Improving Pairs Trading with Cluster-Based Pair Selection

Pairs trading is a classic quantitative trading strategy. Despite its widespread use, it continues to attract research attention. A recent line of research focuses on grouping underlyings into clusters with similar characteristics. We recently discussed such grouping using fundamental metrics.

Reference [1] also attempts to improve the pair selection process, but this time using a purely quantitative, unsupervised machine learning approach. Specifically, it groups the underlyings using density-based methods applied to the correlation-distance matrix. The objective is to identify neighborhoods of underlying that exhibit similar residual return dynamics, that is, comparable co-movement patterns after removing broad market effects, so that cointegration tests and mean-reversion diagnostics are applied primarily within economically coherent groups.

The authors pointed out,

The performance we obtain is encouraging given the constraints of our design: we use only daily adjusted closes, transparent mean-reversion filters, and simple z-score entry/exit rules without any intraday microstructure signals. Across the four clustering specifications in Table 2, annualized Sharpe ratios range from 1.7228 to 2.4935. The best risk-adjusted variant is OPTICS with PC1 removed (Sharpe 2.4935), which also achieves the smallest maximum drawdown (-8.74%). At the same time, the highest terminal wealth is achieved by HDBSCAN with PC1 removed (cumulative return 1020.34%), albeit with materially larger downside risk (max drawdown -28.39%). Overall, the results suggest that a fully reproducible, daily-data pipeline can still recover substantial relative-value structure in a broad ETF universe.

A natural benchmark is a passive investor who buys and holds a broad equity index over the same window. Over 2014–2025, Table 2 shows that SPY (S&P 500) delivers a cumulative return of 302.03% (wealth multiplier 1 + 3.0203 = 4.0203) while QQQ (Nasdaq–100) delivers 542.53% (wealth multiplier 1 + 5.4253 = 6.4253). By contrast, our pairs trading specifications produce cumulative returns between 449.48% and 1020.34%, corresponding to wealth multipliers between 5.4948 and 11.2034…

In summary, this paper shows that a simple, transparent pairs trading framework applied to ETFs using daily data and clustering techniques can achieve strong risk-adjusted performance, with Sharpe ratios between 1.72 and 2.49. The strategy produces cumulative returns between 449% and 1020%, outperforming passive benchmarks such as SPY and QQQ over the 2014–2025 period.

We find the results noteworthy, particularly given that the method is applied to liquid ETFs. As is well known, compared with single-name stocks, ETFs typically carry lower risk and therefore lower expected returns. Nevertheless, the proposed method delivers strong risk-adjusted returns without the use of leverage.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Fuwen Gan and Ramy Mizrachi, Cluster-Based Pairs Trading: Combining Unsupervised Learning with Cointegration Filtering, 2025, Rice University

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Evaluating a Logistic Regression Trading Framework

Regression is one of the oldest predictive methods used in finance and remains widely applied today. Reference [1] revisits this “simple” approach by employing logistic regression, which is particularly suited for modeling binary outcomes, such as whether an asset’s price will increase or decrease.

The author uses cumulative returns over the past 20 days and the past 12 months as predictive variables, capturing short-term and long-term momentum effects. Logistic regression is then applied to classify whether a stock’s return in the upcoming month exceeds that month’s median return. The procedure is implemented on S&P 500 stocks from January 1985 to July 2024 using survivorship-bias-free data.

The author pointed out,

The LRST strategy’s annualized return of 24.61% over the historical period indicates its potential for generating excess returns compared to the S&P 500. However, the accompanying high volatility, as evidenced by an annualized standard deviation of 26.11%, suggests that the strategy is not without significant risk. The Sharpe ratio of 0.7738, while indicative of positive risk-adjusted returns, falls below the optimal threshold of 1, highlighting the need for further refinement to enhance the strategy’s risk-return profile…

Moreover, the recent struggles of the LRST strategy could be attributed to several factors, including the rise of algorithmic trading and macroeconomic shifts that it may not have been designed to capture. This observation resonates with the work of Liu et al. [40], which emphasizes the importance of adapting trading strategies to changing market conditions to maintain profitability. The inability of the LRST strategy to capitalize on market gains during a period of strong growth suggests potential structural weaknesses that [glossary_exclude]warrant [/glossary_exclude]further investigation.

In short, the paper shows that the logistic regression-based strategy delivers an annualized return of 24.61%, outperforming the S&P 500, but its high volatility and Sharpe ratio of 0.77 indicate substantial risk and room for improvement in its risk-return profile. Its recent underperformance may reflect structural weaknesses amid the rise of algorithmic trading and shifting macroeconomic conditions, underscoring the need for adaptation.

