Do Short Selling Costs Affect the Profitability of Stock Anomalies Monday, 27 November, 2017

A new financial research paper related to all long-short equity factor strategies:

Authors: Bekjarovski

Title: How Do Short Selling Costs and Restrictions Affect the Profitability of Stock Anomalies?

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3066750

Abstract:

Short selling frictions cannot explain the persistence of seven prominent stock anomalies. Long-only investing is robust and profitable and can be further enhanced by using a synthetic short. Moreover, portfolios restricted to stocks that are easy to short sell continue to have large and significant short anomaly alphas. I derive cost bounds for switching between implementation methods and show that the cost associated with short anomaly positions is small relative to their profitability contribution using a proprietary database of borrowing fees. Overall, the empirical evidence does not support the implications of arbitrage asymmetry that mispricing is concentrated in short positions where it is too costly to exploit.

Notable quotations from the academic research paper:

"Historical tests of the CAPM have given rise to an abundance of anomalously priced characteristics in the cross-section of stock returns. Focus in academic work has been placed on the performance of zero-cost long-short portfolios. In practice, investment vehicles that provide anomaly exposure tend to be long-only. Evidence on the impact of short selling frictions on anomaly profitability is scarce. The goal of this paper is to fill this gap and explore the effect of short selling costs and short selling restrictions on the viability of anomaly investing.

From the point of view of practitioners, the impact of short selling frictions is relevant as it can help determine the optimal approach to anomaly investing. Should strategies be executed long-only or long-short? Alternatively, can shorting the market, rather than shorting individual securities, be used to improve between anomaly fit and the performance of anomaly combinations?

An explanation of anomalies grounded on arbitrage asymmetry implies two key hypotheses: (1) anomaly profitability should be concentrated in short positions and (2) capturing this profitability should be too costly. The paper finds evidence against both claims for seven prominent stocks anomalies. Extensive mispricing exists in long positions and short selling costs are avoidable and low relative to short position profitability. In other words, short selling frictions cannot account for the persistence of anomalies. The evidence is as follows.

Time series alphas are large and statistically significant in long-only anomaly portfolios. Moreover, the inclusion of long-only anomalies to the investment universe leads to an economically large improvement in risk-adjusted performance in portfolio analysis. Sharpe ratios go up by 32% out-of-sample and 60% in-sample relative to a traditional passive investment in the market. The improvement is significant at the 1% level. The results suggest that there is significant profitability in long-positions which is evidence against the first implication of arbitrage asymmetry that anomaly profitability should be concentrated in short positions. Even investors who do not short sell (for whatever reason) can extensively improve performance by including anomalies to their investment universe.

Is it necessary to short sell individual securities to exploit security overpricing and negative alphas? If short selling individual securities is impossible, how can we further improve upon long-only investing? In the presence of all-encompassing prohibitive shorting frictions on individual securities, the paper proposes the use of a synthetic-short strategy. The synthetic bet goes long the highest alpha decile and an intermediate portfolio whilst shorting the market. The synthetic-short approach aims to achieve two objectives. First, it removes overexposure to the equity premium which otherwise dominates long-only investing. Second, the synthetic-short approach exploits negative alphas in overvalued securities. Ideally, investors want to buy positive alpha whilst short selling negative alpha securities. In practice investors can often only easily short sell the market. In other words, investors can only easily short securities in their value-weighted proportions. A long position in a positive alpha decile combined with a market short implies a net short position on all nine remaining decile portfolios. Taking a short position on the lowest negative alpha decile portfolio is beneficial. This is the standard approach in the unrestricted long-short setting. However, short positions in intermediate portfolios can be suboptimal. In properly priced intermediate portfolios without an alpha, short positions are just redundant bets that needlessly waste capital. More problematically however, intermediate portfolios can also have a positive alpha which can be harmful to strategy profitability. To reduce this risk, I include a positive weight in the second highest alpha portfolio in the synthetic-short. The goal is to reduce the net negative weight assigned to the second highest alpha portfolio in the overall synthetic-short strategy.

