#907 – Estimating Hedge Funds\u2019 Returns Out of Sample<\/a><\/strong><\/p>\n\n\n\n Period of rebalancing:<\/strong> Yearly Source paper:<\/strong><\/p>\n\n\n\n Wilson, Eric: Hedge Funds With(out) Edge<\/strong> #908 – Tail Risk Hedging with Cheap Options<\/a><\/strong><\/p>\n\n\n\n Period of rebalancing:<\/strong> Monthly <\/p>\n\n\n\n Source paper:<\/strong><\/p>\n\n\n\n Neo, Poh Ling and Tee, Chyng Wen: Tail Risk Hedging: The Search for Cheap Options<\/strong> #909 – Deep Momentum<\/a><\/strong><\/p>\n\n\n\n Period of rebalancing:<\/strong> Monthly Source paper:<\/strong><\/p>\n\n\n\n Han, C.: Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning<\/strong> #910 – The Term Structure of Machine Learning Alpha<\/a><\/strong><\/p>\n\n\n\n Period of rebalancing:<\/strong> Quarterly Source paper:<\/strong><\/p>\n\n\n\n Blitz, D. and Hanauer, X. M. and Hoogteijling, T. and Howard, C.: The Term Structure of Machine Learning Alpha<\/strong> #911 – Multi Risk Premia Strategy<\/a><\/strong><\/p>\n\n\n\n Period of rebalancing:<\/strong> Quarterly Source paper:<\/strong><\/p>\n\n\n\n Leveau, Daniel and Sahote, Navdeep: Alternative Risk Premia Prime<\/strong> #912 – Forward Variance Factor Predicts Stock Returns<\/a><\/strong><\/p>\n\n\n\n Period of rebalancing:<\/strong> Monthly Source paper:<\/strong><\/p>\n\n\n\n Deng, Yizhe and Jiang, Fuwei and Wang, Yunqi and Zhou, Ti: International Stock Return Predictability: The Role of U.S. Volatility Risk<\/strong> #913 – Double Bottom Country Trading Strategy<\/a><\/strong><\/p>\n\n\n\n Period of rebalancing:<\/strong> Daily Source paper:<\/strong><\/p>\n\n\n\n Dujava, Cyril and Kal\u00fas, Filip and Vojtko, Radovan: Double Bottom Country Trading Strategy<\/strong> #117 – Lottery Effect in Stocks<\/a> Gu, Ming and Hu, Yi and Xiong, Zhitao: Dissecting the Lottery-Like Anomaly: Evidence from China<\/strong> #460 – ESG Level Factor Investing Strategy<\/a><\/strong><\/p>\n\n\n\n Institute for Monetary and Financial Research, Hong Kong: Doing Well by Doing Good? Risk, Return, and Environmental and Social Ratings<\/strong> #568 – Momentum effect in Chinese B-shares<\/a><\/strong><\/p>\n\n\n\n Chen, Dongxu and Dai, Yue and Qiu, Zhigang: Return Seasonality and Investor Structure: A Tale of Twin Markets in China<\/strong> #697 – Multifactor Corporate Bond Strategy<\/a><\/strong><\/p>\n\n\n\n Bartov, Eli and Faurel, Lucile and Mohanram, Partha S.: The Role of Social Media in the Corporate Bond Market: Evidence from Twitter<\/strong> Dissecting the Performance of Low Volatility Investing<\/strong><\/a><\/p>\n\n\n\n Low volatility investing is an appealing approach to compound wealth in the stock market for the long term. This particular factor investing style exploits the popular naive notion that lower (higher) risk must always equal lower (higher) overall returns. But in fact, this naive assumption is not true, as low-volatility investments often yield more than their high-volatility counterparts. While low-volatility investing has many advantages, it also results in some disadvantages. How to overcome them? Bernhard Breloer, Martin Kolrep, Thorsten Paarmann, and Viorel Roscovan, in their study Dissecting the Performance of Low Volatility Investing, propose a solution.<\/p>\n\n\n\n Performance of Factor Strategies in India<\/strong><\/a><\/p>\n\n\n\n India is a big emerging market, actually the second biggest after China. We primarily look at developed markets, mostly the U.S. and Europe, and from Emerging Markets, China at most, and we are aware that we neglect this prospective country. We would like to correct this notion and give attention to a country that is (along with China) being cited as a new potential rising superpower and already looking to take the lead of Emerging Markets (EM) countries. Today, we would like to review the paper that analyzes the performance of main equity factors (with an emphasis on the Quality factor) and is a good starting point to understand the specifics of factor investing strategies in India.<\/p>\n\n\n\n Plus, the following trading strategies have been backtested in QuantConnect<\/a> in the previous two weeks:<\/strong><\/p>\n\n\n\n 296 – Management Diversity Strategy<\/a> Seven new strategies have been added. Four new related research papers have been included into existing strategy reviews and two short free blog posts<\/strong><\/a> has been published during last few weeks. Plus, four trading strategies have been backtested in QuantConnect<\/strong><\/a> in the previous two weeks.<\/p>","protected":false},"author":22768,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-29170","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/29170","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/users\/22768"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=29170"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/29170\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=29170"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=29170"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=29170"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> funds
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 2008 – 2022
Indicative performance:<\/strong> 8%
Estimated volatility:<\/strong> 15.38%<\/p>\n\n\n\n
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4513205<\/a>
Abstracto:
I propose a new benchmark to evaluate hedge fund performance: the returns to shorting CBOE Volatility Index (VIX) futures. The informativeness of this benchmark leads to a new methodology that is able to predict hedge fund performance. Specifically, it separates hedge funds, ex-ante, into one group that delivers higher sharpe ratios and positive skewness (SR of 0.52 and Skew of 4.30) while the other group has lower sharpe ratios and negative skewness (SR of 0.15 and Skew of -0.83), out-of-sample (OOS). I refer to the former group as those hedge funds with edge, in contrast to the latter group as those hedge funds that are without edge. This approach cannot be explained or replicated by previously kn.64own methods. Lastly, I show that my empirical findings can be explained by a model that features traders with extrapolative expectations.<\/p>\n\n\n\n