This article is insightful as it demonstrates that,

• Even a basic regression framework can serve as a useful predictive tool within a trading system, although further refinement is necessary.
• There might be structural changes in market dynamics, driven by the increasing prevalence of algorithmic trading and artificial intelligence, implying that traders must adapt accordingly.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Conrad O. Voigt, Logistic Regression-Based Systematic Trading: Performance on the S&P 500, 2026, github.io

Originally Published Here: Evaluating a Logistic Regression Trading Framework

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Integrating Fundamental Metrics into Pairs Trading

Statistical arbitrage is a classic quantitative trading strategy. Its core premise relies on the assumption that two similar stocks should move broadly in tandem and, when their prices diverge, are expected to converge over time. To identify similar underlyings, statistical methods such as cointegration and correlation are commonly employed.

It is widely recognized by practitioners that similarity between two stocks is not purely statistical but also fundamental. However, relatively little research has focused on systematically incorporating fundamental similarity into pair selection. Reference [1] addresses this gap by developing a pairs trading selection framework that integrates fundamental characteristics alongside statistical measures.

To identify fundamentally similar pairs, the author utilizes variables such as return on equity, differences in log sales growth over the past five years, differences in the most recently available financial leverage ratios, geographic proximity (e.g., whether firms are headquartered in the same U.S. state), and industry alignment. Each fundamental metric is then assigned a score, and a composite indicator is constructed for pair selection.

The author pointed out,

This paper introduces a novel approach to stock pair selection in pairs trading, using a multi-factor scoring model based on significant variables identified through panel regression. These variables are weighted by their regression coefficients to construct a composite score. I benchmark this inter-pair characteristic-based method against the Gatev et al. (2006) SSD approach, analyzing both extended and updated trading strategies to account for repeated pair selections. The strategy’s robustness is tested with and without the COVID-19 crisis, and all results are reported net of transaction costs.

The key finding is that the multi-factor method consistently outperforms the benchmark with higher risk-adjusted returns. This performance edge persists beyond the COVID-19 period, with a narrower gap, highlighting the scoring model’s advantage in stressful markets. Though differences between updating and extending pairs are modest, the updated strategy delivers superior returns in volatile markets despite higher transaction costs. Nonetheless, the strategy underperforms relative to passive index investing. Even with rolling estimation windows, out-of-sample performance remains challenging.

In short, this paper introduces a novel stock pair selection framework that incorporates fundamental metrics. The results show that the proposed method delivers higher risk-adjusted returns than the benchmark, particularly in volatile markets, although it underperforms passive index investing and faces challenges in out-of-sample performance.

This article represents a meaningful step toward refining statistical arbitrage by incorporating fundamental characteristics into the pair selection process.

Let us know what you think in the comments below or in the discussion forum.

References

[1]  Lukas Reichmann, Pairs trading- Selection via scoring systems, Finance Research Letters Volume 93, March 2026, 109642

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Extreme VIX: Regime Shifts and Return Predictability

The Volatility Index (VIX) is widely regarded as a forward-looking measure of market uncertainty and investor sentiment. Although it has been extensively studied, certain aspects remain insufficiently explored.

Reference [1] investigates the implications of extreme VIX values, defined as VIX > 45, and examines two key research questions in this context.

1. Extreme implied volatility (VIX > 45) is followed by significantly positive equity returns over three-month and one-year horizons, and
2. Negative returns, elevated volatility, and deteriorating sentiment increase the probability of a VIX spike (VIX > 45)

Using U.S. data from 2008 to 2025, the authors employ linear regression, logistic regression, and GARCH(1,1) models to conduct the analysis. They pointed out,

Results show that extreme VIX spikes offer contrarian signals, with significant positive returns over three-month horizons. Logistic regression confirms significance over both three-month and one-year periods. These findings remain robust after controlling for valuation, credit spreads, PMI, sentiment ratios, and interaction effects. While GARCH captures conditional variance, it lacks forward-looking predictive power in high-stress regimes. Overall, the evidence suggests that volatility timing merits consideration as part of a tactical allocation framework. Our findings also contribute to the market efficiency debate by integrating market expectations with economic indicators for risk-aware decision-making.

