Quantpedia Premium Update – July 5th

New strategies:

#886 – Announcement-Adjusted Industry-Relative Reversal Factor

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1973-2021
Indicative performance: 13.76%
Estimated volatility: 10.3%

Source paper:

Wei Dai, Mamdouh Medhat, Robert Novy-Marx, Savina Rizova: Reversals and the returns to liquidity provision
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4339591
Abstract:
Different aspects of liquidity impact the performance of short-run reversals in different ways, consistent with the predictions of microstructure models. Higher volatility is associated with faster, initially stronger reversals, while lower turnover is associated with more persistent, ultimately stronger reversals. These facts also hold outside the US and explain several seemingly disparate results in the literature.

#887 – Pairs Trading in Cryptocurrencies

Period of rebalancing: Daily
Markets traded: cryptocurrencies
Instruments used for trading: cryptocurrencies
Complexity: Complex strategy
Backtest period: 2018-2021
Indicative performance: 70.40%
Estimated volatility: 39.76%

Source paper:

Chiara Lesa, Ronald Hochreiter: Cryptocurrency Pair Trading
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4433530
Abstract:
Pair trading is a strategy which relies on betting on the relative mispricing of the spread between two securities which share a long-term relationship. These strategies have shown to perform well with equities, however not much research has been conducted in the field of cryptocurrencies, even though this asset class has shown characteristics suggesting suitability for pair trading. The Distance Methods and Cointegration Method are applied to a set of cryptocurrencies at formation and trading periods of daily and hourly data. It is shown that the frequency of the selection period does not influence the pairs selected. Cointegration-selected pairs generally outperforms Distance-selected pairs. When trading frequency is analysed, intraday trading is more profitable, but not when using a stop-loss. Cointegration overperforms Distance, as the cost of the latter selection are increased by the higher number of trades.

#888 – Financial Integration and Currency Returns

Period of rebalancing: Monthly
Markets traded: currencies
Instruments used for trading: CFDs, forwards, futures
Complexity: Complex strategy
Backtest period: 1985-2022
Indicative performance: 5.28%
Estimated volatility: 11%

Source paper:

Filippou, Ilias and T. Nguyen, My and Taylor, Mark P.: Investor Attention to News on Financial Integration and Currency Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4364178
Abstract:
In this paper, we examine the economic value of a text-based measure of financial integration. Our attention measure of financial integration is a strong positive predictor of currency excess returns. Specifically, the financial integration measure is positively priced in the cross-section of currency excess returns. Thus, investors require a risk premium for holding currencies with high exposure to financial integration news. Our results are robust to alternative test assets, and the financial integration portfolios offer significant alphas when controlling for other currency investment strategies.

#889 – Improved Cross-Asset Time-Series Momentum I-XTSM

Period of rebalancing: Quarterly
Markets traded: bonds, equities
Instruments used for trading: CFDs, ETFs, funds, futures
Complexity: Simple strategy
Backtest period: 1990-2021
Indicative performance: 20.82%
Estimated volatility: 28.96%

Source paper:

Xu, Dezhong and Li, Bin and Singh, Tarlok and Park, Jung Chul: Cross-Asset Time-Series Momentum Strategy: A New Perspective
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4424602
Abstract:
We propose a new investment strategy, the improved cross-asset time-series momentum (I-XTSM) strategy, to improve investment performance. Using data on 25 investment portfolios and common commodities for the period from January 1990 to April 2021, we find that the I-XTSM strategy increases profitability substantially in the stock market and avoids momentum collapse effectively. We also document that its profitability is driven by the predictive power of the industrial metal assets’ past signals. Even after considering market exposure, the I-XTSM presents a superior performance and explains the excess profits of other momentum strategies.

#890 – Customer Momentum

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1978-2018
Indicative performance: 13.49%
Estimated volatility: 24.09%

Source paper:

Pinchuk, Mykola: Customer Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4338991
Abstract:
This paper examines customer momentum, defined as a positive relationship between a firm’s returns and past returns of its customers. I confirm previous evidence (Cohen and Frazzini 2008) that customer momentum is both statistically and economically significant. Long-short equally-weighted (value-weighted) decile portfolio generates a monthly return of 122 (106) basis points and a t-statistic above 4 (2.8) with respect to Fama-French factor models. The paper reports that customer momentum neither explains nor is explained by price momentum and earnings momentum. Customer momentum is partially driven by the lead-lag relationship between small and large stocks. I find that in the post-discovery sample, customer momentum has a smaller magnitude and loses statistical significance. The results are consistent with the hypothesis that after its discovery, customer momentum decreased due to exploitation by investors.

#891 – Technical Analysis Sentiment Predicts Stock Returns

Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1964-2019
Indicative performance: 10.16%
Estimated volatility: 9.6%

Source paper:

Ding, Wenjie and Mazouz, Khelifa and Gwilym, Owain Ap and Wang, Qingwei: Technical Analysis as a Sentiment Barometer and the Cross-Section of Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4296699
Abstract:
This paper explores an unexamined sentiment channel through which technical analysis can add value. We use a spectrum of technical trading strategies to build a daily market sentiment indicator that is highly correlated with other commonly used sentiment measures. This technical-analysis-based sentiment indicator positively predicts near-term returns and is inversely related to long-term returns in the cross-section. Simple trading strategies based on this sentiment indicator yield substantial abnormal returns. These results are consistent with the explanation that lack of synchronization induces rational arbitrageurs to exploit the mispricing before it is corrected.

