Quantpedia Premium Update – 17th January 2022

New strategies:

#707 –Benchmarks Portfolios with Decreasing Carbon Footprints

Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2005-2019
Indicative performance: 8.2%
Estimated volatility: 16.5%

Source paper:

Jondeau, Eric and Mojon, Benoît and Pereira da Silva, Luiz A.: Building Benchmarks Portfolios with Decreasing Carbon Footprints
https://ssrn.com/abstract=3987186
Abstract:
In this paper, we build portfolios with decreasing carbon footprint, which passive investors can use as new Paris-consistent (PC) benchmarks and have the same risk- adjusted returns as business as usual (BAU) benchmarks. As the distribution of firms’ carbon intensity is very skewed, excluding a small fraction of highly polluting firms can massively reduce the carbon footprint of a portfolio of corporate stocks. We identify the worst polluters globally, exclude them from the portfolio, and re- allocate the proceeds so as to keep sectoral and regional exposures similar to those of the business as usual (BAU) benchmark. This approach limits divestment from corporates in Emerging Countries that would result from implementing exclusions and reinvestment without the objective of preserving regional exposures. We show that reducing the carbon footprint of the portfolio by 64% in 10 years would be obtained by excluding sequentially up to 11% of the corporates, which together amount to less than 6% of the global market portfolio. While this reallocation preserves regional and sectoral exposures similar to those of the BAU benchmark, it does not change its risk-adjusted return. We define PC benchmark portfolios at the global level, for Emerging Countries, Europe, North America, and the Pacific.

#708 –Anti-Matthew Effect

Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1981-2016
Indicative performance: 10.8%
Estimated volatility: 21.29%

Source paper:

Papamichalis, Theofanis: The ‘Matthew Effect’ in Asset Returns: Winners and Losers from Entry
https://ssrn.com/abstract=3918613
Abstract:
Firms differ in their vulnerability to new entrants to their industries. Recent research has shown the costs of entry to have varied over time, being low before the early 80s and having risen since. In a model with monopolistic competition, fixed costs, and heterogeneous markups, I show that changing costs of entry can give rise to endogenous reallocation of resources, a phenomenon known as the “Matthew Effect” (Merton (1968)). In particular, although reductions in entry costs erode incumbents’ monopoly rents, it is mainly firms with low market power who are adversely affected. In contrast, firms with high market power may even benefit from the resulting increase in market-size. A straightforward long-short strategy exploiting this effect would have generated 10.8% per annum since the 1980s. Furthermore, this effect can rationalize a number of different puzzles in equity markets, including the high equity premium and the time variation in the size effect. Finally, it explains the empirical relation between returns and markups, showing that market structure is a strong determinant of equity returns.

#709 –Short-selling Factor in Corporate Bonds

Period of rebalancing: Monthly
Markets traded: bonds
Instruments used for trading: bonds
Complexity: Complex strategy
Backtest period: 2011-2021
Indicative performance: 7.5%
Estimated volatility: 7.5%

Source paper:

Vladimirova, Desislava and Markl, Thomas and Messow, Philip: The Impact of Short Selling in the Cross-Section of Corporate Bond Returns
https://ssrn.com/abstract=3940980
Abstract:
This study fills the literature gap by employing the longest yet-analyzed period and introduces multiple short selling proxies to explain the relationship between short selling information and bond performance. We examine short selling signals derived from bond and equity markets and find both to be predictive of future corporate bond returns after optimization, especially for high yield securities. Additionally, we find the combination of equity and bond short selling signals to be superior to individual factors, generating positive alpha even after costs. The performance of a blended signal is robust against volatility in down-markets, such as the Covid-19 pandemic.

#710 –Quantile Curves and the VRP

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: options, stocks
Complexity: Very complex strategy
Backtest period: 1996-2019
Indicative performance: 32.92%
Estimated volatility: 10.39%

Source paper:

Fritzsch, Simon and Irresberger, Felix and Weiss, Gregor N. F., Characteristic Portfolios, Conditional Quantile Curves, and the Cross-Section of Option Returns
https://ssrn.com/abstract=3864131
Abstract:
Portfolio sorts and cross-sectional regressions are standard tools to test the pricing of asset characteristics. We propose the alternative use of non-parametric machine learning methods to estimate quantile curves of the characteristic of interest conditional on a set of controls. Building portfolios based on conditional quantile curves yields characteristic portfolios that should only reflect the priced risk associated with the characteristic and does not require any assumption on the functional form of the characteristic-return relation. We apply our procedure to the pricing of volatility risk in the cross-section of option returns. The Sharpe ratio of the resultant characteristic portfolios are up to 30% higher than those of comparable strategies.