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> ETFs, funds, futures, options
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 1996-2020
Indicative performance:<\/strong> 7.64%
Estimated volatility:<\/strong> 12.62%<\/p>\n\n\n\n
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4378071<\/a>
Abstracto:
We find that a simple heuristic of sorting liquid equity options by dollar price to construct a portfolio of cheap put options leads to a surprisingly robust tail risk hedge – the superior performance holds even when compared against advanced empirical option strategies. Further investigation reveals the asymmetry in market correlation under different market conditions as the mechanism of this robust hedging performance. The correlation spike accompanying tail risk events leads to most of these options moving into the money, compensating the losses incurred on a broad-base equity index holding. During normal market conditions, these options benefit from the diversification effect due to a lower market correlation, thus mitigating the portfolio drag effect.<\/p>\n\n\n\n
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 1975-2017
Indicative performance:<\/strong> 36%
Estimated volatility:<\/strong> 19.35%<\/p>\n\n\n\n
https:\/\/durham-repository.worktribe.com\/output\/1274919<\/a>
Abstracto:
This paper documents the bimodality of momentum stocks: both high- and low-momentum stocks have nontrivial probabilities for both high and low returns. The bimodality makes the momentum strategy fundamentally risky and can cause a large loss. To alleviate the bimodality and improve return predictability, this paper develops a novel cross-sectional prediction model via machine learning. By reclassifying stocks based on their predicted financial performance, the model significantly outperforms off-the-shelf machine learning models. Tested on the US market, a value-weighted long-short portfolio earns a monthly alpha of 2.4% (t-statistic = 6.63) when regressed against the Fama-French five factors plus the momentum and short-term reversal factors.<\/p>\n\n\n\n
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 2004-2021
Indicative performance:<\/strong> 5.17%
Estimated volatility:<\/strong> 9.97%<\/p>\n\n\n\n
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4474637<\/a>
Abstracto:
Machine learning (ML) models for predicting stock returns are typically trained on one-month forward returns. While these models show impressive full-sample gross alphas, their performance net of transaction costs post 2004 is close to zero. By training on longer prediction horizons and using efficient portfolio construction rules, we demonstrate that ML-based investment strategies can still yield significant positive net returns. Longer-horizon strategies select slower signals and load more on traditional asset pricing factors but still unlock unique alpha. We conclude that design choices are critical for the success of ML models in real-life applications.<\/p>\n\n\n\n
Markets traded: <\/strong>bonds, commodities, currencies, equities
Instruments used for trading:<\/strong> CDFs, ETFs, forwards, futures, stocks, swaps
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 2017-2023
Indicative performance:<\/strong> 8.53%
Estimated volatility:<\/strong> 6.62%<\/p>\n\n\n\n
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4446248<\/a>
Abstracto:
The combination of last year\u2019s large sell-off in the financial markets, a challenging macroeconomic environment, and heightened volatility has led institutional investors to reassess their strategic asset allocation. Guiding these reassessments is the central question of how best to fulfill the dual mandate of generating attractive returns, while providing downside protection for the portfolio.Hedge funds are an important component in institutional investors\u2019 asset allocation. Indeed, several recent surveys indicate an expected increase in allocations to hedge funds and other alternative investment strategies in 2023, with investors increasingly adopting alternative risk premia (ARP) strategies as a substitute for traditional hedge funds. Buoyed by the global trend towards internalization, institutional investors are fashioning bespoke ARP strategies inhouse to profit from improved cost efficiency and increased transparency.This whitepaper explores the theoretical underpinnings of ARP strategies and their historical development. After discussing these fundamental principles, the report presents an empirical study of ARP across all major liquid asset classes.<\/p>\n\n\n\n
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> CFDs, ETFs, funds, futures
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 2001-2019
Indicative performance:<\/strong> 3.48%
Estimated volatility:<\/strong> 8.29%<\/p>\n\n\n\n
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4282212<\/a>
Abstracto:
We construct a common volatility risk factor from U.S. option-implied forward variances and show that it significantly and positively predicts market returns of developed countries, both in- and out-of-sample. This result is robust to inclusion of local volatility factors and other forecasting variables. Moreover, the predictability is stronger during periods of more intense U.S. volatility spillovers and for countries that are more financially linked with the U.S. By contrast, predictability for emerging markets is weak. These findings underscore the unique role of U.S. volatility, which serves as a global risk factor in shaping the risk–return tradeoff across integrated markets.<\/p>\n\n\n\n
