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Axioma Quant Forum Returns, September 2014

Join us for the Axioma Quant Forum in New York and London this September.

Leading practitioners and researchers will present their latest work on effective risk management, superior portfolio construction and optimal portfolio trading.

Join Axioma for a full day of illuminating presentations and thought-provoking discussion.

Axioma Quant Forum - New York

Date: September 8, 2014, 8:30 AM- 6:00 PM
Location: The Princeton Club, 14 W 43rd St., New York

Please note: Registration for the New York event is now full.
Additional registrants will be added to our waiting list and contacted if space becomes available.

Axioma Quant Forum - London

Date: September 24, 2014, 8:30 - 6:00 PM
Location: Andaz Liverpool Street Hotel, London, EC2M 7QN

Analytic Investors, LLC: The Not-so-well-known Three-and-one-half Factor Model show abstract
Equity analysts conceptualize the Fama-French framework as a tool for studying the size and value characteristics of equity portfolios along with the market return. But the market return is not the return to market beta. In other words, in equity risk modeling practice, the basic Fama-French framework includes four factors not just three. Unlike the other three factors, the intercept term (i.e., market factor) does not have a coefficient that varies across securities so can be described as just half a factor. We clarify the nature and role of the “first” factor in equity return models and explain that the distinction between the market portfolio return and the return to the cross-sectional variation in security beta also applies to portfolio performance measurement. Specifically, the realized alphas of low (high) beta portfolios are reduced (increased) when a beta factor is included. The problem of ignoring the beta factor in performance measurement pertains to fully invested portfolios that have a low or high beta based on security selection, not to changes in portfolio beta induced by cash or leverage. hide abstract
Harindra de Silva, PhD, CFA, President, Portfolio Manager show bio

Harindra (“Harin”) de Silva is responsible for the firm’s strategic direction and the ongoing development of its investment processes. Since joining the firm in 1995, he has worked with the investment team to develop innovative strategies including short extension, low volatility, and factor based asset allocation.

Harin has authored several articles and studies on finance-related topics including risk based portfolio construction, the Fundamental Law of Active Management and asset allocation. He, along with his colleagues Roger Clarke and Stephen Thorley, was recognized with the Graham and Dodd Award of Excellence by the CFA Institute for their research in 2002 and 2005. They were also awarded the Bernstein Fabozzi award by Institutional Investor in 2005, 2006 and 2011. Harin currently serves as Associate Editor of the Financial Analysts Journal. Harin has 26 years of investment experience. He holds a Ph.D. in Finance from, University of California, Irvine; M.S. in Econometrics, University of Rochester; MBA, Finance, University of Rochester ; and B.S. in Mechanical Engineering, University of Manchester Institute of Science and Technology hide bio

AQR Capital Management, LLC: To Trade or Not to Trade? Informed Trading with Short-Term Signals for Long-Term Investors show abstract
When long-term investors trade slowly changing portfolios, they are not particularly sensitive to when they should place or modify their bets. Short-term information can be used to guide investors on how to time their trades. Strategic trade modification provides exposure to short-term signals without imposing additional transaction costs or capacity limits. Long-term investors should not ignore short-term information simply because it is too expensive to trade on. hide abstract
Michael Katz, PhD, Principal show bio

Michael is a researcher and portfolio manager heading AQR’s macro and fixed-income team as well as AQR’s inflation-related strategies. Prior to AQR, he was a teaching fellow and research assistant at Harvard University, a teaching and research assistant at Tel Aviv University, and a consultant for Trigger Ltd. (now Trigger-Foresight Ltd.). He also served as an intelligence officer in the Israeli Defense Forces, achieving the rank of Major. His research on how long-term investors can use short-term information has been published in Financial Analysts Journal. Michael earned a B.A. in economics and Middle East history, with honors, at Tel Aviv University, and a Ph.D. and an A.M. in economics from Harvard University. hide bio

