Risk-managed industry momentum and momentum crashes

This paper investigates Barroso and Santa-Clara’s [J. Financ. Econ., 2008, 116, 111–120] risk-managed momentum strategy in an industry momentum setting. We investigate several traditional momentum strategies including that recently proposed by Novy-Marx [J. Financ. Econ., 2012, 103, 429–453]. We moreover examine the impact of different variance forecast horizons on average pay-offs and also Daniel and Moskowitz’s [J. Financ. Econ., 2016, 122, 221–247] optionality effects. Our results show in general that neither plain industry momentum strategies nor the risk-managed industry momentum strategies are subject to optionality effects, implying that these strategies have no time-varying beta. Moreover, the benefits of risk management are robust across volatility estimators, momentum strategies and subsamples. Finally, the ‘echo effect’ in industries is not robust in subsamples as the strategy works only during the most recent subsample.     

Industry momentum strategies and autocorrelations in stock returns

In this study, we examine the sources of profits to momentum strategies of buying past winner industry portfolios and selling short past loser industry portfolios. We decompose the profit into (1) own-autocovariances in industry portfolio returns, (2) cross-autocovariances among industry portfolio returns, and (3) cross-sectional dispersion in mean portfolio returns. Our empirical results show that the industry momentum effect is mainly driven by the own-autocorrelation in industry portfolio returns, not by return cross-autocorrelations or by cross-sectional differences in mean returns. Indeed, the industry momentum strategy generates statistically significant profits only when own-autocorrelations are positive and statistically significant. The evidence is consistent with several behavioral models (e.g. Journal of Financial Economics 45 (1998) 307; Journal of Finance 53 (1998) 1839; Journal of Finance 54 (1999) 2143) that suggest positive own-autocorrelations in stock returns and hence the price momentum.     

Can exchange traded funds be used to exploit industry and country momentum?

There is overwhelming empirical evidence on the existence of country and industry momentum effects. This line of research suggests that investors who buy country and industry portfolios with relatively high past returns and sell countries and industries with relatively low past returns will earn positive risk-adjusted returns. These studies focus on country and industry indexes that cannot be traded directly by investors. This raises the question of whether country and industry momentum effects really can be exploited by investors or whether they are illusionary in nature because they exist only on non-tradable assets. We analyze the profitability of country and industry momentum strategies using actual price data on exchange traded funds (ETFs). We find that over the sample periods during which these ETFs were traded, an investor would have been able to exploit country and industry momentum strategies with an excess return of about 5 % per annum. These returns cannot be explained by unconditional exposures to the Fama–French factors. The daily average bid-ask spreads on ETFs are substantially below the implied break-even transaction cost levels. Hence, we conclude that investors who are not willing or able to trade individual stocks may use ETFs to benefit from momentum effects in country and industry portfolios.     

Risk-managed 52-week high industry momentum,momentum crashes and hedging macroeconomic risk

This is the first study to investigate the profitability of Barroso and Santa-Clara’s [J. Financial Econ., 2015, 116, 111–120] risk-managing approach for George and Hwang’s [J. Finance, 2004, 59, 2145–2176] 52-week high momentum strategy in an industrial portfolio setting. The findings indicate that risk-managing adds value as the Sharpe ratio increases, and the downside risk decreases notably. Even after controlling for the spread of the traditional 52-week high industry momentum strategy in association with standard risk factors, the risk-managed version generates economically and statistically significant pay-offs. Notably, the risk-managed strategy is partially explained by changes in cross-sectional return dispersion, whereas the traditional strategy does not appear to be exposed to such economic risks.     

Reward-risk momentum strategies using classical tempered stable distribution

We implement momentum strategies using reward-risk measures as ranking criteria based on classical tempered stable distribution. Performances and risk characteristics for the alternative portfolios are obtained in various asset classes and markets. The reward-risk momentum strategies with lower volatility levels outperform the traditional momentum strategy regardless of asset class and market. Additionally, the alternative portfolios are not only less riskier in risk measures such as VaR, CVaR and maximum drawdown but also characterized by thinner downside tails. Similar patterns in performance and risk profile are also found at the level of each ranking basket in the reward-risk portfolios. Higher factor-neutral returns achieved by the reward-risk momentum strategies are statistically significant and large portions of the performances are not explained by the Carhart four-factor model.     

The Momentum Effect in Latin American Emerging Markets

We find that momentum strategies yield profits in Latin American emerging markets. Both stock type and country play a major role in explaining the momentum effect in these markets, but stock type is much more important. For risk-averse investors, winner portfolios stochastically dominate loser portfolios in these markets, implying that there are no asset-pricing models consistent with risk-averse investors that can rationalize the momentum effect. The results obtained via the bootstrap procedure without replacement also uphold this conclusion.     

