New strategies:
#932 – Timing Betting-Against-Beta (BAB) Anomaly v.2
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1990-2023
Indicative performance: 12.8%
Estimated volatility: 10%
Source paper:
Bozovic, Milos: VIX-Managed Portfolios
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4507634
Abstract:
We propose a simple portfolio management strategy that gauges the leverage based on the observed implied volatility index (VIX). The strategy involves taking less risk when the cumulative previous-month VIX is high and more when it is low. We show that the strategy yields more stable weights and thus requires less rebalancing than comparable strategies based on realized volatility. As a result, it produces substantially higher spanning regression alphas when transaction costs are taken into account. We document this for ten equity factors, six classes of mean-variance efficient portfolios and 176 anomaly portfolios. We argue that the superior performance of the VIX-based strategy is driven by its ability to time volatility and tail risk simultaneously, resulting from the forward-looking nature of the information entailed in the index and the higher-order return moments embedded in the implied volatility smile.
#933 – The Effect of Market Returns on Factor Returns
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex
Backtest period: 1965-2017
Indicative performance: 14.57%
Estimated volatility: 10.65%
Source paper:
Di Carlo, Michael and Tsiakas, Ilias, The Market State, Mispricing and Asset Pricing Anomalies
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4499390
Abstract:
This paper examines the role of the market state in predicting asset pricing anomalies. We find that the sign, size, and significance of anomaly returns depend crucially on whether they follow a positive or negative market excess return. The predictive power of the negative market state is especially strong for portfolios sorted on an aggregate mispricing measure. We conjecture that these findings can be explained by the loosening of arbitrage capital for investors with short positions and the disposition effect for investors with long positions. Our hypothesis is supported by empirical evidence on short interest, liquidity and fund flows.
#934 – Aggregate Call Order Imbalance Strategy
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: CFDs, ETFs, funds, futures
Complexity: Very complex strategy
Backtest period: 2010-2020
Indicative performance: 4.79%
Estimated volatility: 4.52%
Source paper:
Cao, Jie and Li, Gang and Zhan, Xintong and Zhou, Guofu: Betting Against the Crowd: Option Trading and Market Risk Premium
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4301015
Abstract:
We study how equity option trading affects the market risk premium. We find that a measure of aggregate call order imbalance (ACIB), defined as the cross-sectional average of the difference between open-buy and open-sell volume, negatively forecasts future stock market returns significantly from days to months. Moreover, ACIB represents an option-based investor sentiment measure that accounts for excess option buying or selling, and is highly correlated with the stock investor sentiment. Our findings shed new insights on the distinctions for call and put option trading, index and equity option trading, and cross-sectional and time-series predictions.
#935 – Characteristics Similarity of the Return-Sorted Portfolios
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1962-2021
Indicative performance: 8.73%
Estimated volatility: 9.63%
Source paper:
Seyfi, Seyed Mohammad Sina: Essence of the Cross Section
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4466972
Abstract:
I develop a method to identify the strongest determinants of expected returns among potentially infinite return predictors. Instead of sorting stocks on characteristics, I sort stocks into portfolios based on their realized returns—the variable of interest—at each month in the past and find the average of each characteristic among assets in each portfolio. Then I create out-of-sample portfolios such that they are as similar as possible to the returns-sorted portfolios regarding 206 characteristics. Differences in characteristics of low- and high-mean stocks determine where the dispersion in expected returns emanates from. I find price-based characteristics are the strongest predictors.
#936 – Aggregate Momentum Spillover Factor Predicts Stock Returns
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: CFDs, ETFs, funds, futures, stocks
Complexity: Very complex
Backtest period: 1980-2019
Indicative performance: 4.13%
Estimated volatility: 6.56%
Source paper:
Yu, Henry(Honghai) and Chen, Zhuo and Hao, Xianfeng: Leading the Market: The Role of Momentum Spillovers
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4341966
Abstract:
Measuring the marker-level investor underreaction by momentum spillovers, we find that aggregate momentum spillover (AMS) significantly predicts the market premium in both in-sample and out-of-sample frameworks. Using the LASSO, we identify the leader stocks of the future market regardless of ex-ante stock characteristics. By aggregating the return gap between leader stocks and their followers, we construct an indicator AMS to measure the market level momentum spillover. Economically, our indicator contains profitable information for mean-variance investors by identifying the leading stocks of the future market. Finally, we further explore the source of the predictive ability of the new indicator and illustrate the mechanism of investors’ limited attention and underreaction to the predictability of market returns by order imbalance.