Kepos Capital: Thinking about an Environmental, Social and Governance (ESG) Policy show abstract
The appropriate time path for emissions prices, which economists call the "Social Cost of Carbon," should be thought of as the solution to an optimal control problem. The price of carbon is the brake that society uses to accelerate or decelerate the rate of usage of the atmosphere's unknown capacity to safely absorb emissions. Right now the incentive to reduce emissions is strongly negative, i.e. governments around the world heavily subsidize the creation of emissions. Potential climate-risk tail events, together with societal risk aversion (which is best observed in the equity risk premium) and expectations of technological change determine the appropriate time path for emissions prices. Societal understanding of this issue is at a tipping point. As expectations of incentives being created sooner and higher increase, the valuations of stranded assets, such as coal and coal fired power plants will decline. But understanding how forward expectations of carbon emission prices drive current valuations is complex. It is also important to understand that it is not the act of pricing emissions that destroys the value of these assets – it is the economic externality that has already destroyed their value. What the recognition of that externality will do is to reduce their current false valuations. Exxon and Shell have, in their public discussion of stranded assets, shown that they do not understand this issue. Paraphrasing Upton Sinclair, “It is difficult to get a company to understand something, when the valuation of its assets depends on it not understanding it. hide abstract
Robert Litterman, Chairman of Risk Committee and Partner
QS Investors, LLC: Tactical Timing of Low-Volatility Global Equities show abstract
Many investors remain interested in making a strategic allocation to low-volatility equities to help them better meet their investment objectives. However, many have expressed concerns about current timing due to relative valuation levels of low-volatility stocks and the impact of potential rate increases given low volatility stocks tend to pay higher dividends. In this presentation we examine the validity of these concerns by analyzing which factors have been good drivers and predicators of global low volatility equities over the last 35 years. hide abstract
Sanne De Boer, Research Analyst show bio

Sanne is a research analyst at QS Investors, LLC. Prior to joing QS Investors, Sanne was a quantitative research analyst from 2006 – 2010 at ING Investment Management as well as an Adjunct Assistant Professor at New York University Stern School of Business. Prior to joining ING Investment Management, he held positions measuring and managing various types of risk at Citigroup and American Express. Sanne has an MA in Mathematics and Econometrics at Vrije Universiteit Amsterdam and a Ph.D. in Operations Research from Massachusetts Institute of Technology. hide bio

Axioma, Inc.: Multi-Period Portfolio Optimization with Alpha-Decay show abstract

For all there is to like about traditional Markowitz mean variance optimization (MVO), it is essentially a one-move game that ignores next steps. Yet decisions driven by single-period MVO may have very significant consequences for investors with long-term horizons. Fortunately, there is a way to address the problem. Multi-period optimization gives investors the ability to make “wait and see” decisions, by including approximate forecasts and long-term policy decisions that extend beyond the time horizon of a current rebalancing. In doing so, investors can maintain their focus on the long term, while obtaining flexibility in the short term.

Here we consider portfolio optimization with a composite alpha signal that is composed of a short-term and a long-term alpha signal. The short-term alpha better predicts returns at the end of the rebalancing period but it decays quickly. The long-term alpha has less predictive power than the short-term alpha but it decays slowly. We develop a multi-period portfolio that incorporates this alpha model to construct the optimal portfolio at the end of the rebalancing period. We compare this model with the traditional single-period MVO model on several simulated backtests and show that the multi-period model has a superior realized performance. hide abstract

Sebastian Ceria, PhD show bio

Before founding Axioma, Ceria was an Associate Professor of Decision, Risk and Operations at Columbia Business School from 1993 to 1998. Sebastian has worked extensively in the area of optimization and its application to portfolio management. He is the author of many articles in publications including Management Science, Mathematical Programming, Optima and Operations Research. Most recently, Sebastian's work has focused on the area of robust optimization in portfolio management. He has co-authored numerous papers on the topic, including, "Incorporating Estimation Errors into Portfolio Selection: Robust Portfolio Construction," published in The Journal of Asset Management, “To Optimize or Not to Optimize: Is That the Question?” published in the Oxford Handbook of Quantitative Management, and “Factor Alignment Problems and Quantitative Portfolio Management,” published in the Journal of Portfolio Management. He is a recipient of the Career Award for Operations Research from the National Science Foundation. Ceria completed his PhD in Operations Research at Carnegie Mellon University's Graduate School of Industrial Administration. hide bio

Panel Discussion:
Alignment of Alpha and Risk Factors – How to Resolve the Issue with Real World Portfolios?

