Quantpedia Premium Update – June 29th

New Strategies:

#1140 – U.S. Presidential Election Results Impact the Size Premium

Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple strategy
Backtest period: 1963-2024
Indicative performance: 30.15%
Estimated volatility: 27.64%

Source paper:

Caglayana; Mustafa O.; Celikerb; Umut; Tepe; Mete: Do U.S. Presidential Election Results Impact the Size Premium?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5265365
Abstract: We examine if the size premium in stock returns is affected by the political environment in the U.S. We find that small-cap stocks significantly outperform large-cap stocks —both economically and statistically — especially in the periods following U.S. presidential election wins by the Democratic Party. Moreover, the outperformance of small stocks following Democratic Party wins is found to be strongest when the incumbent president is Republican. Conversely, there is no evidence of a significant size premium following the wins by the Republican Party and/or during non-election years. Our results remain robust after controlling asset-pricing factors, the January effect, and business cycles.

#1141 – Short-term Reversal Factor in REITs

Period of rebalancing: Monthly
Markets traded: REITs
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1987-2023
Indicative performance: 10.43%
Estimated volatility: 11.88%

Source paper:

Letdin; Mariya; Seagraves; Cayman; Sirmans; Stace: REIT Factors
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5250691
Abstract: Employing a transparent, replicable methodology with CRSP-Ziman data from 1987 to 2023, this paper develops and analyzes six REIT return factors—size, value, momentum, quality, low volatility, and short-term reversal. Our REIT-specific factors demonstrate substantially improved explanatory power over general equity asset pricing models, achieving a 33% higher median firm-level R2 and reducing average unexplained alphas by nearly 3% per year. Short-term reversal, momentum, quality, and low volatility factors generate significant risk-adjusted returns in REITs. While the size factor underperforms, the value factor shows a robust premium only after controlling for its negative exposures to REIT momentum, quality, and low volatility. These factors exhibit distinct behaviors across economic regimes and remain robust to transaction costs. Compared to general equity factors, REIT momentum, quality, and reversal yield unique alphas, highlighting real-estate-specific return anomalies. After testing over 15,000 additional predictors, we find that while almost all are subsumed by our six factors, novel signals tied to financial health may provide incremental value. This study advances REIT asset pricing with a comprehensive factor framework and dataset available for download at reitfactors.ai.

#1142 – Quality Factor in REITs

Source paper:

#1143 – The Wikipedia Effect

Period of rebalancing: Weekly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 2016-2023
Indicative performance: 6.50%
Estimated volatility: 6.00%

Source paper:

Pyun; Chaehyun: The Wikipedia Effect: Analyzing Investor Attention for Strategic Investment Decisions
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5172055
Abstract: The industries with the highest increase in Wikipedia page views are the telecommunications, consumer durables, and high-technology sectors. On average, firms with increasing views have higher returns, lower earnings per share, and higher price-to-earnings ratios. Moreover, long-short investment strategies using changes in Wikipedia page views yield profitable portfolio returns, outperforming the market. Factor model regressions demonstrate that these portfolios exhibit positive abnormal returns relative to various benchmark models. In particular, the high-minus-low portfolio shows statistically significant alphas that common risk factors cannot explain. The findings suggest that changes in firms’ Wikipedia page views are a valuable indicator of stock performance.

#1144 – The Implications of Investor Attention for Price and Earnings Momentum

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1967-2022
Indicative performance: 14.99%
Estimated volatility: 27.14%

Source paper:

Hou; Kewei; Loh; Roger K.; Peng; Lin; Xiong; Wei: A Tale of Two Anomalies: The Implications of Investor Attention for Price and Earnings Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5202255
Abstract: We examine the role of investor attention in explaining price and earnings momentum anomalies using an extensive set of established and novel attention measures. Our analysis reveals a striking contrast: price momentum profits are larger among high-attention stocks, while earnings momentum profits are stronger among low-attention stocks. These findings support a dual mechanism through which attention influences momentum: investor inattention leads to underreaction to earnings news, while heightened attention amplifies behavioral biases that drive price momentum. Moreover, we uncover significant heterogeneity in how different attention measures relate to momentum, reflecting variations in investor clientele and attention channels.

#1145 – Multifractal Crypto Strategy

Period of rebalancing: Intraday
Markets traded: crypto
Instruments used for trading: crypto
Complexity: Very complex strategy
Backtest period: 2021-2023
Indicative performance: 68.20%
Estimated volatility: 29.27%

Source paper:

Czellara; Veronika; Iyidogan; Engin: Multifractal Cryptocurrencies
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5194867
Abstract: We propose a multifractal model to capture the high frequency dynamics of cryptocurrency returns and show that our model accurately predicts cryptocurrency returns at five-minute intervals. We then introduce a novel trading strategy which provides trading signals based on risk-return trade-off. When combined with our model, the strategy generates substantial returns even under high trading fees, execution delays, and cryptocurrency crashes. We also document that the number of multifractal volatility states correlates with the cryptocurrency’s age and wealth concentration. Finally, we present evidence of self-similarity in cryptocurrency volatility patterns across frequencies, reinforcing the multifractal dynamics on cryptocurrency returns.

