#26 – Value (Book-to-Market) Anomaly

Authors: Qiao

Title: Conditional Market Exposures of the Value Premium

Link: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2687528

Abstract:

Value strategies exhibit a large positive beta if contemporaneous market excess returns are positive, and a small beta if contemporaneous market excess returns are negative. Value also has a large positive beta after bear markets, but a small beta after bull markets. These facts hold for equity-value strategies in 21 countries, and to a lesser extent for three non-equity-value strategies. Betas conditional on contemporaneous market returns are able to capture expected return variation associated with the book-to-market ratio. These betas partially explain the value premium, and are related to a larger cash-flow risk of value strategies.

Notable quotations from the academic research paper:

"Value strategies exhibit asymmetric betas: a large and positive up-market beta when the contemporaneous market excess returns are positive, and a small or negative down-market beta when the contemporaneous market excess returns are zero or negative. Value strategies also exhibit time-varying betas: after a string of good market returns, or a bull market, value has a small negative bull-market beta. After a string of poor market returns, value has a large positive bear-market beta. Asymmetric betas and time-varying betas also exist for international equity-value strategies, and to a lesser extent, in three non-equity-value strategies.

Asymmetric betas and time-varying betas are plausibly linked through mean-reversion of market returns. Value has a large positive beta in bear markets when market returns have been low. Because market returns tend to mean-revert, expected market returns are high when realized returns have been low. Therefore, value has a large positive beta when expected market returns are high. Value also has a small negative beta when expected market returns are low. Taken together, the time-varying betas combined with mean-reverting market returns translate into asymmetric contemporaneous betas.

Conditional market exposures shed light on the mechanism of value strategies. A decomposition of beta into its cash-flow and discount-rate components reveals asymmetric betas mostly come from cash-flow betas, consistent with the idea that value securities have higher cash-flow risk."

Are you looking for more strategies to read about? Check http://quantpedia.com/Screener

Do you want to see performance of trading systems we described? Check http://quantpedia.com/Chart/Performance

Do you want to know more about us? Check http://quantpedia.com/Home/About

#118 – Time Series Momentum Effect

Authors: Kim, Tse, Wald

Title: Time Series Momentum and Volatility Scaling

Link: http://world-finance-conference.com/papers_wfc2/468.pdf

Abstract:

Moskowitz, Ooi, and Pedersen (2012) show that time series momentum delivers a large and significant alpha for a diversified portfolio of various international futures contracts over the 1985 to 2009 period. Although we confirm these results with similar data,  we find that their results are driven by the volatility-scaled returns (or the so-called risk parity approach to asset allocation) rather than by time series momentum. The alpha of time series momentum monthly returns drops from 1.27% with volatility-scaled weights to 0.41% without volatility scaling, which is significantly lower than the cross-sectional momentum alpha of 0.95%. Using  volatility-scaled positions, the cumulative return of a time series momentum strategy is higher that that of the buy-and-hold  strategy; however, timeseriesmomentuman buy-and-hold offer similar cumulative returns if they are not scaled by volatility. The superior performance of the time series momentum strategy also vanishes in the more recent post-crisis period of 2009 to 2013.

Notable quotations from the academic research paper:

"We revisit the findings of MOP (Moskowitz, Ooi, and Pedersen 2012 time series momentum strategy) using 55 futures contracts over the 1985to 2013 period. One special procedure used by MOP is that they scale the returns of the different futures contracts by a simple lagged estimate of volatility. In particular, an asset with a lower volatility will take a greater position size and have a higher weight in the portfolio. Using the same period as MOP, 1985-2009, and also volatility-scaling returns, we find similar results: A portfolio of 55 futures contracts based on the prior 12-month momentum offers an alpha of 1.27% per monthand the alphas of all the individual contracts (except one) are positive with an average of 1.31%. However, if we use unscaled equal-weighted returns, the portfolio alpha and the average individual alpha drop to 0.41% and 0.42%, respectively.

More specifically, MOP scale the volatility of each individual futures contract to correspond to the volatility of an average stock by effectively leveraging the positions. When we scale the futures contracts toa lower (higher) volatility, we obtain smaller (larger) alphas, and scaling the buy-and-hold strategy produces similar results. Thus the magnitude of the TSMOM strategy appears to be largely due to leveraging a strategy which happened to generate a positive alpha. Without volatility scaling, the monthly time series momentum returns underperform the cross-sectional momentum strategy.

Moreover, while we find a positive alpha when applying a TSMOM (time series momentum) strategy to individual contracts, the individual contract returns do not generally outperform a buy-and-hold futures strategy. Specifically, TSMOM offers higher profits than buy-and-hold for 29 (out of 55) contracts using unscaled returns, and 31 contracts using volatility-scaled returns.

