Quantpedia Premium Update – 21st of June

New strategies:

#880 – Co-Skewness Enhanced Momentum

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1974-2019
Indicative performance: 23.18%
Estimated volatility: 14.87%

Source paper:

Dong, Liang and Dai, Yiqing and Haque, Tariq H. and Kot, Hung Wan and Yamada, Takeshi, Coskewness and Reversal of Momentum Returns: The US and International Evidence
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4247732
Abstract:
The winner-minus-loser (WML) momentum strategy carries an inherent downside as its returns have negative coskewness. We propose a coskewness-volatility-managed momentum strategy that reduces the reversal risk of the baseline WML strategy by 61% and that of the volatility-managed momentum strategy (Barosso and Santa-Clara, 2015) by 20% for US stocks. The returns of our strategy generate a slightly positive skewness in contrast with the negative skewness of the WML and volatility-managed strategies. Since the coskewness of momentum portfolio returns predict future returns for up to 12 months, our strategy is effective for momentum portfolios of holding periods longer than one month. Our strategy also mitigates momentum downside risks in major international stock markets such as the UK, Germany, and France.

#881 – How Satellite Launches Influence Stock Returns

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1957-2019
Indicative performance: 17.2%
Estimated volatility: 20.26%

Source paper:

Do, Hung Xuan and Nguyen, Nhut H. and Nguyen, Quan M. P. and Truong, Cameron, Aerospace Competition, Investor Attention, and Stock Return Comovement
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4376532
Abstract:
The fierce aerospace competition between superpowers results in a strong public attention to satellite launch events in the U.S. Under limited attentional resources, U.S. investors allocate their attention more to market-level shocks than firm-specific shocks, making stock returns to comove more with the market on satellite launch days compared to other days. Particularly, we find that the effect is significantly stronger for the military related satellite launches, for launches before the Soviet dissolution, and for international satellite launches by other competitors, highlighting a greater concern placed for national security. As a practical implication, a trading strategy exploiting the potential satellite-induced mispricing could yield an annualized abnormal risk-adjusted return of up to 17% within the three-day window around the launch date. Our results are robust to a battery of robustness analyses considering different characteristics of satellite launches, an exclusion of firms in the aerospace industry, and stock return comovement with industries

#882 – Opening Range Breakout (ORB) Strategy in QQQ

Period of rebalancing: Intraday
Markets traded: equities
Instruments used for trading: CFDs, ETFs, futures
Complexity: Complex strategy
Backtest period: 2016-2023
Indicative performance: 31%
Estimated volatility: 27.68%

Source paper:

Zarattini, Carlo and Aziz, Andrew: Can Day Trading Really Be Profitable? Evidence of Sustainable Long-term Profits from Opening Range Breakout (ORB) Day Trading Strategy vs. Benchmark in the US Stock Market
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4416622
Abstract:
The validity of day trading as a long-term consistent and uncorrelated source of income for traders and investors has always been a matter of debate. In this paper, we investigate the profitability of the well-known Opening Range Breakout (ORB) strategy during the period of 2016 to 2023. This period encompasses two bear markets and a few events with abnormal volatility. Our results suggest that with the proper use of leverage or leveraged products (such as 3x leveraged ETFs), day trading can empirically produce significant returns when compared to a standard buy and hold strategy on benchmark indexes in the US public equity markets (Nasdaq or NYSE). Without any loss of generality, we studied the results of an ORB strategy implemented in QQQ. By comparing the results of the active day trading approach with a passive exposure in QQQ, we prove that it is possible for the ORB portfolio to significantly outperform the passive investment. In fact, the day trading portfolio produced an annualized alpha of 33% (net of commissions). Nevertheless, due to leverage constraints enforced by brokers, an active trader would have capped the full upside potential given by the ORB strategy. To overcome this issue, we introduced the use of TQQQ, a leveraged ETF of QQQ, which allows day traders to fully exploit the benefit of the active strategy while adhering to leverage constraints. The resulting portfolio would have earned an outstanding return of 1,484% during the same period of 2016 to 2023, while an investment in the QQQ ETF would have earned 169% annualized

#883 – High-Momentum in Liquid Cryptocurrencies

Period of rebalancing: Weekly
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Simple strategy
Backtest period: 2017-2022
Indicative performance: 13.61%
Estimated volatility: 6.49%

Source paper:

Fičura, Milan: Impact of Size and Volume on Cryptocurrency Momentum and Reversal
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4378429
Abstract:
We analyse how cryptocurrency size and trading volume impact the momentum and reversal dynamics of their returns. We show that the previously reported weekly return reversal occurs for small and illiquid coins only (t-stat = -7.31), while the large and liquid coins exhibit weekly momentum effect instead (t-stat = 2.33). Long-term returns exhibit reversal effects, which are, however, insignificant for the large and liquid coins. We further analyse the impact of high momentum on future cryptocurrency returns, measured as the distance of previous-week closing price from the k-week high. High momentum has not been analysed on cryptocurrency markets before, and we show it to be a superior predictor of future returns when compared to regular momentum. The distance from the 1-week high predicts negatively future returns of small and illiquid coins (t-stat = -9.03) and positively future returns of large and liquid coins (t-stat = 4.93). The results are highly robust to different settings of the size and liquidity thresholds. We further show that the short-term reversal of small and illiquid coins is driven mostly by their low trading volumes, while the short-term momentum of large and liquid coins is driven mostly by high market capitalizations and to a lower degree by high trading volumes.

