Northfield’s 27th Annual Research Conference Monday, October 6 th – Wednesday, October 8 th 2014 Stowe Mountain Lodge 7412 Mountain Road Stowe, VT 05672 This year, Northfield’s annual conference returns to Stowe, VT. Northfield takes pride in offering an excellent agenda; and this year’s event is an extremely strong program, filled with a wide variety of financial topics, which are at the forefront of the financial industry. Northfield’s 27th Annual Research Conference will be in a setting that will allow participants to leave their normal workday and focus on the range of presentations while enjoying the Stowe area during peak foliage season! Venue The Stowe Mountain Lodge is located at the base of Mount Mansfield within The Stowe Mountain Resort. The area is surrounded with beautiful covered bridges, sparkling lakes, rambling woodlands and majestic mountain peaks. Calendar Our event will begin with a welcome reception on the evening of Sunday, October 5th, while the conference meeting sessions will begin on Monday, October 6th and finish with lunch on Wednesday, October 8th . CFA Institute Continuing Education Credit Approved CFA Institute has approved this program, offered by Northfield Information Services, Inc, for 12 CE credit hours. If you are a CFA Institute member, CE credit for your participation in this program will be automatically recorded in your CE tracking tool. Travel Arrangements and Accommodations We anticipate a large turnout for this year’s conference, given the desirability of the location. Reservations are on a first come basis so it is a good idea to register early. Please note - we are accepting registrations via online registration only for the conference and hotel accommodations. If you have any difficulties registering, please contact [email protected] for assistance. Hotel accommodations at the reduced conference rate are nearly gone and must be arranged by contacting The Stowe Mountain Lodge, at 888-478-6938 , 802-760-4755 or visit, http://www.stowemountainlodge.com. Monday, October 6, 2014 Agenda 9:00 am Seminar sessions: Junior Ballroom 9:00 am AIG Before, During and After the Crisis Bill Poutsiaka, AIG (CIO) The story of AIG, the iconic global insurance stalwart, is one that has been told and re-told from a variety of perspectives, focusing mainly on the company’s crisis during the economic downturn that began in earnest in 2008. The story that deserves to be told in tandem about the company, however, is more objective and begins long before the crisis sets in. It explores the factors and functions – both internal and external – that brought the AIG to the brink. This presentation will discuss these factors and functions, and will underscore that this is a story not exclusive to AIG. It is a story that the company shares with others on the Street --- companies that created the same situations which, when coupled with the overall economic environment, led to the perfect storm. We will also discuss how AIG has recovered, talk a bit about the controls put in place to ensure the company has a sustainable future, and hopefully thereby set positive precedents for the industry. 10:00 am Decomposing Variance Risk for Long-Term Investors – On a Few Elementary Formulas Yan Ge, CPPIB Risk is often calculated in return space. To gain insight into this notion of risk, we investigate the difference between return space analysis and value space analysis. With that insight, we derive some elementary but exact formulas to decompose the variance risk measure into two terms: a short-horizon term, and a long-horizon term associated with market trend. Percentage return analysis on financial time series involves a simple mathematical feature that carries a financial meaning, namely, an embedded rebalancing strategy whereby the investment is rebalanced to a fixed-dollar (FD) at each time step. Consequently, all risk-return analysis intentionally or unintentionally overlay a strategy on the financial asset in question. The risk-return analysis is therefore more about the performance or Profit and Loss of the FD-strategy than that of the original asset, or that of the buy-and-hold (BH) strategy. Similarly, active risk-return analysis is about the active Profit and Loss between the two FD-strategies, but often mistaken as the active Profit and Loss of the asset versus a benchmark. Another consequence of the overlay strategy is that return analysis introduces additional model assumptions, which may not exist in the actual time series data. Therefore, return space differs from value space in (1) strategy overlay (2) distribution model assumption. While percentage return is consistent with lognormal or GBM model assumptions, one cannot take it for granted that return analysis would result in the right statistics for hedge fund strategies and alternative assets. 3:00pm To Rebalance or Not to Rebalance: A Statistical Comparison of Terminal Wealth of FixedWeight and Buy-and-Hold Portfolio Eddie Qian, Panagora Asset Management “To rebalance or not to rebalance?” This seemingly innocuous question is of fundamental importance to many important investment issues. For example, it is related to the debate about efficient market theory and market inefficiency, and by extension, the distinction between traditional cap-weighted indices and alternative betas. It is also the point of divergence between how many investors carry out their asset allocation policies (to rebalance) and how they adhere to “passive” cap-weighted indices of underlying asset classes (not to rebalance). Which approach is better, from the perspective of risk-adjusted returns? In this presentation, we shall address this question statistically by comparing both expected value and expected variance of terminal wealth of fixed-weighted portfolios and their buy-and-hold counterparts. We also apply the analysis to longonly as well as long-short portfolios. The theoretical results suggest that overall fixed-weight portfolios with portfolio rebalancing are more likely to have better risk-adjusted terminal wealth than buy-and-hold portfolios. 