Quantpedia Premium Update – 21st June

New strategies:

#756 – A New Predictability Pattern in the US Stock Market Returns

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1946-2020
Indicative performance: 10.50%
Estimated volatility: 11.02 %

Source paper:

Valeriy Zakamulin: A New Predictability Pattern in the US Stock Market Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4053277
Abstract:
In this article, we document a new stock market anomaly that seems to have escaped the attention of both investment professionals and academics alike. We find that over more than a century, the monthly market return has been predicted by the monthly market return at lag 5. This predictability is market-wide and is most evident in the returns of portfolios of large and growth stocks. The trading strategy that incorporates this predictability yields superior performance that cannot be attributed to common risk factors. A closer investigation of the new anomaly reveals that not each calendar month possesses predictive ability. Therefore, there is a linkage between the new anomaly and calendar effects in stock returns.

#757 – Market Timing with Merton Rule for Earnings Yield

Period of rebalancing: Monthly
Markets traded: equities, bonds
Instruments used for trading: stocks, bonds
Complexity: Moderately complex strategy
Backtest period: 1997-2021
Indicative performance: 10.02%
Estimated volatility: 9.10%

Source paper:

White, James and Haghani, Victor: Men Doth Not Invest by Earnings Yield Alone
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4025456
Abstract:
The most popular indicator of the attractiveness of the stock market – Shiller’s Cyclically-Adjusted Price Earnings ratio (CAPE) – is currently at 39x in the US, higher than it’s been 98% of the time for the past 120 years. What’s a thinking investor to make of this? Should he stay clear of the US stock market, or stick to some pre-set strategic allocation to equities, or is there something else going on? In this note, we’ll argue that CAPE is far from irrelevant, but on its own, it doesn’t tell an investor how much stock exposure to have. We believe it makes sense for certain long-term investors to use Shiller’s CAPE in a strategy we define as “Excess Earnings Yield Dynamic Asset Allocation.” We find that over the past 120 years (and in three-quarters of the decades in that period), such a strategy would have delivered a significantly higher Risk-Adjusted Return within the US market, assuming the existence and plausible pricing of long-term inflation-indexed bonds throughout. Over the period during which inflation-protected bonds have existed, the improvement in Risk-Adjusted Returns was significant.

#758 – Credit Risk Factor in Bonds

Period of rebalancing: Monthly
Markets traded: bonds
Instruments used for trading: bonds
Complexity: Moderately complex strategy
Backtest period: 2004-2019
Indicative performance: 8.86%
Estimated volatility: 9.40%

Source paper:

Dang, Thuy Duong and Hollstein, Fabian and Prokopczuk, Marcel and Prokopczuk, Marcel: Which Factors for Corporate Bond Returns?
https://ssrn.com/abstract=4012601
We comprehensively analyze the most prominent factors proposed in the corporate bond literature. Using a Bayesian model selection approach, we simultaneously compare all 1,024 different possible subsets of these factors. A model including the bond market, term risk, credit risk, short-term reversal, and volatility risk jointly explains the cross-section of corporate bond returns best. Default risk, downside risk, liquidity risk, and momentum, among others, appear to be redundant factors. The bond market and credit risk contribute the most to explaining cross-sectional and time-series variation in test asset returns, while short-term reversal behaves more like a friction than a systematic factor.

New research papers related to existing strategies:

#558 – Quality Strategy in the Indian Market

Jagarlapudi, Chaitanya and Gupta, Vatsal and Gupta, Rudraksh: How many stocks for maximizing risk-adjusted return? Perspective from Indian Stock Market
https://ssrn.com/abstract=3972603
Abstract:
Portfolio managers and investors alike continuously grapple with trying to outperform the benchmarks, while keeping in mind the right number of stocks in the portfolio for optimal diversification. Through our analysis of the Indian stock market, we show that by random selection of stocks, the odds of outperforming the index are very low. Instead, if investors incorporate the ‘quality’ factor in stock selection, the probability of outperforming the index improves substantially and is statistically significant – at around 5% outperformance per annum at 90% confidence.

By incorporating the quality factor, they can own a fairly small basket of 15 to 25 stocks, which captures 90% of the benefit of owning the benchmark at 90% confidence and generates strong outperformance compared to the index. This is much lower than the 45 stocks required for reducing only the diversifiable risk by 90% with a 90% confidence, as the previous studies suggest. These could serve as practical guidelines for portfolio construction and improving returns.

