Quantpedia Premium Update – 19th December

#812 – Patent Intensity Factor in Equities

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1963 – 2021
Indicative performance: 6.97%
Estimated volatility: 15.61%

Source paper:

Jan Bena, Adlai Fisher, Jiri Knesl, and Julian Vahl: Pricing Technological Innovators: Patent Intensity and Life-Cycle Dynamics
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4218260
Abstract:
Technological innovators are priced differently than other firms, earning high stock returns controlling for standard factors, with less punishment for high capi- tal investment and weak profitability. We create the persistent new firm variable patent intensity (PI), patents received divided by market capitalization, avail- able from 1926. On average, high PI firms account for ten percent of CRSP market value but generate over half of five-year-forward public market patenting. Aged portfolios and standard factors show high alpha and low profitability lasting more than a decade past formation. Adding an expected growth factor, alphas become insignificant at most horizons, and loadings show strong life-cycle dynamics: high but declining growth, aggressive and increasing investment, and weak but improving profitability.

#813 – Option Gamma Predicts Stock Returns

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1996 – 2021
Indicative performance: 8.99%
Estimated volatility: 11.23%

Source paper:

Soebhag, Amar: Option Gamma and Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4256259
Abstract:
Stocks with high net gamma exposure robustly underperform stocks with low net gamma exposure by 10% per year. This effect is distinct from multiple previously documented return predictors, and survives many robustness checks. We show that stocks with low net gamma exposure negatively predicts future realized volatility. We argue that investors command a risk premium to hold low net gamma exposure stocks, which are riskier. Lastly, we show that the volatility predictability stems from a non-informational channel, and not from private information.

#814 – Post Earnings Announcement Drift in China

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2011 – 2021
Indicative performance: 4.69%
Estimated volatility: 7.66%

Source paper:

Lan, Qiujun and Mi, Xianhua and Xie, Yuxuan and Zhao, Xinwei: A Simple But Well-Performing Strategy Based on Earnings Announcement Drift
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4270751
Abstract:
A simple strategy base on PEAD is provided, which only requires going long on dozens of stocks. It gets average 6.39% excess return per quarter in China’s stock market. Also found was that i) compared with earnings announcements, earnings preannouncements are prone to produce a more profitable EAJ strategy; ii) the number of stocks contained in the EAJ strategy has an obvious seasonal cycle, dramatically increased in 2015 and 2021; iii) the Fama-French three factors have little explanatory effect on the returns of the EAJ strategy; and iv) company characteristics also seem to have no significant effects on these earnings.

#815 – Arbitraging Levered ETFs

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: ETFs
Complexity: Simple strategy
Backtest period: 2009-2018
Indicative performance: 4.92%
Estimated volatility: 2.56%

Source paper:

Tosi, Adriano: Leveraged ETPs Across Asset Classes
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4146736
Abstract:
Leveraged exchange traded products (LETPs) exhibit different monthly returns than their underlying geared exchange traded products (ETPs). The effect is known as LETP slippage. This paper studies LETP slippage across five asset classes: equity developed, equity emerging, commodity, fixed income and currency markets. High volatility asset classes show larger slippage than low volatility asset classes. In the cross-section, sorting LETPs by variability measures makes the slippage effect more pronounced. A portfolio of liquid and volatile LETPs yields risk-adjusted returns of 12.50% on an annual basis. Further, LETP slippage is either zero correlated or negative correlated to the same asset class ETP market portfolio. Accordingly, LETP slippage can be used as a diversification instrument when combined with a broad market index.

#816 – Momentum Based on Fractional-Difference Filter

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately Complex strategy
Backtest period: 1972-2020
Indicative performance: 20.30%
Estimated volatility: 22.10%

Source paper:

Chitsiripanich, Soros and Paolella, Marc S. and Polak, Pawel and Walker, Patrick S.: Momentum Without Crashes
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4280465
Abstract:
We construct a momentum factor that identifies cross-sectional winners and losers based on a weighting scheme that incorporates all the price data, over the entire lookback period, as opposed to only the first and last price points of the window. The weighting scheme is derived from the fractional-difference filter- a statistical transformation that preserves memory in the data, and has an economic interpretation of coherently combining reversal and momentum patterns in the returns. Our extensive out-of-sample analysis shows that the new fractional momentum strategy not only achieves significantly higher (risk adjusted) returns, but also mitigates the notoriously large drawdowns of the classical momentum and short-term reversal strategies. The performance results are robust with respect to transaction costs and other real world frictions; excess returns are not explained by other asset pricing factors; and they are pervasive across different asset universes and foreign markets.

