{"id":43443,"date":"2025-11-24T23:30:55","date_gmt":"2025-11-24T22:30:55","guid":{"rendered":"https:\/\/quantpedia.com\/?p=43443"},"modified":"2025-11-24T23:32:34","modified_gmt":"2025-11-24T22:32:34","slug":"quantpedia-premium-update-november-24th","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-november-24th\/","title":{"rendered":"Quantpedia Premium Update – November 24th"},"content":{"rendered":"

New Strategies:<\/h4>\n\n\n\n

#1207 – Volatility Decay and Arbitrage in Leveraged ETFs<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded:<\/strong> equities
Instruments used for trading:<\/strong> ETFs
Complexity:<\/strong> Simple
Backtest period:<\/strong> 2006-2024
Indicative performance:<\/strong> 3.6%
Estimated volatility:<\/strong> 7.7%<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Lin, Cheng-To and Lin, Shih-Kuei and Wang, George Yungchih and Yeh, Zong-Wei: Volatility Decay and Arbitrage in Leveraged ETFs: Evidence from the US and Japan<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5421274<\/a>
Abstract:<\/strong> While financial theory suggests that shorting bear leveraged ETFs (LETFs) is the optimal way to harvest volatility decay, we find the opposite is true in the US market. A beta-neutral arbitrage strategy shorting US bull LETFs yields a Sharpe ratio as high as 2.12, whereas the theoretically superior bear-side strategy is largely unprofitable. This paper resolves this puzzle by examining volatility decay strategies across US and Japanese markets. We find that the overall pairwise beta-neutral strategy is robustly profitable, generating highly positive skewness and offering strong downside protection. The striking cross-market asymmetry is driven not by volatility decay itself, but by the \u201cnon-compounding effect\u201d\u2014a friction tied to the markets\u2019 different replication technologies (swaps in the US vs. futures in Japan). Our results show that the optimal decay, harvesting strategy is necessarily asymmetric: shorting bull LETFs in the US but bear LETFs in Japan. Finally, we develop a jump-diffusion model to provide a theoretical basis for the strategy\u2019s exceptionally large profits during market crises.<\/p>\n\n\n\n

#1208 – Leveraged ETFs in Asset Allocation: Opportunity or Trap?<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded:<\/strong> equities, commodities, bonds
Instruments used for trading:<\/strong> ETFs
Complexity:<\/strong> Moderate
Backtest period:<\/strong> 1926-2025
Indicative performance:<\/strong> 8.88%
Estimated volatility:<\/strong> 9.69%<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Pauchlyov\u00e1, Margar\u00e9ta and Vojtko, Radovan: Leveraged ETFs in Asset Allocation: Opportunity or Trap?<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5756723<\/a>
Abstract:<\/strong> In this article, we explore whether it makes sense to incorporate leveraged ETFs into static and dynamic long-only asset allocation strategies. Leveraged ETFs promise amplified exposure to the underlying asset, offering the potential for significantly higher returns during favorable market conditions. However, this comes at the cost of much higher volatility, path-dependency, and the well-known issue of volatility decay, which can lead to substantial underperformance over longer periods. Our objective is to examine if \u2014 and how \u2014 leveraged ETFs can be systematically integrated into portfolio construction so that their benefits can be captured while mitigating their inherent risks.<\/p>\n\n\n\n

#1209 – Santa Claus Rally<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded:<\/strong> equities
Instruments used for trading:<\/strong> ETFs, futures, CFDs
Complexity:<\/strong> Simple
Backtest period:<\/strong> 1960-2021
Indicative performance:<\/strong> 1.4%
Estimated volatility:<\/strong> –<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Kjaer, Christian: The Santa Claus Rally<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3991532<\/a>
Abstract: <\/strong>A very short note examining the existence of the Santa Claus Rally effect across a number of equity markets. The effect appears surprisingly strong and robust across equity markets.<\/p>\n\n\n\n