Panelists:
  • John Guerard, PhD, Director of Quantitative Research, McKinley Capital Management
  • Andrew Waisburd, PhD, Director of Research, Invesco Quantitative Strategies
  • Todd Wolter, CFA, Principal, Lead Portfolio Manager, Clarivest Asset Management
APG: How Your Portfolio Can Benefit From Forecasting Trade Volumes show abstract
Equity investors use a wide range of sophisticated models to forecast risk, but when it comes down to estimating trade volumes their preferred estimator is a naïve average or median of recent volume. Based on a literature study we have created an estimator which improves the ADV estimates by 20%. As trade volumes are the main driver of impact costs, differences in estimates impact the optimal after-cost portfolio. We demonstrate how volumes may impact this optimal portfolio, and what the potential gains are of having better volume estimates. hide abstract
Peter Korteweg, PhD, CFA, Senior Portfolio Manager show bio

Peter is responsible for the development of mathematical models and analysis tools in a team which oversees the quantitative equity investments of APG, an asset manager for pension funds. He is the co-portfolio manager of the team's low-volatility portfolio and has extensive knowledge of the theory and practice of risk factor models and trade cost models for institutional investors. He holds a PhD in Applied Mathematics and is a CFA charterholder. hide bio

BNP Paribas Asset Management: Beyond Smart Beta: A Risk-Budgeting Approach for Multi-Factor Equity Portfolios show abstract
Recent research recognises that a number of strategies that go under the name of Smart Beta and include Minimum Variance, Maximum Diversification, Risk Parity and Fundamental indexation are in fact deriving their excess returns and risk exposures from well-known factor risk premiums like value, small cap and low volatility. Moreover, it has been suggested that these Smart Beta strategies are clumsy approaches to gain exposure to such factor risk premiums and that optimal combinations of such strategies are difficult to construct. In this research we propose a framework for portfolio construction of multi-factor risk premium portfolios which addresses these pitfalls. The frameworks starts with the selection of the factor premiums to add to a portfolio, followed by the allocation of a risk budget to each of those factor risk premiums and finally the construction of an efficient portfolio which delivers those factor risk premiums under realistic portfolio constraints, e.g. long-only with a limited number of stocks. hide abstract
Raul Leote de Carvalho, Head of Quantitative Research and Investment Solutions show bio

Raul Leote de Carvalho has 14 years of experience in Finance and is the Head of Quantitative Research and Investment Solutions in the Financial and Engineering team of BNP Paribas Asset Management in Paris since 2007. He is responsible for carrying out innovative quantitative research applicable in the development of quantitative strategies for different investment teams in either equities, fixed income or asset allocation and also for the use of advanced quantitative approaches in the design of investment client solutions.

Prior to that, from 2003 to 2007 he held the position of Senior Quantitative Strategist in the Global Strategy team of BNP Paribas Investment Partners located in Paris where he was member of the Asset Allocation Committees and developed a number of quantitative models and strategies for asset allocation. He joined Paribas Asset Management in 1999 in London as a Quantitative Analyst, a position he held until 2002, working mainly on the application of robust portfolio optimisation techniques to portfolio construction, the development of FX and fixed income models and also as fund manager of asset allocation portfolios.

Before he spent 3 years working as a Research Associate in Computational and Theoretical Physics at the University College of London, at the École Normale Supérieure de Lyon and at the University of Wuppertal. He obtained a PhD in Theoretical Physics from the University of Bristol in 1996, an MSc in Condensed Matter Physics in 1992 and a BSc in Chemistry in 1990 both from the University of Lisbon. He is a member of Inquire Europe and the author of a many refereed papers in Finance and Physics published in several academic journals. He passed the Investment Management Certificate in London in 2001. hide bio

Cantab Capital Partners
Massoud Mussavian, Scientist
HOLT Investment Strategy: Generating Excess Returns Across the Road Less Travelled show abstract
The efficacy of stock selection models are frequently examined across a range of parameters and regimes such a size, region, sector, and style. One metric that is commonly overlooked is the depth of analyst coverage. In this presentation we examine whether there is a relationship between the number of analysts covering a company and the power of stock selection. Using a global universe, we examine stock selection performance per analyst coverage grouping and look into the correlation of coverage to company size and valuation multiples. The insights obtained are consistent across a range of samples. hide abstract
Siebert Kruger

Coming soon.hide bio

Macquarie Securities Group: Managing risk, return and active share (by Giuliano De Rossi) show abstract
The concept of active share has received considerable attention since the work of Cremers and Petajisto (2009) who showed that funds with high active share tend to outperform [How Active Is Your Fund Manager? A New Measure That Predicts Performance, Review of Financial Studies 22, 3329-3365]. More and more portfolio managers are incorporating explicit active share targets in their portfolio construction process, in addition to or as a substitute for tracking error targets. We analyse the problem of optimising an active risk / active return objective function subject to active share constraints and derive the resulting efficient frontiers. Moreover, we show that imposing active share constraints can be interpreted as a robust portfolio construction approach. In particular, building on the existing literature on optimisation with gross exposure constraints, we highlight: 1) the equivalence between portfolio optimisation with active share limits and regularisation of the covariance matrix 2) the connection between portfolio construction and lasso regression 3) a Bayesian interpretation of active share constraints. Monte Carlo experiments are used to assess the robustness of the method out of sample. Our research has important implications for portfolio managers: Active share targets should depend on skill and on the number of stocks and concentration of the benchmark. In addition, we argue that active share targets should change over time to reflect changes in market conditions. hide abstract
Ioan Mirciov show bio