#711 –Extrapolation in China

Period of rebalancing: Weekly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 2009-2020
Indicative performance: 43.72%
Estimated volatility: 17.37%

Source paper:

Yang, Siyuan and Li, Siyang: Extrapolation in China’s Stock Market: Returns, Price Crash Risk and Price Informativeness
https://ssrn.com/abstract=3978914
Abstract:
Exploiting novel data from Guba forum in China, we analyze the return extrapolation in the cross-section comprehensively and relate it to return predictability and market quality. We find that investors extrapolate from past returns to form their beliefs with exponentially decaying weight and extrapolative beliefs will negatively predict future returns while residual expectations have positively predictive power. We construct a long-short enhanced trading strategy based on investor sentiment earning a significant weekly return of 0.7% (annually 43%, 23% for long leg). Investors tend to extrapolate more for stocks with higher turnover, less volatility, healthy fundamentals and transparent information environment, while higher degree of extrapolation (DOX) will induce higher stock price crash risk and lower price informativeness. Finally, we highlight the need for improving investors’ financial literacy.

#712 –Sentiment Beta in China

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple strategy
Backtest period: 2006-2020
Indicative performance: 5.79%
Estimated volatility: 11.35%

Source paper:

Fengjiao Lin, Zhigang Qiu: Sentiment Beta, and Asset Prices: Evidence from China
https://ssrn.com/abstract=3941220
Abstract:
This paper examines the relationship between sentiment beta and stock returns in China’s stock market. Stocks with low (negative) investor sentiment beta significantly outperform those with high investor sentiment beta (positive), and an against minus catering sentiment (AMC) pricing factor is identified in Chinese stock market. The negative relationship between sentiment beta and stock returns is stronger when arbitrage is highly restricted but not statistically significant when arbitrage is less restricted. The negative relationship between sentiment beta and stock returns is stronger in stocks with highly subjective valuations.

New research papers related to existing strategies:

#609 – Intraday Reversal in US
#251 – Intraday Momentum in Equities
#681 – Predictable Price Pressure Caused by Option Makers and Leveraged ETFs

Barbon, Andrea and Buraschi, Andrea, Gamma Fragility
https://ssrn.com/abstract=3725454
Abstract:
We document a link between large aggregate dealers’ gamma imbalances and intraday momentum/reversal of stock returns, arising from the potential feedback effects of delta-hedging in derivative markets on the underlying market. This channel relies on limited liquidity of the underlying market, but it is distinct from information frictions (adverse selection and private information) and funding liquidity frictions (margin requirement shocks). We test our joint hypothesis using a large panel of equity options that we use to compute a proxy of stock-level gamma imbalance. We find supporting evidence that intra-day momentum (reversal) is explained by the interaction of negative (positive) ex-ante gamma imbalance and and illiquidity. The effect is stronger for the least liquid underlying securities. Our results help to explain both intra-day volatility and autocorrelation of returns. Moreover, we find that gamma imbalance is related to the frequency and the magnitude of flash crash events.

#513 – Predicting Intraday Returns with Machine Learning methods

Huddleston, Dillon and Liu, Fred and Stentoft, Lars, Intraday Market Predictability: A Machine Learning Approach
https://ssrn.com/abstract=3726765
Abstract:
Conducting, to our knowledge, the largest study ever of five-minute equity market returns using state-of-the-art machine learning models trained on the cross-section of lagged market index constituent returns, we show that regularized linear models and nonlinear tree-based models yield significant market return predictability. Ensemble models perform the best across time and their predictability translates into economically significant Sharpe ratios of 0.98 after transaction costs. These results provide strong evidence that intraday market returns are predictable during short time horizons, beyond what can be explained by transaction costs. Furthermore, we show that constituent returns hold significant predictive information that is not contained in market returns or in price trend and liquidity characteristics. Consistent with the hypothesis that predictability is driven by slow-moving trader capital, predictability decreased post-decimalization, and market returns are more predictable during the middle of the day, on days with high volatility or illiquidity, and in financial crisis periods.

