideal trade and what was actually done.
More informationAlternative Trading Systems in Europe Trading Performance by European Venues Post-MiFID
May 2010 Alternative Trading Systems in Europe Trading Performance by European Venues Post-MiFID Abstract We analyze a sample of trading activity in Europe, spanning primary exchanges, dark pools, and
More informationPROTECTING YOUR PORTFOLIO WITH BONDS
Your Global Investment Authority PROTECTING YOUR PORTFOLIO WITH BONDS Bond strategies for an evolving market Market uncertainty has left many investors wondering how to protect their portfolios during
More informationMiFID II, Research Unbundling, and What it Means for You
AUTHORS Jack Pollina Managing Director Head of Global Commission Management and Hedge Fund Business Development jack.pollina@itg.com CONTACT Asia Pacific +852.2846.3500 Canada +1.416.874.0900 EMEA +44.20.7670.4000
More informationRethinking Fixed Income
Rethinking Fixed Income Challenging Conventional Wisdom May 2013 Risk. Reinsurance. Human Resources. Rethinking Fixed Income: Challenging Conventional Wisdom With US Treasury interest rates at, or near,
More informationAuctions (Opening and Close) in NYSE and NASDAQ
ITG Primer Auctions (Opening and Close) in NYSE and NASDAQ 2009 Investment Technology Group, Inc. All rights reserved. Not to be reproduced or retransmitted without permission. Broker-dealer products and
More informationInvesting on hope? Small Cap and Growth Investing!
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
More informationThe newest technology - unlike any other indicator How and why the Nielsen Indicators can help make you a better trader
The newest technology - unlike any other indicator How and why the Nielsen Indicators can help make you a better trader - 1 - Table of Contents THE LATEST DEVELOPMENT IN ANALYSIS CAN GIVE YOU A REAL ADVANTAGE...
More informationMLC MasterKey Unit Trust Product Disclosure Statement (PDS)
MLC MasterKey Unit Trust Product Disclosure Statement (PDS) Preparation date 1 July 2014 Issued by MLC Investments Limited (MLC) ABN 30 002 641 661 AFSL 230705 This information is general and doesn t take
More informationINDEX & ETF ASSET MANAGEMENT
INDEX & ETF ASSET MANAGEMENT Contact: (925) 594-5001 george.madrigal@penserra.com (916) 730-2065 dustin.lewellyn@penserra.com (925) 594-5026 ernesto.tong@penserra.com The investment professionals at Penserra
More informationEvaluating Trading Systems By John Ehlers and Ric Way
Evaluating Trading Systems By John Ehlers and Ric Way INTRODUCTION What is the best way to evaluate the performance of a trading system? Conventional wisdom holds that the best way is to examine the system
More informationActive vs. Passive Money Management
Active vs. Passive Money Management Exploring the costs and benefits of two alternative investment approaches By Baird s Advisory Services Research Synopsis Proponents of active and passive investment
More informationModerator Timothy Wilson
Investment Symposium March 2012 P2: Portfolio Construction: Asset Allocation versus Risk/Strategy Buckets Marc Carhart Radu Gabudean Moderator Timothy Wilson Beyond Modern Portfolio Theory Radu C. Gabudean
More informationNo duplication of transmission of the material included within except with express written permission from the author.
Copyright Option Genius LLC. All Rights Reserved No duplication of transmission of the material included within except with express written permission from the author. Be advised that all information is
More informationHigh-frequency trading: towards capital market efficiency, or a step too far?