Ioan Mirciov joined the Quantitative team at Macquarie from a buy-side role, where he investigated investment strategies based on volatility derivatives. Prior to that, he worked on the design and efficient replication of credit benchmarks at Barclays Capital. Ioan holds a PhD in Finance from Kellogg School of Management, where he focused on earnings surprises. hide bio

Old Mutual Global Investors: show abstract
As financial markets have globalised, correlations among regional equity indices have increased markedly over the last 20 years, diminishing the scope for diversification within equities. Alternatives can offer some diversification, but some of the popular hedge fund strategies, such as equity long short, in practice can exhibit high correlation with equities. Equity market neutral stands out as the hedge fund strategy that has exhibited one of the lowest level of correlation against traditional asset classes. We show in asset allocation historical simulations that these correlations can be useful in improving the risk return characteristics of portfolios, particularly for low risk investors. Hedge fund strategies are now more widely available to investors via UCITS funds, making investment in these alternative asset classes much more accessible, in terms of funds being regulated, liquid and transparent. But equity market neutral is not a homogeneous space, and fund selection is key if one is to take advantage of the potential diversification benefit this asset class can provide. hide abstract
Amadeo Alentorn, CFA, PhD, Head of Research and Fund Manager - Global Equity Team show bio

Amadeo Alentorn joined Old Mutual Global Investors in 2005. He is Head of Research and Portfolio Manager in the global equities team, responsible for managing over $3bn in assets across global equity hedge funds, absolute return and long only equity funds. He has extensive experience in investment research and software development. Prior to joining Old Mutual, he developed simulation models for systemic and liquidity risk at the Bank of England (2004-2005), and worked as a software developer for CAD systems (2001-2004) and for robotic applications (1998-1999). Amadeo holds a BEng in Robotics (2000) from the University of Plymouth, an MSc in Computer Science (2001) and a PhD in Computational Finance (2006) from the University of Essex. During his doctoral research he developed a new option pricing model based on extreme value theory, and published on the subject. He is a CFA charterholder. hide bio

Pioneer Global Asset Management: Targeting Individual Liabilities for DC Pension Funds Using Stochastic Programming show abstract
Within the realm of the pension funds, the recurring trend of the past years have been that of transitioning from the Defined Benefit (DB) schema to a Defined Contribution (DC). Consequent household exposure to market dynamics increases the likelihood of gaps between post-retirement funding obligations and income. From the perspective of a pension fund sponsor, minimizing such likelihood increases the plans' visibility and utility. To achieve this, we propose the application of Asset Liability Management (ALM) techniques using a multistage dynamic stochastic optimization in our analysis. The results of such dynamic ALM approach to a wide range of risk profiles show conclusively an improvement in plan participant post-retirement solvency over the traditional fixed-mix balanced fund approach. hide abstract
Jung Hun Kim, ALM Analyst show bio

Jung Hun Kim is an Asset-Liability Management (ALM) analyst at Pioneer Investments based in Milan, Italy since May 2012. Jung is responsible for developing and maintaining the dynamic stochastic programming platform to simulate asset class scenarios and optimize ensuing portfolios. Jung has over extensive experience in quantitative analysis across Financial Industry Regulatory Authority (FINRA) in the US and IBM Consulting. He holds a B.A. degree from Brown University and a M. Eng. from Johns Hopkins University. hide bio

Robeco: show abstract
Various studies recommend investing in factor premiums beyond the classic market risk premium, such as the small-cap, value, momentum, and low-volatility premiums. It is unclear, however, if factor investing can best be implemented using a long-only or a long-short approach. We empirically compare both approaches and find that although a long-short approach is superior theoretically, a long-only approach seems to be the preferred alternative in most scenarios, after accounting for practical issues such as benchmark restrictions, implementation costs and factor decay. In fact, we show that costs and decay may completely offset the value added of a long-short implementation. We conclude that investors should carefully consider the pros and cons of long-only and long-short approaches when implementing factor investing. The framework described in this paper is intended to help investors make that decision. hide abstract
Simon Lansdorp, Ph.D., Quant Researcher, Investment Solutions and Research

