Quantpedia Premium Update – 20th January

New strategies

#821 – Accrual Effect in Family Firms

Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2007-2017
Indicative performance: 18.70%
Estimated volatility: not known

Source paper:

Adam Aoun, Leonidas C. Doukakis, Georgios A. Papanastasopoulos: Family Ownership and the Accrual Anomaly
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4278427
Abstract:
Motivated by the unique nature of family firms and the puzzling persistence of the accrual anomaly worldwide, we study the presence and economic significance of the accrual anomaly separately for family and non-family firms using a sample of 27,117 observations from 34 capital markets. At an individual stock level of analysis, we show that the negative relation of accruals with future earnings performance and stock returns is more pronounced within family firms, while it is highly attenuated within non-family firms. Evidence from portfolio-level analysis summarizes the economic significance of this finding. Overall, we conclude that agency problems, information uncertainty, and barriers to arbitrage could be key explanatory factors regarding the accrual anomaly’s occurrence and persistence within family firms.

#822 – Negative ESG Premium in Chinese Stock Market

Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2016-2021
Indicative performance: 8.99%
Estimated volatility: 13.35%

Source paper:

Ni, Yinan and Sun, Yanfei: ESG Premium in Chinese Stock Markets
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4222515
Abstract:
The ESG (Environmental, Social, and Governance) concept has been increasingly adopted in financial markets, this paper studies the evolving effect of corporate ESG performance on the stock returns in China’s stock markets. Utilizing the Paris Agreement and China’s President Xi’s pledge to achieve carbon neutrality by 2060 as ESG shocks, we find that firms with lower ESG scores provide higher stock returns after the announcement of Paris Agreement. The effect of ESG performance heightens after Xi’s pledge. Using sorted portfolios and Fama-French factor models, we find that investors are rewarded for bearing ESG-related risks. Our estimated monthly ESG risk premium is between 0.52% and 0.63%. State-owned firms with larger market capitalizations and better financial and operational performance tend to have better ESG performance.

#823 – Machine Learning and the Cross-Section of Cryptocurrency Returns

Period of rebalancing: Weekly
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Very complex strategy
Backtest period: 2017-2022
Indicative performance: 11.09%
Estimated volatility: not stated

Source paper:

Cakici, Nusret and Shahzad, Syed Jawad Hussain and Bedowska-Sojka, Barbara and Zaremba, Adam: Machine Learning and the Cross-Section of Cryptocurrency Returns
https://ssrn.com/abstract=4295427
Abstract:
We employ a repertoire of machine learning models to explore the cross-sectional return predictability in cryptocurrency markets. While all methods generate substantial economic gains, those that account for nonlinearities and interactions fare the best. The return predictability derives mainly from a handful of simple features—such as idiosyncratic volatility, past alpha, or maximum daily return—and is likely driven by mispricing. Accordingly, abnormal returns originate predominantly from short positions, concentrate in hard-to-arbitrage assets, and gradually decline over time. Despite a high portfolio turnover, machine learning strategies remain a profitable net of trading costs. However, they critically depend on shorting small cryptocurrencies, which may pose challenges in practice.

#824 – Diversified Machine Learning in Emerging Markets

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 2002-2021
Indicative performance: 2.8%
Estimated volatility: 5.32%

Source paper:

Hanauer, Matthias Xaver and Kalsbach, Tobias: Machine Learning and The Cross-Section of Emerging Market Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4287550
Abstract:
This paper compares various machine learning models to predict the cross-section of emerging market stock returns. We document that allowing for non-linearities and interactions leads to economically and statistically superior out-of-sample returns compared to traditional linear models. Although we find that both linear and machine learning models show higher predictability for stocks associated with higher limits to arbitrage, we also show that this effect is less pronounced for non-linear models. Furthermore, significant net returns can be achieved when accounting for transaction costs, short-selling constraints, and limiting our investment universe to big stocks only.

New research papers related to existing strategies:

#720 – Reversal in Small Cryptos
#755 – Mean-reversion and Trend-Following Based on MIN and MAX in BTC

Kozlowski, Steven and Puleo, Michael and Zhou, Jizhou: Cryptocurrency Return Reversals
https://ssrn.com/abstract=4256709
Abstract:
Analyzing a set of 200 cryptocurrencies over the period from 2015 to 2019, we document a significant return reversal effect that holds at the daily, weekly, and monthly rebalancing frequencies and is robust to controls for differences in size, turnover, and illiquidity. Moreover, the reversal effect persists during both halves of our sample period and following periods of both high and low market implied volatility. Consistent with the effect being driven by a combination of market inefficiency and compensation for liquidity provision, we find reversals are most pronounced among smaller capitalization and less liquid cryptocurrencies.