#210 – Adaptive Asset Allocation
#220 – Momentum and Trend Following in Global Asset Allocation

Çağın Ararat, Francesco Cesarone, Mustafa Çelebi Pınar, Jacopo Maria Ricci: MAD Risk Parity Portfolios
https://arxiv.org/abs/2110.12282
Abstract:
In this paper, we investigate the features and the performance of the Risk Parity (RP) portfolios using the Mean Absolute Deviation (MAD) as a risk measure. The RP model is a recent strategy for asset allocation that aims at equally sharing the global portfolio risk among all the assets of an investment universe. We discuss here some existing and new results about the properties of MAD that are useful for the RP approach. We propose several formulations for finding MAD-RP portfolios computationally, and compare them in terms of accuracy and efficiency. Furthermore, we provide extensive empirical analysis based on five real-world datasets, showing that the performances of the RP approaches generally tend to place both in terms of risk and profitability between those obtained from the minimum risk and the Equally Weighted strategies.

#497 – Monetary (FOMC) Momentum in Stocks

Ferriani, Fabrizio, Issuing Bonds during the COVID-19 Pandemic: Is There An ESG Premium?
https://ssrn.com/abstract=4042802
Abstract
We rely on the ESG ratings assigned by four distinct agencies (MSCI, Refinitiv, Robeco, and Sustainalytics) to study the link between ESG scores and firms’ cost of debt financing during the early stage of the COVID-19 pandemic. We document the existence of a statistically and economically significant ESG premium, i.e. better rated companies access debt at lower cost. Despite some heterogeneity across rating agencies, this result is robust to the inclusion of issuer’s credit standing as well as several bond and firms’ characteristics. We find that the effect is mainly driven by firms domiciled in advanced economies whereas creditworthiness considerations prevail for firms in emerging markets. Lastly, we show that the lower cost of capital for highly-rated ESG firms is explained by both risk-based considerations unrelated to corporate credit risk and by investors’ preference towards more sustainable assets.

#341 – Opening Range Breakout within Crude Oil
#442 – Intraday Momentum in Crude Oil ETF
#538 – Oil Intraday Momentum

Wong, Patrick: Predicting Intraday Crude Oil Returns with Higher Order Risk-Neutral Moments
https://ssrn.com/abstract=4019829
Abstract:
High frequency crude oil option data is used to extract the risk-neutral semi-moments from the crude oil market. These risk-neutral moments include the variance, third central moment and the recently developed tail risk variation measures. The returns of second and third semi-moments, and to a lesser extent the returns of the tail risk measures, are found to explain and predict returns in the crude oil and S&P 500 futures at high frequency.

#460 – ESG Level Factor Investing Strategy
#582 – Carbon Risk in the Cross Section of Corporate Bond Returns

Han, Yingwei and Li, Ping and Wu, Sanmang: Does Green Bond Improve Portfolio Diversification? Evidence From China
https://ssrn.com/abstract=4004407
Abstract:
Green bonds are a type of fixed-income instrument that is specifically used to raise funds for projects with environmental benefits to mitigate and adapt to climate change. China’s green bond market expanded rapidly in recent years due to national push for net-zero emissions and the growing appetite of investors. However, little is known about the investment benefits of green bonds from the perspective of portfolio optimization. This paper investigates whether green bonds offer better risk-return profile compared to conventional bonds in China. We compare the out-of-sample diversification benefits of green bonds and conventional bonds using various asset allocation strategies. The results show that the portfolio with green bonds leads to higher risk-adjusted returns than the portfolio with conventional bonds across different asset allocation strategies and risk aversions. This is mainly due to the increase in returns after including green bonds in the portfolio. Overall, our findings suggest that China’s green bonds should be included in optimal portfolios.

#697 – Multifactor Corporate Bond Strategy

Wang, Chen and Weitzner, Gregory: Rating Agency Beliefs and Credit Market Distortions
https://ssrn.com/abstract=3967788
Abstract:
Credit rating agencies (CRAs) make regular forecasts of the future credit market conditions and explicitly incorporate these forecasts in their credit rating processes. We show that CRAs beliefs induce mispricing in corporate bond markets, which in turn affect firms’ financial and investment decisions. We propose a measure of CRA subjective beliefs as the difference in forecasts of future aggregate credit spreads between CRAs and a consensus from many other financial institutions. When CRAs are relatively more optimistic, they issue higher credit ratings despite their lack of additional information regarding future credit market conditions. CRA optimism leads to lower initial yields and subsequent negative returns for newly issued bonds. In response to this mispricing, firms increase their debts, leverage, and investments—where the effects are most pronounced among rated firms—and unrated firms are more likely to be rated.