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> ETFs
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 2000-2023
Indicative performance:<\/strong> 7.79%
Estimated volatility:<\/strong> 16.57%<\/p>\n\n\n\n
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4549609<\/a>
Abstracto:
The article from Quantpedia discusses the methodology of technical analysis, mainly focusing on the double bottom and double top trading strategies. Technical analysis is often viewed skeptically, with some likening it to \u201dastrology for men.\u201d However, the article emphasizes that while both fundamentals and psychology drive the market, it would be unwise to dismiss technical analysis entirely. The history of technical analysis is traced back to figures like Sokyu Honma in the 1600s in Japan and the Dow Theory from the modern world. The article then delves into various technical analysis terms, such as supports, resistances, and moving averages. The main focus is on the double top and double bottom patterns used as reversal indicators in charts. These patterns can indicate strong resistance or support in price charts. The methodology for the study was based on feedback and questions received over the years, indicating that technical analysis remains a popular subject.<\/p>\n\n\n\nNew research papers related to existing strategies:<\/strong><\/h5>\n\n\n\n
#520 – Lottery Stocks and Past Performance<\/a>
#598 – Lottery and Hot Potato Stocks<\/a><\/strong><\/p>\n\n\n\n
https:\/\/ssrn.com\/abstract=4433510<\/a>
Abstracto:
This paper dissects the lottery-like anomaly in Chinese A-share stocks by decomposing total stock returns into overnight and intraday returns. Our findings indicate that the negative overnight returns are concentrated among lottery-like stocks, and the lottery-like anomaly is mainly driven by the overnight returns component. Considering the unique Chinese institutional features, our mechanism analysis reveals that the overnight returns induced lottery-like anomaly is more pronounced in stocks with high retail investors\u2019 gambling preference and high limits of arbitrage. Overall, our results suggest that investors optimism and trading constraints have a substantial impact on market efficiency in China.<\/p>\n\n\n\n
https:\/\/ssrn.com\/abstract=4131043<\/a>
Abstracto:
We analyse the risk and return relationship of firms sorted by environmental and social (ES) ratings. We document that ES ratings do not have a statistically significant relationship with either average stock returns or unconditional market risk measures. Firms with high ES ratings have significantly lower downside risk than firms with lower ES ratings. However, a two standard-deviation move across stocks on ES score results in a decrease in downside risk measuring only 4\u20138% of the underlying downside risk measure\u2019s standard deviation. This decrease in downside risk for high ES firms can be partly attributed to the news sentiment about the firms and institutional trading. Our results suggest that ES investing may not be justified solely based on the risk-return relationship of ES firms.<\/p>\n\n\n\n
https:\/\/ssrn.com\/abstract=4304204<\/a>
Abstracto:
In this paper, we examine the impact of investor structure on return seasonality driven by institutional investors\u2019 seasonal trading in China\u2019s stock markets. Our identification of investor structure is based on the unique twin market setting in China, in which shares with the same firm fundamentals are cross-listed in both A-share and B-share markets. Given that there are more retail investors in A-share market, we find that 12 types of return seasonality in A-shares are significantly stronger than their cross-listed B-share counterparts, consistent with our theory predictions. We further use the Stock Connect program in China as an exogenous shock to control endogeneity and find the change in investor structure indeed has significant effect on return seasonality.<\/p>\n\n\n\n
https:\/\/ssrn.com\/abstract=4255991<\/a>
Abstracto:
Prior studies document the role social media information plays in the stock market as well as the important dissimilarities between the bond and stock markets. Bridging these two literatures, we examine the role of social media information in the corporate bond market. Analyzing a broad sample of messages by Twitter individual users, posted just prior to earnings announcements, containing bond, credit risk, and fundamental information, we find that aggregate Twitter opinion (OPI) predicts upcoming announcement bond returns and changes in CDS spreads, and is associated with future changes in bond yield spreads and credit ratings, thereby providing economically important information to the bond market. This interpretation is bolstered by results from a variety of cross-sectional analyses. Finally, we document an association between OPI and future changes in default risk, which casts light on the nature of the Twitter information underlying our findings. Overall, our findings demonstrate that Twitter appears to disseminate potentially economically important information to even the presumably sophisticated bond and CDS investors, as well as information intermediaries.<\/p>\n\n\n\nAnd several interesting free blog posts have been published during last 2 weeks:<\/strong><\/h5>\n\n\n\n
365 – Timing S&P500 Using a Large Set of Forecasting Variables<\/a>
816 – Momentum Based on Fractional-Difference Filter<\/a>
913 – Double Bottom Country Trading Strategy<\/a><\/strong><\/p>","protected":false},"excerpt":{"rendered":"