MOP also show that time series momentum profits are larger than those from the cross-sectional momentum (XSMOM) strategy of Jegadeesh and Titman (1993). In contrast, examining the foreign exchange market only, Menkoff et al. (2012) find that the TSMOM strategy is less profitable than the XSMOM strategy. We note that when implementing the TSMOM, Menkoff et al. do not volatility-scale their results. In our study, we show that the alpha of the XSMOM, 0.95%, lies between the alphas obtained from the TSMOM using equal (non-volatility-scaled) and volatility-scaled weights. Therefore, the different weighting schemes may explain the conflicting conclusions of MOP and Menkoff et al.

The volatility scale used by MOP is similar to the so-called risk parity approach to asset allocation. A risk parity portfolio is an equally weighted portfolio, where the weights refer to risk (proxied by standard deviation in MOP) rather than dollar amount invested in each asset (Kazemi, 2012). Risk parity balances a portfolio by increasing (decreasing) the weights of low (high) risk assets and using leverageto attain higher portfolio returns.

We also examine the results for several sub-periods: pre and post-2001, and following the financial crisis, 2009-2013. The choice of 2001 is based on a potential structural break in commodity futures markets around the passage of the Commodity Futures Modernization Act (CFMA) in December 2000. We show that the superior performance of TSMOM is concentrated in the pre-2001 period. When we use more recent periods, we find that the performance of TSMOM is worse than that of a buy-and-hold strategy. These results are consistent with the increase in market quality documented for the equity market by Chordia, Roll, and Subrahmanyam (2011)."

Are you looking for more strategies to read about? Check http://quantpedia.com/Screener

Do you want to see performance of trading systems we described? Check http://quantpedia.com/Chart/Performance

Do you want to know more about us? Check http://quantpedia.com/Home/About

#6- Volatility Effect in Stocks – Long-Short Version
#7- Volatility Effect in Stocks – Long-Only Version

Authors: Stefano, Lamperiere, Bevaratos, Simon, Laloux, Potters, Bouchaud

Title: Deconstructing the Low-Vol Anomaly

Link: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2670076

Abstract:

We study several aspects of the so-called low-vol and low-beta anomalies, some already documented (such as the universality of the effect over different geographical zones), others hitherto not clearly discussed in the literature. Our most significant message is that the low-vol anomaly is the result of two independent effects. One is the striking negative correlation between past realized volatility and dividend yield. Second is the fact that ex-dividend returns themselves are weakly dependent on the volatility level, leading to better risk-adjusted returns for low-vol stocks. This effect is further amplified by compounding. We find that the low-vol strategy is not associated to short term reversals, nor does it qualify as a Risk-Premium strategy, since its overall skewness is slightly positive. For practical purposes, the strong dividend bias and the resulting correlation with other valuation metrics (such as Earnings to Price or Book to Price) does make the low-vol strategies to some extent redundant, at least for equities.

Notable quotations from the academic research paper:

"Our main results are as follows:

We do confirm once again the strength and persistence of the low-vol and low-beta effect on a pool of 9 different countries; in fact we find that the P&L of the two anomalies are very strongly correlated ( ≈ 0.9) suggesting that these two anomalies are in fact one and the same. However, since the market neutral low-vol/low-beta strategy has (by construction) a long dollar bias, it is sensitive to the financing rate.

We find that the low-vol anomaly has nothing to do with short-term (one month) stock reversal – at variance with some claims in the literature, as it entirely survives lagging the measure of past volatilities by one month or more. The low-vol effect is therefore a persistent, long-term effect.

We find that, as expected, low-vol (low-beta) portfolios have strong sector exposures. However, the performance of these strategies remains strong even when sector neutrality is strictly enforced. The low-vol effect is therefore not a sector effect.

We find that a large proportion of the low-vol performance is in fact eked out from dividends. This is our central result, that follows from the strong negative correlation between volatility and dividend yields which (oddly) does not seem to be clearly documented in the literature. However, the low-vol anomaly persists for ex-dividend returns which are found to be roughly independent of the volatility level. Therefore risk-adjusted exdividend returns are themselves higher for low-vol stocks, which is in itself an “anomaly”.

We find that the skewness of low-vol portfolios is small but systematically positive, suggesting that the low-vol excess returns cannot be identified with a hidden risk-premium.