In short, the paper's conclusions are as follows,

• The evidence robustly supports the contrarian effect at the 3-month horizon. At the 1-year horizon, the predictive power comes primarily from continuous volatility measures rather than the binary VIX>45 indicator.
• The results clearly demonstrate that extreme volatility episodes are statistically predictable using valuation, sentiment, and medium-term volatility measures. The evidence shows regime transitions are preceded by identifiable market conditions rather than occurring randomly.

This study makes an important contribution by formally examining phenomena long observed by practitioners and by providing results with meaningful practical implications.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Fazio, P., and Spohn, D. (2026). Predictive Signals from VIX Spikes: A Comparative Study of Linear, Logistic, and GARCH-Based Return Forecasting Models. Preprints.org.

Article Source Here: Extreme VIX: Regime Shifts and Return Predictability

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Volume Effects in Pairs Trading Performance

Volume is an important factor that has not been sufficiently studied in the literature, although increasing attention is now being devoted to its role. For example, we recently discussed the link between intraday volume and volatility.

Continuing this line of inquiry, Reference [1] revisits the age-old quantitative strategy of pairs trading and incorporates volume analysis into the framework. The author studied pairs formed from the S&P500 constituent stocks using the conitegration method. The data spans from 2005 to 2024. They pointed out,

This study investigated pairs trading strategies across market segments, parameters, weighting methods, and cost structures using U.S. equity data (2005-2024). Volume emerged as the dominant performance factor, with high-volume pairs consistently outperforming low-volume counterparts across multiple metrics, including superior returns when converging towards the historical relationship. The success of volume-based segmentation may stem partly from grouping stocks with similar trading characteristics, creating more stable price relationships. Notably, optimal parameter configurations were remarkably similar between volume deciles, demonstrating consistent underlying market dynamics across liquidity environments.

Contrary to efficient market predictions, performance improved in the testing period (2015-2024), suggesting structural market changes or institutional constraints have preserved the strategy’s effectiveness despite widespread documentation. Different weighting methodologies produced similar results, with equal weighting combining simplicity and strong performance.

In short, the paper finds that trading volume is a dominant performance driver, with high-volume pairs consistently outperforming low-volume counterparts. Contrary to efficient market predictions, strategy performance improved in the later sample period, while results remained robust across parameter choices and weighting schemes. Overall, pairs trading still remains profitable.

This represents an important contribution that may reinvigorate research on pairs trading and trading volume.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Mustafa Hussein, Can Safety and Profitability Coexist? Performance Analysis of Pairs Trading among S&P 500 Stocks, 2025, University of Gothenburg

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State-Dependent Correlation Between the S&P 500 and the VIX

It is well known that the correlation between the S&P 500 and the VIX index is negative. In fact, arbitrage and hedging strategies have been designed around this relationship, for example, hedging vega risks with delta.

However, practitioners also recognize that this correlation can break down and even turn positive. Reference [1] addresses this issue by examining the time-varying nature of the correlation. The paper first posits and demonstrates that volatility-of-volatility (VoV) risk is distinct from volatility (Vol) risk, and then classifies the market into four regimes:

1. Low VOL risk and low VOV risk (LVOL–LVOV)
2. High VOL risk and low VOV risk (HVOL–LVOV)
3. Low VOL risk and high VOV risk (LVOL–HVOV)
4. High VOL risk and high VOV risk (HVOL–HVOV).

The authors pointed out,

We develop theoretical hypotheses on the correlations between the S&P 500 and VIX based on the VFE and two types of risk in the S&P 500–VIX pair: (1) VOL risk in the S&P 500 (i.e., the underlying asset) and (2) VOV risk in VIX (i.e., its VOL product). According to our hypothesis, the correlations between the S&P 500 and VIX do not remain constant and change across various VOL and VOV risk states. We developed a regime‐switching model with state‐contingent correlations to test our hypotheses.

Our empirical results are consistent with the following notions. First, shocks that drive high VOL risk are not necessarily identical to those that drive VOV risk and thus VOL and VOV do not respond to shocks in the same way. These results imply a four‐state VOL system: high/low VOL risk and high/low VOV risk pairs. Second, the minimum inverse correlation between the S&P 500 and VIX is observed under a high VOL–low VOV risk state. In contrast, the maximum inverse correlation between the S&P 500 and VIX occurs under a high VOL–high VOV risk state. Third, the proposed state‐contingent correlations prove more effective in portfolio construction than conventional time‐dependent correlations.

In short, the paper develops a regime-switching model to show that the relationship between the S&P 500 and the VIX varies across distinct volatility (VOL) and volatility-of-volatility (VOV) risk states. It identifies a four-state system and demonstrates that correlation strength depends on the joint VOL–VOV regime.

