Portfolios

02/17/2015

Recent Research: Highlights from February 2015

"On the Holy Grail of “Upside Participation and Downside Protection"
The Journal of Portfolio Management (Winter 2015)
Edward Qian

Upside participation and downside protection” is a popular motto for many investors. It has taken on much more significance in recent years, in the wake of the global financial crisis. But how do we define and evaluate strategies from the perspective of “upside participation and downside protection”? In this article, the authors present an analytic framework in which they provide a quantitative definition of upside and downside participation ratio, define participation ratio difference as a goodness measure for defensive strategies, and prove a relationship between the participation ratio difference and traditional alpha. As an illustration, they apply this new analysis to the S&P 500 Index and its 10 sectors and show that defensive, low-beta sectors tend to have positive participation ratio differences, while cyclical, high-beta sectors tend to have negative participation ratio differences. This finding is consistent with the low-beta/volatility anomaly and provides another explanation for the popularity of low-beta/volatility strategies.

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11/17/2014

Recent Research: Highlights from November 2014

"Let’s Save Retirement: Repairing America’s Broken System of Funding Workers’ Retirement"
The Journal of Retirement 
Russell L. Olson and Douglas W. Phillip

Far too few American workers can look forward to financial independence as they age. Many will be obliged to extend their working lives, some into their seventies. A patchwork of defined contribution (DC) retirement plans now serve as the primary retirement saving vehicle in the private sector, but they are complex, costly, and challenging for employers and employees to manage. This article presents a comprehensive set of recommendations for a unified private DC pension system to cover all working Americans, with a single set of rules and without cost to the government. A key part is the creation of broadly diversified trusteed retirement funds (TRFs), whose sponsors are trustees, with fiduciary responsibilities. Employee contributions will automatically go into a broadly diversified TRF unless the employee either opts out or selects a preferred TRF or the employer already sponsors a defined benefit (DB) pension plan. TRFs will relieve employers from fiduciary responsibility for all future DC contributions. To protect retirees from inflation, longevity, and asset price volatility risk, retirees will be encouraged to use their TRF savings to buy either an immediate or deferred indexed annuity. A new government agency, the Federal Longevity Insurance Administration, will enable private insurance companies to provide low-cost annuities.

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09/02/2014

Recent Research: Highlights from Summer 2014

"Return Predictability and Dynamic Asset Allocation: How Often Should Investors Rebalance?"
The Journal of Portfolio Management (Summer 2014
Himanshu Almadi, David E. Rapach, and Anil Suri

To exploit return predictability via dynamic asset allocation, investors face the important practical issue of how often to rebalance their portfolios. More frequent rebalancing uses statistically and economically significant short-horizon return predictability to aggressively pursue the dynamic investment opportunities afforded by changes in expected returns. However, the degree of return predictability typically appears stronger at longer horizons, which, along with lower transaction costs, favors less frequent rebalancing. The authors analyze the performance effects of rebalancing frequency in the context of dynamic portfolios constructed from monthly, quarterly, semi-annual, and annual return forecasts for US stocks, bonds, and bills, where the dynamic portfolios rebalance at the same frequency as the forecast horizon. Along the transaction-cost/rebalancing frontier, monthly (annual) rebalancing provides the greatest outperformance when unit transaction costs are below (above) approximately 50 basis points, and dynamic portfolios based on annual rebalancing typically outperform the benchmarks for unit transaction costs well in excess of 400 basis points.

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07/28/2014

Recent Research: Highlights from July 2014

"Going for Broke: Restructuring Distressed Debt Portfolios"
The Journal of Fixed Income (Summer 2014
Sanjiv R. Das and Seoyoung Kim

This article discusses how to restructure a portfolio of distressed debt and what the gains are from doing so, and attributes these gains to restructuring and portfolio effects. This is an interesting and novel problem in fixed-income portfolio management that has received scant modeling attention. We show that debt restructuring is Pareto improving and lucrative for borrowers, lenders, and investors in distressed debt. First, the methodological contribution of the paper is a parsimonious model for the pricing and optimal restructuring of distressed debt, i.e., loans that are under-collateralized and are at risk of borrower default, where willingness to pay and ability to pay are at issue. Distressed-debt investing is a unique portfolio problem in that a) it requires optimization over all moments, not just mean and variance, and b) with debt restructuring, the investor can endogenously alter the return distribution of the candidate securities before subjecting them to portfolio construction. Second, economically, we show that post-restructuring return distributions of distressed debt portfolios are attractive to fixed-income investors, with risk-adjusted certainty equivalent yield pickups in the hundreds of basis points, suggesting the need for more efficient markets for distressed debt, and shedding light on the current policy debate regarding the use of eminent domain in mitigating real estate foreclosures.