#129 – Dollar Carry Trade

de Oliveira Souza, Thiago and de Oliveira Souza, Thiago, Dollar Carry Timing
https://ssrn.com/abstract=3712255
Abstract:
Dollar carry trade risk premiums – unlike dollar-neutral or foreign exchange carry risk premiums – are positively correlated with firm-level dispersions in investment, profitability, and book-to-market in addition to the Treasury-bill rate, long term bond yield, term spread, and default spread. This predictability is also statistically and economically significant out of sample: It generates Sharpe ratios as large as 1.37 (compared to 0.44 unconditionally), for example. Indeed, several forecasting models pin down the few periods responsible for the entire premium. Finally, any detailed narrative (typically based on untestable claims) in which the variables above are proxies for the latent (quantity of) risk and price of risk states – and the business cycle – in the U.S. explains the results in the present paper. However, I avoid making this type of less scientific claims as much as possible and focus on the evidence, instead.

#571 – Post Seasoned Equity Offering Returns in China

Giannone, Danilo Antonino: The Effect of Unscheduled News on Systematic Risk
https://ssrn.com/abstract=3962137
Abstract:
I use intraday prices to explore the time-varying characteristic of the systematic risk around unscheduled firm-level news writing about secondary equity offering (SEO) programs. I show that, around this information flow, the beta drops by a statistically significant and economically important amount. Firm-level news about an SEO program leads to a sharp decrease in the company’s systematic risk of 33.4% on the day that the news is reported. These results are consistent with investors’ rationality and managers’ signals about company valuation. That is, investors sell overvalued firms in favour of more fairly valued companies. Further, the results show that the sentiment of news does not explain the change in the systematic risk. So, investors should closely monitor news taxonomy to understand their risk exposure around information flow. Finally, I show that, through the systematic risk variation documented in this paper, it is possible to explain more than 50% of the negative abnormal return observed on the SEO announcement date.

#441 – Front-Running S&P GSCI Index

Yan, Lei and Irwin, Scott and Sanders, Dwight R., Sunshine vs. Predatory Trading Effects in Commodity Futures Markets: New Evidence from Index Rebalancing
https://ssrn.com/abstract=3715726
Abstract:
Annual rebalancing of the S&P GSCI provides a novel identification of the impact of predictable order flows from index investors in commodity futures markets. Using the 24 commodities included in the S&P GSCI for 2004–2019, we show that cumulative abnormal returns to a long-short strategy peaked at 72 basis points in the middle of the week following the rebalancing period, but the impact declines to near zero within the next week. The findings show that the impact of order flows from financial investors on commodity futures prices is modest and temporary, consistent with the prediction of sunshine trading theory.

#536 – Machine Learning Stock Picking

Tommi Huotari, Jyrki Savolainen and Mikael Collan: Deep Reinforcement Learning Agent for S&P 500 Stock Selection
https://www.mdpi.com/2075-1680/9/4/130/pdf
Abstract:
This study investigated the performance of a trading agent based on a convolutional neural network model in portfolio management. The results showed that with real-world data the agent could produce relevant trading results, while the agent’s behavior corresponded to that of a high-risk taker. The data used were wide in comparison with earlier reported research and was based on the full set of the S&P 500 stock data for twenty-one years supplemented with selected financial ratios. The results presented are new in terms of the size of the data set used and with regards to the model used. The results provide direction and offer insight into how deep learning methods may be used in constructing automatic trading systems.

#27 – Market Timing with Aggregate and Idiosyncratic Stock Volatilities
#673 – Mispricing and Idiosyncratic Volatility Effect in Stocks

Chen, Linda H. and Jiang, George and Xu, Danielle and Yao, Tong, Dissecting the Idiosyncratic Volatility Anomaly
https://ssrn.com/abstract=3718794
Abstract:
The idiosyncratic volatility (IVOL) anomaly, documented in Ang, Hodrick, Xing, and Zhang (2006), has garnered a great deal of attention in the literature. Yet, questions remain regarding the robustness and pervasiveness of the IVOL anomaly, with a particular concern that the IVOL anomaly might simply be the manifestation of market microstructure effect. In this paper, we show that the IVOL anomaly is strong and pervasive after we exclude stocks most susceptible to market microstructure noise – such as microcap stocks, penny stocks, and stocks with strong short-term return reversal. These results are robust to equal-weighting or value-weighting stocks in the IVOL portfolios. Our findings suggest that rather than being the cause of the anomaly, market microstructure noise actually weakens the IVOL anomaly.

#58 – VIX Predicts Stock Index Returns

Dotsis, George, A New Index of Option Implied Absolute Deviation
https://ssrn.com/abstract=3728746
Abstract:
This paper proposes of new index of forward looking absolute deviation extracted from option prices. The new index, named ADIX, is model-free and easy to compute using at-the-money straddle prices. An empirical analysis using S&P 500 options data for the time period 1996-2019 reveals that ADIX has similar behavior to VIX in terms of time series dynamics, risk premiums and forecasting ability. The new index offers an alternative risk measure, more intuitive as a measure of dispersion, to study information embedded in option prices.

