Relative Value Arbitrage

Momentum strategies can be divided into two categories: time series and cross-sectional. In a previous newsletter, I discussed time series momentum. In this post, I focus on cross-sectional momentum strategies.

Cross-Sectional Momentum in the Commodity Market

Momentum trading is often divided into 2 categories: time-series momentum and cross-sectional momentum. Time-series based trading strategies generate trading signals based on the asset’s past returns. A typical time-series trading strategy usually involves buying assets with positive trend signals and selling those with negative trend signals. In contrast, cross-sectional trading strategies generate trading signals based on the relative performance of assets. A typical cross-sectional trading strategy involves buying assets with the highest-ranked trend signals and selling those with the lowest-ranked trend signals. So basically, this is a relative value strategy.

Reference [1] examined trend trading in the commodity market from the cross-sectional momentum perspective. The authors conducted a study on a portfolio of 35 commodity futures.

Findings

-The study introduces a trend factor that uses short-, intermediate-, and long-run moving averages of settlement prices in commodity futures markets.

-The trend factor generates statistically and economically significant returns during the post-financialization period (2004–2020).

-It outperforms the momentum factor by more than nine times in the Sharpe ratio and carries less downside risk.

-Unlike the momentum factor, which delivers insignificant returns in the sample, the trend factor consistently generates large positive returns.

-The trend factor cannot be explained by existing multifactor asset pricing models.

-It also provides a significant positive risk premium, confirming its economic relevance.

-The trend factor is correlated with funding liquidity, as measured by the TED spread.

-Overall, the findings highlight the economic value of using historical price information in commodity futures markets beyond traditional momentum strategies.

In short, cross-sectional momentum exists in the commodity market, and it is possible to construct a profitable trend trading strategy.

Reference

[1] Han, Yufeng and Kong, Lingfei, A Trend Factor in Commodity Futures Markets: Any Economic Gains From Using Information Over Investment Horizons? SSRN 3953845

Profitability of Cross-Sectional Momentum Strategy

Reference [2] examines the profitability of cross-sectional momentum strategies over the past decades

Findings

– The study investigates how different definitions of cross-sectional momentum affect the performance of long-short momentum strategies under varying market conditions.

-A long-short momentum portfolio buys stocks with strong past performance and shorts stocks with weak past performance.

-Standard long-short momentum strategies have delivered declining returns in recent decades, leading researchers and practitioners to search for solutions.

-A key weakness of momentum strategies is their tendency to experience “crashes,” where large gains are followed by sudden and substantial losses.

-The thesis proposes a method to mitigate crash risk, conditional on market states.

-While the literature offers complex methods for constructing long-short portfolios, the study demonstrates that relatively simple adjustments can meaningfully improve outcomes.

-Techniques such as volatility scaling and adjusting long and short positions based on market state enhance performance.

-These adjustments restore the robustness of the momentum premium and improve the risk-return profile of the strategy.

In short, the paper concludes that the profitability of cross-sectional momentum strategies has diminished. The author subsequently proposes an approach to enhance the strategy’s returns.

By applying techniques such as volatility scaling and adjusting long and short positions based on the market state we can significantly enhance the efficacy of the momentum strategy, restoring its former robustness.

Reference

[2] Pyry Pohjantähti, Revisiting (Revitalizing) Momentum, 2024, Aalto University School of Business

Closing Thoughts

In summary, both studies highlight that momentum remains useful but requires refinement. The first shows that a trend factor in commodity futures, built on moving averages, delivers strong returns and outperforms traditional momentum benchmarks. The second finds that cross-sectional momentum, though weakened and crash-prone, can be improved with simple adjustments like volatility scaling and conditioning on market states. Together, they show that momentum strategies are still effective when adapted to market conditions.

Stop loss is a risk management technique. It has been advocated as a way to control portfolio risk, but how effective is it? In this post, I will discuss certain aspects of stop loss.

When Are Stop Losses Effective?

A stop loss serves as a risk management tool, helping investors limit potential losses by automatically triggering the sale of a security when its price reaches a predetermined level. This level is set below the purchase price for long positions and above the purchase price for short positions.

Reference [1] investigates the effectiveness of stop losses by formulating a market model based on fractional Brownian motion to simulate asset price evolution, rather than using the conventional Geometric Brownian motion.