Agenda Advancing economics in business High-frequency trading High-frequency trading: towards capital market efficiency, or a step too far? The growth in high-frequency trading has been a significant development
More informationRisk and return (1) Class 9 Financial Management, 15.414
Risk and return (1) Class 9 Financial Management, 15.414 Today Risk and return Statistics review Introduction to stock price behavior Reading Brealey and Myers, Chapter 7, p. 153 165 Road map Part 1. Valuation
More informationFinancial Assets Behaving Badly The Case of High Yield Bonds. Chris Kantos Newport Seminar June 2013
Financial Assets Behaving Badly The Case of High Yield Bonds Chris Kantos Newport Seminar June 2013 Main Concepts for Today The most common metric of financial asset risk is the volatility or standard
More informationTRADING VENUE LIQUIDITY
TRADING VENUE LIQUIDITY IT S QUALITY, NOT QUANTITY, THAT MATTERS AUTHORS Mike Smith, Partner Benjamin Smith, Partner Daniela Peterhoff, Partner Quinton Goddard, Principal CHANGING THE GAME The concept
More informationThree new stock ETFs for greater global diversification
Three new stock ETFs for greater global diversification Canadian stocks account for less than 4% of publicly traded companies global market value. Investors in Canada, however, allocate 59% of their stock
More informationAlgorithmic Trading. Global Execution Services. Making the world liquid EQUITY MARKETS
Global Execution Services Algorithmic Trading Making the world liquid EQUITY MARKETS CORPORATE & INVESTMENT BANKING / INVESTMENT SOLUTIONS / SPECIALISED FINANCIAL SERVICES 2 Content Our Execution Services
More informationInvestment Technology Group Investor Overview
Investment Technology Group Investor Overview ITG Inc., member NASD, SIPC. 2006 All rights reserved. Not to be reproduced without permission. 91306-61314 Safe Harbor Statement This document may contain
More informationINVESTMENT MANAGER FEES: A CRITICAL LOOK
HEALTH WEALTH CAREER INVESTMENT MANAGER FEES: A CRITICAL LOOK JULY 2015 In this short article, we suggest a number of ways in which the current and common structures for investment managers fees (for traditional,
More informationBenefits of Separately Managed Accounts
703 Market Street, 18th Floor San Francisco, CA 94103 800.541.7774 Benefits of Separately Managed Accounts Separately Managed Accounts are known under a number of other names including individually managed
More informationSetting the Scene. FIX the Enabler & Electronic Trading
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
More informationTrade Cost Analysis: A Component of Risk Management
Trade Cost Analysis: A Component of Risk Management Coordinator: Jim Bryson As transaction cost management moves into the daily forum, transaction costs must be viewed daily and used to manage risk and
More information9 Questions Every ETF Investor Should Ask Before Investing
9 Questions Every ETF Investor Should Ask Before Investing 1. What is an ETF? 2. What kinds of ETFs are available? 3. How do ETFs differ from other investment products like mutual funds, closed-end funds,
More informationSeeking a More Efficient Fixed Income Portfolio with Asia Bonds
Seeking a More Efficient Fixed Income Portfolio with Asia s Seeking a More Efficient Fixed Income Portfolio with Asia s Drawing upon different drivers for performance, Asia fixed income may improve risk-return
More informationPortfolio Management Consultants Perfecting the Portfolio
Portfolio Management Consultants Perfecting the Portfolio Envestnet PMC is the ultimate advisor to the advisor. Our goal is to help advisors strengthen relationships with their clients and improve outcomes
More informationABF PAN ASIA BOND INDEX FUND An ETF listed on the Stock Exchange of Hong Kong
Important Risk Disclosure for PAIF: ABF Pan Asia Bond Index Fund ( PAIF ) is an exchange traded bond fund which seeks to provide investment returns that corresponds closely to the total return of the Markit
More informationMeasures of implicit trading costs and buy sell asymmetry
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
More informationINSURANCE. Life Insurance. as an. Asset Class
INSURANCE Life Insurance as an Asset Class 16 FORUM JUNE / JULY 2013 Permanent life insurance has always been an exceptional estate planning tool, but as Wayne Miller and Sally Murdock report, it has additional