4:00pm Did You Choose Well the When, Where and to Whom of Your Birth? John O’Brien; University of California at Berkeley There is universal agreement that the U.S. has traditionally been, and should continue to be, a land of equal opportunity. Yet, when we observe the nation we see vast inequality of income, wealth, status and influence. However, do these unequal socioeconomic economic outcomes imply that there is any corresponding inequality of opportunity? Once born, we each have a level of control over how we develop. But, we have no control over when, where, and to whom we are born. The philosopher John Rawls suggested that the design of a just and bountiful society should be developed from the perspective that the designers would not know the when, where and to whom of their birth. Rawls suggested the resulting society would be “fair”, although it might well evince a level of inequality of income, etc.; but that level of inequality would be “optimal”. According to the Rawlsian notion of optimality, the least fortunate in a Rawls-optimal society would be better off than they would be in any more-uniformly economically equal society. I propose that financial professionals should establish research programs based on principles of prediction markets to study how to measure and improve the current equality of opportunity. This is important not only from the Rawlsian viewpoint of “fairness” or “social justice”, but also from the viewpoint of economic development of the nation. The basis for this belief is that the value of the nation’s human capital is lessened in proportion to the extent of inequality of opportunity among its existing and future generations of citizens. Unless the potential for “greatness” is merely hereditary, this must be true. Tuesday, October 7, 2014 9:00 am Seminar sessions: Junior Ballroom 9:00 am Correlations, Diversification, and Hedging: A Critical Review of Portfolio Diversification Measures Randy O’Toole, Federated Investors This paper presents a critical review of two popular approaches that aim to quantify diversification properties expressly related to correlations and provide frameworks for constructing diversified portfolios: the Portfolio Diversification Index (PDI) developed by Rudin and Morgan [2006] and the Diversification Ratio (DR) introduced by Choueifaty and Coignard [2008]. These two measures have garnered much interest from practitioners and academics, and both have been evaluated and critiqued in a number of studies. Importantly, the PDI and the DR have become contenders in the quest for achieving diversification through risk-based portfolio construction methods, which most prominently include minimum variance and risk parity strategies. We show that the PDI and DR are in fact very closely related to both of these risk-based approaches to portfolio diversification. First, we establish the link between the PDI and risk parity by showing that the PDI quantifies diversification properties specifically associated with so-called naïve risk parity portfolios, where portfolio weights are inversely proportional to each asset’s volatility. This has important implications for the interpretation of the PDI as a summary measure of diversification and for portfolio construction schemes based on maximizing the PDI. 10:00 am The Art of Tracking Corporate Bond Indices Marielle deJong, Amundi The corporate bond indices, built by index providers to serve as investment benchmarks, contain a great many securities, and are for that reason difficult to replicate. The art is to construct an investible portfolio that captures the general price trend among the several thousands of securities in the index, being limited to selecting few of them. This paper describes a practical approach to this, which combines a well-established portfolio construction technique known as stratified sampling with a modern bond risk measure named the Duration Times Spread. The key idea is to divide the index members into samples related to distinct sources of risk that play in the corporate bond markets, and build small subsamples that capture those risks. As the Duration Times Spread conveys linear- as well as non-linear bond price behavior, it proves an effective measure in this portfolio building process. 3:00pm Valuation of Asset Management Firms Bernd Scherer, EDHEC Asset management firms attract investor’s interest in times of recovering markets as they are seen as ideal recovery plays (due to their aggressive stock market beta). Rising markets will increase assets under management both indirectly (larger inflows as a function of larger wealth and lower risk aversion) as well as directly (performance related increase in assets under management). Another often quoted reason for the interest in asset management firms is the expectation by many market observers that the asset management industry is ripe for consolidation. Cost synergies are obvious but diseconomies of scale are not. How do these thoughts enter the rational valuation of asset management firms? 