卖空约束下动量策略在牛市中的盈利性分析

众多实证研究发现,通过持有赢者组合多头和输者组合空头的动量套利策略可以获取显著的超额收益。考虑到我国A股市场卖空约束,选取沪深300样本股和中小板股票设置不同的对照组和投资期限,采用假设检验方法分析了2005年股改以来两次牛市行情中单一赢者组合及输者组合多头策略的盈利性特征,并检验了A股市场中是否存在动量套利交易的盈利模式。实证显示,即使不考虑交易成本,两种多头策略也都很难有显著的高于样本总体的收益率。     

Using industry momentum to improve portfolio performance

Minimum-variance portfolios, which ignore the mean and focus on the (co)variances of asset returns, outperform mean–variance approaches in out-of-sample tests. Despite these promising results, minimum-variance policies fail to deliver a superior performance compared with the simple 1/N rule. In this paper, we propose a parametric portfolio policy that uses industry return momentum to improve portfolio performance. Our portfolio policies outperform a broad selection of established portfolio strategies in terms of Sharpe ratio and certainty equivalent returns. The proposed policies are particularly suitable for investors because portfolio turnover is only moderately increased compared to standard minimum-variance portfolios.     

Momentum strategies in commodity futures markets

The article tests for the presence of short-term continuation and long-term reversal in commodity futures prices. While contrarian strategies do not work, the article identifies 13 profitable momentum strategies that generate 9.38% average return a year. A closer analysis of the constituents of the long–short portfolios reveals that the momentum strategies buy backwardated contracts and sell contangoed contracts. The correlation between the momentum returns and the returns of traditional asset classes is also found to be low, making the commodity-based relative-strength portfolios excellent candidates for inclusion in well-diversified portfolios.     

Momentum strategies based on reward–risk stock selection criteria

In this paper, we analyze momentum strategies that are based on reward–risk stock selection criteria in contrast to ordinary momentum strategies based on a cumulative return criterion. Reward–risk stock selection criteria include the standard Sharpe ratio with variance as a risk measure, and alternative reward–risk ratios with the expected shortfall as a risk measure. We investigate momentum strategies using 517 stocks in the S&P 500 universe in the period 1996–2003. Although the cumulative return criterion provides the highest average monthly momentum profits of 1.3% compared to the monthly profit of 0.86% for the best alternative criterion, the alternative ratios provide better risk-adjusted returns measured on an independent risk-adjusted performance measure. We also provide evidence on unique distributional properties of extreme momentum portfolios analyzed within the framework of general non-normal stable Paretian distributions. Specifically, for every stock selection criterion, loser portfolios have the lowest tail index and tail index of winner portfolios is lower than that of middle deciles. The lower tail index is associated with a lower mean strategy. The lowest tail index is obtained for the cumulative return strategy. Given our data-set, these findings indicate that the cumulative return strategy obtains higher profits with the acceptance of higher tail risk, while strategies based on reward–risk criteria obtain better risk-adjusted performance with the acceptance of the lower tail risk.     

Is the Mean Return of Hotel Real Estate Stocks Apt to Overreact to Past Performance?

This study examines the return patterns of hotel real estate stocks in the U.S. during the period from 1990 to 2007.We find that the magnitude and persistence of future mean returns of hotel real estate stocks can be predicted based on past returns, past earnings surprise, trading volume, firm size, and holding period. The empirical evidence found from this paper confirms that short-horizon contrarian profits can be partially explained by the lead-lag effects, while in the intermediate-term price momentum profits and long-term contrarian profits can be partially attributed to the firms’ overreaction to past price changes. Our results support the contrarian/overreaction hypothesis, and they are inconsistent with the Fama-French risk-based hypothesis or the underreaction hypothesis. The study also confirms the earning underreaction hypothesis and finds the high volume stocks tend to earn high momentum profits in the intermediate-term. The study finds that the earning momentum effect for hotel stocks is more short-lived and smaller in magnitude than the market average. Price momentum portfolios (or contrarian portfolios) of big hotel firms underperform small hotel firms and the hotel price momentum portfolio (or contrarian portfolios) significantly underperform the overall market over the intermediate-term (or the long-term). These findings imply that the U.S. hotel industry, particularly the big hotel firms, have experienced relatively conservative growth in the sample period. It suggests that a conservative hotel growth strategy accompanied by an internal-oriented financing policy is proper in a period of prosperity.     

Productivity Risk and Industry Momentum

A productivity shock identified through a vector autoregression model is a priced risk factor for one‐month industry momentum portfolios and commands a positive risk premium. Stocks in winning industries have greater sensitivity to productivity news, thereby earning higher average returns than stocks in losing industries. This evidence lends support to an Intertemporal Capital Asset Pricing Model (ICAPM) with human wealth. In many specifications, exposure to productivity risk captures more than half of the observed industry momentum profits. This paper studies the sources of profits and attributes the risks of industry momentum portfolios to the behavior of their underlying cash flows.     

Lead–Lag Effects in Australian Industry Portfolios

This study finds that returns to stocks with relatively high market capitalizations lead returns to stocks with relatively low market capitalizations in Australian industry portfolios. These lead–lag patterns or positive cross-correlations are found in both daily and weekly returns and continue to hold even after the market factor is removed. The results for weekly returns are consistent with the findings of Hou (Rev Fin Stud 20:1113–1138, 2007) who found significant lead–lag effects in weekly returns to US industry portfolios. However while that study found stronger lead–lag effects for industries with relatively small average market capitalizations this study finds that industries with larger average market capitalizations have stronger lead–lag effects. This may be caused by industry momentum created by positive news to the leader stocks of an industry, and implies investors can gain significantly by investing in the smaller firms of prominent industries, following an earlier shock to the corresponding leader stocks.     

