Quantpedia Premium Update – March 27th

New strategies:

#1109 – Election-Driven Arbitrage Strategy

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2024-2024
Indicative performance: 97.32%
Estimated volatility: 44.88%

Source paper:

Jain, Purvesh and Ezhov, Artem and Liang, Michael and Liu, Xiaxuan and Wang, Sicheng and Zhou, Chenyu and Stoikov, Sasha and Cetin, Umu: Election Arbitrage During the 2024 U.S. Presidential Election
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5086104
Abstract:
The emergence of prediction markets during the last election cycle offer a new promise: to better understand the relation between financial markets and political candidates. In this study, we construct of a pair of systematic portfolios designed to perform well, regardless of which candidate wins. We first identify key event windows during which the betting odds changed significantly, thus identifying Harris (H) or Trump (T) favorable periods. Second, we select assets which exceed a performance threshold during H (or T) favorable periods, while ensuring returns remain above a specified floor during unfavorable H (or T) events. Post-election results demonstrate that the combined performance of the asymmetric portfolios significantly outperformed the benchmark, indicating the presence of election-driven arbitrage opportunities.

#1110 – Reducing Option-Writing Risk with IV Rank and Strike Clusters

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: options
Complexity: Very complex strategy
Backtest period: 2016-2025
Indicative performance: 6.96%
Estimated volatility: 14.62%

Source paper:

Melchin, Derek: Reducing Option-Writing Risk with IV Rank and Strike Clusters
https://www.quantconnect.com/research/18766/reducing-option-writing-risk-with-iv-rank-and-strike-clusters/p1
In this research, we examine a strategy that seeks to identify periods when providing insurance to market participants is profitable through writing put Option contracts. The strategy relies on estimates of future volatility and a novel Strike Availability factor, then utilizes clustering to signal when these factors are at elevated levels, indicating upcoming volatility. The results show that the strategy has a high win rate but modest cumulative returns.

#1111 – Switching Regimes Factor Strategy

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1963-2022
Indicative performance: 8.2%
Estimated volatility: 10%

Source paper:

Mulliner, Amara and Harvey, Campbell R. and Xia, Chao and Fang, Ed and van Hemert, Otto: Regimes
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5164863
Abstract:
We propose a new systematic method for detecting the current economic regime and show how to use this information for predicting returns. Rather than presupposing a set of possible regimes, we rely on economic state variables and determine for which historical dates the values of these variables were most similar. To establish our position in an asset today, we identify historically similar periods and measure subsequent performance of the asset. If the historical performance is positive, we initiate a long position; conversely, if it is negative, we initiate a short position. We illustrate the efficacy of our method on six common long-short equity factors over 1985-2024. Our results show that using this information our regime classification leads to significant outperformance. Interestingly, we also find important information in what we call anti-regimes – periods in the past that are the most dissimilar to today.

#1112 – Resurrecting the Value Effect – Tech vs. Non-Tech Sub-Universes

Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1968-2019
Indicative performance: 6.93%
Estimated volatility: 11.48%

Source paper:

Lee, Ryan C.Y.: Resurrecting the value effect: The role of technology stocks
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5145314
Abstract:
This paper examines the decline of the value premium over the past three decades, attributing this deterioration to the rising dominance of technology stocks. Decomposing the Fama-French HML factor reveals that the tech component explains 45% of the time variation in HML returns and significantly weakens the value premium due to the misclassification of high-valuation tech firms as the new economy. To address this, I propose a simple two-industry framework that applies tech and non-tech benchmarks. This industry-adjusted approach effectively accounts for the valuation dynamics and restore the value premium from 0.17% (t=1.00) to 0.56% (t=4.18) in the post-1991 period. These findings underscore the importance of industry composition and offer a refined perspective on the value premium in a tech-driven economy.

#1113 – The Impact of the Inflation on the Performance of the US Dollar

Period of rebalancing: Monthly
Markets traded: currencies
Instruments used for trading: CFDs, ETFs, forwards, futures, swaps
Complexity: Simple strategy
Backtest period: 1990-2005
Indicative performance: 1.96%
Estimated volatility: 5.84%

Source paper:

Vojtko, Radovan and Dujava, Cyril: The Impact of the Inﬂation on the Performance of the US Dollar
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5181351
Abstract:
This study examines the relationship between inﬂation rates and the performance of the US dollar (USD) in foreign exchange markets, based on the analysis presented on Quantpedia. The investigation uses historical data to explore whether inﬂation levels can be used to predict the strength or weakness of the USD against other currencies. The central aim is to provide insights for currency allocation strategies by determining the predictive power of inﬂation on USD valuation. As Quantpedia reported, the ﬁndings suggest a complex and potentially non-linear relationship. The data do not uniformly support the conventional economic wisdom that higher inﬂation weakens a currency. The analysis considers factors such as the Federal Reserve’s monetary policy responses and the global economic environment to explain periods when the USD’s reaction to inﬂation deviates from theoretical expectations. The study likely identiﬁes speciﬁc conditions under which inﬂation is a more reliable indicator of USD performance. These results have implications for investors and currency traders, highlighting the need for nuanced models incorporating macroeconomic factors beyond simple inﬂation metrics. The research contributes to a more sophisticated understanding of currency dynamics, informing the development of improved currency hedging and investment strategies. Future work should focus on reﬁning these models with real-time data and exploring additional variables that impact the inﬂation-USD relationship.