#937 – Headquarter Location Effect
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1996-2020
Indicative performance: 8.52%
Estimated volatility: 14.41%
Source paper:
Gao, Chao and Zeng, Wen: Near is Dear: Remoteness, Soft Information, and Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4509746
Abstract:
Geographic proximity plays an important role in dissemination of soft information. Consistent with incomplete information theories, we find that remotely headquartered stocks outperform proximate stocks by 8.59% annually on a risk-adjusted basis. This highly persistent return pattern is mitigated by geographic dispersion of operations. The post-earnings announcement drift and return predictability of aggregate mutual fund trading are only observed among remote firms, further highlighting the role of soft information in price discovery. We also formally document a causal link between geographic remoteness and lack of soft news in the market. Releases of public soft news surprise the market more and induce larger market reactions for remote firms.
New research papers related to existing strategies:
#485 – Toxical Releases and Stock´s Performance
Yin, Ximing and Yu, Deshui and Chen, Li: The Time-Varying Pollution Premium
https://ssrn.com/abstract=4587729
Abstract:
This paper aims to identify the time variability of risk-adjusted returns (alpha) from the long-short portfolio constructed from firms with high versus low toxic emission intensity. We propose time varying factor model augmented with time-varying volatility, and apply the nonparametric kernel method to estimate the coefficients. Using a monthly sample from 1992 to 2018, we find that the long-short portfolio generates significant abnormal returns in the period of 1995–2005 only and this pattern only slightly varies with regard to factor selection. Moreover, the risk exposures of the long short portfolio returns on commonly risk factors also evolve over time. The time-variation in pollution premium is potentially attributed to the time-variation in investors preference and risk exposures.
#833 – Skewness factor in Chinese Equities
Zhen, Fang: Market Volatility and Skewness Risks in China
https://ssrn.com/abstract=4419181
Abstract:
I examine the pricing of risk-neutral market volatility and skewness risks in the cross-section of stocks in China. I find that stocks with high exposures to innovations in volatility or skewness exhibit low expected returns. Market volatility and skewness are economically important and command risk premia of 2.33% and 1.72% per month, respectively. In contrast to the US, innovations in volatility (skewness) exhibit less (more) negative contemporaneous correlation with market returns. These relationships provide a hedging explanation for my results. The negative risk premium of volatility is robust to empirical settings, whereas that of skewness is sensitive to testing methods.
#654 – Momentum without the Crash Component
Bianchi, Daniele and De Polis, Andrea and Petrella: Taming Momentum Crashes
https://ssrn.com/abstract=4182040
Abstract:
The returns on conventional momentum portfolios exhibit a predominantly negative, time-varying skewness which deepens during the so-called “momentum crashes”. This has important implications for the dynamics of the risk-return trade-off associated with momentum investing: the relation between the portfolio expected return and its volatility is time-varying and depends on the conditional skewness of the returns. We explore the economic value of accounting for time-varying skewness to measure momentum risk by comparing the performance of different risk-managed momentum portfolios. A dynamic, skewness-adjusted maximum Sharpe ratio strategy significantly improves upon popular volatility-scaling approaches. Finally, we show that the dynamics of skewness in momentum returns cannot be fully reconciled with an asymmetric exposure to upside and downside market risk.
#554 – Size factor in China
#555 – Value factor in China
#557 – Profitability Factor in Chinese Equities
Lian, Xiangbin and Shi, Chuan: A Composite Four-Factor Model in China
https://ssrn.com/abstract=3928587
Abstract:
We investigate investors’ overreaction and underreaction and their implications to asset pricing in China stock market. The study first picks anomaly variables representing investors’ overreaction and underreaction and then measures these two effects quantitatively. Both of them deliver significant excess returns, both statistically and economically, in China stock market. We then equip these two effects with the market and the size factor to construct a composite four-factor model and study how they price other assets. Extensive empirical analysis shows that this new model is suitable for China stock market. The maximum annual Sharpe ratio spanned by the four factors is 2.02, which is one time higher than those spanned by similar models such as Stambaugh and Yuan (2017) and Daniel, Hirshleifer and Sun (2020). In addition, using 149 anomaly candidates as test assets, the composite four-factor model exhibit good pricing capability, as there is only one test asset whose abnormal return given the model exceeds the 3.0 t-statistic threshold.
