Financial Analysts Journal

Does asset allocation policy explain 40 percent, 90 percent, or 100 percent of performance? According to some well-known studies, more than 90 percent of the variability of a typical plan sponsor's performance over time is attributable to asset allocation. However, few people want to explain variability over time. Instead, an analyst might want to know how important it is in explaining the differences in return from one fund to another, or what percentage of the level of a typical fund's return is the result of asset allocation. To address these aspects of the role of asset allocation policy, we investigated these three questions. 1. How much of the variability of returns across time is explained by asset allocation policy? 2. How much of the variation of returns among funds is explained by differences in asset allocation policy? 3. What portion of the return level is explained by returns to asset allocation policy? We examined 10 years of monthly returns to 94 balanced mutual funds and 5 years of quarterly returns to 58 pension funds. For the mutual funds, we used return-based style analysis for the entire 120-month period to estimate policy weights for each fund. We carried out the same type of analysis on quarterly returns of 58 pension funds for the five-year 1993-97 period. For the pension funds, rather than estimated policy weights, we used the actual policy weights and asset-class benchmarks of the pension funds. We answered the three questions as follows: In summary, our analysis shows that asset allocation explains about 90 percent of the variability of a fund's returns over time but explains only about 40 percent of the variation of returns among funds. Furthermore, on average across funds, asset allocation policy explains slightly more than 100 percent of the levels of returns. Thus, the answer to the question of whether asset allocation policy explains 40 percent, 90 percent, 100 percent of performance, depends on how the question is interpreted.

With the recent flurry of articles declaiming the death of the rational markets hypothesis, it is well to pause and recall the very sound reasons this hypothesis was once so widely accepted at least in academic circles. Although academic models often assume that all investors are rational, this is clearly an expository device not to be taken seriously. However, what is in contention is whether markets are "rational" in the sense that prices are set as if all investors are rational. Even if markets are not rational in this sense, there may still not be abnormal profits opportunities. In that case, we say the markets are "minimally rational". This article maintains that developed financial markets are minimally rational and, with two qualifications, even achieve the higher standard of rationality. In particular, it contends that realistically, market rationality needs to be defined so as to allow investors to be uncertain about the characteristics of other investors in the market. It also argues that investor irrationality, to the extent it affects prices, is particularly likely to be manifest through overconfidence, which in turn is likely to make the market in an important sense hyper-rational. To illustrate, the paper ends by re-examining some of the most serious evidence against market rationality: excess volatility, the risk premium puzzle, the size anomaly, closed-end fund discounts, calendar effects and the 1987 stock market crash.

This paper contains three parts: a discussion of the tax advantages of household capital (owner-occupied housing and consumer durables) relative to business capital (structures and producers durables) ,an analysis of alternative mechanisms for reducing these advantages (including the use of the mechanisms since 1965) ,and a brief enumeration of various attempts to lower the residential mortgage rate relative to other debt yields that have been employed during the past two decades or are currently being advocated.

U.S. investors hold much less international stock than is optimal according to mean–variance portfolio theory applied to historical data. We investigated whether this home bias can be explained by Bayesian approaches to international asset allocation. In comparison with mean–variance analysis, Bayesian approaches use different techniques for obtaining the set of expected returns by shrinking the sample means toward a reference point that is inferred from economic theory. Applying the Bayesian approaches to the field of international diversification, we found that a substantial home bias can be explained when a U.S. investor has a strong belief in the global mean–variance efficiency of the U.S. market portfolio, and in this article, we show how to quantify the strength of this belief. We also found that one of the Bayesian approaches leads to the same implications for asset allocation as the mean–variance/tracking-error criterion. In both cases, the optimal portfolio is a combination of the U.S. market portfolio and the mean–variance-efficient portfolio with the highest Sharpe ratio.

Chicago-based Morningstar Inc. rates the investment performance of mutual funds using a rating system of one to five stars. This article first documents the method Morningstar uses in assigning these widely circulated ratings. It then establishes that (1) a fund with a long history is less likely to receive the top rating of five stars than a fund with a short history and (2) nearly half of the no-load, diversified, domestic equity funds receive an overall Morningstar rating of four or five stars whereas slightly more than a quarter of these funds receive one or two stars. The disproportionate number of high ratings for these funds is a result of the interaction between the broad comparison group Morningstar uses in its rankings and the handicaps it gives to load and specialized equity funds.

There are no established benchmarks for evaluating currency investment manager performance. Some analysts have suggested that known investing styles like momentum, purchasing power parity, and carry serve as benchmarks. Challenges for this approach include: there is no market portfolio; there are many alternative generic factor constructions; different constructions of the same factor may have low correlations; the 3 factors may not provide diversification; and there is no “buy and hold” in the FX market. An evaluation of professional currency managers’ returns indicates that they are often generated independently from the generic style factors. Skill in timing is what investors should pay for and some managers demonstrate superior skill in timing the factors. Managers are also skilled at minimizing drawdowns relative to the generic factors. The use of generic style factors may be a worst case scenario instead of returns to which an FX investor may aspire.

