Three New Academic Research Papers Related to Momentum in Stocks Friday, 5 October, 2018

Related to:

#14 - Momentum Effect in Stocks

Author: Muller, Muller

Title: The Remarkable Relevance of Characteristics for Momentum Profits

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3240609

Abstract:

This paper provides a comprehensive analysis of a large set of momentum enhancing strategies for global equity markets. Our findings reveal the relevance of characteristics in enhancing and explaining momentum after accounting for possible interrelations with idiosyncratic volatility and extreme past returns. Out of a set of eighteen stock characteristics, we find particularly age, book-to-market, maximum daily return, R², information diffusion, and 52-week high price to matter for momentum profits. Overall, and consistent with behavioral explanation attempts, momentum appears to work best for hard-to-value firms with high information uncertainty. There are however substantial cross-country differences with regard to which characteristics truly enhance momentum. Our results imply that the link between idiosyncratic volatility, extreme past returns, and momentum profits itself is unable to comprehensively explain enhanced momentum returns and corroborate the heterogeneity of stock markets around the globe.

Author: Abhyankar, Filippou, Garcia-Ares, Haykir

Title: Overcoming Arbitrage Limits: Option Trading and Momentum Returns

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3206873

Abstract:

Returns to cross-sectional momentum in the U.S. equity market, over 1996-2016, are fifty percent lower and statistically insignificant relative to the previous two decades. The decline is linked to larger arbitrage capital flows, lower stock trading costs, and greater investor awareness after publication. During this period stocks with traded options rose to more than seventy percent of all listed stocks. We find strong evidence that the reduction in momentum profits is also related to stock option trading that offers alternate avenues for short sales and information flows that contribute to more efficient stock pricing.

Author: Avramov, Hore

Title: Cross-Sectional Factor Dynamics and Momentum Returns

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3033349

Abstract:

This paper proposes and implements an inter-temporal model wherein aggregate consumption and asset-specific dividend growths jointly move with two mean-reverting state variables. Consumption beta varies through time and cross sectionally due to variation in half-lives and stationary volatilities of the dividend signals. Winner (Loser) stocks exhibit high (low) half-lives and stationary volatilities, and thus exhibit high (low) consumption beta commanding high (low) risk-premium. The model also rationalizes the "momentum crashes" phenomenon discussed in Daniel and Moskowitz (2014). High half-lives of dividend signals in Winners keep their consumption betas low long after recovering from a prolonged economic downturn, while low half-lives in Losers make their consumption betas grow rather quickly. Thus, coming out of a recession, the long Winner/short Loser strategy reduces in consumption beta and, hence, risk-premia.

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VVIX Index Predicts Value/Growth Return Spread Thursday, 27 September, 2018

A new financial research paper has been published and is related value/growth style returns:

#26 - Value (Book-to-Market) Anomaly

Author: Krause

Title: Risk and Uncertainty in Style Rotation

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3209491

Abstract:

The effectiveness of the VIX index as a leading indicator of style returns has been examined in the finance literature, finding that increases in this “fear index” lead to outperformance of “value” vs “growth” stocks, although the effect has attenuated over time. This study introduces the concept of “uncertainty” as an additional indicator of returns to value, as measured by the CBOE® VVIX (“volatility of volatility”), that that may be considered as a proxy for “uncertainty” in the Knightian sense. Increases in uncertainty (the VVIX index) lead to negative short-term returns to value. Additional macroeconomic variables provide additional incremental information regarding these phenomena.

Notable quotations from the academic research paper:

"this study examines the effectiveness of the two CBOE® volatility indices as leading indicators of style returns (value vs. growth), and the results of the analysis indicate that the CBOE® VVIX index provides significant incremental information regarding the interaction of returns, volatility, and uncertainty on a lead-lag basis. The initial analysis of the VIX index relative to style returns is consistent with Boscaljon et al. (2011) since it finds largely insignificant short-term effects of the VIX index on returns to value.

However, innovations in the VVIX index indicate significant negative returns to value. The inclusion of several macroeconomic variables provides additional explanatory information since the VIX index indicates positive returns to value under certain conditions. The main contribution to the literature of this paper is the introduction of the additional concept of “uncertainty” into the returns to value analysis using highly liquid ETFs. The availability of these products, and their recent exponential growth, provides an opportunity to examine the relation of expected volatility and uncertainty to growth and value using similar, easily tradable and low-cost instruments.

In order to further explore the returns to value from uncertainty as proxied by the VVIX index, in Table 5, changes in the VVIX index are included in the estimations of Equation 2 as a potentially further explanatory, independent variable. In this estimation, there is one indication of the potential returns to value from volatility in conjunction with uncertainty. In Panel A, for the large-cap ETFs, the results for five-day returns to value are significantly positive for changes in the VIX index (volatility) at the five percent level, although some other coefficients (10- and 20- day) are significant at the ten percent level. Additionally, the coefficients are significant and negative for changes in the VVIX index (uncertainty) over five- to thirty-day time periods (the 20-day coefficient is marginally significant) at the five percent level.

