{"id":41489,"date":"2025-07-10T23:49:14","date_gmt":"2025-07-10T21:49:14","guid":{"rendered":"https:\/\/quantpedia.com\/?p=41489"},"modified":"2025-07-13T21:21:09","modified_gmt":"2025-07-13T19:21:09","slug":"quantpedia-premium-update-july-10th","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-july-10th\/","title":{"rendered":"Quantpedia Premium Update – July 10th"},"content":{"rendered":"

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

#1146 – First Month of a Quarter Positively Predicts the Second Month’s Return<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded:<\/strong> equities
Instruments used for trading:<\/strong> ETFs, futures, CFDs
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 1926-2023
Indicative performance:<\/strong> 8.32%
Estimated volatility:<\/strong> 22.49%<\/p>\n\n\n\n

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

Guo, Hongye and Wachter, Jessica A.: Correlation neglect in asset prices<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5268329<\/a>
Abstract: <\/strong>The U.S. stock market\u2019s return during the \ufb01rst month of a quarter positively predicts the second month\u2019s return, which then negatively predicts the \ufb01rst month\u2019s return of the next quarter. The pattern arises from a model in which investors do not fully recognize that earnings announced in the second month of a quarter are inherently similar to those announced in the \ufb01rst month, thereby overreacting to such predictably repetitive earnings. The same pattern exists in the cross-section and time series of industry returns. Evidence from survey data lends support to the mechanism of correlation neglect.<\/p>\n\n\n\n

#1147 – A Dual Approach For VIX ETNs Trading<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded:<\/strong> equities
Instruments used for trading:<\/strong> ETFs
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 2008-2025
Indicative performance:<\/strong> 16.3%
Estimated volatility:<\/strong> 16.4%<\/p>\n\n\n\n

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

Zarattini, Carlo and Mele, Antonio and Aziz, Andrew: The Volatility Edge: A Dual Approach For VIX ETNs Trading<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5316487<\/a>
Abstract: <\/strong>Volatility isn\u2019t just a measure of market fluctuations; it is the underlying asset of a large number of tradable instruments. After providing a concise overview of the history of volatility trading, this paper demonstrates how individual investors can construct portfolios designed to capture the volatility risk premium using only VIX-linked exchange-traded notes (ETNs). We test four rule sets, starting with a constant short-volatility allocation and ending with a dynamically sized strategy that responds to both the option market premium and the slope of the VIX term structure. Over 2008-2025, and after realistic costs, the final version compounds at 16.3% per year, delivers a Sharpe ratio of 1, and keeps equity-market correlation near 15%. Blending even a modest slice of this strategy into a passive SPY portfolio can lift the combined Sharpe ratio by 20%. We also outline how the rules can be automated through a standard broker API. In conclusion, volatility trading is no longer the exclusive domain of institutional hedge funds. With the right tools and discipline, individual investors and systematic traders can now access and exploit volatility-based strategies. However, one must always be mindful: volatility itself is volatile\u2014and should be handled with care.<\/p>\n\n\n\n

#1148 – Pre-Announcement Drift Strategy in Foreign Central Banks<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded:<\/strong> equities
Instruments used for trading:<\/strong> ETFs
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 2010-2025
Indicative performance:<\/strong> 3.06%
Estimated volatility:<\/strong> 4.6%<\/p>\n\n\n\n

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

Vojtko, Radovan and Dujava, Cyril: Pre-Announcement Drift for BoE, BoJ, SNB: Do Markets Move Before the Word Is Out?<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5289134<\/a>
Abstract: <\/strong>We\u2019ve previously examined how central bank policy decisions\u2014particularly those by the Federal Reserve and the European Central Bank (ECB)\u2014impact stock market behavior. The price drift in U.S. equities around the Federal Open Market Committee (FOMC) meetings is a well-documented phenomenon. Likewise, our research study of the ECB revealed a pre-announcement drift, underscoring the anticipatory nature of equity markets ahead of key policy events and the potential opportunities for trading strategies. But are such price drifts unique to the Fed and ECB? In this article, we broaden the scope to investigate whether similar market behavior occurs around monetary policy announcements by other major central banks: mainly the Swiss National Bank (SNB), the Bank of England (BoE), and the Bank of Japan (BoJ).<\/p>\n\n\n\n