Coming soon.hide bio

State Street Global Advisors: Low Volatility Investing: Lower Risk with Market-Like Returns show abstract
Since the global financial crisis of 2007-2008, increased interest in low volatility investing has spawned many different ways investors can capture the low volatility anomaly. This paper categorizes the various low volatility investment choices into two main approaches: heuristic and optimized; and looks at several prominent implementations within each. It compares construction methodologies, historic performance, drawdown, turnover, and other portfolio characteristics of the various implementations. Investors looking to allocate to low volatility should find a strategy whose characteristics best match the requirements and constraints of their investment objectives. This paper helps identify these characteristics. hide abstract
Christopher Cheung, Senior Researcher, Global Equity Beta Solutions show bio

Chris is a Vice President of State Street Global Advisors and a Senior Portfolio Manager and Research Analyst in Global Equity Beta Solutions. He specializes in optimized, tax-efficient and Advanced Beta strategies for the passive indexing group. His responsibilities include managing portfolios for institutional and high net worth clients in addition to performing quantitative research in alternative equity products.

Chris holds a Bachelor degree in Electrical Engineering and a Master degree in Engineering Management from Cornell University. He was awarded a graduate fellowship at the Carroll Graduate School of Management at Boston College where he earned Master degrees in Business Administration and in Finance. Chris has also earned the Chartered Financial Analyst designation, and is a member of the CFA Institute and the Hong Kong Society of Financial Analysts. hide bio

UBS: From Forecasts to Portfolios: Making the Most of Your Insights show abstract
This talk describes a framework for analysing and using single and multiple forecasts. As a first step the forecasts are decomposed into factor and stock specific views. For single forecasts it is then possible to estimate the breadth of the forecast. For multiple forecasts, the forecasts can be scaled and checked for consistency. Finally any inconsistencies must be resolved – a process which involves the judgement and knowledge of a portfolio manager. Once these inconsistencies have been removed the amended forecasts can be combined and a portfolio constructed. hide abstract
David Jessop, Managing Director – Global Head of Equities Quantitative Research show bio

David Jessop is the Global Head of Equities Quantitative Research at UBS. His areas of interest include risk modelling and portfolio construction. David joined the quantitative research team at UBS in 2002. Prior to this, he spent seven years at Citigroup as Head of Global Quantitative Marketing. Before moving to the sell side, he spent six years at Morgan Grenfell Asset Management, where he managed index funds, asset allocation funds and also an option overwriting fund. David graduated from Trinity College, Cambridge with an MA in Mathematics. hide bio

Axioma, Inc.: Multi-Period Portfolio Optimization with Alpha-Decay show abstract

For all there is to like about traditional Markowitz mean variance optimization (MVO), it is essentially a one-move game that ignores next steps. Yet decisions driven by single-period MVO may have very significant consequences for investors with long-term horizons. Fortunately, there is a way to address the problem. Multi-period optimization gives investors the ability to make “wait and see” decisions, by including approximate forecasts and long-term policy decisions that extend beyond the time horizon of a current rebalancing. In doing so, investors can maintain their focus on the long term, while obtaining flexibility in the short term.

Here we consider portfolio optimization with a composite alpha signal that is composed of a short-term and a long-term alpha signal. The short-term alpha better predicts returns at the end of the rebalancing period but it decays quickly. The long-term alpha has less predictive power than the short-term alpha but it decays slowly. We develop a multi-period portfolio that incorporates this alpha model to construct the optimal portfolio at the end of the rebalancing period. We compare this model with the traditional single-period MVO model on several simulated backtests and show that the multi-period model has a superior realized performance. hide abstract

Sebastian Ceria, PhD show bio

Before founding Axioma, Ceria was an Associate Professor of Decision, Risk and Operations at Columbia Business School from 1993 to 1998. Sebastian has worked extensively in the area of optimization and its application to portfolio management. He is the author of many articles in publications including Management Science, Mathematical Programming, Optima and Operations Research. Most recently, Sebastian's work has focused on the area of robust optimization in portfolio management. He has co-authored numerous papers on the topic, including, "Incorporating Estimation Errors into Portfolio Selection: Robust Portfolio Construction," published in The Journal of Asset Management, “To Optimize or Not to Optimize: Is That the Question?” published in the Oxford Handbook of Quantitative Management, and “Factor Alignment Problems and Quantitative Portfolio Management,” published in the Journal of Portfolio Management. He is a recipient of the Career Award for Operations Research from the National Science Foundation. Ceria completed his PhD in Operations Research at Carnegie Mellon University's Graduate School of Industrial Administration. hide bio

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