The P&L of the low-vol strategy is ∼ −0.5 correlated with the Small-Minus-Big (Size) Fama-French factor, ∼ 0.2 correlated with the High-Minus-Low (Value) factor and ∼ 0.5 correlated with the Earning-to-Price factor, which is expected since earnings and dividends are themselves strongly correlated. Once these factors are controlled for, the residual performance of low-vol becomes insignificant. This result ties with Novy-Marx’s observations: profitability measures explain to a large degree the low-vol (low-beta) effect.

We find that part of the low-vol effect can be explained by compounding, i.e. the mere fact that a stock having plummeted −20% must make +25% to recoup the losses. Although significant, this mechanism is only part of the story.

By analyzing the holding of mutual funds, we find that (at least in the U.S.) these mutual funds are indeed systematically over-exposed to high vol/small cap stocks and underexposed to low-vol/large cap stocks, in agreement with the leverage constraint and/or bonus incentives stories alluded to above. A similar observation was made in Ref. [13] concerning the behaviour of Japanese institutional investors.

Our overall conclusion is that, while the low-vol (/low-beta) effect is indeed compelling in equity markets, it is not a real diversifier in a factor driven portfolio that already has exposure to Value type strategies, in particular Earning-to-
Price and Dividend-to-Price. Furthermore, the strong observed dividend bias makes us believe that the effect is probably not as convincing in other asset classes such as bonds."

Are you looking for more strategies to read about? Check http://quantpedia.com/Screener

Do you want to see performance of trading systems we described? Check http://quantpedia.com/Chart/Performance

Do you want to know more about us? Check http://quantpedia.com/Home/About

#118 – Time Series Momentum Effect

Authors: Baltas

Title: Trend-Following, Risk-Parity and the Influence of Correlations

Link: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2673124

Abstract:

Trend-following strategies take long positions in assets with positive past returns and short positions in assets with negative past returns. They are typically constructed using futures contracts across all asset classes, with weights that are inversely proportional to volatility, and have historically exhibited great diversification features especially during dramatic market downturns. However, following an impressive performance in 2008, the trend-following strategy has failed to generate strong returns in the post-crisis period, 2009-2013. This period has been characterised by a large degree of co-movement even across asset classes, with the investable universe being roughly split into the so-called Risk-On and Risk-Off subclasses. We examine whether the inverse-volatility weighting scheme, which effectively ignores pairwise correlations, can turn out to be suboptimal in an environment of increasing correlations. By extending the conventionally long-only risk-parity (equal risk contribution) allocation, we construct a long-short trend-following strategy that makes use of risk-parity principles. Not only do we significantly enhance the performance of the strategy, but we also show that this enhancement is mainly driven by the performance of the more sophisticated weighting scheme in extreme average correlation regimes.

Notable quotations from the academic research paper:

"More generally and more aggressively, following the recent financial crisis in 2008, assets from different asset classes (and not just commodities) have started exhibiting stronger co-movement patterns, with the diversification benefits being dramatically diminished.In an environment of increased asset co-movement, the volatility-parity weighting scheme can be deemed a suboptimal choice. By ignoring the covariation between assets, volatility-parity fails to allocate equal amount of risk to each portfolio constituent. This is the reason why volatility-parity is also often called as naïve risk-parity (Bhansali, Davis, Rennison, Hsu and Li, 2012). Following these observations, one possible reason for the recent lacklustre performance of trend-following can be the suboptimal weighting scheme that ignores pairwise correlations (see e.g. Baltas and Kosowski, 2015). Our aim is to address this particular feature of the strategy and construct a portfolio that formally accounts for pairwise correlations.

At this stage, it is important to stress that the profitability of a trend-following strategy depends on two factors: (i) the existence of serial-correlation in the return series and (ii) the efficient combination of assets from various asset classes. It is obvious that the first factor is of utmost importance for the profitability of the strategy; non-existence of persistent price trends cannot be alleviated by a more robust weighting scheme. By amending the volatility-parity scheme in a way that accounts for pairwise correlations, we can only address any inefficiency in the risk allocation between portfolio constituents.

In principle, an optimal allocation to risk that would also account for correlations would optimally over-weight assets, which correlate less with the rest of the universe and under-weight assets that correlate more with the rest of the universe in an effort to improve the overall portfolio diversification. This is the principle of the risk-parity portfolio construction methodology (also known as the Equal Risk Contribution scheme). That is, to equate the contribution to risk from each portfolio constituent, after accounting for any pairwise correlation dynamics.

The empirical question is whether this more sophisticated scheme can overcome the limitations of volatility-parity and consequently hedge against drawdowns experienced in high-pairwise-correlation states. Our findings show that the trend-following portfolio that employs risk-parity principles constitutes a genuine improvement to the traditional volatility-parity variant of the strategy. The Sharpe ratio of the strategy increases from 1.31 to 1.48 over the entire sample period (April 1988 – December 2013), but most importantly it more than doubles over the post-crisis period (January 2009 – December 2013) from 0.31 to 0.78. The improvement is both economically and statistically significant. A correlation event study shows that the improvement is mainly driven by the superior performance of the risk-parity variant of the strategy in extreme average correlation conditions."