Underreaction to Political Information and Price Momentum

In this study, we examine whether momentum in stock prices is induced by changes in the political environment. We find that momentum profits are concentrated among politically sensitive firms and industries. From 1939 to 2016, a trading strategy with a long position in winner portfolios (industries or firms) that are politically unfavored and a short position in losers that are politically favored does not generate significant momentum profits. Furthermore, our political‐sensitivity‐based long‐short portfolio explains 23% to 27% (42% to 43%) of monthly stock (industry) momentum alphas. This explanatory power is concentrated around presidential elections, when the level of political activity is high. Collectively, our results suggest that investor underreaction to political information generates momentum in stock and industry returns.     

Momentum and Autocorrelation in Stock Returns

This article studies momentum in stock returns, focusing onthe role of industry, size, and book-to-market (B/M) factors.Size and B/M portfolios exhibit momentum as strong as that inindividual stocks and industries. The size and B/M portfoliosare well diversified, so momentum cannot be attributed to firm-or industry-specific returns. Further, industry, size, and B/Mportfolios are negatively autocorrelated and cross-seriallycorrelated over intermediate horizons. The evidence suggeststhat stocks covary "too strongly" with each other. I argue thatexcess covariance, not underreaction, explains momentum in theportfolios.     

Trend following,risk parity and momentum in commodity futures

We show that combining momentum and trend following strategies for individual commodity futures can lead to portfolios which offer attractive risk adjusted returns which are superior to simple momentum strategies; when we expose these returns to a wide array of sources of systematic risk we find that robust alpha survives. Experimenting with risk parity portfolio weightings has limited impact on our results though in particular is beneficial to long–short strategies; the marginal impact of applying trend following methods far outweighs momentum and risk parity adjustments in terms of risk-adjusted returns and limiting downside risk. Overall this leads to an attractive strategy for investing in commodity futures and emphasises the importance of trend following as an investment strategy in the commodity futures context.     

Idiosyncratic momentum and the cross‐section of stock returns: Further evidence

In this article, we evaluate the profitability and economic source of the predictive power of the idiosyncratic momentum effect, by using five popular asset pricing models to construct the idiosyncratic momentum. We show that all five idiosyncratic momentum strategies produce similar return predictability and consistently outperform the conventional momentum strategy in the cross‐sectional pricing of equity portfolios and individual stocks. This positive effect of idiosyncratic momentum on returns is consistent with the investment capital asset pricing model (CAPM). Further analysis reveals that the firm‐level idiosyncratic momentum effect cannot extend to the aggregate stock market.     

Momentum,reversal, and the trading behaviors of institutions

We identify two types of momenta in stock returns—one due to returns relative to other stocks and one due to firm-specific abnormal returns, where abnormal is determined by a stock's idiosyncratic return variation. Despite similar performances over the first year, these momentum portfolios perform dramatically differently beyond year one. Relative-return momentum reverses strongly; abnormal-return momentum continues for years. This complexity in return momentum challenges the current theories of momentum. We propose that both momenta are consequences of agency issues in the money management industry and provide empirical support for this economic rationale of momentum in returns. Incentives induce institutions to chase relative returns and to underreact to firm-specific abnormal returns.     

Are good-news firms riskier than bad-news firms?

This paper examines the relative risk of good-news firms, i.e., those with high standardized unexpected earnings (SUE), and bad-news (low SUE) firms using a stochastic discount factor approach. We find that a stochastic discount factor constructed from a set of basis assets helps explain post-earnings-announcement drift (PEAD). The risk exposures on the pricing kernel increase monotonically from the lowest to highest SUE sorted portfolios. Specifically, good-news firms always have higher risk exposures than bad-news firms in both 10 SUE sorted portfolios and 25 size and SUE sorted portfolios. However, the estimated expected risk premium is too small to explain the observed magnitude of returns on the PEAD strategy. Our risk adjustment can explain only about one-fourth of the total magnitude of the average realized return to the PEAD strategy. As a result, the average risk-adjusted returns of earnings momentum strategies are mostly positive and significant. Overall, our results support the view that at least some portion of the returns to the earnings momentum strategies examined represent compensation for bearing increased risk.     

Do Industries Explain Momentum?

This paper documents a strong and prevalent momentum effect in industry components of stock returns which accounts for much of the individual stock momentum anomaly. Specifically, momentum investment strategies, which buy past winning stocks and sell past losing stocks, are significantly less profitable once we control for industry momentum. By contrast, industry momentum investment strategies, which buy stocks from past winning industries and sell stocks from past losing industries, appear highly profitable, even after controlling for size, book-to-market equity, individual stock momentum, the cross-sectional dispersion in mean returns, and potential microstructure influences.