History Is Worse Than Useless. In a series of articles we published in1 we show that relative valuations predict subsequent returns for both factors and smart beta strategies in exactly the same way price matters in stock selection and asset allocation. The fact they are expensive has two uncomfortable implications. We, as investors, extrapolate that part of the historical alpha at our peril.

As with asset allocation and stock selection, relative valuations can predict the long-term future returns of strategies and factors—not precisely, nor with any meaningful short-term timing efficacy, but well enough to add material value. These findings are robust to variations in valuation metrics, geographies, and time periods used for estimation.

Two assumptions widely supported in the finance literature form the basis for how most investors forecast factor alpha and smart beta strategy alpha. The two assumptions we take issue with are that past performance of factor tilts and smart beta strategies is the best estimate of their future performance, and that factors and smart beta strategies have constant risk premia value-add over time.

Common sense tells us that current yield begets future return. Nowhere is this more intuitive than in the bond market. In the equity market, at least since the s, we know that the cyclically adjusted price-to-earnings CAPE ratio, as demonstrated by Robert Shiller, and the dividend yield are both good predictors of long-term subsequent returns. If relative valuation, and the implication it has for mean reversion, is useful for stock selection and for asset allocation, why would it not matter in choosing factor tilts and equity strategies?

In our smart beta series, we offer evidence that relative valuations are important in the world of factors and smart beta strategies. We show that variations in valuation levels predict subsequent returns and that this relationship is robust across geographies, strategies, forecast periods, and our choice of valuation metrics. Our research tells us that investors who too often select strategies based on wonderful past performance are likely to have disappointing performance going forward.

For many, mean reversion toward historical valuation norms dashes their hopes of achieving the returns of the recent past. These conclusions are, of course, just qualitative. To make them practical, we need to quantify the effects we observe.

In this article we do precisely that. We measure the richness of selected factors based on their relative valuations versus their respective historical norms and calculate their implied alphas. Why Valuations Matter We can easily see the link between valuation and subsequent performance on a scatterplot created using these two variables.

The two scatterplots in Figure 1 are from Arnott, Beck, and Kalesnik a and are examples of the historical distributions of valuation ratios and subsequent five-year returns for a long—short factor, the classic Fama—French definition of value, and for a smart beta strategy the low volatility indexas of March 31, In Junewe identified the former as the cheapest factor, relative to its history, and the latter as the most expensive strategy, relative to its history.

The value factor consists of a long value portfolio and a short growth portfolio. We measure performance and relative valuation by comparing the value portfolio relative to the growth portfolio. For the low-volatility index we measure performance and relative valuation by comparing the low-volatility portfolio with the cap-weighted stock market. The dotted line shows the average relationship between valuations and subsequent five-year performance.

Both scatterplots show negative slope: We use the same method for other factors and smart beta strategies. For most strategies and factors across multiple geographic regions the relationship is both statistically and economically significant. Comparing Alpha-Forecasting Models Many investors expect the alpha of a strategy to be its historical alpha, so much so that this assumption itself is an example of an alpha-forecasting model.

One of the cornerstones of any investment process is an estimate of forward-looking return. We argue that a good alpha-forecasting model, whether for a strategy or a factor tilt, should have three key attributes:. These criteria provide useful metrics for us to compare different alpha-forecasting models. We select six models for comparison.

One model assumes an efficient market: Two of the models use only past performance and ignore valuations, and four of the models are based on valuation levels relative to historical norms.

Similarly, Model 0 assumes the risk-adjusted alpha of a factor tilt or smart beta strategy is approximately zero. We measure the mean squared error relative to an expected alpha of zero. Recent past return most recent five years. This model uses the most recent five-year performance of a factor or strategy to forecast its future return. Because our research tells us that investors who select strategies based on wonderful past performance are likely buying stocks with high valuations, we expect this model will favor the strategies that are currently expensive and have low future expected returns.

Long-term historical past return inception to date. Long-term historical factor returns are perhaps the most widely accepted way to estimate factor premiums expected returnsboth in the literature and in the practitioner community. Doing so requires that we extrapolate historical alpha to make the forecast: Averaging performance over a very long period of time should theoretically mitigate vulnerability to end-point richness.

Valuation dependent overfit to data. This model is a simple and intuitive valuation-dependent model, as illustrated by the log-linear line of best fit in Figure 1. Valuation dependent shrunk parameters. A model calibrated using past results may be overfitted, and as a result provide exaggerated forecasts that are either too good or too bad to be true.