#1149 – Mispricing as a Short-Term Excess Return<\/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 strategy
Backtest period:<\/strong> 1968 -2022
Indicative performance:<\/strong> 23.48%
Estimated volatility:<\/strong> 17.95%<\/p>\n\n\n\n

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

Han, Chulwoo and Kang, Jangkoo and Lee, Geongon: Mispricing and Correction in Short-Term Returns<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5191605<\/a>
Abstract: <\/strong>Our study addresses a limitation of the traditional short-term reversal strategy, which presumes that all stocks have the same expected return and identifies mispricing solely based on realized returns. We estimate the conditional expected return of a stock using firm characteristics and machine learning and measure the mispricing of a stock as Short-Term Excess Return (STER), the difference between its realized return and conditional expected return. Our results show that machine learning methods produce more accurate conditional expected returns and that a STER-based reversal strategy generates significantly higher returns than the traditional short-term reversal strategy. Our findings are consistent with the investor overreaction-based explanation. <\/p>\n\n\n\n

#1150 – Congressional Effect<\/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 strategy
Backtest period:<\/strong> 1897 -2000
Indicative performance:<\/strong> 5.3%
Estimated volatility:<\/strong> –<\/p>\n\n\n\n

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

Ferguson, Michael F. and Witte, H. Douglas: Congress and the Stock Market<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=687211<\/a>
Abstract: <\/strong>We find a strong link between Congressional activity and stock market returns that persists even after controlling for known daily return anomalies. Stock returns are lower and volatility is higher when Congress is in session. This \u201cCongressional Effect\u201d can be quite large\u2014more than 90% of the capital gains over the life of the DJIA have come on days when Congress is out of session. The Effect varies systematically with the public’s opinion of Congress: returns are lower and volatility higher when a relatively unpopular Congress is active. Public opinion appears to play a fundamental role in market prices. This is consistent with a mood-based explanation that sees Congress as \u2018depressing\u2019 the average investor. Alternatively, our results can also be reconciled with rational explanations that view Congressional activity as a proxy for regulatory uncertainty or rent-seeking behavior.<\/p>\n\n\n\n

#1151 – High Trend Strategy<\/a><\/h5>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded:<\/strong> equities, bonds, commodities, REITs
Instruments used for trading:<\/strong> ETFs
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 1926 -2019
Indicative performance:<\/strong> 12.51%
Estimated volatility:<\/strong> 8.18%<\/p>\n\n\n\n

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

Faber, Meb: All Time Highs. A Good Time To Invest? No. A Great Time.<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=5271319<\/a>
Abstract:<\/strong> In this paper, we examine the counterintuitive proposition that investing at all-time highs may not only be prudent but highly effective. Contrary to the widespread fear of \u201cbuying the top,\u201d our analysis demonstrates that entering markets near their highs\u2014particularly through simple, trend-following strategies\u2014can lead to superior risk-adjusted returns and reduced drawdowns across asset classes. We evaluate this behavior in equities, real estate, and commodities, and show that momentum-driven frameworks help investors remain invested during strength while avoiding major declines. Our findings support a systematic, evidence-based approach to allocating capital, even when markets appear overextended.<\/p>\n\n\n\n

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

#527 – Employee Satisfaction, ESG and Stock Returns<\/a><\/strong>
#692 – Employee Satisfaction Factor<\/a><\/strong>
#806 – Employee Sentiment and Stock Returns<\/a><\/strong><\/p>\n\n\n\n