#530 – Jump Risk in Stocks
Alexiou, Lykourgos and Rompolis, Leonidas: Jump Tail Risk Exposure and the Cross-Section of Stock Returns
https://ssrn.com/abstract=4556446
Abstract:
We introduce a new jump tail risk measure retrieved from option prices. We examine the cross-sectional pricing of stocks according to their sensitivities to jump tail risk. We find a negative market price of jump tail risk. A high-low portfolio sorted by jump tail risk betas delivers a statistically and economically significant negative premium of -9.95% per year. Risk-adjusted returns are also negative and highly significant. We document that the negative jump tail risk premium is mainly driven by its downside jump tail risk component. On the contrary, the premium of the high-low portfolio sorted by upside jump tail risk betas is insignificant. The negative premium of downside jump tail risk is significant when controlling for various risk factor loadings and firm characteristics, and remains strong for large firms. Our results carry over to a predictive setting, in which we compare subsequent realized returns of the quintile portfolios sorted by downside jump tail risk betas estimated over the previous period.
#822 – Negative ESG Premium in Chinese Stock Market
Wu, Yanran and Zhou, Riwang and Zhang, Chao: Size and ESG Pricing
https://ssrn.com/abstract=4573721
Abstract:
We examine ESG pricing in the Chinese stock market. The results show that holding stocks with high ESG scores does not provide investors with higher future excess returns. On the contrary, stocks with low ESG scores perform better. However, this negative ESG premium feature is robust only in small-cap stocks. As size increases, the negative ESG premium fades away and is characterized by a positive premium in larger stock subgroups. We further examine the source of the negative ESG premium in small-cap stocks. The results show that this negative premium can not be explained by firm characteristics, short-term reversal effects, and lottery characteristics of stocks, but is associated with ESG investors. Specifically, the higher the ESG score with more ESG investors in small-cap stocks, the lower the expected excess return of the stock. This result implies that firms may benefit from ESG performance and disclosure, while investors may suffer from ESG strategies. Based on the results, we remind investors that they should be cautious in using ESG indicators to guide their investment decisions.
And several interesting free blog posts have been published during last 2 weeks:
Which Alternative Risk Premia Strategies Works as Diversifiers?
In the ever-evolving world of finance, the quest for stable returns and risk mitigation remains paramount. Traditional asset classes, such as stocks and bonds, have long been the cornerstone of investment portfolios, but their inherent volatilities and susceptibilities to market fluctuations necessitate a more diversified approach. Enter the domain of alternative risk premia (ARP) – strategies designed to capture returns from diverse sources of risk, often orthogonal to traditional market risks. Our exploration in this blog post delves deep into this subject, shedding light on which ARP strategies can truly serve as robust diversifiers in the complex financial tapestry.
Estimating Stocks-Bonds Correlation from Long-Term Data
There are a few concepts in the world of finance that are taken for granted, and one of them is the free lunch of diversification. Investors like to mix stocks and bonds into a simple allocation portfolio and hope for better outcomes than investing in just one asset. But the favorable return-to-risk profile of those asset allocation strategies relies on the low correlation between those two asset classes, which, as we will see from today’s contribution, we can’t take for granted. We hope the recent study sheds more light on this topic.
Is It Good to Be Bad? – The Quest for Understanding Sin vs. ESG Investing
What are our expectations from the ESG theme on the portfolio management level? The question is whether ESG investing also offers some kind of “alternative alpha”, or outperformance against the traditional benchmarks. There are managers and academics who are enthusiastic and hope for the outperformance of the good ESG stocks. However, the academic research community is really split. Some academic papers show positive alpha for “Saints” (good ESG stocks); others show significantly positive alpha for “Sinners” (bad ESG stocks). So, how it’s in reality? Is it “Good to be Bad”? Or the other way around?
Plus, the following trading strategies have been backtested in QuantConnect in the previous two weeks:
914 – Bitcoin Leads Altcoins on Intraday Basis
930 – Influence of Liquidity, Institutional Ownership & Lottery Effect on Stocks
931 – Forecasting Crude Oil Prices