Parameter shrinkage is a common way to reduce model overfitting to rein in extreme forecasts. Appendix A provides more information on how we modify the parameters estimated in Model 4 to less extreme values.

Valuation dependent shrunk parameters with variance reduction. Model 5 further shrinks Model 4 by dividing its output by two. The output of this model is perfectly correlated with the output of Model 4, with the forecast having exactly two times lower variability.

Linear model look-ahead calibration. Model 6 allows look-ahead bias. With our log-linear valuation model we estimate using the full sample. Nevertheless, it provides a useful benchmark—a model that, by definition, has perfect fit to the data—against which we can compare our other models.

How close can we come to this impossible ideal? For our model comparison we use the same eight factors in the US market as we use in our previously published research. The description of our factor construction methodology is available in Appendix B. We use the first 24 years of data Jan —Dec in the initial model calibration, encompassing several valuation cycles, and use the remaining data Jan —Oct to run the model comparison. These data end in because we are forecasting subsequent five-year performance; an end date in October allowed us to conduct our model comparison analysis in November and December.

We report the comparison results in Table 1. Model 0 and Model 2 are our base cases. We need to beat a static zero-alpha assumption Model 0 in order to even argue for the use of dynamic models in alpha forecasting. And we need to beat Model 2 to demonstrate the usefulness of a valuation-based forecasting model.

Assuming that future alpha is best estimated by the past five years of performance, Model 1 provides the least accurate forecast of alpha i.

Further compounding its poor predictive ability, its forecasts are negatively correlated with subsequent factor performance. Focusing on recent performance—the way many investors choose their strategies and managers—is not only inadequate, it leads us in the wrong direction.

Model 2, which uses a much longer period of past performance to forecast future performance, provides a significant improvement in accuracy over Model 1, as reflected by a much smaller MSE. Still, as with Model 1, its forecasts are negatively correlated with subsequent performance, and its forecast accuracy is worse than the zero-factor-alpha Model 0.

Selecting strategies or factors based on past performance, regardless of the length of the sample, will not help investors earn a superior return and is actually more likely to hurt them. The negative correlations of the forecasts of both Models 1 and 2 with subsequent factor returns imply that factors with great past performance are likely overpriced and are likely to perform poorly in the future. Valuation-dependent Models 3—6 all have positive correlations between their forecasts and subsequent returns, and all beat Model 0 in this regard; the correlation is undefined for Model 0 because its forecasts are always constant.

Models 4—6 beat Model 0 in forecast accuracy, with all having a lower MSE than Model 0. All four models that forecast using valuations Models 3—6 are able to substantially improve forecast accuracy compared to Models 1 and 2, which use only past returns.

Model 4 shrinks parameter estimates away from extreme values, mitigating the risk of overfitting the data. It also provides a more realistic out-of-sample alpha forecast compared with Model 5.

We therefore apply it in the next section while cheerfully acknowledging it could likely be further improved to investigate what current valuations are telling us about the alpha forecasts for factors and smart beta strategies. Readers who are more interested in the current forecasts of Model 5, which is also a very good model, merely need to cut these forecasts in half.

Factor and Smart Beta Strategy Alpha Forecasts Using Model 4, we calculate the alpha forecasts over the next five-year horizon for a number of factors and smart beta strategies.

Factors We find that almost all popular factors in the US, developed, and emerging markets have shown strong historical returns. This outcome is utterly unsurprising: The only popular factors with negative but insignificant past performance are illiquidity and low beta in the developed markets, and size in the emerging markets.

Figure 2, Panel A, plots the historical excess return and historical volatility, and Panel B the five-year expected return and expected volatility, at year-end for a number of common factors in the US market, constructed as long—short portfolios.

We provide the same data for the developed and emerging markets in Appendix C. The results can also be found in tabular form later in the article in Table 2, Panel A. The alpha forecasts are plotted against the projected volatilities, which are estimated as an extrapolation of recent past volatility. The volatilities of the factor portfolios are a measure of the volatility of a long—short portfolio; in other words, these volatilities measure the volatility of the return difference between the long and the short portfolios.

Take, for example, the low beta factor in the United States, which has a volatility second only to the momentum factor. Does this mean that low arbitrage binary option ultimatum review stocks have high volatility? The factor portfolio that goes long in low beta stocks and short in high beta stocks carries with it a substantial negative net beta, which contributes to the volatility of the factor.