New research papers related to existing strategies:

#930 – Influence of Liquidity, Institutional Ownership & Lottery Effect on Stocks
#1020 – Momentum and Investors’ Lottery-Like Preference

Zhu, Zhaobo and Lin, Tse-Chun: Arbitrage Asymmetry and Lottery-Related Anomalies
Abstract:
This paper explicitly documents the coexistence of positive and negative relations between lottery features and subsequent returns by using an ex-ante mispricing measure to identify underpriced lottery stocks from overpriced lottery stocks. Such a positive relation exists mainly among underpriced stocks, in which the underpricing is more severe for lottery stocks than for non-lottery stocks possibly because underpriced lottery stocks have less buy-side arbitrage from institutional investor peers due to institutions’ preference for nonlottery stocks. In contrast, the negative relation mainly exists among overpriced stocks preferred by individual investors, in which the overpricing is more severe for lottery stocks than for non-lottery stocks possibly because institutional arbitrageurs stay away from these overpriced lottery stocks due to high arbitrage risk. Moreover, consistent with arbitrage asymmetry, when aggregating all stocks, the negative relation dominates possibly because the aggregate market is relatively overpriced due to that buying is easier than shorting.

#576 – Boosted Regression Trees in Corporate Bonds
#678 – Systematic Trading of Municipal Bonds
#696 – Fair Spread Value Factor in Corporate Bonds
#697 – Multifactor Corporate Bond Strategy
#872 – Machine Learning – Random Forests Predicts Cross Section of Corporate Bonds

Bell, Sebastian and Kakhbod, Ali and Lettau, Martin and Nazemi, Abdolreza: Glass Box Machine Learning and Corporate Bond Returns
https://ssrn.com/abstract=5047456
Abstract:
Machine learning methods in asset pricing are often criticized for their black box nature. We study this issue by predicting corporate bond returns using interpretable machine learning on a high-dimensional bond characteristics dataset. We achieve state-of-the-art performance while maintaining an interpretable model structure, overcoming the accuracy-interpretability trade-off. The estimation uncovers nonlinear relationships and economically meaningful interactions in bond pricing, notably related to term structure and macroeconomic uncertainty. Subsample analysis reveals stronger sensitivities to these effects for small firms and long-maturity bonds. Finally, we demonstrate how interpretable models enhance transparency in portfolio construction by providing ex ante insights into portfolio composition.

Li, Delong and Lu, Lei and Qi, Zhen and Zhou, Guofu: International Corporate Bond Returns: Uncovering Predictability Using Machine Learning
https://ssrn.com/abstract=5055237
Abstract:
This paper examines cross-sectional predictability of corporate bond returns using a novel international dataset and machine learning techniques. We find significant predictability in both U.S. and non-U.S. markets, with predicting factors differing substantially. Downside risk and illiquidity have a greater influence on corporate bond returns in non-U.S. markets. We further show that corporate bonds in developed economies, compared with those in emerging markets, are more integrated with the U.S. corporate bond market. Developed economies also have a stronger integration between corporate bonds and stocks. These findings shed light on bond pricing and diversification opportunities among international corporate bond markets.

#408 – Cointegrated Cryptocurrency Portfolios
#887 – Pairs Trading in Cryptocurrencies

Saji, Thazhungal Govindan Nair: Pairs trading in cryptocurrency market: A long-short story
https://ssrn.com/abstract=5135627
Abstract:
Pairs trading that is built on ‘Relative-Value Arbitrage Rule’ is a popular short-term speculation strategy enabling traders to make profits from temporary mispricing of close substitutes. This paper aims at investigating the profit potentials of pairs trading in a new finance area-on cryptocurrencies market. The empirical design builds upon four well-known approaches to implement pairs trading, namely: correlation analysis, distance approach, stochastic return differential approach, and cointegration analysis , that use monthly closing prices of leading cryptocoins over the period January 1, 2018,-December 31, 2019. Additionally, the paper executes a simulation exercise that compares long-short strategy with long-only portfolio strategy in terms of payoffs and risks. The study finds an inverse relationship between the correlation coefficient and distance between different pairs of cryptocurrencies, which is a prerequisite to determine the potentially market-neutral profits through pairs trading. In addition, pairs trading simulations produce quite substantive evidence on the continuing profitability of pairs trading. In other words, long-short portfolio strategies, producing positive cumulative returns in most subsample periods, consistently outperform conservative long-only portfolio strategies in the cryptocurrency market. The profitability of pairs trading thus adds empirical challenge to the market efficiency of the cryptocurrency market. However, other aspects like spectral correlations and implied volatility might also be significant in determining the profit potentials of pairs trading.

