New strategies:
#669 – Volatility Risk Premium in Currencies
Period of rebalancing: Monthly
Markets traded: currencies
Instruments used for trading: futures, forwards, CFDs
Complexity: Very complex strategy
Backtest period: 1998-2013
Indicative performance: 4.95%
Estimated volatility: 8.15%
Source paper:
Della Corte, Pasquale and Ramadorai, Tarun and Sarno, Lucio: Volatility Risk Premia and Exchange Rate Predictability
https://ssrn.com/abstract=2233367
Abstract:
We discover a new currency strategy with highly desirable return and diversification properties, which uses the predictive capability of currency volatility risk premia for currency returns. The volatility risk premium — the difference between expected realized volatility and model-free implied volatility — reflects the costs of insuring against currency volatility fluctuations, and the strategy sells high-insurance-cost currencies and buys low-insurance-cost currencies. The returns to the strategy are mainly generated by movements in spot exchange rates rather than interest rate differentials, and the strategy carries a large weight in a minimum-variance portfolio of commonly employed currency strategies. We explore alternative explanations for the profitability of the strategy, which cannot be understood using traditional risk factors.
#670 – Machine Learning Pairs Trading Strategy
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1980-2020
Indicative performance: 24.8%
Estimated volatility: 12.3%
Source paper:
Han, Chulwoo and He, Zhaodong and Toh, Alenson Jun Wei: Pairs Trading via Unsupervised Learning
https://ssrn.com/abstract=3835692
Abstract:
This paper develops a pairs trading strategy via unsupervised learning. Unlike conventional pairs trading strategies that identify pairs based on return time series, we identify pairs by incorporating firm characteristics as well as price information. Firm characteristics are revealed to provide important information for pair identification and significantly improve the performance of the pairs trading strategy. Applied to the US stock market from January 1980 to December 2020, the long-short portfolio constructed via the agglomerative clustering earns a statistically significant annualized mean return of 24.8% and a Sharpe ratio of 2.69. The strategy remains profitable after accounting for transaction costs and removing stocks below 20% NYSE-size quantile. A host of robustness tests confirm that the results are not driven by data snooping.
#671 – ESG Premium in Options
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks, options
Complexity: Complex strategy
Backtest period: 2004-2018
Indicative performance: 9.12%
Estimated volatility: 3.06%
Source paper:
Jie Cao, Amit Goyal, Xintong Zhah and Weiming Elaine Zhang : Unlocking ESG Premium from Options
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3878123
Abstract:
We find that option expensiveness, as measured by implied volatility, is higher for low-ESG stocks, showing that investors pay a premium in the option market to hedge ESG-related uncertainty. Using delta-hedged option returns, we estimate this ESG premium to be about 0.3% per month. All three components of ESG contribute to option pricing. The effect of ESG performance heightens after the announcement of Paris Agreement, after speeches of Greta Thunberg, and in the aftermath of Me-Too movement.We find that investors pay ESG premium to hedge volatility, jump, and other higher moment risks. The influence of ESG on option premia is stronger for firms that are closer to end-consumers, facing severer product competition, with higher investors’ ESG awareness, and without corporate hedging activity.
#672 – Growth Potential and Options Returns
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks, options
Complexity: Complex strategy
Backtest period: 1996-2018
Indicative performance: 17.6%
Estimated volatility: 10.6%
Source paper:
Panayiotis C. Andreou, Turan G. Bali, Anastasios Kagkadis, Neophytos Lambertides: Firm Growth Potential and Option Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3874674
Abstract:
This paper shows that rm growth potential – representing a rm’s yet-unexercised growth opportunities – is associated with option overpricing and low future delta- hedged option returns. We provide an explanation of this phenomenon based on the idea that retail investors exert buying pressure and tend to overpay for the call options of growth-oriented rms because they overestimate the potential pro ts arising from the return skewness of the underlying stocks. We further show that the e ect is stronger among stocks that are more likely to exhibit high skewness, are more prone to limits- to-arbitrage and are more exposed to informational frictions.