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09/18/2013

Asset Prices and Ambiguity

ABSTRACT

Modern portfolio theory, developed in the expected utility paradigm, focuses on the relationship between risk and return, assuming away ambiguity, uncertainty over the probability space. In this paper, we present an asset pricing model developed by Izhakian (2011), which incorporates ambiguity as a second factor (in addition to “risk”). Our contribution is two fold; we propose an ambiguity measure that is derived theoretically and computed from intra-day stock market prices. Second, we use it in conjunction with risk measures to test the basic relationship between risk,ambiguity and return. We find that our ambiguity measure has a consistently negative effect on returns and that our risk measure has mostly a positive effect. The best evidence, judging by statistical significance, is obtained when we use the change in volatility alongside the measure of ambiguity.

–Menachem Brenner and Yehuda Izhakian

Webinar-8-2013

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09/02/2013

The ETFG Monthly Liquidation Watch List

June’s bond market rout captivated investors’ attention as fixed income ETFs saw their largest monthly redemptions in history. But this month’s ETF Liquidation Watch List, compiled by ETF Global, contains only four fixed income products, one less than last month, and they are all inverse funds.

The monthly compilation, found on ETF Global's website, quantifies all exchange traded products that hold below $5 million in AUM, have existed for at least two years and had negative performance for the trailing 12 months. That said, most fixed income ETFs that are older than two years still have positive performance for the trailing 12 months and have more than $5 million in assets. That is not the case for equity products that meet the three criteria and have seen their ranks swell on the list that contains a record 78 ETPs this month.

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08/28/2013

Recent Research: Highlights from August 2013

"Standing Out From the Crowd: Measuring Crowding in Quantitative Strategies"
The Journal of Portfolio Management (Summer 2013)
Rochester Cahan and Yin Luo

One of the most frequently cited criticisms of quantitative investing has been the charge that everyone uses the same factors and models. In other words, the popular strategies of the last few decades, such as value and momentum, have become crowded, leaving little room for investors to generate alpha. But is this actually true? The authors propose an empirical framework for measuring crowdedness, and use this to study the crowding in common systematic strategies.

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02/20/2013

Recent Research: Highlights from February 2013

"Volatility, Correlation, and Diversification in a Multi-Factor World"
The Journal of Portfolio Management (Winter 2013)
Richard Roll

In a multi-factor world, diversification benefits do not generally depend on correlation. Investors can restructure portfolios to align factor sensitivities. This implies that diversification benefits depend only on the idiosyncratic volatility that remains after restructuring. Similarly, the risk reduction that follows adding an asset to an existing portfolio does not depend on the asset’s correlation with the portfolio. These implications evince the fundamental importance of measuring the underlying factors and estimating factor sensitivities for every asset. Other researchers have investigated several methods for measuring factors. An easy-to-implement general method involves specifying a group of heterogeneous indexes or traded portfolios. Exchange-traded funds (ETFs) could be well suited to this purpose.

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11/13/2012

Recent Research: Highlights from November 2012

"The Volume Clock: Insights into the High-Frequency Paradigm"
The Journal of Portfolio Management (Fall 2012)
David Easley, Marcos M. Lopez de Prado, and Maureen O’Hara

Over the last two centuries, technological advantages have allowed some traders to be faster than others. In this article, the authors argue that contrary to popular perception, speed is not the defining characteristic that sets high-frequency trading (HFT) apart. HFT is the natural evolution of a new trading paradigm that is characterized by strategic decisions made in a volume-clock metric. Even if the speed advantage disappears, HFT will evolve to continue exploiting structural weaknesses of low-frequency trading (LFT). LFT practitioners are not defenseless against HFT players, however, and this article offers options that can help them survive and adapt to this new environment.