#187 – CEO Interviews Effect

Flam, Rachel and Green, Jeremiah and Sharp, Nathan Y.: Do Investors Respond to CEO Facial Expressions of Anger During Television Interviews?
https://ssrn.com/abstract=3740755
Abstract:
Televised media interviews with public company CEOs occur nearly every trading day. During these interviews, investors observe visual cues in addition to hearing the verbal information managers disclose. Building on findings in the psychology and communications literature, we ask whether investors learn from CEO facial expressions. Using a sample of 959 interviews on CNBC from 2014-2018, we focus on CEO expressions of anger, an emotion generally associated with negative outcomes. We find that CEOs are more likely to show facial expressions of anger when the CEO is more expressive generally, when the journalist shows an angry facial expression, and when recent stock returns are lower. We also find that investors respond negatively to CEO facial expressions of anger and that CEO anger can nullify the benefits of a positive message from journalists.

#470 – Macroeconomic Announcement Beta Strategy
#212 – Scheduled Economic Announcements Effect in Stocks

Chen, Jingjing and Jiang, George, Asset Pricing Around Anticipated Macroeconomic Announcements: A (Mood) Swing of Three Days
https://ssrn.com/abstract=3690077
Abstract:
Prior literature documents significant beta premium and market excess return on scheduled macroeconomic announcement days. We find that beta premium and market excess return are negative on days prior to and post announcements, such that the three-day averages are insignificant. We further show that the three-day changes in beta premium and market excess return are both driven by high-beta stock returns. Performing cross-sectional and time-series tests, we provide evidence that an investor mood swing around macroeconomic announcements contributes to changes in high-beta stock valuation. Investors are averse to high-beta stocks prior to announcements but favor high-beta stocks on announcement days.

#408 – Cointegrated Cryptocurrency Portfolios

Miroslav Fil and Ladislav Kristoufek: Pairs Trading in Cryptocurrency Markets
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9200323
Abstract:
Pairs trading is a strategy based on exploiting mean reversion in prices of securities. Even though these strategies have been shown to perform well for equities, their performance is unknown for the field of cryptocurrencies, usually perceived as inefficient and predictable. We apply the distance and cointegration methods to a basket of 26 liquid cryptocurrencies traded on the Binance exchange, specifically at 5-minute, 1-hour and daily frequencies. In our backtests, the strategies underperform classical benchmarks. However, the results are quite sensitive to parameter settings and external factors such as transaction costs or execution windows. Higher-frequency trading delivers significantly better performance, and while the most common daily distance method returns −0.07% monthly, this increases to 11.61% monthly for 5-minute frequency. Additionally, we find evidence of simple mean-reverting behavior in intraday prices that is missing in daily data, and which provides further support for the inefficiency of cryptocurrency markets.

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

Periodicity in Cryptocurrencies – Recurrent Patterns in Volatility and Volume

The high-frequency data in cryptocurrency markets is available at any time of the day, which facilitates the studies of periodicity measures beyond what’s possible in other markets. The research paper by Hansen, Kim, and Kimbrough (2021) investigates the periodicity in volatility and liquidity in two major cryptocurrencies, Bitcoin and Ether, using data from three exchanges, Binance, Coinbase Pro, and Uniswap V2. In particular, the authors measure relative volatility and relative volume across days, hours, and minutes. Their results have confirmed the presence of recurrent patterns in volatility and volume in studied cryptocurrencies for the periods day-of-the-week, hour-of-the-day, and within the hour.

Quality Factor in Sector Investing

The critical question of this research is to examine whether the quality factor could be found in the aggregated groups of similar stocks such as industries or sectors. Additionally, instead of constructing a comprehensive quality metric like other papers, we examine the individual ratios aggregated to the whole sector. The aim is to investigate the fundamental ratios on which quality is based rather than the composite quality score of sectors.

Lottery Effect in ETFs Across Several Asset Classes

This research aimed to examine the widely recognized Skewness Effect and the possibility of using ETFs as trading instruments. The skewness was found in many asset classes, and the ETFs could be utilized as trading instruments to exploit this effect. We examined two skewness anomalies – the short one-month and the long twelve-months skewness across two samples of commodity ETFs, sector ETFs, and country ETFs.