#673 – Mispricing and Idiosyncratic Volatility Effect in Stocks
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 1967-2016
Indicative performance: 22.13%
Estimated volatility: 17.32%
Source paper:
Barroso, Pedro and Detzel, Andrew L. and Maio, Paulo F., The Volatility Puzzle of the Low-Risk Anomaly
https://ssrn.com/abstract=3882108
Abstract:
The volatility of betting-against-beta (BAB) and -idiosyncratic volatility (BAV) factors negatively forecasts their respective Sharpe ratios and abnormal returns. This predictability causes significant performance gains from volatility timing these factors and provides new time-series evidence on leading theories of the low-risk anomaly. Consistent with the limits-to-arbitrage theory, we show that the abnormal returns of the volatility-managed BAV strategy are concentrated in overpriced stocks. However, controlling for mispricing, arbitrage risk, lottery demand, and multiple risk factors has no effect on the timing benefits of BAB. We further show that the leverage constraints model predicts a counterfactual positive relation between volatility and subsequent BAB Sharpe ratios, and highly active institutions shift from high- to low-beta stocks as volatility increases, suggesting their demand contributes to the abnormal returns of BAB. Overall, the predictive power of volatility challenges our current understanding of the low-risk anomaly.
New research papers related to existing strategies:
#669 – Volatility Risk Premium in Currencies
Ornelas, Jose Renato Haas: Expected Currency Returns and Volatility Risk Premia
https://ssrn.com/abstract=2809141
Abstract:
This paper addresses the predictive ability of currency volatility risk premium – the difference between an implied and a realized volatility – over US dollar exchange rates using a time series perspective. The intuition is that, when risk aversion sentiment increases, the market quickly discounts the currency, and latter this discount is accrued, leading to a future currency appreciation. Based on two different samples with a diversified set of 32 currencies, I document a positive relationship between currency volatility risk premium and future currency returns. Results remain robust even after controlling for traditional fundamental predictors like Purchase Power Parity and interest rate differential.
#640 – Climate Beta and Mutual Funds
Kuang, Huan and Liang, Bing: Carbon Risk Exposure in the Mutual Fund Industry
https://ssrn.com/abstract=3750244
Abstract:
We construct a novel carbon risk measure for mutual funds based on stock holding data and explore how carbon risk affects mutual funds’ performance, risk, and flows. We find that funds with higher carbon risk exposure have lower performance and higher unexplained risk in factor models. Furthermore, carbon risk is not fully captured in traditional Environmental, Social, and Governance (ESG) measures. We also find that institutional investors take carbon risk more seriously than retail investors do. Institutional funds with higher carbon risk exposure experience a negative flow shock in the subsequent period and their flow–performance relation is more sensitive. Moreover, when there is more news coverage on climate change, carbon risk exposure affects fund flows more negatively. These findings do not apply to retail funds.
#578 – Combining Smart Factors Momentum and Market Portfolio
Kadan, Ohad and Liu, Fang and Tang, Xiaoxiao: Recovering Conditional Factor Risk Premia
https://ssrn.com/abstract=3803993
Abstract:
We offer an approach for recovering option-implied time-varying forward-looking risk premia of systematic factors—even if they do not possess actively-traded options. We apply this approach to the market, size, value, and momentum factors. We find that factor premia are highly volatile. Both the market and the value premia tend to be higher during slowdowns and recessions and during turbulent times. By contrast, the momentum premium is higher during periods of high economic growth and low volatility. We use the recovered factor premia to construct trading strategies, which mitigate market and momentum crash risk and to predict returns of individual stocks even if they do not possess traded options.
#632 – Classification of Stocks as Anomaly Longs
#536 – Machine Learning Stock Picking
Dong, Xi and Li, Yan and Rapach, David and Zhou, Guofu: Anomalies and the Expected Market Return
https://ssrn.com/abstract=3562774
Abstract:
We provide the first systematic evidence on the link between long-short anomaly portfolio returns—a cornerstone of the cross-sectional literature—and the time-series predictability of the aggregate market excess return. Using 100 representative anomalies from the literature, we employ a variety of shrinkage techniques (including machine learning, forecast combination, and dimension reduction) to efficiently extract predictive signals in a high-dimensional setting. We find that long-short anomaly portfolio returns evince statistically and economically significant out-of-sample predictive ability for the market excess return. Economically, the predictive ability of anomaly portfolio returns appears to stem from asymmetric limits of arbitrage and overpricing correction persistence.