Are you looking for more strategies to read about? Check http://quantpedia.com/Screener

Do you want to see performance of trading systems we described? Check http://quantpedia.com/Chart/Performance

Do you want to know more about us? Check http://quantpedia.com/Home/About

#14 – Momentum Effect in Stocks

Authors: Goetzmann, Huang

Title: Momentum in Imperial Russia

Link: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2663482

Abstract:

Some of the leading theories of momentum have different empirical predictions about its profitability conditional on market composition and structure. The overconfidence explanation provided by Daniel, Hirshleifer, and Subrahmanyam (1998), for example, predicts lower momentum profits in markets with more sophisticated investors. The information-based theory of Hong and Stein (1999) predicts lower momentum profits in markets with lower informational frictions. The institutional theory of Vayanos and Woolley (2013) predicts lower momentum profits in markets with less agency. In this paper we use a dataset from a major 19th century equity market to test these predictions. Over this period there was no evidence of delegated management in Imperial Russia. A regulatory change in 1893 made speculating on the St. Petersburg stock market more accessible to small investors. We find a momentum effect that is similar in magnitude to those in modern markets, and stronger during the post-1893 period than during the pre-1893 period, consistent with the overconfidence theory of momentum.

Notable quotations from the academic research paper:

"Momentum strategies, also known as relative strength strategies, are a class of long-short trading strategies that buy past winners and sell past losers. Jegadeesh and Titman (1993) first document the profitability of momentum strategies in a sample of U.S. stocks during the period from 1965 to 1989. Many other studies extend this initial finding by documenting momentum profitability in the post-1989 period, in equity markets outside of the U.S., and in other asset classes While there is an abundance of empirical evidence for momentum, the debate on its underlying mechanism remains unsettled. For instance, Daniel, Hirshleifer, and Subrahmanyam (1998) propose a model in which investor overcondence about the precision of private information generates the momentum effect. On the other hand, in Hong and Stein's (1999) model, the interaction of boundedly rational agents and the slow diffusion of information generate initial underreaction and subsequent overreaction. More recently, Lou (2012) and Vayanos and Woolley (2013) propose explanations of momentum driven by flows into and between institutional money managers.

While these theories all generate the momentum effect, they have different predictions about momentum profitability conditional of market composition and structure. Tests of these predictions require significant variation for identification. In this paper we use a comprehensive dataset of monthly stock returns from the St. Petersburg Stock Exchange in the 19th and early 20th centuries to test these predictions. The St. Petersburg Stock Exchange provides an ideal setting for investigating momentum because: 1) it is, as yet, an untouched sample for finance researchers; 2) there was no evidence of a delegated management structure in the Russian Empire over this period; and 3) a regulatory change in 1893 substantially reduced the costs of speculative trading for less sophisticated investors. The institutional theory predicts a muted momentum effect in such a market as the institutions that the theory relies on to generate momentum were absent. The overcondence theory predicts lower momentum profits during the pre-1893 period than during the post-1893 period, because in the later period there was more market participation by those who are more susceptible to being overconfident. In contrast, the information diffusion theory predicts higher momentum profits during the pre-1893 period, because market participation was narrower and information flow was slower.

Our results are consistent with the investor overcondence theory. Despite the absence of a delegated management structure in our setting, we find that past medium-term winners outperform past medium-term losers by as much as 74 basis points per month, which is similar in magnitude to momentum prots in modern markets. Exposure to the market factor cannot explain this outperformance. In addition, we find that the momentum effect is small and insignificant during the pre-1893 period, but large and highly significant during the post-1893 period. A placebo test using momentum returns from the London Stock Exchange shows that the same empirical regularity is not observed in a market that did not undergo a similar regulatory change.

Daniel and Moskowitz (2013) document that the momentum trade occasionally experiences large crashes. This suggests that, whatever the mechanism, momentum profits could compensate for an infrequently occurring risk factor. Based on this idea, they propose a method to manage this extreme left-tail risk.Interestingly, in our Russian sample extending more than 40 years, we find that while momentum returns are somewhat negatively skewed, extraordinary crashes like those that occurred in the U.S. are absent."