New research papers related to existing strategies:

#304 – Seasonality in Treasury Auctions Strategy

Herb, Patrick: The Treasury Auction Risk Premium
https://ssrn.com/abstract=4247371
Abstract:
I empirically show that uniform-price U.S. Treasury auctions are underpriced relative to the secondary market settlement date price, and that this underpricing is explained by risk premia. I posit that intermediaries demand a risk premium to offset future secondary market price uncertainty, in which uncertainty is captured by Treasury auction return volatility. I show that return volatility forecasts explain the bulk of Treasury auction underpricing. I also show that forecasts of expected risk-adjusted Treasury auction returns explain the bid-to-cover ratio and auction tenders.

#526 – Principal Portfolios

Bessembinder, Hendrik (Hank) and Burt, Aaron Paul and Hrdlicka, Christopher M.: Factor Returns and Out-of-Sample Alphas: Factor Construction Matters
https://ssrn.com/abstract=4281769
Abstract:
Portfolios formed on a time-varying basis from the principal components of the factors compiled by Chen and Zimmerman (CZ) and Jensen, Kelly, and Pedersen (JKP) display large and robust out-of-sample Sharpe ratios, implying that the factors strongly forecast the cross-section of stock returns. However, average Sharpe ratios obtained based on the CZ factors are notably larger than when using the JKP factors. We investigate this divergence, documenting the roles of the weighting methods used to construct factor returns, the number of quantile portfolios employed to construct factor returns, and divergences in the numbers and composition of the factors contained across databases. We also show that factor principal components beyond the first few contribute substantially to the factors’ ability to forecast the cross-section of returns.

#726 – Technical Indicators Predict Cross-Sectional Expected Stock Returns

Un, Kuok Sin and Li, He: Technical Analysis and Stock Returns: An Aggregate Approach
https://ssrn.com/abstract=4277254
Abstract:
We propose an aggregate technical trading index by extracting the most relevant forecasting information contained in 7,846 technical trading rules to predict equity risk premium in the U.S. The proposed method is based on the false discovery rate and partial least squares approaches that alleviate the impact of data-snooping bias and idiosyncratic noise components in technical indicators. We find evidence that the aggregate technical trading index exhibit statistically and economically significant power in forecasting stock market excess returns. Moreover, the new index can deliver sizable economic gains for mean-variance investors in asset allocation analysis even taking both transaction costs and data snooping bias into account. We find that the predictive power of the aggregate technical trading index derives from its ability to predict returns in business-cycle expansions and returns of telecom and technology stocks.

#118 – Time Series Momentum Effect

Xu, Dezhong and Li, Bin and Singh, Tarlok and Park, Jung Chul: Cross-Asset Time-Series Momentum Strategy: A New Suggestion
https://ssrn.com/abstract=4231887
Abstract:
We propose a new investment strategy, the improved cross-asset time-series momentum (I-XTSM) strategy, to improve investment performance. Using data on 25 investment portfolios and common commodities for the period from January 1990 to April 2021, we find that the I-XTSM strategy increases profitability substantially in the stock market and avoids momentum collapse effectively. We also document that its profitability is driven by the predictive power of the industrial metal assets’ past signals. Even after considering market exposure, the I-XTSM presents a superior performance and explains the excess profits of other momentum strategies.