Findings

-In long positions, stop loss levels are placed below purchase prices, while in short positions, they are positioned above to protect invested capital.

-Stop-loss strategies improve buy-and-hold returns when asset prices display long-range dependence, capturing fractal characteristics of financial market behavior over time.

-The Hurst parameter, expected return, and volatility significantly influence stop-loss effectiveness, making their measurement crucial for optimizing strategy performance.

-Simulation results confirm that optimizing stop-loss thresholds for these variables can significantly enhance investment returns and reduce downside risks.

-Polynomial regression models were developed to estimate the optimal relationship between stop-loss thresholds and influencing variables for better trading outcomes.

-In mean-reverting market conditions, stop losses tend to reduce risk-adjusted returns, highlighting the importance of adapting strategies to market regimes.

In short, the paper formulated a market model based on fractional Brownian motion. Using this model, we can formally study the effectiveness of stop losses. It showed that stop losses enhance the risk-adjusted returns of the buy-and-hold investment strategy when the asset price is trending.

We note, however, that when the underlying asset is in the mean-reverting regime, stop losses decrease the risk-adjusted returns.

Reference

[1] Yun Xiang  and Shijie Deng, Optimal stop-loss rules in markets with long-range dependence, Quantitative Finance, Feb 2024

Fixed and Trailing Stop Losses in the Commodity Market

Building on previous discussion of the theoretical foundations of stop-loss strategies, Reference [2] examines their real-world application in the commodity market. It evaluates the performance of fixed and trailing stop losses, uncovering key factors that influence their effectiveness and impact on returns.

Findings

-The study analyzed fixed and trailing stop-loss strategies in commodity factor trading, focusing on their effectiveness in improving returns and reducing risk exposure.

-Results showed unmanaged factors performed poorly after accounting for transaction costs, while applying simple stop-loss rules significantly improved factor performance at the asset level.

-Fixed-stop strategies achieved an average Sharpe ratio of 0.92, whereas trailing-stop strategies delivered a higher average Sharpe ratio of 1.28.

-Both fixed and trailing stop-loss approaches maintained maximum drawdowns below 20 percent, with generally positive return skewness except for the skewness factor.

-The effectiveness of stop-loss strategies was not regime-dependent, but influenced by the quality of trading signals, commodity return volatility, and serial correlations.

-Transaction costs also played a significant role in determining stop-loss strategy performance, highlighting the importance of cost-efficient execution in commodity markets.

-Dynamically adjusting stop-loss thresholds based on realized volatility further enhanced factor performance compared to static fixed thresholds, especially in volatile trading environments.

-Stop-loss strategies were most effective when applied to factors built with high-conviction weighting schemes, maximizing their potential to capture commodity premia.

-Positive return autocorrelation and higher commodity return volatility were key conditions under which stop-loss strategies delivered the most meaningful performance improvements.

In short, in the commodity market, stop losses are effective when the autocorrelation of returns is positive, which is consistent with the findings of Reference [1]. Additionally, the volatility of returns influences how effective stop losses are.

A notable result of this study is that using trailing-stop with dynamic thresholds could enhance factor performance compared to using fixed thresholds.

Reference

[2] John Hua FAN, Tingxi ZHANG, Commodity Premia and Risk Management, 2023

Closing Thoughts

In summary, the first paper formulates a market model based on fractional Brownian motion to formally study the effectiveness of stop losses. It finds that stop losses improve the risk-adjusted returns of a buy-and-hold strategy when the asset price exhibits trending behavior, but reduce returns in mean-reverting regimes. The second paper focuses on the commodity market and shows that stop losses are effective when return autocorrelation is positive, aligning with the first study’s findings. It also highlights that return volatility affects stop loss effectiveness, and notably, that trailing stops with dynamic thresholds can enhance factor performance compared to fixed thresholds.

In trading system design, out-of-sample (OOS) testing is a critical step to assess robustness. It is a necessary step, but not sufficient. In this post, I’ll explore some issues with OOS testing.

How Well Overfitted Trading Systems Perform Out-of-Sample?

In-sample overfitting is a serious problem when designing trading strategies. This is because a strategy that worked well in the past may not work in the future. In other words, the strategy may be too specific to the conditions that existed in the past and may not be able to adapt to changing market conditions.