#887 – Pairs Trading in Cryptocurrencies

Landi, Alexandre and Lemishko, Tetiana: Real-World Viability of Cointegration-Based Forex Pairs Trading Strategy with Walk-Forward Optimization
https://ssrn.com/abstract=5068086
Abstract:
This study considers the application of Walk-Forward Optimization (WFO) to evaluate the real-world viability of a cointegration-based pairs trading strategy in the Forex market. In previous research, we observed that pairs trading benefited from the use of a cointegration-based filter such that trades on some given pair would be executed in the test window only when cointegration was observed in the training window. However, such improved performance relied on an arbitrary set of fixed parameters that produced decent results over the entire 17-year period. In this study, we apply WFO to dynamically recalibrate strategy parameters over time to enable adaptation to evolving market conditions. Results show that the use of WFO not only did not worsen historical performance, but also reduced maximum drawdowns, thus improving riskadjusted performance with respect to using the fixed parameter set.

#183 – Optimized Currency Portfolios
#230 – Mean Variance Carry Trade Strategy

Dauber, Moritz and Umlandt, Dennis: Common Factors in Currency Characteristics
https://ssrn.com/abstract=5118807
Abstract:
We study the factor structure of currency characteristics by employing a threedimensional tensor factor model that simultaneously captures the variation in characteristics of the G10 currencies over time. We show that factor-mimicking portfolios derived from these common factors in currency characteristics are able to price individual currency returns better than standard factor models derived from univariate sorts on the same characteristics. The variation in currency characteristics can be well captured by a parsimonious two-factor model, where the first factor closely resembles the carry trade and the second factor acts as a hedge against carry crash risk, that is composed of signals from FX momentum, FX value and the term spread. A potential third factor, which dynamically weights several characteristics, incrementally improves the fit of the total variation but has a high Sharpe ratio.

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

The Impact of the Inflation on the Performance of the US Dollar

Inflation is one of the key macroeconomic forces shaping financial markets, influencing asset prices across the board. In our previous analysis, we examined how gold and Treasury prices react to changes in the inflation rate, uncovering patterns that suggested inflation dynamics also impact the US dollar. In this follow-up, we shift our focus entirely to the dollar, analyzing how it responds to both accelerating and decelerating inflation. As the world’s reserve currency, the dollar’s movements have far-reaching implications, affecting global trade, monetary policy, and asset allocation. Our goal is to determine whether inflation serves as a clear driver of dollar performance and, if so, in what ways.

Trading the Spread: Bitcoin ETFs vs. Cryptocurrencies Infrastructure ETFs

In this study, we explore the application of simple spread trading strategies using Bitcoin ETFs and cryptocurrency infrastructure ETFs—two highly correlated asset classes due to the broader influence of cryptocurrency market movements. Given their strong relationship, this setup provides a compelling case for implementing pair trading strategies based on mean reversion principles. Building on our previous work, How to Build Mean Reversion Strategies in Currencies, we adapt and extend these models to the cryptocurrency ETF space, demonstrating their broader applicability beyond traditional currency markets. Specifically, we test two sub-methods of mean reversion: linear and exponential. Our goal is to offer a clear and practical example of how traders can leverage these techniques across different asset classes.

How Global Neutral Rates Impact Currency Carry Strategies?

Market practitioners often rely on experience-based wisdom to navigate currency markets, and one such widely held belief is that low dispersion in global bond yields signals weak future returns for carry trades (and high dispersion implies high future carry returns). While this intuition makes sense—when yield differentials are compressed, the incentive to exploit them diminishes—a recent academic study provides a solid theoretical foundation for this idea. The research not only confirms this observation with rigorous empirical analysis but also explains the underlying financial mechanisms that drive the relationship. By quantifying the effect and presenting clear visualizations, the study transforms an intuitive market rule of thumb into a well-grounded principle backed by data.

How Mega Tech Stocks Impact Factor Strategies

The dominance of mega-tech stocks, particularly the “Magnificent 7,” in both U.S. and global equity indexes has a profound impact on factor portfolios. When constructing value-weighted smart beta strategies, these portfolios often end up heavily concentrated in a few individual stocks. This concentration introduces idiosyncratic risk, skewing the risk profiles of factor strategies. While no active strategy can entirely avoid the influence of these high-market-cap stocks, it is critical to limit their exposure to reduce idiosyncratic risk and improve the stability of factor-based approaches.