#884 – Predicting Stock Outperformance by Machine Learning

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 2004-2018
Indicative performance: 15.25%
Estimated volatility: 12.48%

Source paper:

Breitung, Ch.: Automated Stock Picking using Random Forests
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3978532
Abstract:
We derive a stock ranking by applying a technical features based random forest model on an international dataset of liquid stocks. Rather than predicted return, our ranking is based on outperfomance probability. By applying a decile split, we find that long-short portfolios achieve Sharpe ratios of up to 1.95 and a highly significant yearly six-factor alpha of up to 21.79%. Moreover, we show that outperformance probabilities serve as a superior measure of future returns in the context of portfolio optimization. Mean-variance portfolios using this measure are less volatile and more profitable than equally- or value-weighted portfolios. Our findings are robust to firm size, regional restrictions, and non-crisis periods, and cannot be explained by limits to arbitrage.

#885 – Network Diversification for a Robust Portfolio Allocation

Period of rebalancing: Monthly
Markets traded: bonds, commodities, equities, REITs
Instruments used for trading: ETFs
Complexity: Very complex strategy
Backtest period: 1996-2022
Indicative performance: 4.92%
Estimated volatility: 4.95%

Source paper:

Jaeger, M. and Marinelli, D.: Quantifying Narratives and their Impact on Financial Markets
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4068889
Abstract:
Portfolio allocation strategies often seek risk budgeting and diversification by relying only on correlation matrices to model relationships between assets. Although this approach can capture, in normal times, most of the dependencies between asset prices, it faces several challenges in terms of noise resistance, capturing non-linear relations that can naturally appear in the market and extreme allocations in long-short portfolio strategies.

This paper presents novel network-based strategies that combine equal volatility allocation with network centrality measures to construct efficiently diversified portfolios and deliver stable strategies also suitable for long-short investments.

Networks can encode linear and non-linear relationships between asset prices. To encode several layers of information simultaneously multiplex networks – a particular form of a multilayer network – can be deployed. Associated centrality measures can agnostically account for each asset’s (ir)relevance in diversifying the risks of the portfolios.

The results show that network-based portfolios can outperform several competing alternatives, maintaining a favourable risk characteristic.

New research papers related to existing strategies:

#460 – ESG Level Factor Investing Strategy

De Nard, Gianluca and Engle, Robert F. and Kelly, Bryan T.: Factor Mimicking Portfolios for Climate Risk
https://ssrn.com/abstract=4388326
Abstract:
We propose and implement a procedure to optimally hedge climate change risk. First, we construct climate risk indices through textual analysis of newspapers. Second, we present a new approach to compute factor mimicking portfolios to build climate risk hedge portfolios. The new mimicking portfolio approach is much more efficient than traditional sorting or maximum correlation approaches by taking into account new methodologies of estimating large-dimensional covariance matrices in short samples. In an extensive empirical out-of-sample performance test, we demonstrate the superior all-around performance delivering markedly higher and statistically significant alphas and betas with the climate risk indices.

Berg, Florian and Lo, Andrew W. and Rigobon, Roberto and Singh, Manish and Zhang, Ruixun: Quantifying the Returns of ESG Investing: An Empirical Analysis with Six ESG Metrics
https://ssrn.com/abstract=4367367
Abstract:
We quantify the financial performance of environmental, social, and governance (ESG) portfolios in the U.S., Europe, and Japan, based on data from six major ESG rating agencies. We document statistically significant excess returns in ESG portfolios from 2014 to 2020 in the U.S. and Japan. We propose several statistical and voting-based methods to aggregate individual ESG ratings, the latter based on the theory of social choice. We find that aggregating individual ESG ratings improves portfolio performance. In addition, we find that a portfolio based on Treynor-Black weights further improves the performance of ESG portfolios. Overall, these results suggest there is a significant signal in ESG rating scores that can be used for portfolio construction despite their noisy nature.

#75 – Federal Open Market Committee Meeting Effect in Stocks

Baglioni, Tommaso and Ribeiro, Ruy, Corporate Bonds Distress and FOMC Announcement Returns
https://ssrn.com/abstract=4400095
Abstract:
This paper documents that the ex-ante level of the corporate bond market distress is a good predictor for the pre-FOMC announcement return, subsuming the relevant information of equity market uncertainty highlighted by the previous literature. We compute the orthogonal components of distress and uncertainty, and we find that only distress can predict the pre-announcement return, which tends to be positive (negative) when distress is high (low), regardless of the level of uncertainty. These results hold also after 2011, when the average pre-announcement return is flat, but it is possible to predict it using distress.