The volatility of the low beta factor in this long—short framework therefore suggests that a long-only low beta investor should expect large tracking error with respect to the market, even if the portfolio is much less risky than the market. Momentum also typically leads to high easy ways to make legit money online error, while the investment guardian stockbrokers reviews leads to low tracking error.

Viewing projected alpha and relative risk together gives us an insight into the likely information ratios currently available in these factors. Factors with negative forecasted alpha. Forecasted alphas for low beta factors are negative in all markets. Having experienced capm indian stock market today strong bull market from through earlyand even after a large pullback over the second half oflow beta factors are still quite expensive relative to their historical valuation norms.

We hesitate to speculate if this is due to the rising popularity of the factor driving the relative valuation higher or the soaring valuation driving the rising popularity. As anyone in the social sciences knows, correlation is not causation. Either way, the data suggest we should not expect low beta strategies to add much value to investor portfolios until their valuations are more consistent with their past norms. We also hesitate to dismiss the low beta factor solely because of its relative valuation.

Diversification and the quest for return are london stock exchange earl street address important goals. Even at current valuation levels, low volatility can serve an daily forex signal indicator download role in both reducing and diversifying risk.

A sensible response is to rely on the low beta factor less than we might have in the past. Alpha forecasts for the size factor small cap versus large cap are negative in all markets. Put another way, the wow can you make money jewelcrafting factor in all regions is expensive relative to its own historical average.

In the United States this relationship has flipped from a year ago: This huge move takes the size factor in the United States from somewhat cheap a year ago to neutral now. Size has lower long-term historical performance compared to other factors in most regions, so modest overvaluation outside the United States is enough to drive our alpha forecasts negative. Other factors with less attractive projected alphas are illiquidity in the US market and gross profitability in the developed markets, both forecast to have close to zero expected return over the next five years.

Factors with positive forecasted alphas. Value outperformed handily inbut not enough to erase the relative cheapness of the strategy in most markets, especially in the emerging markets. Increasing valuation dispersion around the globe has opened up many great opportunities for the patient value investor, the mirror image—tumbling popularity, tumbling relative valuations, and tumbling historical returns—of the picture painted by low beta.

We look at value two ways. The first, a composite, is one of the factors with the highest projected expected returns across all regions. The composite is constructed using four valuation metrics, each measuring the relative valuation multiples of the long portfolio value relative to the short portfolio growth: Unlike the value composite, it has close to fidelity stock plan services number projected return.

The lower forecasted return may be associated with the big gap in profitability observed among companies today versus in the past. After a stock market development determinants second half ofmomentum has flipped from overpriced to underpriced. It turns out that, although for most factors relative valuation plays out slowly over a number of years, valuation is a pretty good short-term predictor for momentum performance.

Across all markets, we expect momentum to deliver respectable future performance slightly above historical norms. Finally, we are projecting good performance for gross profitability in the US market over the next five years, a switch from last spring.

Our return forecasts are all before trading costs and fees. In the case of momentum, trading costs can dwarf fees. Smart Beta Strategies In addition to factors, which are theoretical difficult-to-replicate long—short portfolios, we estimate the expected risk—return characteristics for a selection of the more-popular smart beta strategies. The list of strategies and the description of their methodologies is available in Appendix B.

In order to produce forecasts we what happens options stock split the strategies using the published methodologies of the underlying indices. Any replication exercise is subject to deviation from the original due operating strategies of binary options forum differences in databases, rebalancing dates, interpretations of the written methodologies, omitted details in the methodology description, and so forth; our replication is no exception.

The results for the smart beta strategies yield a number of interesting observations, some of which are quite similar to our observations about factors. Like popular factors, all popular strategies in all regions with the exception of small cap in emerging markets have positive historical returns.

Again, this should not be surprising because these strategies would not be popular without strong historical returns! Note many of the strategies are simulated backtests for most of the historical test span. Accordingly, as with factors, the high historical returns for long-only investment strategies should be adjusted downward for selection bias.

The historical and expected alphas for the smart beta strategies, as well as their respective tracking errors, implied by current US valuation levels are shown in the scatterplots in Figure 3. Appendix D presents the same data for the developed and emerging markets. The data are also provided in tabular form later in the article in Table 2, Panel B. Smart beta strategies with negative forecasted alphas.

Like our findings regarding the low beta factor, we project that the low beta and low-volatility strategies will underperform their respective benchmarks across all regions. Even after some pretty disappointing results during the second half ofthese strategies still trade at premium valuations.