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08/09/2012

Recent Research: Highlights from August 2012

"Diversification Return and Leveraged Portfolio"
The Journal of Portfolio Management (Summer 2012)
Edward Qian

It is widely accepted that portfolio rebalancing adds diversification return to fixed-weight portfolios, but this is only true for long-only unleveraged portfolios. Qian provides analytical results regarding portfolio rebalancing and the associated diversification returns for different kinds of portfolios including long-only, long-short, and leveraged. He shows that portfolio rebalancing is linked to underlying portfolio dynamics. For long-only unleveraged portfolios, rebalancing amounts to a mean-reverting strategy, and the diversification return is always non-negative. But for short (or inverse) and leveraged portfolios, portfolio rebalancing on the top-down level amounts to a trend-following strategy that detracts from diversification return. Qian analyzes diversification returns of risk parity portfolios and shows that the diversification return of a leveraged long-only portfolio can generally be decomposed into two parts, both of which are related to a scaled unleveraged portfolio. The first part is the positive diversification return from rebalancing among individual assets at the bottom-up level, which is amplified by leverage. The second part is the negative diversification return caused by the leverage of the overall portfolio. His numerical examples show that diversification return is, in general, positive for leveraged risk parity portfolios when the leverage ratio is not too high. In addition, he shows that low correlations between different assets are crucial in achieving positive diversification return and reducing portfolio turnover for risk parity portfolios.

"The Rubber Starts to Meet the Road: Achievable Results in US Housing Finance Reform"
The Journal of Structured Finance (Summer 2012)
Chris DiAngelo

This article begins by noting that the US Congress and the Administration both remain stymied in the area of housing finance reform, notwithstanding numerous “white papers,” “requests for information,” “roundtables,” and the like. After almost four years, Fannie Mae and Freddie Mac remain in conservatorship, with no clear exit plan. Looking past the top level (Congress and the White House), however, one will see that the two government-sponsored enterprises (GSEs) themselves and their regulator/conservator, the Federal Housing Finance Agency, have begun to make real progress in several areas. In early 2012, the FHFA released a strategic plan for the GSEs and followed that up with a “scorecard” that sets forth in some detail a “to do” list of items that the FHFA intends to get done, along with target dates for those items. The article focuses on four items in particular: the REO disposition program, the “new securitization platform” initiative, the “single security” concept, and the possibility of privatizing the multifamily business. The conclusion is that more progress is being made than the public generally believes.

"Kicking the Habit: How Experience Determines Financial Risk Preferences"
The Journal of Wealth Management (Fall 2012)
Joachim Klement and Robin E. Miranda

Conventional explanations for the diversity of risk preferences among individual investors offer only limited insight. Recent research in neuroscience, genetics, and behavioral decision making underscore the importance of experience in financial risk taking. The authors review these findings and argue that not only does individual experience influence risk taking, so do the collective experiences of groups. Additionally, there seems to be a significant genetic component to financial risk taking, suggesting that “evolutionary experience” also needs to be considered when analyzing the risk preferences of individual investors. The authors introduce some simple tools to identify the influence of experience on financial risk preferences. These tools can help financial advisors to accurately assess investors’ risk preferences to help them achieve their goals at an acceptable level of risk.


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07/10/2012

Recent Research: Highlights from July 2012

"Specification Risk and Calibration Effects of a Multifactor Credit Portfolio Model"
The Journal of Fixed Income (Summer 2012)
Gregor Dorfleitner, Matthias Fischer, and Marco Geidosch

This article examines a crucial source of specification risk when calibrating a typical industry-type, Merton-based credit portfolio model. It emerges from the necessity of having to choose a proxy for creditworthiness. In addition to equity prices and asset values, which are the classical choices, the authors consider credit default swap (CDS) spreads and expected default frequencies (EDF, from Moody’s KMV) as alternatives. Based on 40 large European companies from different industries, the authors calibrate a macroeconomic factor model with an OLS regression analysis for each specification and calculate the corresponding economic capital. Eighteen macroeconomic and financial variables are considered as risk factors. Their findings are: a) on average, 2 to 3 risk factors are needed to adequately model creditworthiness on the obligor level, b) stock market variables are the most important risk factors, c) model-implied credit correlation is extremely sensitive to the choice of the proxy for creditworthiness, and d) only the EDF specification leads to less economic capital compared with regulatory capital, according to Basel II, while it is exceeded substantially by all other specifications. In particular, credit correlation in the CDS specification by far exceeds any estimate mentioned in the literature. Most important, the authors show that the economic capital of their sample portfolio can be reduced by 78%, depending on which variable is chosen as a proxy for creditworthiness.