The no arbitrage relation between futures and spot prices implies an analogous relation between futures and spot volatilities as measured by daily range. Long memory features of the range-based volatility estimators of the two series are analyzed, and their joint dynamics are modeled via a fractional vector error correction model (FVECM), in order to explicitly consider the no arbitrage constraints. We introduce a two-step estimation procedure for the FVECM parameters and we show the properties by a Monte Carlo simulation. The out-of-sample forecasting superiority of FVECM, with respect to competing models, is documented. The results highlight the importance of giving fully account of long-run equilibria in volatilities in order to obtain better forecasts.

A study of one brokerage house's recommendations to its individual customers during the 1960s suggests that they were genuinely valuable, even after allowing for transactions costs and risk. On the other hand, the recommendations were useful in selection, rather than in market timing: The ratio of buys to sells varied little over the period studied. Abnormal returns were associated primarily with buy, rather than sell, recommendations. Positive in the six months prior to the recommendation, they peaked in the month of recommendation and remained essentially zero thereafter. One possible explanation is that, in the months prior to being recommended, companies enjoyed abnormal prosperity accompanied by a series of favorable news items. Their prosperity caught the attention of the brokerage house, whose research staff than uncovered additional positive news, encapsulating in recommendations to customers what might otherwise have been several more months of slowly emerging information. If large positive returns in the month of the recommendation were merely the result of trading pressure induced by the recommendation, those returns would have been followed by reversals. The absence of such reversals suggests that the brokerage house's recommendations were associated with genuine changes in the value of the securities.

"Nowhere does history indulge in repetitions so often or so uniformly as in Wall Street," observed legendary speculator Jesse Livermore. History tells us that periods of major technological innovation are typically accompanied by speculative bubbles as economic agents overreact to genuine advancements in productivity. Excessive run-ups in asset prices can have important consequences for the economy as firms and investors respond to the price signals, resulting in capital misallocation. On the one hand, speculation can magnify the volatility of economic and financial variables, thus harming the welfare of those who are averse to uncertainty and fluctuations. But on the other hand, speculation can increase investment in risky ventures, thus yielding benefits to a society that suffers from an underinvestment problem.

This paper proposes a scalable heuristic for portfolio selection problems in which investments are illiquid and return distributions change over time.

We construct an equally-weighted index of commodity futures monthly returns over the period between July of 1959 and March of 2004 in order to study simple properties of commodity futures as an asset class. Fully-collateralized commodity futures have historically offered the same return and Sharpe ratio as equities. While the risk premium on commodity futures is essentially the same as equities, commodity futures returns are negatively correlated with equity returns and bond returns. The negative correlation between commodity futures and the other asset classes is due, in significant part, to different behavior over the business cycle. In addition, commodity futures are positively correlated with inflation, unexpected inflation, and changes in expected inflation.

Diversification return is an incremental return earned by a rebalanced portfolio of assets. The diversification return of a rebalanced portfolio is often incorrectly ascribed to a reduction in variance. We argue that the underlying source of the diversification return is the rebalancing, which forces the investor to sell assets that have appreciated in relative value and buy assets that have declined in relative value, as measured by their weights in the portfolio. In contrast, the incremental return of a buy-and-hold portfolio is driven by the fact that the assets that perform the best become a greater fraction of the portfolio. We use these results to resolve two puzzles associated with the Gorton and Rouwenhorst index of commodity futures, and thereby obtain a clear understanding of the source of the return of that index. Diversification return can be a significant source of return for any rebalanced portfolio of volatile assets.

Historically, commodity futures have had excess returns similar to those of equities. But what should we expect in the future? The usual risk factors are unable to explain the time-series variation in excess returns. In addition, our evidence suggests that commodity futures are an inconsistent, if not tenuous, hedge against unexpected inflation. Further, the historically high average returns to a commodity futures portfolio are largely driven by the choice of weighting schemes. Indeed, an equally weighted long-only portfolio of commodity futures returns has approximately a zero excess return over the past 25 years. Our portfolio analysis suggests that the a long-only strategic allocation to commodities as a general asset class is a bet on the future term structure of commodity prices, in general, and on specific portfolio weighting schemes, in particular. In contrast, we provide evidence that there are distinct benefits to an asset allocation overlay that tactically allocates using commodity futures exposures. We examine three trading strategies that use both momentum and the term structure of futures prices. We find that the tactical strategies provide higher average returns and lower risk than a long-only commodity futures exposure.

This paper traces the evolution of the concept of "mortgage yield", starting with the yield to prepayment which held sway until the mid-seventies, to the cash flow yield which dominated until the late eighties, to the option adjusted yield which is intellectually dominant today. It is argued that while each of these concepts represented an improvement over the one that preceded it, the cash flow yield should have given way to the holding period yield, and then to an option adjusted holding period yield of which the (currently fashionable) option adjusted yield is merely a special case. The holding period yield is the ideal tool for scenario analysis because of its sensitivity to the particular circumstances of the user, and the option adjusted variant provides better information about whether a security is correctly priced because it does not prejudge the market’s consensus holding period.