#129 – Dollar Carry Trade

Bybee, Leland and Gomes, Leandro and Valente, Joao Paulo: Macro-Based Factors for the Cross-Section of Currency Returns
https://ssrn.com/abstract=4400205
Abstract:
We use macroeconomic characteristics and exposures to Carry and Dollar as instruments to estimate a latent factor model with time-varying betas with the instrumented principal components analysis (IPCA) method by Kelly et al. (2020). On a pure out-of-sample basis, this model can explain up to 78% of cross-sectional variation of a Global panel of currencies excess returns, compared to only 27.9% for Dollar and Carry and 51% for a static PCA model. The latent factor and time-varying exposures are directly linked to macroeconomic fundamentals. The most relevant are exports exposures to commodities and US trade, credit over GDP, and interest rate differentials. This model, therefore, sheds light on how to incorporate macroeconomic fundamentals to explain time-series and cross-section.

#710 – Quantile Curves and the VRP

Korn, Olaf and Trappe, Niklas and Volkmann, David: A New Look at the Cross-Section of Option Returns and Volatility
https://ssrn.com/abstract=4418628
Abstract:
This paper re-examines two volatility-related patterns in the cross-section of stock option returns: the low-volatility effect and the expensiveness effect. Intermediary asset pricing theory suggests specific linkages between these effects. As our empirical results show, the low-volatility effect is primarily present for expensive options and the expensiveness effect increases with volatility. In this way, the paper provides new evidence on the role of volatility in the economics of options markets and on the importance of intermediaries and market imperfections. Our results offer potential benefits for investors, as the conditional effects cannot be explained by common risk factors or market inefficiencies.

#555 – Value factor in China

Jiang, Zhaohui and Anderson, Keith P. and Stafylas, Dimitrios: Can Investor Sentiment Predict Value Premium in China?
https://ssrn.com/abstract=4385668
Abstract:
We explore the value premium in the Chinese stock market and how to exploit it using a new investor sentiment index. We extensively discuss the performance of BM, CFP, EP and SP factors in China. Consistent with the experience of other countries, BM generates more of a value premium in small cap performance, while EP generates more of a value premium in large cap stocks in the Chinese stock market. First, we construct a novel value factor based on BM, EP and SP. We obtain the loading weights of each value indicator in each market value by partial least squares. The novel value factor outperformed all other value factors. Second, we explore the relationship between value premium and investor sentiment. Different from evidence from most developed countries, the value stocks perform better than growth stocks in the bull market in China. Our evidence suggests investing in value stocks can get more profit when market sentiment is low.

#822 – Negative ESG Premium in Chinese Stock Market

Chen, Yan and Zheng, Yijia and Lv, Gaotian and Zhang, Wenjie: ESG News and Stock Returns: Evidence from China.
https://ssrn.com/abstract=4417945
Abstract:
This study examines the impact of ESG-related news on Chinese stock markets. We find that ESG news significantly influences stock markets, indicating that investors consider ESG news as a crucial source of information for investment decisions. The effects of ESG news with varying attributes, such as sentiments and sources, on stock markets exhibit significant diversity.The stock market response to ESG-related news varies across listed enterprise with different attributes, including industry and ownership structure. These findings suggest that ESG-related news can act as a supplementary source of information for ESG ratings, facilitating a more accurate assessment of enterprise ESG performance.

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

ESG Ratings Disagreement in 2023

Sustainable investing is a topic we cover extensively in the form of systematic ESG investing strategies and/or blogs. Enormous capital allocation decisions are based on ESG ratings given by various agencies. The problem is that there is no actual normalization and standardization, which creates wrinkles on the faces of hedge and pension fund managers when making those critical individual equity allocations, be they inclusions or exclusions.

Ehling, Paul and Sørensen, Lars Qvigstad (January 2023) new paper analyzes the portfolio choice consequences arising from the well-known divergence of ESG scores. From a risk point of view, the optimized ESG portfolios differ more across each other than they differ relative to the benchmark, suggesting that the different rating agencies’ scores result in substantially different portfolios.

And how dissimilar are ratings among the agencies? We find staggeringly comic that ratings of Warren Buffett’s (and Charlie Munger’s) Berkshire Hathaway (NYSE: BRK.B) disagree by a large margin (see red ellipses in Figure 2 below). While Sustainalytics give it an outperforming rating, FTSE and MSCI regard it as one of the top laggards.

Which Investors Drive Factor Returns?

If different investors share a common goal, why are there differences in strategy choices and portfolio characteristics across investor classes? Elsaify (2022) attempts to provide an answer. In his study, he documents heterogeneity in investors’ processing abilities, which is the key factor influencing investor’s strategy choice and finds that such heterogeneity stems from factor timing ability.

According to the results, hedge funds seem to have the highest attention capacity, the most precise information and excel at factor timing. On the other hand, long-term investors (insurance companies and pension funds), brokers, and short-sellers exhibit low attention capacity because of their timing inability. They spend relatively more attention on the fundamental, their portfolios have the least dispersion and variance and their impact on factor returns is limited.