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06/12/2012

Recent Research: Highlights from June 2012

"Some Like It Hot: The Role of Very Active Mandates across Equity Segments in a Core-Satellite Structure"
The Journal of Investing (Summer 2012)

Frank Nielsen, Giacomo Fachinotti, and Xiaowei Kang

This article reviews the active management opportunity in different market segments, and discusses the role of very active mandates across segments in a core–satellite portfolio structure. Research based on manager performance data over the last 10 years indicates that there is little evidence that average emerging market or small-cap managers have produced higher or more persistent risk-adjusted returns relative to their developed market large-cap peers. Therefore, institutional investors may consider active and passive management as complementary strategies across all equity segments. Due to the outperformance of high active risk mandates over the analyzed period, a simulated core–satellite structure across different equity segments achieved a higher information ratio than a combination of low active risk managers. The outperformance of high active risk mandates may reflect links between higher manager skill, higher investment conviction, and/or fewer constraints. Depending on investment beliefs, institutional investors might explore such a core–satellite structure to implement the global equity allocation.

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01/11/2012

Recent Research: Highlights from January 2012

"Optimal Hedge Fund Allocation with Improved Estimates for Coskewness and Cokurtosis Parameters"
The Journal of Alternative Investments (Winter 2012)
Asmerilda Hitaj, Lionel Martellini, and Giovanni Zambruno

Since hedge fund returns are not normally distributed, mean–variance optimization techniques are not appropriate and should be replaced by optimization procedures incorporating higher-order moments of portfolio returns. In this context, optimal portfolio decisions involving hedge funds require not only estimates for covariance parameters but also estimates for coskewness and cokurtosis parameters. This is a formidable challenge that severely exacerbates the dimensionality problem already present with mean–variance analysis. This article presents an application of the improved estimators for higher-order co-moment parameters, in the context of hedge fund portfolio optimization. The authors find that the use of these enhanced estimates generates a significant improvement for investors in hedge funds. The authors also find that it is only when improved estimators are used and the sample size is sufficiently large that portfolio selection with higher-order moments consistently dominates mean–variance analysis from an out-of-sample perspective. Their results have important potential implications for hedge fund investors and hedge fund of funds managers who routinely use portfolio optimization procedures incorporating higher moments.

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12/28/2011

Harry Markowitz - Father of Modern Portfolio Theory - Still Diversified

Markowitz2Harry Markowitz’s Nobel Prize winning Modern Portfolio Theory was put to the supreme test in The Great Recession of 2008. The stock market plunged nearly 40%, stock and corporate bond markets crashed, the money markets froze up. Uncle Sam had to bail out major banks, while letting Bear Stearns and Lehman Brothers fail.

It raised the big question: Does Modern Portfolio Theory hold up during once-in-a-lifetime events?

“It is sometimes said that portfolio theory fails during a financial crisis because all asset classes go down and all correlations go up,” Markowitz said in a telephone interview from his office in San Diego, CA.

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12/08/2011

Mathematical Role Models

Whether you are an aspiring risk manager, quantitative analyst, or a trader, your first order of business is to manage risk. Fundamental analysis can tell you what a company is worth or whether a sector is undervalued theoretically, but market prices and volatility have their say also. You come to find that entries and exits are important, but ultimately risk management comes down to position sizing.

From there you can purchase expensive backtesting/simulation software and a subscription to the data that you need to run through it, but unless you have been trained in what all the boxes you've checked before you click "Run" mean, you will likely have data mined and over optimized your way to hypothetical success.

At various times of my career I have used such simulation software. Admittedly, it is very helpful and still is in some ways. It is very fast in its calculations and can also run Monte Carlo simulations for you, for example. However, nothing will replace the knowledge you will get from having to learn to do it from the ground up. I have done that too. My first model was created by hand on spreadsheets: first Lotus 123 cum Excel.

Constructing a portfolio that will provide you and your clients with the best risk-adjusted returns, while suitably diversifying your holdings, is done by teams these days. Teams that have a great deal of resources and intellectual capital. You need to learn how to construct such a model in order to compete in the global marketplace that is seeming to become more and more uncertain each day, perhaps by yourself at the beginning. It may appear to be easy in theory, but it is much harder in practice. One of the best things you can do to get started in an affordable manner is to become very proficient in Excel.



Quantitative hedge funds (Quant funds), such as AQR and Renaissance, spend enormous amounts of time, money, and effort to make sure they sift through oceans of data to find the most profitable opportunities. They also invest in the best personnel...many of whom have PhD's in Quantitative Finance or hold designations such as the CQF (Certification in Quantitative Finance). The competition is brutal, and this is before you have to fight off the high frequency traders...