VVIX vs value/growth stocks

"


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Bitcoin Returns Resemble Returns of High Sentiment Beta Stocks Friday, 21 September, 2018

A new financial research paper has been published and is related to cryptocurrency trading strategies:

Author: Jo, Park, Shefrin

Title: Bitcoin and Sentiment

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3230572

Abstract:

On the surface, cryptocurrencies share important features in common with high sentiment beta stocks. Baker and Wurgler (2007) identify high sentiment betas with small startup firms that have great growth potential. This paper investigates the degree to which, during the period July 18, 2010 to February 26, 2018, the return to bitcoin displayed the characteristics of a high sentiment beta stock. Using a sentiment-dependent factor model, the analysis indicates that in large measure, bitcoin returns resembled returns to high sentiment beta stocks.

Notable quotations from the academic research paper:

"The main objective of this paper is to investigate the degree to which bitcoin resembles a “high sentiment beta” stock, a term introduced by Baker and Wurgler (2007). They note that some stocks are more vulnerable to being mispriced than others, stating: “stocks of low capitalization, younger, unprofitable, high-volatility, non–dividend paying, growth companies … are likely to be disproportionately sensitive to broad waves of investor sentiment.” This statement implies that when investors become excessively optimistic about stocks in general, they become even more optimistic about stocks of small firms that, while not currently profitable, are perceived as holding great potential for future profitability. Baker and Wurgler describe stocks that are disproportionately sensitive to investor sentiment as featuring “high sentiment beta.”

bitcoin's alpha

Table 3 presents the coefficient estimates of CAPM, Fama-French three, Carhart four, and Fama-French five factor models for U.S. daily excess returns on excess Bitcoin Return. We note that Jensen’s alpha is significant and positive at the 0.1% significance level in all the above asset pricing models during our sample period. Notice too that all of the coefficients of CAPM and Fama-French market and other factors are insignificant, suggesting that bitcoin returns are largely nonsystematic, at least from the perspective of a traditional factor pricing model.

There are at least three different channels by which sentiment can impact bitcoin returns. The first channel is bitcoin-specific, which is reflected in bitcoin’s Jensen’s alpha. The second channel involves the sensitivity of bitcoin’s price to general market sentiment. Baker and Wurgler (2007) describe such sentiment as general optimism about stocks.

The third channel involves the manner in which sentiment mediates fundamental factor loadings, as noted by Baker and Wurgler (2006). Of special interest is the impact of sentiment on factor loadings associated with size (SMB) and profitability (RMW), because of the analogy between bitcoin and Baker and Wurgler’s association of high sentiment beta to small startup firms that are not yet profitable but possess great growth potential. Because bitcoin is the cryptocurrency most closely associated with a blockchain technology, and blockchain activity is positively related to general economic activity, our prior expectation is that bitcoin returns will be statistically related to the market risk premium.

factor loadings

The results displayed in Table 5 Panel A indicate the following. First, the returns to bitcoin are statistically related to the market risk premium. Moreover, the interaction term involving the market risk premium features a negative coefficient. Therefore, when sentiment declines, bitcoin returns become more sensitive to the market risk premium.

Factor loading estimates for two other interaction terms are statistically significant. The first pertains to size (SMB), with a negative sign, and the second pertains to investment (CMA), with a positive sign. The bitcoin size effect is that when sentiment declines, bitcoin returns share a common feature with the stocks of small firms. This finding has the same flavor as Baker and Wurgler’s finding that the size effect only applies in connection with periods of negative sentiment. In particular, this finding is in line with the intuitive association of new cryptocurrencies to the stocks of small startups. The bitcoin investment effect is that when sentiment declines, bitcoin returns share a commonality with firms that invest aggressively rather than conservatively. In this respect, aggressive investment reduces firms’ free cash flows and equity returns, whose effect on bitcoin is most pronounced during times in which investors generally become more bearish.

Table 5 Panel A indicates that bitcoin returns are statistically related to the coefficient on the Fama-French profitability factor RMW (𝛽5 ), and with a positive sign. Therefore, the return to holding bitcoin is generally positive during periods when the stocks of higher profitability firms outperform the stocks of lower profitability firms. At first glance, this finding appears to be at odds with bitcoin returns resembling the returns to the stocks of unprofitable firms. However, in interpreting this finding, it is important to keep in mind that the return to small growth stocks cannot be explained by the Fama-French three-factor model, as those stocks have historically earned low returns, not the high returns predicted by the model. Similarly, in the extended Fama-French factor model, profitability is related differently to small, growth stocks than to other stocks.

