Quantpedia Update – 27th August 2016

New strategies:

#318 – Option Trading Volume Predicts Equity Returns

Period of rebalancing: weekly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Bactest period: 1996-2014
Indicative performance: 10.37%
Estimated volatility: 10.99%
Source paper:

Kang, Kim, Lee: Stock Return Predictability of Out-of-the-Money Option Trading
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2803724
Abstract:
We find evidence that unsigned out-of-the money call (put) trading volume, which is publicly observable data, predicts positive (negative) future stock returns. The return predictability is significant both in daily and weekly horizons, and interestingly, it is more significant in larger stocks but unrelated to stock short interests. Using the out-of-the money option trading volume, we propose an implementable investment strategy that offers 20 bp weekly returns (equivalently, about 10% annual returns). We also find evidence that the option volume measure predicts the occurrence of material corporate events such as takeover announcement or earnings surprise. Our finding suggests that the information contained in option markets are relatively slowly incorporated into the stock price.

New research paper related to existing strategy:

#55 – Pairs Trading with Country ETFs

Doering: Does Pairs Trading with ETFs Work?
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2816849
Abstract:
Several studies have demonstrated that pairs trading with single stocks achieved significant excess returns over long periods, with diminishing returns in recent years. However, practitioners extended pairs trading to ETFs. We discuss potential benefits of ETF over stock pairs and test whether ETF pairs trading is profitable, using NYSE Arca trading data over a time span from 2001 to June 2016, and compare our results to stock pairs trading. We find that ETF pairs achieve average excess returns of up to 27 bps per month, but are usually less profitable than stock pairs. Additionally, our results suggest that though ETF pairs trading involves lower arbitrage risks compared to stock pairs, this advantage is more than outweighed by a higher market efficiency effect.

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

Blitz, Vidojevic: The Profitability of Low Volatility
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2811144
Abstract:
Low-risk stocks exhibit higher returns than predicted by established asset pricing models, but this anomaly seems to be explained by the new Fama-French five-factor model, which includes a profitability factor. We argue that this conclusion is premature given the lack of empirical evidence for a positive relation between risk and return. We find that exposure to market beta in the cross-section is not rewarded with a positive premium, regardless of whether we control for the new factors in the five-factor model. We also observe stronger mispricing for volatility than for beta, which suggests that the low-volatility anomaly is the dominant phenomenon. We conclude that the low-risk anomaly is not explained by the five-factor model.

#213 – Long-Term Reversal Combined with a Momentum Effect
#279 – Long-Term Reversal Combined with a Momentum Effect in Industry Portfolios

Blackburn, Cakici: Overreaction and the Cross-Section of Returns: International Evidence
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2800188
Abstract:
A number of theories have been posited linking price momentum with price reversals (Barberis, Shleifer, and Vishny (1998), Daniel, Hirshleifer, and Subrahmanyam (1998), and Hong and Stein (1999)). The models generally rely on behavioral descriptions of irrational investors who push prices beyond their fundamental value thus leading to an inevitable price reversal. While significant empirical evidence has shown the presence of momentum in global equity returns, there have been no large-scale global studies of long-term price reversals. We study returns from twenty-three developed countries categorized into the regions of North America, Europe, Japan, and Asia, over 1993-2014 and find evidence supporting the global presence of long-term price reversal. The return differential between long-term losers and long-term winners is economically and statistically significant. Results from independent double sorts and from Fama-MacBeth regressions show that significant long-term performance helps explain the cross-section of returns after controlling for size, book-to-market equity, and momentum.

Three additional related research papers have been included into existing free strategy reviews during last 2 week:

#5 – FX Carry Trade

1. Volatility and liquidity risk factors explain Carry strategy:

Shehadeh, Li, Moore: The Forward Premium Bias, Carry Trade Return and the Risks of Volatility and Liquidity
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2789141

Abstract:
In this paper, we analyse the relationship between the currency carry return and volatility and liquidity risk factors. We find that both categories of risk factors are relevant to understanding and explaining carry return, with an outperformance for volatility ones especially the global FX volatility risk factor. Consistent with the poor performance of currency carry trades during high FX volatility regime, we also show that the well-established negative slope coefficient in the Fama regression tends to be more positive and even above unity in times of high FX volatility. The paper, overall, contributes to the risk-based solution of the forward premium bias puzzle.

2. FX variance and negative skewness risk factors explain Carry strategy:

Broll: The Carry Trade and Implied Moment Risk
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2775663
Abstract
The carry trade is a zero net investment strategy that borrows in low yielding currencies and subsequently invests in high yielding currencies. It has been identified as highly profitable FX strategy delivering significantly excess returns with high Sharpe ratios. This paper shows that these excess returns are especially compensation for bearing FX variance and negative skewness risk. Additionally, factor risks that affect foreign money changes, foreign inflation changes, as well as changes to a newly developed Carry Trade Activity Index and the VIX index, as a proxy for global risk aversion, make up the carry trade risk anatomy. These findings are not exclusively important for carry traders, but also contribute to the understanding of currency risk in the cross-section. This is directly linked to asset pricing tests from Lustig et al. (2011), which have shown that currency baskets sorted on their interest rate differentials are all exposed to carry trade returns as a risk factor. Furthermore, this paper finds evidence that a decreased level of funding liquidity potentially leads to carry trade unwindings, controlling for equity and FX implied variance and skewness effects, which supports the theoretical model of liquidity spirals developed by Brunnermeier and Pedersen (2009).

Related to multiple equity long/short strategies…

A very important academic paper suggests that investors should trade global equity markets if they want to pursue equity long-short strategy …

Jacobs, Muller: Anomalies Across the Globe: Once Public, No Longer Existent?
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2816490
Abstract:
Motivated by McLean and Pontiff (2016), we study the pre- and post-publication return predictability of 138 anomalies in 39 stock markets. Based on more than a million anomaly country-months, we find that the United States is the only country with a statistically significant and economically meaningful post-publication decline in long/short returns. The surprisingly large differences between the U.S. and international markets cannot be fully explained with general time effects or differences in limits to arbitrage, in-sample anomaly profitability, data availability, or local risk factor exposure. Our results have implications for the recent literature on arbitrage trading, data mining, and market segmentation.

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