#710 – Quantile Curves and the VRP

Schlag, Christian and Sichert, Tobias and Sichert, Tobias: The Shape of the Pricing Kernel and Expected Option Returns
https://ssrn.com/abstract=3708791
Abstract:
A growing literature analyzes the cross-section of single stock option returns, virtually always under the (implicit or explicit) assumption of a monotonically decreasing pricing kernel. Using option returns, we non-parametrically provide significant and robust evidence that the pricing kernel as a function of single stock returns is indeed U-shaped. This shape of the pricing kernel has strong implications for the impact of volatility on expected options returns. For example, we show both theoretically and empirically that higher volatility can increase or decrease expected call option returns, depending on moneyness. Furthermore, on the basis of a U-shaped pricing kernel, we shed new light on some recent findings from the literature on expected option returns, such as anomalies related to ex-ante option return skewness and to lottery characteristics of the underlying stock.

#14 – Momentum Factor Effect in Stocks

Gao, Xiang and Koedijk, Kees and Walther, Thomas and Wang, Zhan: Relative Investor Sentiment Measurement
https://ssrn.com/abstract=4122594
Abstract:
This paper proposes a new metric to gauge investor sentiment using a relative valuation method. We combine investor behavioral finance traits and option-implied standard deviations under both the real-world probability (P) valued most in the view of uninformed investors and the risk-neutral space (Q) adopted when there exists no cognitive error. Given that investor sentiment can be thought of as risk-taking by the uninformed exceeding their informed peers, we postulate that the differences between the variance, skewness, and kurtosis of P and Q measures for investors with various behavioral traits matter. We hence construct our investor sentiment proxy by summing these differentials of variance, skewness, and kurtosis in weighted forms. It is documented that such relative investor sentiment metric exhibits economically and statistically strong return predictability for momentum portfolios. Our findings contribute to the extant literature by (1) complementing the Baker-Wurgler market-based investor sentiment index from the theoretical perspective, (2) modeling investor sentiment via utilizing the informational content of options prices, and (3) supporting the Barberis-Shleifer-Vishny definition of investor sentiment to be differences in financial market participant behavior.

#670 – Machine Learning Pairs Trading Strategy

Findsen, Frederik and Pedersen, Jens: A Stochastic Spread & Co-integration Approach to Pairs Trading in a Regime- Switching Environment
https://ssrn.com/abstract=4128453
Abstract:
This paper applies two statistical arbitrage algorithms on the U.S. equities market, using daily historical prices from January 2005 to March 2012. The algorithms construct portfolios using two different frameworks, namely, the Vasicek model and co-integration approach, with a Markov regime-switching component. The empirical results show that the strategies deliver annualised returns of 12 and 10 per cent and Sharpe ratios of 0.61 and 1.49, which is found to be superior to a comparable benchmark, i.e., the broad U.S. stock index Russell 3000. Our findings corroborate previous literature on the topic of pairs trading, and in addition to this, extends the literature by introducing a regime-switching component in the co-integration framework for pairs trading.

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

Quantum Computing as the Means to Algorithmic Trading

The topic of quantum computing has been gaining popularity recently, and both the scientific community and investors seem to have high hopes for its future. It seems that this brand-new technology could revolutionize various aspects of computing as we currently know them. Great contributions could be made in the fields of medicine and healthcare, security, and computability [1], as well as in the field of finances, which interests us here at Quantpedia the most. Quantum computers are especially great in optimization tasks, so optimizing a portfolio could be one of the key contributions in our interest. [2] In this article, we would like to introduce the concept of quantum computers, their current state, their potential use in finance, and more.

Stock Returns vs Inflation Expectations

What happens to the stock prices when inflation expectations decrease or increase? The authors Manav Chaudhary and Benjamin Marrow, in their paper Inflation Expectations and Stock Returns, explore this topic and find that when inflation expectation is high, stock prices also rise in their value. The evidence they present suggests that stocks have been a hedge against expected inflation for the last couple of decades and that this effect is present across stocks from all industries.

100 Years of Historical Market Cycles

Which assets perform best when rates are rising, and inflation is high? And what happens if rates are still rising but inflation is already falling? And what’s the impact of the business cycle? These are the questions that everyone is currently trying to answer. Today, we will start a longer series of articles with the goal of giving an exact quantitative answer to all questions related to cycles in inflation, interest rates, and economic growth. This series of articles can also serve as an introduction to the methodology that we will use in the upcoming Quantpedia Pro report.