More informationWhether you re new to trading or an experienced investor, listed stock
Chapter 1 Options Trading and Investing In This Chapter Developing an appreciation for options Using option analysis with any market approach Focusing on limiting risk Capitalizing on advanced techniques
More informationHow to manage your portfolio and emotions during volatile markets. Video Transcript. Recorded on March 6, 2015
How to manage your portfolio and emotions during volatile markets Video Transcript Recorded on March 6, 2015 Featuring: Michael Santoli, senior columnist, Yahoo! Finance Matthew Diczok, managing director,
More informationValueCharts TradeStation
ValueCharts TradeStation ValueCharts TradeStation indicator suite can be located in the TradeStation Strategy Network under MicroQuantSM products. Free trial subscription periods are available for all
More informationUnderstanding the Equity Summary Score Methodology
Understanding the Equity Summary Score Methodology Provided By Understanding the Equity Summary Score Methodology The Equity Summary Score provides a consolidated view of the ratings from a number of independent
More informationBECS Pre-Trade Analytics. An Overview
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
More informationLovers by night, strangers by day? An investigation of simple Overnight Trading Strategies. Abstract: The Day- and Night-Puzzle:
Lovers by night, strangers by day? An investigation of simple Overnight Trading Strategies. Chrilly Donninger Chief Scientist, Sibyl-Project Sibyl-Working-Paper, September 2014 http://www.godotfinance.com/
More informationPattern Recognition and Prediction in Equity Market
Pattern Recognition and Prediction in Equity Market Lang Lang, Kai Wang 1. Introduction In finance, technical analysis is a security analysis discipline used for forecasting the direction of prices through
More informationInvestment Section INVESTMENT FALLACIES 2014
Investment Section INVESTMENT FALLACIES 2014 Simulation of Long-Term Stock Returns: Fat-Tails and Mean Reversion By Rowland Davis Following the 2008 financial crisis, discussion of fat-tailed return distributions
More informationRisk management: Building an effective process
Deutsche Bank Group DB Advisors Risk Management: Building an effective process Risk management: Building an effective process When crisis hit global markets, many managers were caught unprepared. In the
More informationThe Cost of Algorithmic Trading: A First Look at Comparative Performance
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 idomowitz@itginc.com Henry Yegerman Director
More informationPROFESSIONAL FIXED-INCOME MANAGEMENT
MARCH 2014 PROFESSIONAL FIXED-INCOME MANAGEMENT A Strategy for Changing Markets EXECUTIVE SUMMARY The bond market has evolved in the past 30 years and become increasingly complex and volatile. Many investors
More informationEQUITY RISK CONTROLS. FPL Risk Management Committee
EQUITY RISK CONTROLS FPL Risk Management Committee TABLE OF CONTENTS Objective...3 Overview...3 The Client/Broker Relationship...4 Benefits of Risk Controls...4 Typical Workflow...5 Implementation of Risk
More informationInsights. Did we spot a black swan? Stochastic modelling in wealth management
Insights Did we spot a black swan? Stochastic modelling in wealth management The use of financial economic models has come under significant scrutiny over the last 12 months in the wake of credit and equity
More informationUBS Global Asset Management has
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. renato.staub@ubs.com Deploying Alpha: A Strategy to Capture
More informationFutures Trading Using the 14-day Stochastic Signal as Defined and Published by Robert McHugh, Ph.D.,
Futures Trading Using the 14-day Stochastic Signal as Defined and Published by Robert McHugh, Ph.D., by David Zaitzeff, futures broker at PFG West (Camarillo, CA) 800-656-0443 (office) Robert McHugh, Ph.D.,
More informationSEI Japan Equity Fund As at 30th June, 2009
Benchmark TOPIX Base Currency JPY Currencies Available EUR, GBP Fund Complex SEI Global Assets Fund PLC SEI Japan Equity Fund As at 30th June, 2009 SEI MANAGER OF MANAGERS PHILOSOPHY SEI employs a sophisticated
More information