Momentum in stocks

Top Ten Blog Posts on Quantpedia in 2019

29.December 2019

The end of the year is a good time for a short recapitulation. Apart from other things we do (which we will summarize in our next blog in a few days), we have published around 50 short blog posts / recherches of academic papers on this blog during the last year. We want to use this opportunity to summarize 10 of them, which were the most popular (based on Google Analytics tool). Maybe you will be able to find something you have not read yet …

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Three Methods to Fix Momentum Crashes

12.November 2019

Everyone who lived during the 2007 and 2009 crisis knows what the biggest weakness of the equity momentum strategy was. It was right during the spring of 2009 when the financial markets were on its inflection point when the momentum strategy crashed. Right after that inflection point, stocks which were the biggest losers during the previous year performed exceptionally well and caused strong under-performance of classical long-short momentum strategy. How can we prevent this situation from happening again? That’s the topic of our favorite new recent study written by Matthias Hanauer and Steffen Windmueller. They analyze three momentum risk management techniques – idiosyncratic momentum, constant volatility-scaling, and dynamic scaling, to find the remedy for momentum crashes. It’s our recommended read for this week for equity long-short managers …

Authors: Matthias Hanauer and Steffen Windmueller

Title: Enhanced Momentum Strategies

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Momentum Explains a Bunch Of Equity Factors

10.October 2019

Financial academics have described so many equity factors that the whole universe of them is sometimes called “factor zoo”. Therefore, it is no surprise that there is a quest within an academic community to bring some order into this chaos. An interesting research paper written by Favilukis and Zhang suggests explaining a lot of equity factors with momentum anomaly. They show that very often, up to 50% of the equity factor returns can be linked to returns of momentum strategy. This link is especially prevalent in short legs of equity factors.

Authors: Favilukis, Zhang

Title: One Anomaly to Explain Them All

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Three New Insights from Academic Research Related to Equity Momentum Strategy

4.August 2019

What are the main insights?

– The momentum spread (the difference of the formation-period recent 6-month returns between winners and losers) negatively predicts future momentum profit in the long-term (but not in the following month), the negative predictability is mainly driven by the old momentum spread (old momentum stocks are based on whether a stock has been identified as a momentum stock for more than three months)

– The momentum profits based on total stock returns can be decomposed into three components: a long-term average alpha component that reverses, a stock beta component that accounts for the dynamic market exposure (and momentum crash risk), and a residual return component that drives the momentum effect (and subsumes total-return momentum)

– The profitability and the optimal combination of ranking and holding periods of momentum strategies for a sample of Core and Peripheral European equity markets the profitability vary across markets

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Equity Momentum in Years 1820-1930

10.June 2019

Once again, our favorite type of study – an out of sample research study based on data from 19th and beginning of 20th century.  Interesting research paper related to all equity momentum strategies …

Authors: Trigilia, Wang

Title: Momentum, Echo and Predictability: Evidence from the London Stock Exchange (1820-1930)



We study momentum and its predictability within equities listed at the London Stock Exchange (1820-1930). At the time, this was the largest and most liquid stock market and it was thinly regulated, making for a good laboratory to perform out-of-sample tests. Cross-sectionally, we find that the size and market factors are highly profitable, while long-term reversals are not. Momentum is the most profitable and volatile factor. Its returns resemble an echo: they are high in long-term formation portfolios, and vanish in short-term ones. We uncover momentum in dividends as well. When controlling for dividend momentum, price momentum loses significance and profitability. In the time-series, despite the presence of a few momentum crashes, dynamically hedged portfolios do not improve the performance of static momentum. We conclude that momentum returns are not predictable in our sample, which casts some doubt on the success of dynamic hedging strategies.

Notable quotations from the academic research paper:

"This paper studies momentum and its predictability in the context of the rst modern stock market, the London Stock Exchange (LSE), from the 1820s to the 1920s.