Petkevich, Alex and Teterin, Pavel: Distracted Banks and Corporate Bond Pricing
https://ssrn.com/abstract=3968248
Abstract:
Bank attention plays an important role in the pricing of corporate debt. Prior studies show that bank loans serve as certification and monitoring mechanisms that signal firm quality to bond markets. To separate continuous monitoring effects from certification effects, we use a measure of bank distraction that results from exogenous shocks to unrelated parts of the bank loan portfolio. Firms with distracted bank lenders have higher credits spreads, especially when distraction is caused by negative attention-grabbing events. The effect is stronger in lower-rated bond issues and when default risk and information asymmetry are high. Bank distraction remains important even for bond issues with a high degree of covenant protections. Greater monitoring losses arise from the distraction of industry specialist banks. Overall, our findings suggest that a temporary decrease in bank monitoring due to unrelated attention-shifting shocks creates negative externalities in the public debt market.

#536 – Machine Learning Stock Picking

Byun, Suk Joon and Cho, Sangheum and Kim, Da-Hea: Can a Machine Learn from Behavioral Biases? Evidence from Stock Return Predictability of Deep Learning Models
https://ssrn.com/abstract=4001583
Abstract:
We examine how the return predictability of deep learning models varies with stocks’ vulnerability to investors’ behavioral biases. Using an extensive list of anomaly variables, we find that the long-short strategy based on deep learning signals generates greater returns for stocks that are more vulnerable to behavioral biases, that is, stocks that are small, unprofitable, volatile, non-dividend-paying, far from the 52-week high, and lottery-like. This performance of deep learning models becomes more pronounced for stocks held by less sophisticated investors. These results suggest that deep learning models with nonlinear structures are useful for capturing mispricing induced by behavioral biases.

#631 – US Climate Policy News and the Cross-section of Stocks
#656 – Machine Learning for Extracting Pessimism from Newspaper Pictures and Text

Anese, Gianluca and Corazza, Marco and Costola, Michele and Pelizzon, Loriana: Impact of Public News Sentiment on Stock Market Index Return and Volatility
https://ssrn.com/abstract=3937901
Abstract:
Recent advances in natural language processing have contributed to the development of market sentiment measures through text content analysis in news providers and social media. The effectiveness of these sentiment variables depends on the imple- mented techniques and the type of source on which they are based. In this paper, we investigate the impact of the release of public financial news on the S&P 500. Using automatic labeling techniques based on either stock index returns or dictionaries, we apply a classification problem based on long short-term memory neural networks to extract alternative proxies of investor sentiment. Our findings provide evidence that there exists an impact of those sentiments in the market on a 20-minute time frame. We find that dictionary-based sentiment provides meaningful results with respect to those based on stock index returns, which partly fails in the mapping process between news and financial returns.

#530 – Jump Risk in Stocks
#559 – Jump Risk in Commodities

Kanniainen, Juho and Magris, Martin: Detecting Intra-Day Jumps in Stock Prices with High-Frequency Option Data
https://ssrn.com/abstract=3727234
Abstract:
We develop a novel, option-based approach for detecting intraday jumps in stock prices. One of the components involved in intraday jump detection is instantaneous volatility, by which intraday returns are scaled. The existing intraday jump detection approaches assume that volatility does not change drastically over a short period, which, however, is in conflict with empirical evidence that volatility can exhibit large movements. We tackle this problem by introducing a method with a completely new proxy for instantaneous volatility, extracted from the option-implied volatility index. This allows us to detect jumps while there are large movements in volatility. We verify our approach with extensive ex-post Monte Carlo experiments. The results show that our approach is of high statistical power, is more robust to variation in volatility, and outperforms the baseline approaches from the literature in terms of both spurious and actual jumps. In the empirical part, we use eight years of high-frequency data on S&P 500 index options. We find that, in comparison with the conventional baseline approaches, our approach identifies fewer jumps, suggesting that true price variation coming from jumps is overstated. Moreover, our method identifies different locations for a large portion of jumps, which emphasizes the important role played by the volatility proxy.

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

Best Performing Value Strategies – Part 2

We focused the first part of this article more on tendencies and trends among these US Equity Value strategies. Today we will talk solely about specific top-performing equity value strategies. More specifically, about numbers #7 to #1 from Quantpedia’s ranking. We will also take a look at combining all the value strategies together into a diversified portfolio of value strategies.

Trend-Following in the Times of Crisis

When someone mentions a financial crisis, most people immediately think of the global financial crisis of 2007-2008. Even though this is the most significant economic crisis in recent years, there have been many more significant crisis periods in the past 100 years. This article examines the biggest crises in three asset classes: stocks, bonds, and commodities, during the past century. Additionally, we analyze the behavior of our trend-following strategy during each of the crisis periods and propose it as a hedge for the stock, bond, and/or commodity markets.

Too Tech to Fail?

Phenomenal innovation, new technologies, growth of social media, and e-commerce have been characteristics of the last decades. BigTech companies such as Google, Facebook (Meta), Amazon, Apple, and Microsoft are becoming so increasingly popular. So now, in connection to the actual carnage on the financial markets, the question arises: are BigTech firms the new “Too Big to Fail”?