We develop a model for pricing risky debt and valuing credit derivatives that is easily calibrated to existing variables. Our approach is based on expanding the Heath-Jarrow-Morton (1990) term-structure model and its extension, the Das-Sundaram (2000) model to allow for defaultable debt with rating transitions. The framework has two salient features, comprising extensions over the earlier work: (i) it employs a rating transition matrix as the driver for the default process, and (ii) the entire set of rating categories is calibrated jointly, allowing, with minimal assumptions, arbitrage-free restrictions across rating classes, as a bond migrates amongst them. We provide an illustration of the approach by applying it to price credit sensitive notes that have coupon payments that are linked to the rating of the underlying credit.

Changes in variance, or volatility, over time can be modeled using the approach based on autoregressive conditional heteroscedasticity. Another approach is to model variance as an unobserved stochastic process. Although it is not easy to obtain the exact likelihood function for such stochastic variance models, they tie in closely with developments in finance theory and have certain statistical attractions. This article sets up a multivariate model, discusses its statistical treatment, and shows how it can be modified to capture common movements in volatility in a very natural way. The model is then fitted to daily observations on exchange rates.

Previous research suggests that the market for index-linked bonds is not entirely efficient and that these inefficiencies can be exploited by including inflation forecasts in trades on break-even inflation. Inspired by those results, we test the informational content of inflation expectations using survey data generated by the Survey of Professional Forecasters. We develop trading strategies speculating on the movement of break-even inflation. The results indicate that the market for US inflation-indexed government bonds offers the possibility to obtain excess returns. These results are fairly consistent regardless of market frictions introduced in the return calculation.

There exists a widespread consensus among mainstream academics and investors that socially responsible investing (SRI) leads to inferior, rather than superior, portfolio performance. Using Innovest’s well-established corporate ecoefficiency scores, we provide evidence to the contrary. We compose two equity portfolios that differ in eco-efficiency characteristics and find that our highranked portfolio provided substantially higher average returns compared to its low-ranked counterpart over the period 1995-2003. Using a wide range of performance attribution techniques to address common methodological concerns, we show that this performance differential cannot be explained by differences in market sensitivity, investment style, or industry-specific components. We finally investigate whether this eco-efficiency premium puzzle withstands the inclusion of transaction costs scenarios, and evaluate how excess returns can be earned in a practical setting via a best-in-class stock selection strategy. The results remain significant under all levels of transactions costs, thus suggesting that the incremental benefits of SRI can be substantial.

In the study reported here, we estimated the forward-looking long-term equity risk premium by extrapolating the way it has participated in the real economy. We decomposed the 1926–2000 historical equity returns into supply factors-inflation, earnings, dividends, the P/E, the dividend-payout ratio, book value, return on equity, and GDP per capita. Key findings are the following. First, the growth in corporate productivity measured by earnings is in line with the growth of overall economic productivity. Second, P/E increases account for only a small portion of the total return of equity. The bulk of the return is attributable to dividend payments and nominal earnings growth (including inflation and real earnings growth). Third, the increase in the equity market relative to economic productivity can be more than fully attributed to the increase in the P/E. Fourth, a secular decline has occurred in the dividend yield and payout ratio, rendering dividend growth alone a poor measure of corporate profitability and future growth. Our forecast of the equity risk premium is only slightly lower than the pure historical return estimate. We estimate the expected long-term equity risk premium (relative to the long-term government bond yield) to be about 6 percentage points arithmetically and 4 percentage points geometrically.

U.S. Treasury Inflation Protection Securities (TIPs) were first issued in January 1997. Through the end of 2002, eleven TIPs have been issued with maturities ranging from a few years through thirty years. One TIP bond has already matured. Returns on TIPs have been positively correlated with returns on nominal bonds and negatively correlated with equity returns over the past five years. TIPs real durations are longer than nominal bonds because their real yields are low. However, their effective nominal durations are much shorter because they are not as sensitive to changes in expected inflation. TIPs volatility has displayed marked variation over time. It was relatively low during 1999- 2000 and considerably higher during 2001-2002. This suggests that real interest rate volatility has increased recently. TIPs can be used to estimate the real yield curve. The real and nominal yield curves can then be combined to estimate the term structure of anticipated inflation. Because of their taxation, TIPs yields may not be entirely independent of inflation. Given plausible assumptions about future expected returns, an investment portfolio diversified across equities and nominal bonds would be improved by the addition of TIPs.

This article takes a critical look at the equity premium puzzle the inability of standard intertemporal economic models to rationalize the statistics that have characterized U.S. financial markets over the past century. A summary of historical returns for the United States and other industrialized countries and an overview of the economic construct itself are provided. The intuition behind the discrepancy between model prediction and empirical data is explained. After detailing the research efforts to enhance the model's ability to replicate the empirical data, I argue that the proposed resolutions fail along crucial dimensions

Presented are an overview of the findings from the recent literature on the cost of U.S. equity trades for institutional investors and new evidence on trading costs from a large sample of institutional trades. The findings discussed have important implications for policymakers and investors: Implicit trading costs are economically significant; equity trading costs vary considerably and vary systematically with trade difficulty and order-placement strategy; and whether a trade price represents "best execution" depends on detailed data for the trade's entire order-submission process, especially information on pretrade decision variables, such as the trading horizon.