Are you looking for more strategies to read about? Check http://quantpedia.com/Screener

Do you want to see performance of trading systems we described? Check http://quantpedia.com/Chart/Performance

Do you want to know more about us? Check http://quantpedia.com/Home/About

#5 – FX Carry Trade

Authors: Ready, Roussanov, Ward

Title: Commodity Trade and the Carry Trade: A Tale of Two Countries

Link: http://jacobslevycenter.wharton.upenn.edu/wp-content/uploads/2015/05/Commodity-Trade-and-the-Carry-Trade-4.3.15.pdf

Abstract:

Persistent differences in interest rates across countries account for much of the profitability of currency carry trade strategies.  The high-interest rate "investment" currencies tend to be "commodity currencies,"  while low interest rate "funding" currencies tend to belong to countries that export finished goods and import most of their commodities.  We develop a general equilibrium model of international trade and currency pricing in which countries have an advantage in producing either basic input goods or final consumable goods. The model predicts that commodity-producing countries are insulated from global productivity shocks through a combination of trade frictions and domestic production, which forces the final goods producers to absorb the shocks.  As a result, the commodity country currency is risky as it tends to depreciate in bad times, yet has higher interest rates on average due to lower precautionary demand, compared to the final-good producer.  The carry trade risk premium increases in the degree of specialization, and the real exchange rate tracks relative technological productivity of the two countries.  The model's predictions are strongly supported in the data.

Notable quotations from the academic research paper:

"A currency carry trade is a strategy that goes long high interest rate currencies and short low interest rate currencies. A typical carry trade involves buying the Australian dollar, which for much of the last three decades earned a high interest rate, and funding the position with borrowing in the Japanese yen, thus paying an extremely low rate on the short leg. Such a strategy earns positive expected returns on average, and exhibits high Sharpe ratios despite its substantial volatility.  In the absence of arbitrage this implies that the marginal utility of an investor whose consumption basket is denominated in yen is more volatile than that of an Australian consumer. Are there fundamental economic dierences between countries that could give rise to such a heterogeneity in risk?

One  source  of  dierences  across  countries  is  the  composition  of  their  trade. Countries that specialize in exporting basic commodities, such as Australia or New Zealand, tend to have high interest rates.  Conversely, countries that import most of the basic input goods and  export  finished  consumption  goods,  such  as  Japan  or  Switzerland,  have  low  interest rates on average.  These differences in interest rates do not translate into the depreciation of
"commodity currencies" on average; rather, they constitute positive average returns, giving rise  to  a  carry  trade-type  strategy. In  this  paper  we  develop  a  theoretical  model  of  this phenomenon, document that this empirical pattern is systematic and robust over the recent time period, and provide additional evidence in support of the model's predictions for the dynamics of carry trade strategies.

We show that the differences in average interest rates and risk exposures between countries  that  are  net  importers  of  basic  commodities  and  commodity-exporting  countries can be explained by appealing to a natural economic mechanism:  trade costs.

We model trade costs by considering a simple model of the shipping industry.  At any time the cost of transporting  a  unit  of  good  from  one  country  to  the  other  depends  on  the  aggregate  shipping capacity available.  While the capacity of the shipping sector adjusts over time to match the demand for transporting  goods between countries,  it  does so slowly,  due to  gestation  lags in the shipbuilding industry.  In order to capture this intuition we assume marginal costs of shipping an extra unit of good is increasing – i.e., trade costs in our model are convex.  Convex shipping costs imply that the sensitivity of the commodity country to world productivity shocks is lower than that of the country that specializes in producing the final consumption good, simply because it is costlier to deliver an extra unit of the consumption good to the commodity  country  in  good  times,  but  cheaper  in  bad  times.   Therefore,  under  complete financial markets, the commodity country's consumption is smoother than it would be in the absence of trade frictions, and, conversely, the commodity importer's consumption is riskier. Since the commodity country faces less consumption risk, it has a lower precautionary saving demand and, consequently, a higher interest rate on average, compared to the country producing manufactured goods.  Since the commodity currency is risky – it depreciates in bad times – it commands a risk premium.  Therefore, the interest rate differential is not offset on average by exchange rate movements, giving rise to a carry trade.

We show empirically that sorting currencies into portfolios based on net exports of finished (manufactured) goods or basic commodities generates a substantial spread in average excess returns, which subsumes the unconditional (but not conditional) carry trade documented by Lustig,  Roussanov,  and  Verdelhan  (2011).   Further,  we  show  that  aggregate  consumption of  commodity  countries  is  less  risky  than  that  of  finished  goods  producers,  as  our  model predicts"

Are you looking for more strategies to read about? Check http://quantpedia.com/Screener

Do you want to see performance of trading systems we described? Check http://quantpedia.com/Chart/Performance

Do you want to know more about us? Check http://quantpedia.com/Home/About