New research papers related to existing strategies:

#33 – Post-Earnings Announcement Effect

Xu, Zhiwei and Yang, Yinan: Differences of Opinion, Overpricing and Market Underreaction to Earnings Announcements
https://ssrn.com/abstract=4341969
Abstract:
Prior studies find an insignificantly association between differences of opinion (DO) before earnings announcements (EAs) and the returns around the EAs and thereby reject the resale option theory that DO drives overpricing. We posit two issues that may lead to their conclusion to be one-sided. First, these studies used noisy DO proxies that are confounded by adverse selection. Second, DO and the resulting overpricing may not be largely reduced by the immediate EAs because of investors’ underreaction to the EA news. With a less noisy DO measure (DIFOPN), we verify the insignificant association between pre-EA DO and the EA returns. More importantly, we also find a significantly negative (positive) association between pre-EA DIFOPN and post-EA returns (contemporaneous returns), suggesting that pre-EA DO indeed results in overpricing, which gets corrected with a delay. We further confirm that stocks with higher pre-EA DO suffer greater underreaction to the immediate EAs.

#436 – A Multi Strategy Approach to Trading Foreign Exchange Futures
#563 – Currency Factor Momentum

Pallasena Ranganathan, Ananthalakshmi and Lohre, Harald and Nolte (Lechner), Sandra and Braham, Houssem: An Integrated Approach to Currency Factor Investing
https://ssrn.com/abstract=4437325
Abstract:
Using the G10 currencies, we show that parametric portfolio policies can help guide an optimal currency strategy when tilting towards cross-sectional factor characteristics. While currency carry serves as the main return generator in this tilting strategy, momentum and value are implicit diversifiers to potentially balance the downside of carry investing in flight-to-quality shifts of foreign exchange investors. Drawing insights from a currency timing strategy, according to time series predictors, we further examine the parametric portfolio policy’s ability to mitigate the downside of the carry trade by incorporating an explicit currency factor timing element. This integrated approach to currency factor investing outperforms a naive equally weighted benchmark as well as univariate and multivariate parametric portfolio policies.

#697 – Multifactor Corporate Bond Strategy

Dickerson, Alexander and Mueller, Philippe and Robotti, Cesare: Priced Risk in Corporate Bonds
https://ssrn.com/abstract=4398449
Abstract:
Recent studies document strong empirical support for multifactor models that aim to explain the cross-sectional variation in corporate bond expected excess returns. We revisit these findings and provide evidence that common factor pricing in corporate bonds is exceedingly difficult to establish. Based on portfolio- and bond-level analyses, we demonstrate that previously proposed bond risk factors, with traded liquidity as the only marginal exception, do not have any incremental explanatory power over the corporate bond market factor. Consequently, this implies that the bond CAPM is not dominated by either traded- or nontraded-factor models in pairwise and multiple model comparison tests.

#465 – Equity Momentum Leads Corporate Bonds

Li, Sophia Zhengzi and Yuan, Peixuan and Zhou, Guofu: Corporate Bond Moments and Predictability of Equity Returns
https://ssrn.com/abstract=4374753
Abstract:
We document that the first and third cross-sectional moments of corporate bond returns significantly and positively predict future stock market returns both in- and out-of-sample. The predictability emerges from informed bond trading and gradual diffusion of information. Particularly, the moments contain information about future aggregate firm fundamentals and real economic activity. The lead-lag effect is more pronounced when the lack of integration between the two markets is more severe. Moreover, the predictive power extends to various size- and value-sorted portfolios and industries.

#80 – Earnings Announcement Premium

Liu, Hong and Mao, Yingdong and Tang, Xiaoxiao and Zhou, Guofu: Earnings Announcements: Ex-Ante Risk Premia
https://ssrn.com/abstract=4342267
Abstract:
In this paper, we provide an estimate of the ex-ante risk premia on earnings announcements based on the option market. We find that the risk premia are time-varying and have predictive power on future stock returns. With our ex-ante risk premia as a measure of uncertainty before each earnings announcement, we find that the earnings-returns relation is much weaker when the uncertainty is high. The well-documented positive post-earnings-announcement drift (PEAD) is present only when the risk premia are high. After controlling for the announcement risk premia, the PEAD factor of the literature no longer has any abnormal returns. Moreover, while trading option straddles is not profitable unconditionally, conditional on high ex-ante risk premia, it becomes profitable even net of transaction costs.

And several interesting free blog posts have been published during last 2 weeks:

Why Naively Pursuing Premiums at the Industry and Country Levels Often Does Not Add Value

Sector/industry picking or country picking can be a profitable trading style but is usually much more challenging than it seems at first sight. Building a good trading model requires a lot of research and dedication. Unfortunately, due to the limited numbers of industries and countries, sorting them on aggregate characteristics can wash out important cross-sectional variations in the characteristics and lead to concentrated portfolios prone to noisier realized returns.

In their fresh Dimensional Fund Advisors research piece, Dong, Huang, and Medhat (2023) touch on the question of whether investors should systematically emphasize certain industries or countries to increase expected returns. Their overhead view provides new insights and sums that investors will likely be better off pursuing premiums in the larger cross-section of individual securities and maintaining broad diversification across the smaller cross-sections of industries and countries.

Combining Gold, Bonds and Low Volatility Stocks

Even though gold is generally a volatile asset, it is often considered a key diversifier, hedging against inflation or protecting during economic uncertainties. According to the authors (Pim van Vliet and Harald Lohre), in times of extreme macroeconomic events, including war, hyperinflation, or major economic recessions, gold investing is widely regarded as a safe haven. However, using gold as a hedge comes at the cost of lower returns. The authors explored the importance of gold in investment portfolios and its ability to reduce the risk of losses combined with bonds and stocks. Compared to many existing studies, they also consider a longer timeframe and the impact of inflation.