New strategy:
#363 – Technology Momentum
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Bactest period: 1963-2012
Indicative performance: 8.60%
Estimated volatility: 18.17%
Source paper:
Lee, Charles M.C. and Sun, Stephen Teng and Wang, Rongfei and Zhang, Ran: Technological Links and Predictable Returns
https://ssrn.com/abstract=3036241
Abstract:
This paper finds evidence of return predictability across technology-linked firms. Employing a classic measure of technological closeness between firms, we show that the returns of technology-linked firms have strong predictive power for focal firms’ returns. A long-short strategy based on this effect yields monthly alpha of 117 basis points. This effect is distinct from industry momentum, and is more pronounced for more innovative firms, firms with higher investor inattention, and firms with higher costs of arbitrage. We find a similar lead-lag relation between the earnings surprises, analyst revisions, and innovation-related activities (such as patent and citation counts) of technology-linked firms. Our results are broadly consistent with sluggish price adjustment to more nuanced technological news.
New research papers related to existing strategies:
#210 – Adaptive Asset Allocation
Parker: ACHIEVE YOUR GOALS MORE OFTEN: A CASE FOR ACTIVE ALLOCATION
http://www.naaim.org/wp-content/uploads/2017/05/2017-1st-Place-Winner_ACHIEVE-YOUR-GOALS-MORE-OFTEN-A-Case-for-Active-Allocation-1.pdf
Abstract:
We propose a dynamic portfolio optimization procedure which uses markets to predict asset returns as well as risks. Differing from other approaches to outperformance, we couch this approach firmly in the concept of efficient markets. In effect using the efficiency of markets to outperform alternate buy-and-hold strategies. We also incorporate goals-based portfolio theory in an effort to create a strategy which can be used to help investors achieve their goals more often, as this is why most investors interact with public markets in the first place. To build the optimization strategy, we use option market implied volatility to forecast the standard deviation of an asset in the coming month. To forecast returns in the coming month, we utilize the US Treasury yield curve spread (10-Year minus 3-month) as a probability indicator of coming recessions, then use the probability-weighted sum of returns as the expected portfolio return in the coming month. This information is then used in place of historical return and variance expectations in the optimization model, and the asset allocation is re-optimized (and thus updated) each month. We tested 108 months (9 years), spanning the years 2007 through 2015. When compared against a historically mean-variance optimized, passively-allocated portfolio, the active allocation approach presented and tested here delivers significant alpha, generally lower beta, and considerably higher probabilities of goal achievement. We find that the monthly increase in return over the passive portfolio (+10.25 basis points, +52.15 basis points, and +64.05 basis points) generated by this strategy is statistically significant at the 5% significance level, though in one test we could not reject the null hypothesis at that level of significance. We further find that, when compared to a simple “buy-and-hold the S&P 500” strategy, the active allocation strategy delivers alpha of 9.70, average excess monthly returns of +62 basis points (statistically significant at the 5% level), lower beta (β= 0.57), and considerably better risk/return efficiency (165% higher Sharpe Ratio). These results are robust even after accounting for the effects of diversification, which leads us to conclude that the superiority of the approach can be attributed to the information content of market-based forecasts.
#341 – Opening Range Breakout with Crude Oil
Lundstrom: Day trading returns across volatility states
http://umu.diva-portal.org/smash/get/diva2:732318/FULLTEXT02.pdf
Abstract:
This paper measures the returns of a popular day trading strategy, the Opening Range Breakout strategy (ORB), across volatility states. We calculate the average daily returns of the ORB strategy for each volatility state of the underlying asset when applied on long time series of crude oil and S&P 500 futures contracts. We find an average difference in returns between the highest and the lowest volatility state of around 200 basis points per day for crude oil, and of around 150 basis points per day for the S&P 500. This finding suggests that the success in day trading can depend to a large extent on the volatility of the underlying asset.
Five additional related research papers have been included into existing free strategy reviews during last 2 weeks:
A new financial research paper related to:
#22 – Term Structure Effect in Commodities
Bianchi: Carry Trades and Tail Risk: Evidence from Commodity Markets
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3035453
Abstract:
In this paper I document that carry trades in commodity markets are subject to potential large and infrequent losses, that is, tail risk. Also, I show that shocks to carry trades and volatility have persistent tail-specific effects which last from four to twelve weeks ahead. The main empirical results are consistent with existing theoretical models in which carry traders are subject to limited risk capacity and liquidity constraints. In this respect, I provide evidence that money managers, index traders, and more generally non-commercial traders, tend to unwind their net-long futures positions when exposed to deteriorating aggregate financial conditions and increasing market uncertainty. Methodologically, I make use of panel quantile regressions with non-additive fixed effects, which allow to identify the tail-specific effect of carry on the conditional distribution of commodity futures excess returns.
And an interesting series about factor investing for a long autumn evenings, related to multiple factor strategies:
Arnott, Beck, Kalesnik, West: How Can 'Smart Beta' Go Horribly Wrong?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3040949
Abstract:
Factor returns, net of changes in valuation levels, are much lower than recent performance suggests. Value-add can be structural, and thus reliably repeatable, or situational—a product of rising valuations—likely neither sustainable nor repeatable. Many investors are performance chasers who in pushing prices higher create valuation levels that inflate past performance, reduce potential future performance, and amplify the risk of mean reversion to historical valuation norms. We foresee the reasonable probability of a smart beta crash as a consequence of the soaring popularity of factor-tilt strategies.
Arnott, Beck, Kalesnik: Timing 'Smart Beta' Strategies? Of Course! Buy Low, Sell High!