This is an important contribution, as it quantifies the dynamic relationship between the S&P 500 and the VIX, thereby providing useful guidance for refining hedging and arbitrage strategies.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Leon Li, Carl R. Chen, Volatility Risk and Volatility-of-Volatility Risk: State-Dependent Correlations Between VIX and the S&P 500 Stock Index and Hedging Effectiveness, Journal of Futures Markets, 2025; 1–20

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Multifractality and Its Underlying Drivers in Cryptocurrency Markets

Cryptocurrencies, like other financial time series, can be analyzed using traditional time series and econometric methods. However, they present additional challenges due to their distinctive characteristics, including extreme volatility, heavy-tailed distributions, and long-range temporal correlations, which [glossary_exclude]warrant [/glossary_exclude]specialized examination.

Reference [1] analyzes the multifractality characteristics of major cryptocurrencies and investigates their underlying sources.  The authors analyzed data from January 1, 2018, to December 31, 2024, and pointed out,

Taken together, these results reinforce the importance of applying multifractal frameworks to digital markets. They offer practical insights for volatility forecasting, systemic risk monitoring, and understanding the maturation of blockchain-based financial ecosystems. For example, the strong correlations between BTC and ETH on various time scales can be used in optimal portfolio construction [80]. The decay of the autocorrelation function, which indicates the average size of the volatility cluster [37], may also be used in risk management. The consistency of the log-return tail distributions with the inverse cubic power-law allows for the selection of appropriate distributions in various risk mitigation methods, such as Value-at-Risk. Moreover, the greater hierarchical dependence of correlations at the level of larger fluctuations compared to smaller ones, documented by left-sided asymmetry of the multifractal spectra, may potentially allow for extending risk management strategies to include fluctuation-specific correlation patterns. The direct link between multifractality and risk management is an interesting subject for further studies.

Briefly, the article finds that digital asset markets, including BTC, ETH, DEX trading, and NFTs, exhibit clear multifractal scaling, but this multifractality is driven primarily by temporal correlations, not by heavy-tailed return distributions alone.  It also shows strong multifractal cross-correlations between BTC and ETH, especially during large fluctuations. Overall, the study argues that genuine market complexity in crypto stems from persistent correlation structure rather than from fat tails alone.

This study provides important insights into cryptocurrency dynamics, enabling portfolio and risk managers to design more appropriate trading and risk management strategies.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Drozdz, S.; Kluszczynski. R., Kwapien. J.: Watorek. M., Multifractality and its sources in the digital currency market. Future Internet, 2022, 1, 0.

Article Source Here: Multifractality and Its Underlying Drivers in Cryptocurrency Markets

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Improving Momentum Strategies with Machine Learning

Machine learning (ML) is increasingly prominent in modern finance and is being adopted across a wide range of applications. However, using ML to extract alpha remains nontrivial. Reference [1] proposes a novel approach to improving the risk-adjusted returns of momentum strategies using machine learning.

It employs three ML methods—linear regression, random forest, and neural networks—to predict next month’s information coefficients (ICs) using information available at the end of the previous month. These predicted ICs are then combined with momentum factors to construct momentum and reversal strategies.

The authors pointed out,

Machine learning helps improve momentum strategies by capturing strong correlations between stocks and their returns using information coefficients (IC). Shallow machine learning enables us to trace the sources of advantage and adapt to non-linear correlations that traditional methods might overlook. We confirm that a unidirectional momentum strategy can yield significant returns. Furthermore, we observe that implementing a bidirectional approach leads to even more pronounced gains. We find that shorting weak stocks outperforms longing strong stocks, indicating the persistence of the "winners keep winning, losers keep losing" phenomenon. Additionally, we observe that price reversals do not occur within a month. Optimizing the number of stocks in the portfolio leads to the highest excess returns within a certain threshold range. However, implementing inverse trades using machine learning models does not yield positive returns, indicating that the predictive ability of the models may be inadequate to identify profitable opportunities in these situations. Finally, we discover that momentum factors with longer holding periods are more powerful predictors, accurately capturing the correlation between stocks and their returns. We assume that momentum factors with shorter observation periods are less effective, possibly due to the effect of mean reversion. Our research underscores the importance of accurately predicting IC values and the effectiveness of momentum strategies in generating positive returns.

In short, the paper applies machine learning models to momentum investing by predicting future information coefficients and using them to construct momentum and reversal portfolios. It finds that bidirectional momentum strategies outperform unidirectional ones, longer-horizon momentum signals are more informative, and portfolio performance is maximized when the number of selected stocks is optimized within a threshold.

