## Is Momentum Effect Result of Over- of Under-reaction? Wednesday, 25 November, 2015

**#3 - Sector Momentum – Rotational System
#14 - Momentum Effect in Stocks**

Authors: **Heidari**

Title: **Over or Under? Momentum, Idiosyncratic Volatility and Overreaction**

Link: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2687480

Abstract:

Several studies have attributed the high excess returns of the momentum strategy in the equity market to investor behavioral biases. However, whether momentum effects occur because of investor underreaction or because of investor overreaction remains a question. Using a simple model to illustrate the linkage between idiosyncratic volatility and investor overreaction as well as the stock turnover as another measure of overreaction, I present evidence that supports the investor overreaction explanation as the source of momentum effects. Furthermore, I show that when investor overreaction is low, momentum effects are more due to industries (industry momentum) rather than stocks.

Notable quotations from the academic research paper:

"The existence of significantly positive excess returns from momentum strategies is well established in the literature. However, there is no consensus over what drives these returns. Finding a risk-based explanation for the momentum effects is a tremendously difficult task and momentum constitutes perhaps "the toughest challenge for rational theories of the cross-section of stock returns" (Nagel, 2001). As an alternative, behavioral theories of momentum effects have been suggested by a number of researchers.

The models in this behavioral literature can be divided into two camps: those that characterized price momentum as investor underreaction and those that view it as an investor overreaction to information.

The contributions of this paper are as follows. First, I provide a channel for the contribution of investor overreaction to the idiosyncratic volatility of stocks. Second, I present empirical evidence of the relationship between momentum effects, idiosyncratic volatility and stock turnover. I argue that these results are consistent with the overreaction explanation of momentum effects and also provide a clearer explanation for the connection between momentum and idiosyncratic volatility. Third, I shed some light on the relationship between stock momentum and industry momentum and show that the contribution of the industries in the momentum varies with the level of firm-specific information which is proxied by idiosyncratic volatility.

In this paper, I verify that the momentum effect is stronger among stocks with higher idiosyncratic volatility. I then examine an alternative null hypothesis that does not rely on mispricing or limits to arbitrage. I employ a simple model and show that idiosyncratic volatility is related to the investor overreaction. By introducing the relationship between idiosyncratic volatility and overreaction, I conclude that the higher momentum effect among stocks with higher idiosyncratic volatility is due to investor overreaction. I, therefore, provide support for the overreaction explanation of momentum effects.

In this study, I analyze the relationship between industry and stock momentum from a different perspective. I show that industry momentum contributes more to stock momentum when idiosyncratic volatility is low. Among stocks with high idiosyncratic volatility, most of the momentum returns are driven by stocks rather than industries. This is consistent with the overreaction explanation: when idiosyncratic volatility is low, it implies that investors' overreaction is lower, so momentum returns (which is lower than those of the higher idiosyncratic volatility stocks) are more due to industry momentum. When idiosyncratic volatility is high, there is higher investor overreaction to the stock level information and most of the momentum effect is solely individual security momentum rather than industry momentum."

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## Time-Varying Conditional Market Exposures of the Value Premium Thursday, 19 November, 2015

**#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."

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## Deconstructing the Time-Series Momentum Strategy Wednesday, 11 November, 2015

**#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)."

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