4:00pm TRC Networks and Systemic Risk Roger M. Stein, MIT We introduce a new approach for identifying and monitoring systemic risk by combining network analysis and tail risk contribution (TRC). Network analysis provides great flexibly in representing and exploring linkages between institutions, but can be overly general in describing the risk exposures of one entity to another. Systemic TRC provides a more focused view of key systemic risks along with richer financial intuition, but it may miss important linkages between financial institutions. Integrating these two methods can provide information on key relationships between institutions that may become relevant during periods of systemic stress. We demonstrate this approach using exposures of money market funds to major financial institutions during July 2011. The results for our example suggest that TRC networks can highlight both institutions and funds that may become distressed during a financial crisis. Wednesday, October 8, 2014 9:00 am Seminar sessions: Junior Ballroom 9:00am What Would Yale Do If It Were Taxable? Lisa Goldberg, Aperio Group and the University of California at Berkeley The phenomenal success of Yale's endowment has been an inspiration to many investors. However, if Yale’s endowment had to pay the same taxes as individual investors, its portfolio would be constructed very differently. This paper presents a simple model for incorporating tax considerations into a pretax asset allocation such as Yale's. With illustrative examples, we demonstrate the profound impact that taxes can have on optimal portfolio weights as well as the interplay between taxes and risk. Once taxes are included our model tends to lower allocations to tax-inefficient asset classes such as hedge funds and increase allocations to tax-efficient strategies. However, with optimal tax management, hedge fund allocation can still be preserved so long as their returns are uncorrelated with those of equity. 10:00am Multiperiod Portfolio Selection and Bayesian Dynamic Models Petter Kolm, New York University Planning a sequence of trades extending into the future is a very common problem in finance. Merton (1969) and Samuelson (1969) considered agents seeking decisions which maximize total anticipated utility over time, a departure from the one-period portfolio selection theories of the time. All trading is costly, and the need for inter-temporal optimization is more acute when trading costs are considered. The total cost due to market impact is known to be super-linear as a function of the trade size (Almgren et al. (2005) measured an exponent of about 0.6 for impact itself, hence 1.6 for total cost), implying that a large order may be more efficiently executed as a sequence of small orders. Indeed, optimal liquidation paths had already been studied by Almgren and Chriss (1999) under an idealized linear impact model, leading to quadratic total cost. Using techniques inspired by Bayesian statistics, we provide an elegant solution to the classic investment problem of optimally planning a sequence of trades in the presence of transaction costs. 11:00am Taking the Art out of Smart Beta Ed Fishwick, Blackrock The reasons for the outperformance of “smart-beta” portfolios remains somewhat mysterious. This paper extends previous literature on the link between portfolio performance and macroeconomic factors by exploring in detail the relationship between low volatility portfolios and interest rate movements. We propose a method where we use sign changes in interest rates over the past 60 years as a partial determinant of high and low beta returns. In a CAPM framework we find strongly heterogeneous returns to high and low beta dependent on the sign of any interest rate movement, indicating a change in behavior around a zero change. We validate the existence of this zero threshold with a grid search along the likelihood function of our data. We find that the low volatility “anomaly” is strongly related to interest rate changes over the period in question. 12:00pm On a Positive Definition of Asset Specific Risk Dan diBartolomeo, Northfield Investment models routinely make distinctions between factor and idiosyncratic (asset specific) risk. This division is enshrined in theories such as the CAPM and the APT. The estimated magnitudes of stock specific risks are also a key metric of the opportunity set for active equity managers, and are widely used in the scaling of alpha expectations. In the conventional process of constructing factor risk models, we arrive at an estimate of idiosyncratic risk for stocks by virtue of a negative rather than positive specification. We take idiosyncratic risk of an asset to be closely related to the residual portion of the asset’s observed return variance that we cannot explain by virtue of our specification of factors rather than actually trying to directly estimate what the true degree of idiosyncratic risk actually is. Such conventional processes have numerous implications that should be of interest to investors. For example, it implies that different factor specifications of risk models may arrive at different estimates of asset specific risk even with the same input data. In this presentation, we will first examine whether variation of estimates of asset specific risk across models is likely to be statistically significant or economically material. We will then consider a positive definition of specific risk at both the firm and individual security level based on imposing a no-arbitrage condition on the capital structure of a firm. Once we have a prescriptive estimate of specific risk, we will conclude with a discussion of how conditioning the estimates on alternative information sources such as quantification of text news reports can be used to capture time series variation in the true, but unobservable level of asset specific risk. 1:00pm Closing Luncheon –Poolside Our final meal will be a buffet luncheon to encourage everyone to eat together and enjoy a final dose of local camaraderie. If you do need to catch a plane and have to run, there will be boxes available so you can get your sandwich to go.