Sovbetov, Ihlas: Does employee happiness create value for firm performance?<\/strong>
https:\/\/ssrn.com\/abstract=5266338
Abstract:<\/strong> This study examines the impact of employee happiness on firm performance in the UK using data from the “Best 100 British Companies to Work For” list (2001-2020). Applying the Carhart four-factor model to monthly data, we find that happier firms outperform the market, generating a monthly alpha of 32 basis points (3.86% annualized), which increases further to 34 basis points when weighted by ranking. Newly listed firms experience significant abnormal returns, whereas delisted firms show no excess returns, suggesting asymmetric market reactions to inclusion versus removal. Disaggregated analysis by industry shows the highest alpha in the technology sector, where human capital and innovation amplify the role of employee satisfaction. The industrial sector yields more modest alphas, likely reflecting capital intensity, more rigid work environments, and sectoral heterogeneity. A longevity analysis indicates that the market fully incorporates the intangible benefits of listing only after 36 months. Robustness checks using Fama-MacBeth regressions confirm that employeerelated factors-such as workplace conditions, rewards, happiness, and demographicssignificantly influence firm performance.<\/p>\n\n\n\n

#224 – P<\/a><\/strong>rofitability Factor Combined with Value Factor<\/strong><\/a>
#746 – Combined Value and Profitability in US and Chinese Equities<\/a><\/strong><\/p>\n\n\n\n

Ai, Hengjie and Li, Jun and Tong, Jincheng: Equilibrium Value and Profitability Premiums<\/strong>
https:\/\/ssrn.com\/abstract=5267354<\/a>
Abstract:<\/strong> Standard production-based asset pricing models cannot simultaneously explain the value premium and the gross profitability premium. Empirically, we show that value and profitability sorted portfolios differ in the persistence of productivity. We develop a general equilibrium model where firm-level productivity has a two-factor structure with different persistence. We demonstrate that with capital adjustment costs and variable capital utilization, our model can simultaneously account for both the gross profitability premium and the value premium.<\/p>\n\n\n\n

#1067 – Factor Pockets<\/a><\/strong><\/p>\n\n\n\n

Cakici, Nusret and Fieberg, Christian and Neumaier, Tobias and Poddig, Thorsten and Zaremba, Adam: The Devil in the Details: How Sensitive Are “Pockets of Predictability” to Methodological Choices?<\/strong>
https:\/\/ssrn.com\/abstract=5234227<\/a>
Abstract: <\/strong>The growing complexity of forecasting models increases the number of decision nodes in the research process, raising the risk of overfitting to specific design choices. We illustrate this issue using the recent concept of “pockets of predictability,” which posits that return predictability is time-varying and that short windows of high predictability can be identified ex-ante. In this study, we reassess the robustness and practical applicability of this approach. By analyzing 19,440 variations of the original methodology, we find that its effectiveness depends critically on various seemingly minor methodological decisions. Furthermore, return predictability has declined significantly in recent decades, and the potential economic gains are highly sensitive to trading costs. Overall, strategies based on pockets of predictability should be approached with caution.<\/p>\n\n\n\n

#699 – Stock and Bond Returns Predict Currency Returns<\/a><\/strong><\/p>\n\n\n\n

Yamani, Ehab Abdel-Tawab: The Role of Stock and Bond Returns in the Cross-Section of Currency Returns<\/strong>
https:\/\/ssrn.com\/abstract=5286382<\/a>
Abstract: <\/strong>This paper offers a new risk-based explanation for the cross-section of currency excess returns using financial market drivers emanating from returns in stock and bond markets across 20 countries for the period of 1996-2019. We identify a global stock (and bond) risk factor, defined as a long-short portfolio of buying currencies with high sensitivities to stock (bond) returns and selling currencies with low sensitivities to stock (bond) returns. Results show that currencies with high exposures to stock (bond) returns have high (low) expected returns, and vice versa, even after controlling for popular determinants of currency returns.<\/p>\n\n\n\n

