{"id":40122,"date":"2025-05-09T23:32:04","date_gmt":"2025-05-09T21:32:04","guid":{"rendered":"https:\/\/quantpedia.com\/?p=40122"},"modified":"2025-05-27T11:41:34","modified_gmt":"2025-05-27T09:41:34","slug":"quantpedia-premium-update-may-6th","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-may-6th\/","title":{"rendered":"Quantpedia Premium Update – May 6th"},"content":{"rendered":"

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

#1125 – Piotroski’s F-Score in the Chinese Stock Market<\/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> 2000-2024
Indicative performance:<\/strong> 6.42%
Estimated volatility:<\/strong> 9.21%<\/p>\n\n\n\n

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

Shi, Chuan: F-Score in China<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5144971<\/a>
Abstract: <\/strong>This study examines the applicability and effectiveness of Piotroski\u2019s F-score in the Chinese stock market. We investigate whether a long-short hedged portfolio formed using the F-score can earn significant excess returns unexplained by common asset pricing models. Our findings reveal that portfolios based on the F-score demonstrate significant excess returns, with the High \u2212Low portfolio achieving a monthly average excess return of 0.57% under equal weighting and 0.50% under value weighting. Additionally, the integration of the F-score with other fundamental and behavioral finance metrics, like the book-to-market ratio and short-term reversal, further enhances investment performance. The expectation error and FAR (Fundamental-Anchored Reversal) portfolios significantly outperform those based solely on the F-score, indicating the synergy between different information dimensions. These results underscore the importance of fundamental information in the cross-section of expected returns and highlight the potential for combining it with other factors to develop robust investment strategies in the Chinese market. The robustness of our findings is confirmed through various robustness checks.<\/p>\n\n\n\n

#1126 – Pre-ECB Drift Strategy<\/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> Very Complex
Backtest period:<\/strong> 2003-2025
Indicative performance:<\/strong> 3.63%
Estimated volatility:<\/strong> 5.16%<\/p>\n\n\n\n

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

Vojtko, Radovan and Dujava, Cyril: Uncovering the Pre-ECB Drift and Its Trading Strategy Applications<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5227788<\/a>
Abstract: <\/strong>As the world\u2019s attention shifts from the US-centric equity markets to international equity markets (which strongly outperform on the YTD basis), we could review some interesting anomalies and patterns that exist outside of the United States. In the world of monetary policy, traders have long observed a notable positive drift in U.S. equities on days surrounding Federal Reserve (FOMC) meetings. Interestingly, a similar\u2014but slightly shifted\u2014pattern emerges in European markets around European Central Bank (ECB) press conferences. Our quantitative analysis reveals that European equity markets tend to exhibit a strong and consistent upward drift on the day before the ECB\u2019s scheduled press conference. The reason for this timing difference lies in logistics: since the ECB typically speaks at 14:15 CET (8:15 a.m. EST), well before the major U.S. markets open, investors often front-run the potential market-friendly signals from the central bank. Rather than risk holding positions into the uncertainty of the announcement itself, market participants gradually build up exposure the day before, pricing in expectations of dovish or supportive policy moves.<\/p>\n\n\n\n

#1127 – Short-Term Correlated Stress Reversal Trading<\/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> Moderate
Backtest period:<\/strong> 2004-2025
Indicative performance:<\/strong> 6.48%
Estimated volatility:<\/strong> 8.89%<\/p>\n\n\n\n

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

Vojtko, Radovan and Dujava, Cyril: Short-Term Correlated Stress Reversal Trading<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5235835<\/a>
Abstract: <\/strong>Short-term reversal strategies in U.S. large-cap equity indexes, such as the S&P 500, are well-documented and widely followed. These reversals often occur in response to brief periods of market stress, where sharp declines are followed by quick recoveries (as we have experienced in the last few weeks). Traditional approaches typically identify such stress periods using only the price action of the equity index itself. In this research, however, we explore a broader perspective\u2014one that leverages the behavior of other asset classes, including gold, oil, and intermediate-term U.S. Treasuries. We demonstrate that using signals from these correlated assets to detect stress events can enhance the timing and robustness of reversal trades in equities. Furthermore, we show that combining signals across multiple markets leads to a more effective and diversified reversal strategy.<\/p>\n\n\n\n

#1128 – Industry-Based Long-Only Trend-Following<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong>  Daily
Markets traded:<\/strong> equities
Instruments used for trading:<\/strong> ETFs
Complexity:<\/strong> Complex
Backtest period:<\/strong> 1926-2024
Indicative performance:<\/strong> 7.7%
Estimated volatility:<\/strong> 13.7%<\/p>\n\n\n\n