New research papers related to existing strategies:

#17 – Momentum Effect in Anomalies/Trading Systems
#293 – Momentum Effect in Anomalies v2
#510 – Factor Momentum
#1021 – Timing the Factor Zoo in the US

David Blitz: Caveats of Simple Factor Timing Strategies
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5256938
Abstract: This short article discusses the caveats of apparently simple yet highly effective factor timing strategies. Applying various such strategies to the standard U.S. Fama-French factors we find t-statistics up to 5, with combination strategies reaching even higher levels. However, some factor timing strategies have questionable after-cost profitability because of high turnover. Others suffer from substantial performance decay in recent years. Next to such general concerns we identify various strategy-specific concerns. We propose to establish minimum standards for future factor timing studies, including carefully examining explicit or implicit loadings on the phenomena described in this article, clearly establishing the distinctiveness of reported alphas, and addressing susceptibility to known limitations.

#544 – Impact of intangible assets on B/M
#687 – Intangible Factor in US Equities
#893 – Intangibles-Adjusted Profitability Factor

Lin Li: The Role of Intangible Investment in Predicting Stock Returns: Six Decades of Evidence
https://arxiv.org/abs/2505.16336
Abstract: Using an intangible intensity factor that is orthogonal to the Fama–French factors, we compare the role of intangible investment in predicting stock returns over the periods 1963–1992 and 1993–2022. For 1963–1992, intangible investment is weak in predicting stock returns, but for 1993–2022, the predictive power of intangible investment becomes very strong. Intangible investment has a significant impact not only on the MTB ratio (Fama–French high minus low [HML] factor) but also on operating profitability (OP) (Fama–French robust minus weak [RMW] factor) when forecasting stock returns from 1993 to 2022. For intangible asset-intensive firms, intangible investment is the main predictor of stock returns, rather than MTB ratio and profitability. Our evidence suggests that intangible investment has become an important factor in explaining stock returns over time, independent of other factors such as profitability and MTB ratio.

#152 – Momentum Factor Effect in REITs
#159 – Value Effect in REITs

Mariya Letdin, Cayman Seagraves, Stace Sirmans: REIT Factors
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5250691
Abstract: Employing a transparent, replicable methodology with CRSP-Ziman data from 1987 to 2023, this paper develops and analyzes six REIT return factors—size, value, momentum, quality, low volatility, and short-term reversal. Our REIT-specific factors demonstrate substantially improved explanatory power over general equity asset pricing models, achieving a 33% higher median firm-level R2 and reducing average unexplained alphas by nearly 3% per year. Short-term reversal, momentum, quality, and low volatility factors generate significant risk-adjusted returns in REITs. While the size factor underperforms, the value factor shows a robust premium only after controlling for its negative exposures to REIT momentum, quality, and low volatility. These factors exhibit distinct behaviors across economic regimes and remain robust to transaction costs. Compared to general equity factors, REIT momentum, quality, and reversal yield unique alphas, highlighting real-estate-specific return anomalies. After testing over 15,000 additional predictors, we find that while almost all are subsumed by our six factors, novel signals tied to financial health may provide incremental value. This study advances REIT asset pricing with a comprehensive factor framework and dataset available for download at reitfactors.ai.

#21 – Momentum Effect in Commodities
#285 – Spread (Basis) Momentum within Commodities
#322 – Rank Effect for Commodities
#549 – Basis Momentum Commodity Premia in China
#551 – Momentum Commodity Premia in China

Timotheos Angelidis, Athanasios Sakkas, Nikolaos Tessaromatis: Predicting Commodity Returns: Time Series vs. Cross Sectional Prediction Models
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5205084
Abstract: Commodity cross-sectional models based on the commodity momentum, basis, and basismomentum factors generate superior time-series and cross-sectional commodity return forecasts compared to the historical average and time-series forecasting models that use financial, macroeconomic, and commodity-specific variables as predictors. Timing and longshort strategies based on the commodity premium forecasts from cross-sectional models achieve significant utility gains compared to strategies based on the historical average or time series predictive models’ forecasts. Our evidence is robust across many commodities and different forecasting methodologies.

And several interesting free blog posts that have been published during the last 2 weeks:

Absolute Valuation Models for the Stock Market: Are Indexes Fairly Priced?

Valuation models for equity indexes are essential tools for investors seeking to assess long-term market conditions. Traditional models like the CAPE ratio, introduced by Robert J. Shiller, or the Buffett Indicator often rely on macroeconomic variables such as corporate earnings or GDP. While informative, these models can be complex and dependent on data that may be revised or vary across regions. In this article, we introduce a simpler alternative: a valuation ratio based solely on the inflation-adjusted total return of the index, offering a streamlined and transparent approach to index valuation. Finally, our goal would be to answer the question from the title – Are the indexes fairly priced at the moment?

Why Most Markets and Styles Have Been Lagging US Equities?

Over the past decade and a half, the US equities have set the hard-to-beat performance benchmark. Nearly all of the other countries, no matter if small or big, emerging or developed, have lagged behind. However, what are the forces behind this outperformance? Why did most of the other markets and even investing styles bow to the US large-cap growth dominance? A new paper written by David Blitz nicely analyses the rise of the behemoth.

Can We Profit from Disagreements Between Machine Learning and Trend-Following Models?

When using machine learning to forecast global equity returns, it’s tempting to focus on the raw prediction—whether some stock market is expected to go up or down. But our research shows that the real value lies elsewhere. What matters most isn’t the level or direction of the machine learning model’s forecast but how much it differs from a simple, price-based benchmark—such as a naive moving average signal. When that gap is wide, it often reveals hidden mispricings. In other words, it’s not about whether the ML model predicts positive or negative returns but whether its view disagrees sharply with what a basic trend-following model would suggest. Those moments of disagreement offer the most compelling opportunities for tactical country allocation.