#460 – ESG Level Factor Investing Strategy
Amon, J.; Rammerstorfer, M.; Weinmayer, K. Passive ESG Portfolio Management — The Benchmark Strategy for Socially Responsible Investors
https://www.mdpi.com/2071-1050/13/16/9388/pdf
Abstract:
In this article, we investigate the notion of doing well while doing good from the perspective of passive portfolio strategies. We analyze a number of asset allocation strategies based on ESGweighting and compare their financial and ESG performance for the US and Europe. We find no significant difference in the financial performance but superior ESG performance of ESG-based strategies. It can be concluded that, compared to a naive strategy, socially responsible investors are willing to pay a small premium for the impact of the portfolio via transaction costs when rebalancing the portfolio according to their preferences for social responsibility. In addition, when comparing the ESG-based strategies to a value-weighted strategy, we observe no significant difference in ESG performance but a high degree of significance in the superior financial performance of the ESG-based strategy. We also analyze the strategies with regards to the factor loadings given by the Fama–French five-factor model and a sixth factor denoted GMB (Good minus Bad) and find significant differences across the regions and strategies. Overall, the results show strong support of ESG-based strategies being preferred by socially responsible investors but also suggest that such strategies might be preferred by conventional investors looking for a passively managed alternative compared to a valueweighted index. Furthermore, it seems that such a strategy might be a more adequate benchmark for active SRI funds.
#628 – Social Media Sentiment Factor
Lindskog, Sebastian and Serur, Juan Andrés: Reddit Sentiment Analysis
https://ssrn.com/abstract=3887779
Abstract:
It has never been easier for individual investors to get started trading stocks or options. Companies like Robinhood and Webull even offer zero commission trades, no account minimum size, and incentives like a free stock if a user creates an account. Recently, there has been a huge increase in the growth of users of these types of platforms. The combination of many people out of work because of the coronavirus and the US government stimulus package appears to have sparked this. This surge in new investors has sparked tons of activity on popular social media websites like Reddit. There users regularly post stock recommendations and trading strategies. It appears that many Reddit traders are grouping together and causing irrational stock market moves. Panic buying stocks for companies that just declared bankruptcy, betting against Warren Buffet, and ignoring the impact of the coronavirus on airlines and cruise ships are a few of the unusual market moves lately. The purpose of this project is to identify if there is a relationship between the Reddit sentiment on stocks and performance.
And several interesting free blog posts have been published during last 2 weeks:
Community Alpha of QuantConnect – Part 3: Adjusted Social Trading Factor Strategies
This blog post is the continuation of series about Quantconnect’s Alpha market strategies. Part 1 is here and Part 2 can be found here. This part is related to the factor strategies notoriously known from the majority of asset classes. We continue in the examination of factor strategies built on top of social trading strategies, but in this part, the investment universe is reduced based on the insights of the last part. So, without further ado, we continue where we have left last time.
Introduction to Clustering Methods In Portfolio Management – Part 2
October’s is coming, and we continue our short series of introductory articles about portfolio clustering methods we will soon use in our new Quantpedia Pro report. In the previous blog, we introduced three clustering methods and discussed the pros and cons of each one. Additionally, we showed a few examples of clustering, and we presented various methods for picking an optimal number of clusters.
This section demonstrates the Partitioning Around Medoids (PAM) – a centroid-based clustering method, Hierarchical Clustering, which uses machine learning and Gaussian Mixture Model based on probability distribution and applies all three methods to an investment portfolio that consists of eight liquid ETFs.
The CAPM model was a breakthrough for asset pricing, but the times where the market factor was most widely used are long gone. Nowadays, if we exaggerate a bit, we have as many factors as we want. Therefore, it might not be straightforward which factor model should be used.
Hanauer et al. (2021) provide several insights into factor models. The authors postulate that the factor models should be examined in the international samples since this can be understood as a test for asset pricing models. The domestic Chinese A-shares stock market seems to be an excellent “playground” for the factors models, given the size of the Chinese stock market, but mainly because of its uniqueness. The paper compares the models (and factors) based on various methods (performance, data-driven asset pricing framework, test assets, turnovers and even transaction costs). Apart from valuable insights into the several less-known factors, the key takeaway message could be that the “US classic” Fama-French factor models perform poorly in China. The modified Fama-French six-factor model or q-factor is better, but overall, it seems that factor models designed for China, such as the model of Liu, Stambaugh and Yuan (2019), are the best.
Introduction to Clustering Methods In Portfolio Management – Part 3
This is the third and final article from the clustering series. If you’ve missed the previous parts, here you can find the first and second parts of the series. This section examines trading strategies based on previously introduced clustering methods. The complete Portfolio Clustering report will be available for our Quantpedia Pro clients next week.
Plus, the following five trading strategies have been backtested in QuantConnect in the previous two weeks:
#139 – Volatility Spread in Puts/Calls Predicts Stock Returns
#172 – Cold IPOs Effect
#190 – Put-Call Spread Predicts Earnings Announcement Returns
#209 – Volatility Of Volatility Effect in Stocks
#659 – Robust Quality in Stocks
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