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3040956
Abstract:
In our paper — “How Can ‘Smart Beta’ Go Horribly Wrong?” — we show that performance chasing can be as dangerous in smart beta as it is in stock selection, fund selection, or asset allocation. We differentiate between “revaluation alpha” and “structural alpha.” The former is the part of the past return that came from rising valuations. Revaluation alpha is nonrecurring, and is at least as likely to reverse as to persist. Rising valuations create an illusion of alpha and encourage performance chasing.
Structural alpha is the part of the past return that was delivered net of any impact from rising valuations. Why do we emphasize rising valuations? Because factors and strategies with tumbling valuations are rarely noticed in the data mining so pervasive throughout the finance community. For some factors, such as low beta, we show that most or all past performance was revaluation alpha, which could easily reverse from current valuation levels. For smart beta strategies, the picture is a bit better: most established products have respectable structural alpha.
In our paper “To Win with ‘Smart Beta’ Ask If the Price Is Right,” we show that valuations are predictive of future returns. We demonstrate that this result is robust across time, in international and emerging markets, and holds for various metrics used to measure valuations. We also point out that — for the moment, at least — many so-called smart beta strategies are trading in the top quartile, and even top decile, of historical valuations. We caution those who believe past is prologue and are tempted to extrapolate past “alpha” into expected future returns without regard to current valuation levels.
In this paper we explore whether active timing of smart beta strategies and/or factor tilts can benefit investors. We find that performance can easily be improved by emphasizing the factors or strategies that are trading cheap relative to their historical norms and by deemphasizing the more expensive factors or strategies. We also observe that aggressive bets (favoring only the cheapest factor or smart beta strategy) can severely erode Sharpe ratios, so that gentle or moderate tilts toward that factor or strategy would seem to be a sensible compromise. Finally, we note that both factor and smart beta strategies have typically been identified and accepted as potentially alpha generating by the finance and investing communities after a period of impressive success — indeed, many of our own tests include a span that predates their discovery. We show that out-of-sample tests, after a strategy or factor has been discovered, are often far less impressive.
Arnott, Beck, Kalesnik: Forecasting Factor and Smart Beta Returns (Hint: History Is Worse than Useless)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3040953
Abstract:
In a series of papers we published in 2016, 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. To many, one surprising revelation in that series is that a number of “smart beta” strategies are expensive today relative to their historical valuations. The fact they are expensive has two uncomfortable implications. The first is that the past success of a smart beta strategy—often only a simulated past performance—is partly a consequence of “revaluation alpha” arising because many of these strategies enjoy a tailwind as they become more expensive. We, as investors, extrapolate that part of the historical alpha at our peril. The second implication is that any mean reversion toward the smart beta strategy’s historical normal relative valuation could transform lofty historical alpha into negative future alpha. 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.
Arnott, Kalesnik, Wu: The Incredible Shrinking Factor Return
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3040964
Abstract:
This is the first in a series of papers we will publish in 2017 that demonstrate factor tilts generally deliver far less alpha in live portfolios than they do on paper, or put another way, investment managers generally fail to capture the returns that would be expected based on their factor tilts. We break our research into four parts. In this paper we show that the factor returns realized by fund managers differ starkly from the theoretical factor returns constructed from long–short paper portfolios. Notably, the market, value, and momentum factors are far less rewarding in live fund management than their theoretical long–short paper portfolio returns.
In the second paper of the series, we challenge the idea that factor tilts — portfolios combining several theoretical factor portfolios — are the same as smart beta strategies. We show, using Fundamental Index™, equal-weight, and low-volatility strategies as illustrative examples, that factor tilts cannot successfully replicate smart beta strategies. Although the factor tilts of these strategies are easy to replicate, the resulting portfolios look very different from the originals, with the replication portfolios having far higher turnover, lower performance, and smaller capacity.
In a third paper of the series, we show that the relative valuations of factor loadings can give us the courage to buy mutual funds when factor tilts are at their cheapest, hence, the most out of favor. Along with fees, turnover, and past performance — where low fees, low turnover, and low (yes, low!) past performance are predictive of better future returns — factor loadings can help us improve our forecasts of fund returns. We find the best predictor is prior three-year performance, but with the wrong sign: buying the losers is the winningest strategy.
Finally, a fourth paper will take a closer look at momentum, for which we find the realized alpha in live portfolios is essentially zero compared to a theoretical alpha of around 6% a year. We show why momentum doesn’t work in live portfolios, and also show how momentum can be saved as a useful source of alpha.
Arnott, Clements, Kalesnik: Why Factor Tilts are Not Smart 'Smart Beta'
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3040970
Abstract:
We challenge the common view that smart beta strategies and factor tilts are equivalent. Initially, the term “smart beta” referred to strategies that broke the link between the price of a stock and its weight in the portfolio or index. Capitalization weighting does not do that — neither does a portfolio that applies factor tilts to a cap-weighted starting portfolio.
Some have suggested that certain smart beta strategies are essentially factor tilt strategies in disguise, which can be replicated with factor tilts applied to a cap-weighted market portfolio. We test this assertion by replicating three first-generation smart beta strategies — Fundamental Index™, equal weight, and minimum variance — with factor tilts. Creating factor-replicated portfolios that match the factor loadings of these smart beta strategies is easy, but the factor-replicated portfolios are poor substitutes for their smart beta counterparts: performance is poor, turnover is high, and capacity is terrible. Why? The simple answer is that construction details matter in achieving both lower trading costs and higher performance.