#008 – Currency Momentum Factor<\/a><\/strong><\/p>\n\n\n\n

Bartel, Merlin and Hanke, Michael and Petric, Sebastian: FX Factor Momentum Pre-and Post-GFC<\/strong>
https:\/\/ssrn.com\/abstract=5289093<\/a>
Abstract: <\/strong>Building on the well-documented role of dollar and carry factors in global currency research, this paper investigates FX factor momentum in a systematic manner. We analyze the performance of factor momentum strategies on various sets of developed and emerging market currencies. We find that the performance of FX factor momentum strategies depends on the time period (pre- vs.\\ post-GFC) and the particular set of currencies used to construct the factors. Comparing statistical factors from PCA across time reveals structural shifts in the cross-section of currencies that affect the performance of these strategies. Relating economic factors to statistical factors yields further insights into the causes for performance differences between different variants of FX factor momentum strategies.<\/p>\n\n\n\n

#454 – Time Series Momentum Strategies Using Deep Neural Networks<\/a><\/strong>
#909 – Deep Momentum<\/a><\/strong><\/p>\n\n\n\n

Giantsidi, Sofia and Claudia, Tarantola: Deep Learning for Financial Forecasting: A Review of Recent Advancements<\/strong>
https:\/\/ssrn.com\/abstract=5263710<\/a>
Abstract: <\/strong>Deep Learning (DL) has revolutionized financial forecasting. This paper presents a comprehensive literature review of DL studies on financial forecasting implementation, specifically examining articles published in the Scopus research database between 2020 and 2024. We examine key DL models, including Deep Multilayer Perceptrons, Recurrent Neural Networks such as Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Units, Reservoir Computing, Convolutional Neural Networks, and Autoencoders. The studies are categorized based on their application areas, including stock price, index, forex, commodity, bond, cryptocurrency, and volatility forecasting. Within each category, we further classify them according to their DL model choices, distinguishing between standalone models and hybrid approaches, with a focus on data preprocessing, technical indicators, sentiment analysis, and novel methodologies. We attempted to envision the future of the field by taking a snapshot of the current state, highlighting key challenges, and outlining future directions. Findings reveal a slight dominance of hybrid models over standalone ones, with RNN-based architectures, particularly LSTMs, excelling. Data preprocessing, technical indicators, and sentiment analysis play a crucial role in improving predictive accuracy. While index and stock price forecasting remain prevalent due to liquidity and historical data availability, cryptocurrency forecasting is gaining attention, driven by its continuous 24\/7 trading nature.<\/p>\n\n\n\n

And an interesting free blog post that has been published during the last 2 weeks:<\/h4>\n\n\n\n
An Empirical Analysis of Conference-Driven Return Drift in Tech Stocks<\/a><\/h5>\n\n\n\n

Corporate conferences have long been recognized as pivotal events in financial markets, serving as catalysts that signal upcoming innovations<\/a> and strategic shifts. Scheduled corporate events induce market reactions that can be systematically analyzed to reveal predictable return patterns. In this work, we focus on examining the return drift exhibited by technology stocks in the days surrounding their respective conferences, employing simple quantitative methods with daily price data.<\/p>\n\n\n\n

The hypothesized return drift is premised on the notion that investor sentiment and market dynamics are significantly altered by the information disseminated at these conferences. Investors, reacting to both anticipatory signals and post-announcement adjustments, tend to drive prices in a measurable manner in the windows immediately preceding, during, and after the events. By systematically analyzing stocks of companies such as Apple, Google, and Microsoft<\/a>, this study aims to validate the existence of these drift patterns and shed light on the underlying mechanisms, thereby enhancing mutual understanding of event-driven asset pricing dynamics.<\/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

#1138 – The Intersection of Expected Returns<\/a>
#1139 – Market Timing Nifty 50 Index using S&P500 daily performance<\/a>
#1140 – U.S. Presidential Election Results Impact the Size Premium<\/a><\/strong><\/p>\n\n\n\n

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

Six new strategies have been added. Six new related research papers have been included into existing strategy reviews and one short free blog post<\/strong><\/a> has been published during last few weeks. Plus, three 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-41489","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/41489","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=41489"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/41489\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=41489"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=41489"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=41489"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}