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

Zarattini, Carlo and Antonacci, Gary: A Century of Profitable Industry Trends<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4857230<\/a>
Abstract: <\/strong>This paper evaluates the profitability of an industry-based long-only trend-following portfolio. Utilizing 48 industry portfolios from 1926 to 2024, our analysis explores the model\u2019s profitability over a century, highlighting its adaptability and effectiveness across diverse market epochs. We assess the overall profitability of the model and examine the distribution of long-term returns and associated risks. Our analysis includes the impact of individual industry contributions on overall portfolio performance, focusing on the frequency and average profitability of trades at both the portfolio and industry levels. The Timing Industry strategy achieves an average annual return of 18.2% with an annual volatility of 12.6%, resulting in a Sharpe Ratio of 1.39, compared to the US equity market\u2019s 9.7% return, 17.1% volatility, and 0.63 Sharpe Ratio. The model\u2019s outperformance is underscored by an annualized alpha of 10.9%, with the timing strategy reducing drawdown by almost 60% compared to a passive long exposure. Further investigations reveal the active strategy\u2019s ability to fully participate during market upswings while significantly limiting exposure during downturns. In the final section, we introduce 31 sector ETFs provided by State Street Global Advisors and backtest the same trading methodology over the last 20 years. The ETFs successfully replicate the model\u2019s exposure and returns. We also assess the impact of commissions and slippage, demonstrating that the active timing strategy remains largely profitable even with high trading costs.<\/p>\n\n\n\n

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

#45 – Short Interest Effect – Long-Short Version<\/a>
#46 – Short Interest Effect – Long Only version<\/a><\/strong><\/p>\n\n\n\n

Da, Zhi and Fu, Chengbo and Lin, Nanying and Lu, Lei: Short-selling Profitability, Stock Lending Fees, and Asset Pricing Anomalies<\/strong>
https:\/\/ssrn.com\/abstract=5116351<\/a>
Abstract: <\/strong>We measure a stock\u2019s short-selling profitability (SSP) as its price sensitivity to short-selling activities over recent periods. Our findings show that short-selling strongly and negatively predicts future returns, particularly among high-SSP stocks. Furthermore, we identify SSP as a novel determinant of stock lending fees in the cross-section. While the profitability of anomalies decreases when accounting for short-selling fees, they remain exploitable among high-SSP stocks. These results support the presence of a stock lending market in which lenders allow short sellers to retain a portion of arbitrage profits. This suggests that short-selling constraints alone do not fully explain the persistence of anomalies, especially among high-SSP stocks.<\/p>\n\n\n\n

#57 – Term Spread Premium<\/a><\/strong><\/p>\n\n\n\n

Vincenz, Stefan: Harvesting the Term Premium: International Out-of-Sample Evidence<\/strong>
https:\/\/ssrn.com\/abstract=5176661<\/a>
Abstract: <\/strong>The existing evidence for predictability of international bond risk premia raises questions about whether significant statistical in-sample results can be translated into economic gains. Moreover, existing findings offer limited information on their practical applicability. This study examines a broad set of existing bond risk premia models, extends it to international markets, and highlights the benefits of using a forecasting approach that utilizes information from the cross-section of countries. Such an approach, combining information from multiple international markets, better captures drivers in international bond risk premia than other approaches, including solely local information. The out-of-sample findings illustrate how government bond investors can use the presented approach to enhance their efficient frontier, though for most models the overall economic gains remain limited.<\/p>\n\n\n\n

And several interesting free blog posts that have been published during the last 2 weeks:<\/h4>\n\n\n\n

Revisiting Pragmatic Asset Allocation: Simple Rules for Complex Times<\/a><\/h4>\n\n\n\n

Pragmatic Asset Allocation (PAA) represents a portfolio construction approach that seeks to balance the benefits of systematic trend-following with the realities faced by semi-active investors (mainly taxes and lack of time to manage positions). Approximately a month ago, we ran a test and filtered asset allocation strategies from our Screener and looked for those that performed well on a YTD basis. One of those models that fared surprisingly well was the PAA model, and given the challenging market conditions so far in 2025, with mixed signals across asset classes and increased macroeconomic uncertainty, we believe it is an ideal time to revisit the PAA framework. This analysis may help clarify whether a pragmatic, rules-based approach can still hold its ground\u2014or even outperform\u2014in a year when many models have struggled.
<\/a><\/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

1115 – Margin Debt Market Timing Strategy<\/a>
1120 – Righ Tail vs Left Tail Stock Picking Strategy in China<\/a>
1121 – Similar-Attention Stocks Effect in Chinese Stocks<\/a>
1122 – Volatility-Scaled Momentum in Cryptocurrencies<\/a>
1123 – Switching Between Momentum and Reversal Strategies in Equities<\/a><\/strong><\/p>\n\n\n\n

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

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

Four new strategies have been added. Two new related research papers have been included into existing strategy reviews and one short free blog post<\/strong><\/a> have been published during last few weeks. Plus, five trading strategies have been backtested in QuantConnect<\/strong><\/a> 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-40122","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/40122","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=40122"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/40122\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=40122"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=40122"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=40122"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}