Successful risk managers know the limitations of VaR, the Kelly formula, and CAPM. As Aaron Brown, Risk Manager of AQR wrote in his new book, Red Blooded Risk, "Successful risk taking is not about winning a big bet or even a long series of bets. Success comes from winning a sufficient fraction of a series of bets, where your gains and losses are multiplicative.

In order to get to the place where you can affect such trades, you need to test the data. I believe that this is best accomplished these days in an affordable manner using Excel. Thankfully, Excel has dozens of built-in mathematical functions that which can utilize advanced techniques that can give you the answers you need with the statistical significance to boot.

Without these answers, you will not have the emotional nor statistical confidence to manage the risk, nor will you have the answers to the questions that are frequently being asked in today's environment: "What happens if....?"

 

–Michael Martin

Michael Martin has been a successful trader for over 20 years. He is the creator of "Martin Kronicle," author of The Inner Voice of Trading, and instructor of the NYSSA Certificate in Commodity Trading & Trend Following.

11/16/2011

Recent Research: Highlights from November 2011

"Breadth, Skill, and Time"
The Journal of Portfolio Management (Fall 2011)
Richard C. Grinold and Ronald N. Kahn


The information ratio determines the potential of an investment process to add value, and according to the fundamental law of active management, adding value depends on a combination of skill and breadth. Grinold and Kahn use an equilibrium dynamic model to provide insight into the concept of breadth, as well as a refined notion of skill. In equilibrium, the arrival rate of new information exactly balances the decay rate of old information. Grinold and Kahn denote the information turnover rate g. It is relatively easy to measure for any investment process. If the investment process forecasts returns on N assets, the breadth of the strategy i is g · N. Skill—the correlation of forecasts and returns—increases with the return horizon for small horizons, but then asymptotically decays to zero for very long horizons. The authors’ main result is that the ex ante information ratio is Breadth, Skill, and Time , where κ is a measure of skill.

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10/24/2011

Martin L. Leibowitz: Alpha Orbits

CFA

Let us ponder, for a moment, a hypothetical market consisting of only two assets: (1) cash and (2) one big Treasury bond. To keep things simple, let’s make the bond a perpetual with a 5 percent coupon and a current yield of 5 percent so that its price is 100. Further, let us assume that there is no credit risk whatsoever.

Now let us suppose that some investors own only cash, some own only the bond, and others own a mix of both.

We all know what happens to the bond price when the yield goes up or down: If the bond yield rose to 6 percent, the price would drop from 100 to 5/(0.06) = 83.33; if the yield fell to 4 percent, the price would rise to 5/(0.04) = 125.

But we also know that when the price goes up for any reason, the bond’s yield must fall.

In our hypothetical model, the all-cash investors are risk averse as a group, probably believing that bonds are simply not the “right kind” of asset for their highly risk-averse type of fund. But now let’s suppose that one of these all-cash funds suddenly receives an unanticipated contribution that raises its portfolio asset value to a level that modestly increases its risk tolerance. The fund decides to break out of its all-cash stance and buy some bonds, which nudges the bond price up to 101.

Then, a second all-cash fund, noticing that an all-cash fund’s buying bonds has become more acceptable, proceeds to take a nibble. The price moves up to 102.

When a third all-cash fund sees that owning a few bonds has become reasonably respectable, its bond purchases move the price up to 104.

At a cocktail reception at the next All-Cash Funds Conference, these few radical bondholders are the “talk of the town.” Having at least a small bond allocation quickly changes from being a novelty to being downright fashionable.

With this new surge in motivated buying, the price moves up to 107.

Some momentum investors observe this price action and begin to salivate. They don’t hesitate long, and their purchases raise the price to 110.

(Let’s not muddy the waters by wondering who is selling these beautiful bonds. A few contrarians are always lurking in the wings.)

Thus, over the course of a year, the bond’s 5 percent coupon, plus the 10 percent price appreciation, produces a hefty 15 percent total return.

Now let us consider a long-term investor with a 50/50 cash/bond portfolio. The assumed expected return for the bond was set at 5 percent in the last mean–variance optimization. With the dramatic shift in the structure of market returns, the fund decides to call for a new study.

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09/15/2011

Portfolio Heat: When Corn Starts Popping

When your portfolio heat increases too fast, too soon, you need to cut your position size(s) down to lower the overall risk to your portfolio. Else you have a Jiffy Pop portfolio.

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