Quantpedia Premium Update – 19th July

New strategies:

#764 – Overconfidence Factor in China

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 1999-2016
Indicative performance: 16.49%
Estimated volatility: 11.78 %

Source paper:

Liu, Jinpeng: Behavioural Factors in China Stock Market
https://ssrn.com/abstract=4134890
Abstract:
Based on three behavioural biases: overconfidence; disposition effect and herding, we construct three behavioural factors for China stock market. Compared with traditional factors, behavioural factors are able to provide incremental explanation ability and predictability of portfolio returns. To accommodate significant anomalies, our behavioural models outperform the traditional factor models and two popular new factor models. This paper sheds light on the behavioural approach to study China stock market both for academia and industry.

#765 – Credit-Informed Tactical Asset Allocation

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: ETFs, futures
Complexity: Complex Strategy
Backtest period: 1997-2021
Indicative performance: 18.1%
Estimated volatility: 22.5%

Source paper:

Klein, David: Credit-Informed Tactical Asset Allocation – 10 Years On
https://ssrn.com/abstract=3900737
Abstract:
This paper takes a look back at the original Credit-Informed Tactical Asset Allocation paper published in June 2011 and extends the model to address some of the weaknesses identified in the original paper.

#766 – Sentiment Factor in the Cross-Section of Commodity Futures

Period of rebalancing: Monthly
Markets traded: commodities
Instruments used for trading: futures
Complexity: Moderately complex strategy
Backtest period: 2010-2020
Indicative performance: 7.26%
Estimated volatility: 9.60%

Source paper:

Fan, John Hua and Binnewies, Sebastian and De SILVA, Sanuri: Wisdom of Crowds and Commodity Pricing
https://ssrn.com/abstract=4104888
Abstract:
We extract commodity-level sentiment from the Twittersphere in 2009-2020. A long-short systematic strategy based on sentiment shifts more than doubles the Sharpe ratio of extant commodity factors. The sentiment premium is unrelated to fundamentals but is exposed negatively to basis risk and is more pronounced during periods of macro contraction and deteriorating funding liquidity. Sentiment-induced mispricing is asymmetric, i.e., commodities with low (high) sentiment shifts tend to be overvalued (undervalued) when the aggregate market is in backwardation (contangoed). Furthermore, the observed premium arises almost entirely from commodities with the most retweet activities, while retweets and likes themselves do not exhibit stronger predictive ability compared to non-influential tweets.

#767 – Institutional Ownership Enhances Macro Factor Returns

Period of rebalancing: Quarterly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately Complex strategy
Backtest period: 1980-2019
Indicative performance: 8.21%
Estimated volatility: 14.5%

Source paper:

Jin, Zhao, Which Factors Are Priced? It Depends on Who You Ask: Investor Heterogeneity and Factor Pricing
https://ssrn.com/abstract=4046898
Abstract:
Institutional ownership plays important and strikingly opposite roles in factor pricing for macro-related and characteristic factors. Early studies have found that the beta-sorted portfolios of both types of factors have a tiny and insignificant return spread. However, the macro-related (characteristic) factors have a significantly larger (smaller) risk premium within stocks held by institutions compared to stocks held by retail investors. For long-short portfolios based on macro-related composite beta, those formed with stocks with high institutional ownership have a return spread that is 66 basis points higher per month relative to those owned mainly by retail investors. In contrast, composite characteristic beta-sorted long-short portfolios formed with stocks owned mainly by retail investors earn a return spread that is 81 basis points higher per month relative to those owned mainly by institutions. This empirical evidence indicates that the “flat” risk premium of pervasive factors can be partly attributed to the tug-of-war between institutions and retail investors, with the former behaving more consistently with the fundamental risk-averse investors described in classic models and the latter behaving in the opposite way. It is also consistent with the conjecture that macro-related factors are more likely to be a proxy for risk, whereas characteristic factors are more likely to be a proxy for mispricing.

#768 – Retail Ownership Enhances Non-Macro Factor Returns

Period of rebalancing: Quarterly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately Complex Strategy
Backtest period: 1980-2019
Indicative performance: 10.16%
Estimated volatility: 16.00%

Source paper:

New research papers related to existing strategies:

#354 – ETF Creation/Redemption Activity and Return Predictability

Xiao, Han: The Economics of ETF Redemptions
https://ssrn.com/abstract=4096222
Abstract:
This paper provides novel evidence of redemptions in corporate bond exchange-traded funds (ETFs). I first investigate economic incentives for choosing redemption baskets in the primary market. ETFs dispose of bonds with high price pressure exposures, and authorized participants (APs) select assets negatively co-move with liquidity in AP portfolios. Regarding the economic impacts, redemptions decrease ETF returns, liquidity, and efficiency in the less elastic secondary market. APs profit from redemptions by correcting arbitrage between ETFs and underlying assets. Lastly, new policies in the COVID-19 pandemic consistently impact ETFs in both primary and secondary markets.