Factors' performance. Compared to the U.S. post-1926, we find that the market has been less profi table – averaging 5% annually (but also less volatile). Its Sharpe ratio has been 0.34, not too far from the 0.43 of CRSP. The Small-Minus-Big (SMB) factor delivered a 4.85% average annual return, much higher than that found in U.S. post-1926. The risk-free rate, as proxied by the interest on British Government's consols, has been close to 3.3% throughout the period, despite the many large changes in supply (i.e., in the outstanding stock of public debt). As for momentum (UMD), consistent with the existing evidence it has been the most profi table factor – with an average annual return close to 9% – and the most volatile – with 20% annual standard deviation.

Momentum in years 1820-1930

Dissecting momentum returns. Recent literature debates whether momentum is long or short term. In our sample, UMD profi ts strongly depend on the formation period: they average at 10.6% annually for long-term formation (12 to 7 months) and 3.8% for short-term formation (6 to 2 months). So, our out-of-sample test confi rms that momentum is better described as a within-year echo.

To investigate the role of fundamentals as drivers of price momentum, we construct two sets of earnings momentum portfolio. The first earnings momentum portfolio is constructed based on the past dividend paid by the firm relative to its market cap. The portfolio buys stocks of the highest dividend-paying firms over a 12 to 2 months formation period, and shorts the stocks of the lowest ones. We find strong evidence that our dividend momentum (DIV) strategy is pro fitable across our sample: it yields a 5% average annual return with a standard deviation of 12%.

The second earnings momentum portfolio is constructed based on the dividend innovations. Speci cally, we look at the change of dividend year to year, and construct the DIV portfolio. The portfolio buys stocks with the highest change in dividend paid and shorts the stocks with the lowest ones. The DIV portfolio yield an over 24% return with a standard deviation of only 13.2%.

To discern whether price momentum seems driven by dividend momentum, we also test whether the alpha of the static UMD portfolio remains signi ficant and positive after we control for the Fama-French three factors plus the dividend momentum portfolio. In the EW sample, price momentum delivers excess returns of about 8.8% after controlling for the Fama-French three factors, signifi cant at the 1%. However, introducing DIV momentum reduces the alpha to 2.9%, and the alpha is insigni ficantly di fferent from zero. As for VW portfolios, they deliver higher alphas but are less precisely estimated. In this case, the annualized alpha of price momentum drops by half from 11.2% to 5.8% after controlling for DIV momentum.

Momentum crashes. We find that the distribution of monthly momentum returns is left skewed and displays excess kurtosis. Within the five largest EW (VW) momentum crashes, investors lost 18% (26%) on average. The difference between the beta of the winners and that of the losers has been -2.4 (-3.5), on average, and the losses stemmed mostly from the performance of the losers, which averaged at 24% (21%) monthly return. We find little action in the winners portfolio, which returned on average 2% (-6%).

Predictability and dynamic hedging. Dynamic hedging consists in levering the portfolio when its realized volatility has been low and/or the market has been under-performing, and de-levering otherwise. We begin our analysis by looking at whether set of variables helps predicting momentum returns in our sample, and we find that it does not. Probably, this is because the crashes in our sample are more heterogeneous both in terms of origins and in terms of length. In particular, they do not necessarily occur when the market rebounds after a long downturn, and they tend to last for shorter periods of time. As a consequence, our out-of-sample test of the dynamic hedged UMD strategy shows that either it underperforms static momentum, or it does not improve its returns.


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The Size Effect Has a Lottery-Style Payoff

11.January 2019

A new research paper related mainly to:

#25 – SIze Premium

Authors: McGee, Olmo

Title: The Size Premium As a Lottery



We investigate empirically the dependence of the size effect on the top performing stocks in a cross-section of risky assets separated by industry. We propose a test for a lottery-style factor payoff based on a stochastic utility model for an under-diversified investor. The associated conditional logit model is used to rank different investment portfolios based on size and we assess the robustness of the ranking to the inclusion/exclusion of the best performing stocks in the cross-section. Our results show that the size effect has a lottery-style payoff and is spurious for most industries once we remove the single best returning stock in an industry from the sample each month. Analysis in an asset pricing framework shows that standard asset pricing models fail to correctly specify the size premium on risky assets when industry winners are excluded from the construction of the size factor. Our findings have implications for stock picking, investment management and risk factor analysis.