The results show that using a synthetic-short improves Sharpe ratios by 40% out-of-sample and 80% in-sample relative to long-only investing. Improvements are statistically significant at the 2% level. The findings suggest that long-only investing can be extensively improved by using only a market short. The evidence goes against the second hypothesis as it shows that short position profitability can partly be exploited using a cheap execution method such as a market short.

Short selling individual securities in the short leg of anomalies is profitable in the absence of shorting costs. Short alphas capture 63% of long-short profitability. In addition, portfolio analysis shows that short selling individual securities improves the Sharpe ratio by 64% out of sample and 24% in-sample relative to the synthetic-short approach. Improvements are statistically significant at the 2% level (4% in-sample). Overall, the evidence suggests that short selling restriction on either individual securities or the market can severely reduce the profitability of anomalies. However, they do not completely annul their investment potential.

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A Few Tips for Volatility Trading Tuesday, 21 November, 2017

A new financial research paper related to volatility selling strategies:

Authors: Sepp

Title: Gaining the Alpha Advantage in Volatility Trading

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3032098

Abstract:

We present some empirical evidence for short volatility strategies and for the cyclical pattern of their P&L. The cyclical pattern of the short volatility strategies produces an alpha in good times but collapses to the beta in bad times. We introduce a factor model with risk-aversion to explain the risk-premium of short volatility strategies as a compensation to bear losses in bad market regimes. We then consider an econometric model for statistical inference of market regimes and for optimal position sizing. Finally, we illustrate model applications for generating alpha from volatility strategies.

Notable presentation slides from the academic research paper:

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Better Small Cap Premium Tuesday, 14 November, 2017

A new financial research paper related to:

#25 - Small Capitalization Stocks Premium

Authors: Stefano, Serie, Simon, Lemperiere, Bouchaud

Title: The 'Size Premium' in Equity Markets: Where Is the Risk?

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3018454

Abstract:

We find that when measured in terms of dollar-turnover, and once beta-neutralised and Low-Vol neutralised, the Size Effect is alive and well. With a long term t-stat of 5.1, the “Cold-Minus-Hot” (CMH) anomaly is certainly not less significant than other well-known factors such as Value or Quality. As compared to market-cap based SMB, CMH portfolios are much less anti-correlated to the Low-Vol anomaly. In contrast with standard risk premia, size-based portfolios are found to be virtually unskewed. In fact, the extreme risk of these portfolios is dominated by the large cap leg; small caps actually have a positive (rather than negative) skewness. The only argument that favours a risk premium interpretation at the individual stock level is that the extreme drawdowns are more frequent for small cap/turnover stocks, even after accounting for volatility. This idiosyncratic risk is however clearly diversifiable.

Notable quotations from the academic research paper:

"One of the best known – and perhaps most controversial – effects in the market folklore is the so-called “size premium”, which states that small-cap stocks are on average undervalued and outperform large-caps. But while still highly popular among equity managers, strong doubts about the very existence of a size premium have been expressed in a number of scientific publications.

Here we want to advocate another, complementary picture. We argue that market capitalisation is not an optimal indicator of an otherwise genuine “size” effect. Indeed, the dependence of a stock “beta” on market capitalisation is non-monotonic, which induces spurious biases in a (market neutral) portfolio construction. The resulting SMB portfolios have a strong short Low-Vol exposure.

We propose instead the average daily volume (ADV) of transaction (in dollars) as an alternative indicator of size, and show that the above mentioned biases are substantially reduced. The choice of ADV is further motivated by two independent arguments, often put forth in the literature. One is that the size effect might in fact be a liquidity risk premium, i.e., “Cold” stocks, that are more difficult to liquidate, are traded at a discount. The other is that heavily traded, “Hot” stocks are scrutinized by a larger number of market participants, therefore reducing pricing errors.