#1210 – The End-Of-Month Effect in Value-Growth and Real-Estate-Equity Spreads<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded:<\/strong> equities, REITs
Instruments used for trading:<\/strong> ETFs
Complexity:<\/strong> Simple
Backtest period:<\/strong> 2000-2025
Indicative performance:<\/strong> 3.27%
Estimated volatility:<\/strong> 5.43%<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Vojtko, Radovan and Dujava, Cyril: The End-Of-Month Effect in Value\u2013Growth and Real-Estate\u2013Equity Spreads<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5631030<\/a>
Abstract: <\/strong>The clustering of excess returns on the final trading days of the month constitutes a robust empirical regularity with significant implications for portfolio construction. We document a monthend premium that is both statistically and economically significant, distinct from the canonical turn-of-the-month (ToM) effect. Our strategy highlights systematic style rotations-particularly shifts in value versus growth exposures, as proxied by the IVE-IVW spread-and documents parallel contemporaneous dislocations between real-estate and broad-equity benchmarks, as measured by the IYR-SPY spread. <\/p>\n\n\n\n

#1211 – Alpha in Analysts<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded:<\/strong> equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Very Complex
Backtest period:<\/strong> 1999-2024
Indicative performance:<\/strong> 11.3%
Estimated volatility:<\/strong> 24.07%<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Cartea, \u00c1lvaro and Jin; Qi: Alpha in Analysts<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5171848<\/a>
Abstract: <\/strong>This paper examines the investment value in sell-side analyst price targets. We treat each analyst as a portfolio manager and use their price targets to construct 12-month implied return forecasts. We invest in self-financing long-short portfolios for individual analysts, where we go long on stocks with positive forecasts and go short on those with negative forecasts, and the weights in the portfolio are proportional to the magnitude of the implied returns. Our empirical analysis shows that while the average analyst does not generate statistically significant alpha relative to the returns of a long-only portfolio benchmark, a subset of analysts exhibits persistent alpha. Motivated by this heterogeneity, we introduce a \u201cfund-of-analysts\u201d framework that first predicts analyst performance and then dynamically allocates weights across analysts based on predicted analyst performances. Our results show that this meta-portfolio strategy can yield significant alpha over long-only benchmarks, providing new insights into the role of analyst heterogeneity in equity market pricing.<\/p>\n\n\n\n

New research papers related to existing strategies:<\/h4>\n\n\n\n

#1209 – Santa Claus Rally<\/a><\/strong><\/p>\n\n\n\n

Molinaro, Mat\u00e9o and Chebrek, Ryhan and Moury, Julien and Domingues, Theo: Santa Claus Rally: A Global Equity Markets and International Factor-Based Analysis (slides)<\/strong>
https:\/\/ssrn.com\/abstract=5402581<\/a>
Abstract: <\/strong>This study investigates the presence and statistical significance of the Santa Claus Rally (SCR) across global equity markets and factor portfolios. Using adjusted p-values and cross-country factor returns (MKT, SMB, HML, FF, and Devil factors), we document a positive and economically relevant SCR effect in several regions, notably Canada, New Zealand, the United States, and broader North America. For example, Canada\u2019s SMB factor exhibits a SCR return proportion of nearly 97%, with statistical significance reaching 30.76%. However, overall significance declines substantially after multiple-testing corrections, in some cases by over twelvefold, largely due to the disproportionate sample size between SCR days and non-SCR days (97.3% of the total sample). Our results suggest that while the SCR remains visible in certain factors and geographies, its robustness weakens under rigorous statistical scrutiny. Future work will compare significance assessments with and without bootstrap-based adjustments.<\/p>\n\n\n\n