This represents an interesting twist on applying machine learning to investing, using predicted information coefficients to guide portfolio construction rather than directly forecasting returns.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Chang, H.-L., Cheng, H.-W., Lan, Y.-M., & Yu, J.-P. (2025). Machine Learning in Momentum Strategies. EFMA

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Portfolio Timing and Allocation with the Variance Risk Premium

The volatility risk premium (VRP) refers to the systematic difference between implied volatility and subsequent realized volatility. Much of the academic literature focuses on developing strategies to harvest the VRP directly. However, Reference [1] departs from this conventional approach by proposing the use of the VRP as a market-timing mechanism.

Specifically, the author computes the VRP for four major index ETFs—SPY, QQQ, IWM, and DIA—and constructs a composite market-level VRP by averaging the normalized VRPs across these assets, yielding a daily indicator bounded between 0 and 1.

In addition, the author incorporates the VIX/VIX3M ratio as a defensive overlay. Based on these two indicators, market conditions are classified into offensive or defensive regimes, within which asset allocation is determined using 100-day moving averages.

The author pointed out,

The above summary highlights the superiority of VOLTEX-G across key investment metrics compared to the SPY. Specifically, there is a notable increase in annualized return from 14.74% to 17.87%, which is accompanied by a significant reduction in volatility from 17.40% to 12.78%, making VOLTEX-G more stable. Additionally, both the Sharpe Ratio and Sortino Ratio show substantial improvement, indicating a higher quality of risk adjusted returns. The Max Drawdown further confirms these advantages with a reduction from 33.72% to 16.34%, underscoring the strategy’s capital protection capability. Moreover, the significant rise in the Recovery Factor demonstrates SPY’s inability to rebound effectively after major drawdowns. Although improvements in other metrics vary in magnitude sometimes more pronounced and sometimes more moderate, the overall picture clearly reflects the superiority of VOLTEX-G.

In short, the results indicate that using the VRP as a timing indicator improves the portfolio’s risk–adjusted return, while the inclusion of technical filters enhances overall stability.

This represents an innovative application of the VRP that [glossary_exclude]warrants [/glossary_exclude]further investigation and experimentation.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Charalampopoulos, A. (2025). Using Variance Risk Premium to Time a Portfolio of Stock and Bond ETFs. University of Piraeus

Originally Published Here: Portfolio Timing and Allocation with the Variance Risk Premium

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Dynamic Delta Hedging with Confidence-Weighted Signals

Delta hedging is a critical component of option portfolio management. In the research literature, most studies assume strict delta hedging, where portfolio delta is maintained at zero. Reference [1] relaxes this restriction by introducing a partial delta hedging technique that conditions the hedge ratio on the confidence of the underlying’s directional prediction.

Specifically, the approach applies a multiplier to the hedge ratio: when delta is positive, and the model anticipates an upward move, the hedge is reduced to retain more exposure, and similarly, when delta is negative, and a decline is predicted, the hedge magnitude is again reduced. The authors pointed out,

We introduces a confidence-based hedge adjustment mechanism that integrates ML forecasts into option portfolio construction. By scaling hedge ratios according to model confidence, the approach moves beyond rigid delta neutrality and captures incremental returns otherwise missed. Empirically, we find that confidence scaling materially affects portfolio risk–return trade-offs: moderate scaling delivers the highest Sharpe ratios, while aggressive scaling yields higher volatility and weaker long-term performance. Our objective is not to identify the most predictive ML model or factor set, but to demonstrate a paradigm shift in hedging design. By relaxing the strict delta-neutral constraint, predictive signals can be more effectively incorporated into option strategies. Future research could extend this framework to multi-asset portfolios, employ more advanced ML models, or explore higher-dimensional feature sets, which may yield even stronger results and further validate the practical relevance of confidence-scaled hedging.

In short, the paper proposes a confidence-based delta hedging framework that dynamically adjusts hedge ratios based on the directional confidence of machine-learning classifiers. Results show that moderate scaling improves Sharpe ratios relative to a benchmark, while aggressive scaling increases volatility and deteriorates long-term performance.

This is a meaningful contribution, as it reinforces the importance of identifying market regimes and dynamically under- or over-hedging according to prevailing conditions.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Li, B., & Wu, C. (2026). Beyond delta neutrality: Confidence-scaled hedging with machine learning forecasts. Finance Research Letters, 87, 109098.

Originally Published Here: Dynamic Delta Hedging with Confidence-Weighted Signals

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