Although these arguments sound reasonable, we will show below that they fail to capture the mechanism underlying the profitability of Cold-Minus-Hot (CMH) portfolios. Perhaps surprisingly, standard skewness measures do not conform to the idea of that the size effect is a risk premium. In fact, the single name skewness of small cap/small ADV stocks is positive, and declines to zero as the market cap/ADV increases. At the portfolio level, small cap/ADV stocks do not contribute to skewness either. In fact, the SMB portfolio is only weakly negatively skewed, whereas the CMH portfolio is not skewed at all; furthermore, large gains/losses at the portfolio level mostly come from the short leg (corresponding to large cap/ADV stocks). All these results suggest that “prudence” (i.e. aversion for negative skewness and appetite for positive skewness) should favour small cap/ADV stocks, in contradiction with the idea that SMB or CMH are risk premia strategies.

Interestingly, however, higher moments of the return distribution, such as the kurtosis or the downside tail probability, show a clear decreasing pattern as a function of market cap or ADV. In other words, extreme in both directions are more common for small stocks, even after factoring out volatility. Even if, quite unexpectedly, large upside events are more likely than large downside events for small stocks, “safety first” considerations might be enough to deter market participants from investing in these stocks. This scenario would allow one to think of the size anomaly as a risk premium – albeit a rather non conventional one."


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800 Years of Risk-Free Rate Tuesday, 7 November, 2017

All of us at Quantpedia are history freaks, thefore we absolutely LOVE papers like this:

Authors: Schmelzing

Title: Eight Centuries of the Risk-Free Rate: Bond Market Reversals from the Venetians to the ‘VaR Shock’

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3062498

Abstract:

This paper presents a new dataset for the annual risk-free rate in both nominal and real terms going back to the 13th century. On this basis, we establish for the first time a long-term comparative investigation of ‘bond bull markets’. It is shown that the global risk-free rate in July 2016 reached its lowest nominal level ever recorded. The current bond bull market in US Treasuries which originated in 1981 is currently the third longest on record, and the second most intense.

The second part of this paper presents three case studies for the 20th century, to typify modern forms of bond market reversals. It is found that fundamental, inflation-led bond market reversals have inflicted the longest and most intense losses upon investors, as exemplified by the 1960s market in US Treasuries. However, central bank (mis-) communication has played a key role in the 1994 ‘Bond massacre’. The 2003 Japanese ‘VaR shock’ demonstrates how curve steepening dynamics can create positive externalities for the banking system in periods of monetary policy and financial uncertainty.

The paper finally argues that the inflation dynamics underlying the 1965–70 bond market sell-off in US Treasuries could hold particular relevance for the current market environment.

Notable quotations from the academic research paper:

"The most interesting charts:

Nominal risk-free rate:

Risk Free Rate

Bond bull markets comparison:

Bond Bull Markets

Real risk-free rate:

Real Risk Free Rate

Regression and averages of real risk-free rate:

Averages

Inflation:

Inflation
"


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Autumn Readings about Factor Investing Saturday, 28 October, 2017

An interesting series about factor investing for a long autumn evenings, related to multiple factor strategies:

 

Authors: Arnott, Beck, Kalesnik, West

Title: How Can 'Smart Beta' Go Horribly Wrong?

Link: 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.

 

Authors: Arnott, Beck, Kalesnik

Title: Timing 'Smart Beta' Strategies? Of Course! Buy Low, Sell High!

Link: 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.

 

Authors: Arnott, Beck, Kalesnik

Title: Forecasting Factor and Smart Beta Returns (Hint: History Is Worse than Useless)

Link: 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.

 

Authors: Arnott, Kalesnik, Wu

Title: The Incredible Shrinking Factor Return

Link: 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.

 

Authors: Arnott, Clements, Kalesnik

Title: Why Factor Tilts are Not Smart 'Smart Beta'

Link: 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.

 


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