They will reduce portfolio volatility and are complementary to many other strategies. We also project small-cap and equally weighted strategies to have negative returns over the next five years.

After a sharp run-up in small versus large stocks during the second half ofthe size factor is now expensive relative to average historical valuations in all regions. Smart beta strategies with positive forecasted alphas. Momentum-oriented strategies in all regions—in stark contrast to a year ago—tend to have decent projected returns, gross of trading costs which we discuss in the next section.

Given the current high capm indian stock market today of dispersion in profitability across companies, many high-quality companies are trading at reasonably attractive valuations. Finally, the RAFI Size Factor strategy is projected to have a much higher return in the US and developed markets than other small cap—oriented strategies. Instead of trying to capture the Fama—French SMB small minus big factor, one of the factors with weak long-term empirical support, RAFI Size Factor tries to capture other well-documented factor premia within this segment of small stocks having higher risk and higher potential for mispricing.

We quants have the luxury of residing in a world of theory and truly vast data. Investors operate in the real world. As such, no discussion of forecast returns would be complete without addressing the costs associated with implementing an investment strategy. All of our preceding analysis—as well as the backtests and simulated smart beta strategy and factor investing performance touted in the market today—deals with paper portfolios.

Management fees are highly visible and investors are starting to pay a ways to make money fast in runescape members more attention to them. We applaud this development. We find it puzzling however that, in order to save a few basis points of visible fees, some investors will eagerly embrace dozens of basis points of trading costs, missed trades, transition costs for changing strategies, and other hidden costs.

Monitoring manager performance relative to an index is insufficient to gauge implementation costs. One of the dirty secrets of the indexing world is that indexers can adjust their portfolios for changes in index composition or weights, and changes in the published index take bearville money maker after these trades have already moved prices. To quantify the effect of trading costs on different strategies we use the model developed by our colleagues Aked and Moroz The price impact defined by their model is linearly proportional to the amount of trading in individual stocks, measured relative to the average daily volume ADV.

Many of the strategies still show quite attractive performance. The heaviest toll from trading costs is on the momentum and low-volatility strategies. Momentum strategies, typified by high turnover and by fierce competition to buy the same stocks at the same time on the rebalancing dates, are likely associated with high trading costs.

Low-volatility strategies, already operating from a baseline of low projected returns due to their currently rich valuations, are particularly vulnerable to the impact of trading costs. Low-volatility index calculators and managers should pay close attention to ways to reduce how to make money with alchemy wow mop. Again, these strategies have merit for risk reduction and diversification, but we would caution against expecting the lofty returns of the past.

Five-Year Forecasts We summarize the valuation ratios, historical returns, historical returns net of valuation changes, and expected returns along with estimation errors for the most popular factors and strategies in Table 2.

Panel A shows the results for factors, and Panel B shows the results for smart beta strategies. All of these results reflect our method of calculating relative valuation and relative return forecasts, as described in the published methodology for each of these strategies.

These forecasts have uncertainty that, in most cases, is larger than the alpha forecast. Although large, these tables represent only a portion of the multitude of layers and dimensions that investors should consider when evaluating these strategies.

We encourage investors and equity managers to use the tables as a reference point when making factor allocation decisions. As time passes, valuations change, and the expected returns in the table need to be updated to stay relevant. Strategies that seem vulnerable today may be attractively priced tomorrow, and vice versa. The good news is that we will be providing this information, regularly updated, for these and many more strategies and factors on a new interactive section of our website.

We encourage readers to visit frequently and to liberally provide feedback. Our three-part series covers the topics we believe investors should consider before allocating to such strategies. In our earlier research, we explained how smart beta can go horribly wrong if investors anchor performance expectations on recent returns.

Expecting the past to be prologue sets up two dangerous traps. First, if past performance was fueled by rising valuations, that component of historical performance—revaluation alpha—is not likely to repeat in the future. Worse, we should expect this revaluation alpha to mean revert because strong recent performance frequently leads to poor subsequent performance, and vice versa.

We discussed that winning with smart beta begins by asking if the price is right. Valuations are as important in the performance of factors and smart beta strategies as they are in the performance of stocks, bonds, sectors, regions, asset classes, or any other investment-related category.

Starting valuation ratios matter for factor performance regardless of region, regardless of time horizon, and regardless of the valuation metric being used. We showed how valuations can be used to time smart beta strategies. We know factors can be a source of excess return for equity investors, but that potential excess return is easily wiped out or worse!