#685 – Boosted Trees and Cryptocurrency Return Prediction

Luo, Changqing and Pan, Lurun and Chen, Binwei and Xu, Huiru: Prediction of Cryptocurrency Price Based on Multiscale Analysis and Deep Learning
https://ssrn.com/abstract=4092347
Abstract:
In recent years, digital currencies have flourished on a considerable scale, and the markets of digital currencies have generated a nonnegligible impact on the whole financial system. Under this background, the accurate prediction of cryptocurrency prices could be a prerequisite for managing the risk of both cryptocurrency markets and financial systems. Considering the multiscale attributes of cryptocurrency price, we match the different machine learning algorithms to corresponding multiscale components and construct the ensemble prediction models based on machine learning and multiscale analysis. The Bitcoin price series, respectively, from 2017/11/24 to 2020/4/21 and 2020/4/22 to 2020/11/27, is selected as the training and prediction datasets. The empirical results show that the ensemble models can achieve a prediction accuracy of 95.12%, with better performance than the benchmark models, and the proposed models are robust in the upward and downward market conditions. Meanwhile, the different algorithms are applicable for components with varying time scales.

#614 – Climate sentiment, carbon prices and Emission minus Clean Portfolio

Yang, Runfeng and Caporin, Massimiliano and Jiménez-Martin, Juan-Angel: ESG Sentiment Exposure: ESGSenRisk
https://ssrn.com/abstract=4082332
Abstract:
Recent empirical studies show that ESG sentiment, the attitude of investors toward a company’s ESG performance, is a major factor that affects stock performance. While investing in ESG could bring potential risk deduction benefits, changing ESG sentiments in the market will lead to additional stock price movements and thus create additional risk. Therefore, it is important to measure how the changing ESG sentiment affects the risk profile of an investment. This paper studies the impact of ESG sentiment to the downside risk of companies in the US market using ESG sentiment contribution (△ESGSenRisk), a measurement based on the co-movement between ESG sentiment and downside risk. We show that when there is a sudden increase in the ESG sentiment, the downside risk of high-ESG companies become more positive. Such ESG sentiment impact is positively correlated with the ESG performance and company size and varies among different sectors. In addition, during the COVID-19 crisis period, ESG sentiment had a higher impact than at normal times. Our paper provides investors with a new method for ESG risk management. For regulators, our new measurement offers some references on evaluating the impact of ESG-related policies through quantifying the ESG sentiment contribution.

#542 – Committee Portfolio Selection

A. P. Santos, Andre and Torrent, Hudson: Markowitz Meets Technical Analysis: Building Optimal Portfolios by Exploiting Information in Trend-Following Signals
https://ssrn.com/abstract=4106932
Abstract:
Technical indicators are widely used by market participants to identify trends in asset prices and in trading volumes. However, it is unclear how to reconcile this approach with a portfolio selection policy that guide investment decisions in many assets at the same time. We bridge the gap between Markowitz approach to mean-variance portfolios and technical analysis by devising a portfolio strategy in which optimal weights are directly parameterized as a function of multiple trend-following signals. We present an empirical application in which four commonly used technical indicators are employed to obtain portfolios of all constituents of the S&P500 index.

#582 – Carbon Risk in the Cross Section of Corporate Bond Returns
#679 – Carbon Emmision Intensity in Stocks
#707 – Benchmarks Portfolios with Decreasing Carbon Footprints

Lezmi, Edmond and Roncalli, Thierry and Xu, Jiali: Multi-Period Portfolio Optimization
https://ssrn.com/abstract=4078043
Abstract:
In this article, we consider a multi-period portfolio optimization problem, which is an extension of the single-period mean-variance model. We discuss several formulations of the objective function, constraints and coupling relationships. We then derive three numerical algorithms that can be used to solve such problems: the alternating direction method of multipliers, the block coordinate descent algorithm and the augmented quadratic programming method. We illustrate the differences between single-period and multi-period solutions by considering three asset allocation problems: transition management (Rattray, 2003), total variation regularized portfolio (Corsaro et al., 2020) and trading trajectory modeling (Gârleanu and Pedersen, 2013). Finally, we solve the portfolio alignment problem of Le Guenedal and Roncalli (2022) when the fund manager has to dynamically control the carbon footprint of his investment portfolio by integrating a carbon reduction scenario. Comparing the single-period and multi-period solutions shows that the active share between the two portfolios may be greater than 25%. This figure can also reach 40% if we include carbon trends and they are increasing.

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

The Worst One-Day Shocks and The Biggest Geopolitical Events of the Past Century

We dedicated several articles to how we created 100-year history for bonds, stocks, and commodities . Now we analyze the 50 worst one-day shocks and the following days in each of the abovementioned asset classes. In addition to that, we also look at how the multi-asset trend-following strategy performed during the same periods. Further, the second part of this article focuses on critical geopolitical events (the starts of major wars, international crises, and deterioration of US presidents’ health) and their effect on bonds, stocks, commodities, and the multi-asset trend-following strategy.

Takeover Factor Explains the Size Effect

The size effect assumes a negative relationship between average stock returns and firm size. In other words, it states that low capitalization stocks outperform stocks with large capitalization. Although generally accepted, the size effect keeps being challenged. Researchers have been asking how important the firm size characteristic actually is, and whether it is possible to replace the traditional size factor of Fama and French asset pricing model (1993) with more accurate factor. Recently, one potential challenger has emerged – so-called takeover factor, employed by Easterwood et al. (2022). In their study, they work on the assumption that small firms are often targets of takeovers, which gives us a different perspective on merger and acquisition news in regards to size effect. Their results show that M&A component of average returns explains the size premium entirely.