#53 – Sentiment and Style Rotation Effect in Stocks

Alburaythin, Yazeed and Fifield, Suzanne G.M. and Paramati, Sudharshan Reddy: Interaction between Investor Sentiment, Limits to Arbitrage and the Returns of Stock Market Anomalies: Evidence from the UK Stock Market
https://ssrn.com/abstract=4259098
Abstract:
This study investigates the role of two prominent concepts in finance: the role of limits to arbitrage and investor sentiment on stock prices. The study examines how changes in market-wide investor sentiment and limits to arbitrage affect the performance of nine UK stock market anomalies. The existing literature relating to investor sentiment focuses mainly on the US stock market, whilst research on the UK market typically examines aggregated index-level data. In addition, previous studies tend to focus on examining investor sentiment and limits to arbitrage separately. Using data from UK-listed companies over the period January 1997 to December 2019, the study finds that five stock market anomalies are related to changes in UK investor sentiment and produce significantly higher returns following periods of high investor sentiment, while the effect of limits to arbitrage is mostly limited. However, the interaction analysis provides support to the limits to arbitrage theory and demonstrates that the effect of high investor sentiment on stock market anomalies is more pronounced when combined with high limits to arbitrage and has less effect during periods characterised by low limits to arbitrage.

#703 – Machine Learning in News Articles Predicts Stock Returns

Briere, Marie and Huynh, Karen and Laudy, Olav and Pouget, Sebastien: What do we Learn from a Machine Understanding News Content? Stock Market Reaction to News
https://ssrn.com/abstract=4252745
Abstract:
Using textual data extracted by Causality Link platform from a large variety of news sources (news stories, call transcripts, broker research, etc.), we build aggregate news signals that take into account the tone, the tense and the prominence of various news statements about a given firm. We test the informational content of these signals and examine how news is incorporated into stock prices. Our sample covers 1,701,789 news-based signals that were built on 4,460 US stocks over the period January 2014 to December 2021. We document large and significant market reactions around the publication of news, with some evidence of return predictability at short horizons. News about the future drives much larger reactions than news about the present or the past. Stock returns also react more to high-coverage news, fresh news and purely financial news. Finally, firms’ size matters: stocks that are not components of the Russell 1000 index experience larger reactions to news compared to those that are Russell 1000 components. Implications of our results for financial analysts and investors are offered and related to the links between news, firms’ market value and investment strategies.

#460 – ESG Level Factor Investing Strategy

Varmaz, Armin and Fieberg, Christian and Poddig, Thorsten: Portfolio Optimization for Sustainable Investments
https://ssrn.com/abstract=4278621
Abstract:
In mean-variance portfolio optimization, factor models can accelerate computation, reduce input requirements, facilitate understanding and allow easy adjustment to changing conditions more effectively than full covariance matrix estimation. In this paper, we develop a factor model-based portfolio optimization approach that takes into account aspects of the environment, social responsibility and corporate governance (ESG). Investments in assets related to ESG have recently grown, attracting interest from both academic research and investment fund practice. Various literature strands in this area address the theoretical and empirical relation among return, risk and ESG. Our portfolio optimization approach is flexible enough to take these literature strands into account and does not require large-scale covariance matrix estimation. An extension of our approach even allows investors to empirically discriminate among the literature strands. A case study demonstrates the application of our portfolio optimization approach.

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

Tham, Eric and Kang, Young Dae: How Do Carbon Prices Impact the Carbon Premium? https://ssrn.com/abstract=4260719
Abstract:
Governments institute carbon pricing to curb emissions. We show increasing carbon futures prices on the European Trading Scheme (ETS) decrease the carbon \textit{news} equity premium but only in brown firms. This premium is obtained from a panel regression on firm-level sentiment scores. The premium varies across industries and countries, and separate into short and long term components identified by news topics on emissions and environmental innovation respectively. The changes in carbon premium are in line with increased investors’ concerns on environmental issues post COP 2016. Regression results on the carbon premium with the carbon futures returns as explanatory economic variables show $R^2$ up to $41\%$ in Germany and $21\%$ in United States. A firm model of investment is developed to explain the economic impact of carbon prices on the carbon equity premium and validate the empirical findings.

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

Which ESG Funds Perform Greenwashing?

Environmental, social, and governance (ESG) investing is rapidly growing in popularity. As more investors grow interested in the ESG investing, the funds theoretically have more reason to highlight their engagement with the ESG-related activities. In the research paper by Andrikogiannopoulou et al. (2022), authors first use textual analysis to assess how and how much the funds talk about ESG-related topics in their prospectuses, and then they compare this measure with the funds’ actual ESG engagement. The discrepancy between the words in their prospectus (high rate of mentioning ESG investing-related topics) and the fund’s acts (not being as green as illustrated in the prospectus) allows the authors to identify the greenwashing funds and take a closer look at their performance.

What Is an Optimal Allocation to Cryptocurrencies?

Cryptocurrencies are a very controversial asset class. Some people may hate it, others may glorify it, and a significant part may ignore it. But what’s the opportunity cost of complete ignorance? Are we able to numerically calculate it? That’s a hard question that Duchin, Solomon, Tu, and Wang tried to answer in their recent paper, and we will take a look at some of their findings and discuss it.