Investors fare better if we diversify across factors and strategies, with a preference for those that have recently underperformed and are now relatively cheap because of it. In this article, we offer our estimation of expected returns going forward, based on the logic and the framework we develop in our prior three articles. We hope investors find our five-year forecasts useful in managing expectations about their existing portfolios, and perhaps also in creating winning combinations of strategies, positioned for future—not based on past—success.

Appendix A Technical description of Model 4. Model 4 modifies valuation-dependent Model 3, shrinking the parameters to less extreme values. A more detailed description of the expected returns methodology is available on our website. We define the US large-cap equity universe as stocks whose market capitalizations are greater than the median market cap on the NYSE.

The large-cap universe is then subdivided by various factor signals to construct high-characteristic and low-characteristic portfolios, following Fama and French for the US, and Fama and French for international markets.

Note that slight variations in data cleaning and lagging, as well as different investability screens, could lead to slight differences between our factors and those of Fama and French. As an example, in order to simulate the value factor in the United States, we construct the value stock portfolio from stocks above the 70 th percentile on the NYSE by book-to-market ratio, and we construct the growth stock portfolio from stocks below the 30th percentile by the same measure.

Internationally, we construct the value stock portfolio from stocks above the 70 th percentile in their region North America, Japan, Asia Pacific, Europe, and Emerging Markets by book to market, and the growth stock portfolio from stocks below the 30 th percentile in their region.

The stocks are then market-cap weighted within each of the two portfolios, which are used to form a long—short factor portfolio. Portfolios are rebalanced annually each July with the exception of momentum, low beta, and illiquidity, which are rebalanced monthly. The US data extend from July to Decemberdeveloped data from July to Decemberand emerging markets data from July to Decemberand has been filtered to exclude ETFs and uninvestable securities such as state-owned enterprises and stocks with little to no liquidity.

The signals used to sort the various factor portfolios are:. Aked, Michael, and Max Moroz. Buy Low, Sell High! Frazzini, Andrea, and Lasse Heje Pedersen. The material contained in this document is for general information purposes only. Research results relate only to a hypothetical model of past performance i. No allowance has been made for trading costs or management fees, which would reduce investment performance. Actual results may differ.

Index returns represent back-tested performance based on rules used in the creation of the index, are not a guarantee of future performance, and are not indicative of any specific investment. Indexes are not managed investment products and cannot be invested in directly. Research Affiliates is not responsible for any errors or omissions or for results obtained from the use of this information.

Nothing contained in this material is intended to constitute legal, tax, securities, financial or investment advice, nor an opinion regarding the appropriateness of any investment. The information contained in this material should not be acted upon without obtaining advice from a licensed professional. Research Affiliates, LLC, is an investment adviser registered under the Investment Advisors Act of with the U.

Securities and Exchange Commission SEC. Our registration as an investment adviser does not imply a certain level of skill or training. Investors should be aware of the risks associated with data sources and quantitative processes used in our investment management process.

Errors may exist in data acquired from third party vendors, the construction of model portfolios, and in coding related to the index and portfolio construction process. While Research Affiliates takes steps to identify data and process errors so as to minimize the potential impact of such errors on index and portfolio performance, we cannot guarantee that such errors will not occur.

Research Affiliates is the owner of the trademarks, service marks, patents and copyrights related to the Fundamental Index methodology. Any use of these trade names and logos without the prior written permission of Research Affiliates, LLC is expressly prohibited.

Research Affiliates, LLC reserves the right to take any and all necessary action to preserve all of its rights, title and interest in and to these terms and logos. The views and opinions expressed are those of the author and not necessarily those of Research Affiliates, LLC.

The opinions are subject to change without notice. View the discussion thread. Skip to main content. History Is Worse Than Useless March 06, Rob Arnott, Noah Beck, Vitali Kalesnik. Key Points Using past performance to forecast future performance is likely to disappoint. By significantly extending the period of past performance used to forecast future performance, we can improve predictive ability, but the forecasts are still negatively correlated with subsequent performance: Using relative valuations, we forecast the five-year expected alphas for a broad universe of smart beta strategies as a tool for managing expectations about current portfolios and constructing new portfolios positioned for future outperformance.

These forecasts will be updated regularly and available on our website. For a larger view, please click on the image above. Find your next ETF Asset Class: All Asset Classes Alternatives Asset Allocation Commodities Currency Equity Fixed Income. All Regions Asia-Pacific Developed Markets Emerging Markets Europe Frontier Markets Global Global Ex-U.

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