Notable quotations from the academic research paper:

" Firms with small market capitalization tend to outperform larger companies. Investors are attracted to lottery-like assets with positively skewed returns because they o ffer a very large payoff with a small probability, which the investors overweight. This demand makes positively skewed securities overpriced and likely to earn low returns. In this article we test whether the size/market capitalization attribute, and associated factor-mimicking portfolios, receive a lottery-like payoff . The implications of this are that most small stocks do not payoff and the returns to a size strategy are driven by a small number of winners. This type of payo ff can be captured through diversi fication but leaves an under-diversifi ed investor exposed. The risk being that they will not include winning stocks and their resulting return expectation is negative.

To investigate the e ffect of winning stocks on the performance of investment portfolios based on the size we propose a conditional logit model for ranking di fferent investment portfolios based on size and assess the robustness of the ranking to the inclusion/exclusion of the best performing stocks in the cross-section. This parametric choice is embedded within a stochastic utility model for explaining the investment decisions of under-diversi fied size investors aiming to exploit the so-called size premium. under-diversifi ed individuals maximize their expected utility in each period by choosing the stock that is predicted to yield the highest return (highest positive skew). This choice is driven by market capitalization of the portfolio and modeled parametrically using the conditional logit model.

In order to obtain cross-sectional variation on the relationship between the size e ffect and portfolio performance we split the whole cross-section of stocks into di fferent industries and fi t the conditional logit model to each industry separately. We apply the conditional logit model at an industry-speci fic level across three ranked sorted portfolios based on market capitalization: a small, mid-size and big portfolio created from the stocks in each tercile of the cross-section of assets in a speci fic industry ranked by asset size. This exercise is repeated for 20 industries over the period January 1970 to November 2015. Our results reveal that the size e ffect vanishes once the top performing stocks in an industry are removed from the sample.

size lottery

Our empirical findings also highlight the role of industry momentum in determining the relationship between market capitalization and portfolio performance. Speci fically, market capitalization has signi ficantly better predictive ability for portfolio return performance in the months following a positive return in an industry than in the months following a negative industry return.

Given these findings, we investigate further the influence of the winning stocks in industry-speci fic size portfolios. In particular, we propose an alternative size portfolio that we denominate as the winner-weighted index, based on the forecast rank probabilities of stocks provided by the conditional logit model. Intuitively, those stocks that are predicted to be winners in the next period receive a larger allocation of wealth than those stocks that have a low probability of becoming winners. More formally, the allocation of wealth to each asset in the portfolio is determined by the forecast winning probabilities obtained from the conditional logit model and driven by asset size. The performance of this portfolio is compared against a cap-weighted index benchmark portfolio. The weights in the latter portfolio are also driven by market capitalization, however, in contrast to our winner-weighted index portfolio, smaller stocks within an industry receive a smaller allocation of wealth. We consider statistical and economic measures such as the Sharpe ratio, Sortino ratio, the certainty equivalent return of a mean-variance investor and portfolio turnover. We observe the existence of two regimes in portfolio performance. During positive industry momentum periods, the winner-weighted index outperforms the cap-weighted portfolio for 19 out of 20 industries, the exception being the utilities industry. This result is, however, reversed in periods of negative industry momentum for which the cap-weighted index outperforms the winner-weighted index in 18 out of 20 industries.

Our second objective is to explore the influence of winning stocks on the size portfolio pricing factor widely used in the empirical asset pricing literature. Our empirical results for both a top-minus-bottom trading portfolio and a long-only portfolio show that standard asset pricing models are not able to adequately capture the contribution of the size premium to the overall risk premium when the winning stocks are removed from the size factor portfolio. In contrast, we note that the factor loadings ( Beta's) associated to the size portfolio pricing factor in standard models are robust to the inclusion/exclusion of the winning stocks. The removal of winning stocks is a ffecting the risk premium rather than the covariance of portfolios with the risk factor."

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