#525 – Double-Sorting all Possible Strategies<\/a><\/strong><\/p>\n\n\n\n

Wang, Jianye and Peng, Xi: Do machine learning models improve higher-order moment forecasts? Evidence from Fama-French factors<\/strong>
https:\/\/ssrn.com\/abstract=5579893<\/a>
Abstract: <\/strong>This study systematically examines whether Machine Learning (ML) models improve the forecasting of higher-order moments, compared to linear factor models. Using monthly U.S. industry portfolios from January 1975 to April 2025, we implement a rolling-window forecasting framework to compare Random Forest, XGBoost, and LightGBM against the Fama-French five-factor model augmented by industry dummies. Our empirical results show that the linear specification consistently outperforms ML models in terms of forecasting skewness and kurtosis. Robustness tests with alternative window lengths and model specifications confirm these results. Portfolio tests further reveal that ML-based forecasts generate negative annualized returns and Sharpe ratios, whereas OLS-based forecasts obtain modest but positive performance and the lowest maximum drawdowns. Our findings indicate the enduring value of linear models for capturing distributional risks and challenge the notion of ML’s universal superiority.<\/p>\n\n\n\n

#1206 – Revaluation Alpha<\/a><\/strong><\/p>\n\n\n\n

Raju, Rajan: Timing Indian Equity Factors: Structural vs. Revaluation Signals<\/strong>
https:\/\/ssrn.com\/abstract=5513940<\/a>
Abstract:<\/strong> We examine Indian equity factor premia using the structural-revaluation decomposition of Arnott, Ehsani, Harvey and Shakernia (2025), applied to Fama-French style portfolios (value, size, profitability, investment) and momentum over 2004-2025 with the Invespar Indian factor library. Factor returns are separated into a structural component linked to fundamentals via price-to-book and a revaluation component reflecting valuation spread changes. Long-run premia are predominantly structural: Value, size, and momentum show persistent structural means, while revaluation averages to zero but contributes substantially to volatility and is strongly negatively correlated with structure. Predictive regressions and out-of-sample tests indicate limited timing ability, with only momentum at the 12-month horizon showing significance and forecast-error gains below economic thresholds. Robustness checks (alternative anchors, weighting schemes, sub-periods) confirm the results.<\/p>\n\n\n\n

And several interesting free blog posts that have been published during the last 2 weeks:<\/strong><\/p>\n\n\n\n

How to Design a Simple Multi-Timeframe Trend Strategy on Bitcoin<\/a><\/h5>\n\n\n\n

Bitcoin is one of the most widely discussed financial assets of the modern era. Since its inception, it has evolved from a niche digital experiment into a globally recognized investment instrument with institutional adoption and billions in daily trading volume. Despite its inherent volatility, Bitcoin has demonstrated a strong long-term growth trajectory, making it an attractive candidate for trend-based and momentum-oriented trading strategies. In this study, we apply concepts from technical analysis to construct and refine a trend-following strategy for Bitcoin, progressing step by step from a simple MACD setup toward an improved multi-timeframe model.<\/p>\n\n\n\n

Leveraged ETFs in Asset Allocation: Opportunity or Trap?<\/strong><\/a><\/h5>\n\n\n\n

In this article, we explore whether it makes sense to incorporate leveraged ETFs into static and dynamic long-only asset allocation strategies. Leveraged ETFs promise amplified exposure to the underlying asset, offering the potential for significantly higher returns during favorable market conditions. However, this comes at the cost of much higher volatility, path-dependency, and the well-known issue of volatility decay, which can lead to substantial underperformance over longer periods. Our objective is to examine if \u2014 and how \u2014 leveraged ETFs can be systematically integrated into portfolio construction so that their benefits can be captured while mitigating their inherent risks.<\/p>\n\n\n\n

Plus, the following trading strategies have been backtested in QuantConnect<\/a> in the previous two weeks:<\/h4>\n\n\n\n

311 – Currency Option Delta-Hedging Strategy
1194 – Tactical Allocation for Vanguard Investors
1195 – Elastic Momentum Factor in Cryptocurrencies<\/strong><\/p>\n\n\n\n

<\/p>\n\n\n\n

 <\/p>","protected":false},"excerpt":{"rendered":"

Five new strategies have been added. Three new related research papers have been included into existing strategy reviews and two new short free blog posts have been published during last few weeks. Plus, three trading strategies have been backtested in QuantConnect in the previous two weeks.<\/p>","protected":false},"author":25721,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-43443","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/43443","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\/25721"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=43443"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/43443\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=43443"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=43443"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=43443"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}