One of the main implications of the regression analysis above is that sentiment impacts bitcoin returns indirectly through traditional factors, but with no direct discernable direct effect. This leaves open the question of how bitcoin returns and sentiment have coevolved over time. To investigate this issue, we employ Vector Auto Regression (VAR) models that focus on the dynamic relationship between Investor sentiment index and Bitcoin Return.

VAR model

The results suggest that while Bitcoin Returns do not Granger-cause Investor Sentiment Index for the Investor Sentiment equations (Panel B), Investor Sentiment Index does Granger-cause Bitcoin Returns for the ratio of the Bitcoin Returns equation (Panel A). In particular, bullish (bearish) investor sentiment significantly drives Bitcoin Returns positively (negatively). It is noteworthy that although the time period of the bitcoin price decrease (i.e., January-February, 2018) is much shorter, and the percentage of bearish investor sentiment is somewhat lower, the negative impact of bearish investor sentiment on Bitcoin Returns seems to be a bit larger than the positive impact of bullish investor sentiment on Bitcoin Returns.

We further conduct VAR analysis with alternative measure of investor sentiment, Volatility Index (VIX) for robustness, and present the results in Table 9.

sensitivity to vix

In Bitcoin Return equation, the coefficient on VIX (lagged 1) is significantly negative, which implies that a change in VIX index from day t–30 to day t negatively affects bitcoin return. This negative impact of VIX on Bitcoin Returns can be interpreted to mean that investors’ fear about the future market tends to induce a decrease in the price of bitcoin. Intuitively, when the volatility index increases, investors grow afraid of increased volatility, and sell bitcoin, lowering the bitcoin price. Expressed differently, if the VIX decreases, then the market is less volatile, and investors become willing to take risks to earn above average returns by buying bitcoin, thereby driving up its price."


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Beta Herding and Low-Beta Anomaly Tuesday, 11 September, 2018

A new financial research paper has been published and is related to:

#77 - Beta Factor in Stocks

Author: Hwang, Rubesam, Salmon

Title: Overconfidence, Sentiment and Beta Herding: A Behavioral Explanation of the Low-Beta Anomaly

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3224321

Abstract:

We investigate asset returns using the concept of beta herding, which measures cross-sectional variations in betas induced by investors whose beliefs about the market are biased due to changes in confidence or sentiment. Overconfidence or optimistic sentiment causes beta herding (compression of individual assets’ betas towards the market beta), while under-confidence or pessimistic sentiment leads to adverse beta herding (dispersion of betas away from the market beta). We find that beta herding is related to the low-beta anomaly, as high beta stocks underperform low beta stocks on a risk-adjusted basis exclusively following periods of adverse beta herding. As an explanation of the low-beta anomaly, we propose the persistence of bias in betas (i.e., a large difference in betas) that lasts for more than one year as market uncertainty continues.

Notable quotations from the academic research paper:

"In this study we fill the gap in the literature by investigating the bias in cross-sectional asset returns when individual asset prices move together regardless of their fundamentals. We propose a mechanism that explains this comovement in asset returns with respect to two well-known behavioral biases in finance: investor overconfidence and sentiment.

We demonstrate that the cross-sectional difference in the expected returns and betas of individual assets is suppressed when investors are overconfident about signals of the market outlook, and thus their posterior prediction of the market return is overly affected by these signals. A comparable compression of betas arises in the presence of investor sentiment. When optimistic views about the market outlook prevails, individual betas are biased towards the market beta. The opposite case is also possible: when investors are under-confident about the market outlook or their sentiment is pessimistic, the difference between individual betas increases.

This type of cross-sectional bias in betas is referred to as “beta herding” in this study because individual betas are biased (herd) towards the market beta regardless of their equilibrium risk-return relationship, when investors’ market outlook is excessively affected by their optimism or overconfidence about the market outlook. In practice, when beta herding arises, investors may buy assets whose returns increase less than the market because these assets would appear relatively cheap. Likewise, they may sell assets whose returns increase more than the market because these assets would seem to be relatively expensive and the opportunity for taking apparent profits might be hard to resist. In fact, the more confident or optimistic investors are about their market outlook, the more likely they are to trade at a price close to their view.

In our model, high and low beta stocks are not affected differently by beta herding, and thus beta herding can be easily measured by the cross-sectional variance of standardized-betas, which are equivalent to the t-statistics of beta estimates. The standardized-beta provides information on the precision of the beta estimate in addition to its magnitude, and more importantly, makes it possible to compare the dynamics of beta herding over different periods.

Beta herding

We find that the low-beta anomaly is observed only after periods of adverse beta herding, when the dispersion of standardized-betas increases: for value-weighted decile portfolios formed on standardized-betas, the risk-adjusted return of the high-minus-low portfolio over the 12 months following adverse beta herding is -11.4% per year, whereas the returns are not different from zero following periods of no beta herding or high beta herding. The effects of adverse beta herding on standardized-beta sorted portfolios are quite persistent and remain significant over two years."


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