One way to avoid in-sample overfitting is to use out-of-sample testing. This is where you test your strategy on data that was not used to develop the strategy. Reference [1] examined how well the in-sample optimized trading strategies perform out of sample.

Findings

-In-sample overfitting occurs when trading strategies are tailored too closely to historical data, making them unreliable in adapting to future, changing market conditions and behaviors.

-The study applied support vector machines with 10 technical indicators to forecast stock price directions and explored how different hyperparameter settings impacted performance and profitability.

-Results showed that while models often performed well on training data, their out-of-sample accuracy significantly dropped—hovering around 50%—highlighting the risk of misleading in-sample success.

-Despite low out-of-sample accuracy, about 14% of tested hyperparameter combinations outperformed the traditional buy-and-hold strategy in profitability, revealing some potential value.

-The highest-performing strategies exhibited chaotic behavior; their profitability fluctuated sharply with minor changes in hyperparameters, suggesting a lack of consistency and stability.

-There was no identifiable pattern in hyperparameter configurations that led to consistently superior results, further complicating strategy selection and tuning.

-These findings align with classic financial theories like the Efficient Market Hypothesis and reflect common challenges in machine learning, such as overfitting with complex, high-dimensional data.

-The paper stresses caution in deploying overfitted strategies, as their sensitivity to settings can lead to unpredictable results and unreliable long-term performance in real markets.

The results indicated that most models had a high in-sample accuracy but only around 50% when applied to out-of-sample data. Nonetheless, a significant proportion of the models managed to outperform the buy-and-hold strategy in terms of profitability.

However, it’s noteworthy that the most profitable strategies are sensitive to system parameters. This is a cause for concern.

Reference

[1] Yaohao Penga, Joao Gabriel de Moraes Souza, Chaos, overfitting, and equilibrium: To what extent can machine learning beat the financial market?  International Review of Financial Analysis Volume 95, Part B, October 2024, 103474

How Reliable Is Out-of-Sample Testing?

Out-of-sample testing is a crucial step in designing and evaluating trading systems, allowing traders to make more informed and effective decisions in dynamic and ever-changing financial markets. But is it free of well-known biases such as overfitting, data-snooping, and look-ahead? Reference [2] investigated these issues.

Findings

-Out-of-sample testing plays a vital role in evaluating trading systems by assessing their ability to generalize beyond historical data and perform well under future market conditions.

-Although useful, out-of-sample testing is not immune to biases such as overfitting, data-snooping, and especially look-ahead bias, which can distort the validity of results.

-A common issue arises when models are developed or tuned using insights gained from prior research, creating an indirect dependency between development and test data.

-Researchers found that excessively high Sharpe ratios in popular multifactor models can be largely explained by a subtle form of look-ahead bias in factor selection.

-Many out-of-sample research designs still overlap with datasets used in earlier studies, leading to results that reflect known patterns rather than genuine model performance.

-The ongoing and iterative nature of financial research makes it difficult to construct fully unbiased validation frameworks that truly represent out-of-sample conditions.

-When alternative evaluation methods were applied, Sharpe ratio estimates dropped significantly, indicating the extent to which traditional approaches may inflate performance expectations.

-This reduction in Sharpe ratios is actually encouraging, as it better reflects the realistic outcomes investors can expect when implementing these models in real time.

-Despite these findings, the paper emphasizes that multifactor models still improve on CAPM, though the improvements are smaller than widely claimed.

In short, out-of-sample testing also suffers, albeit subtly, from biases such as overfitting, data-snooping, and look-ahead.

We agree with the authors. We also believe that out-of-sample tests, such as walk-forward analysis, also suffer from selection bias.

Then how do we minimize these biases?

Reference

[2] Easterwood, Sara, and Paye, Bradley S., High on High Sharpe Ratios: Optimistically Biased Factor Model Assessments (2023). SSRN 4360788

Closing Thoughts

The results indicated that most models achieved high in-sample accuracy, but only around 50% when applied to out-of-sample data. While out-of-sample testing is an essential tool for evaluating trading strategies, it is not entirely free from biases such as overfitting and look-ahead. Research shows that these biases can inflate performance metrics like Sharpe ratios, leading to overly optimistic expectations.

Quantitative trading based on market sentiment is a less developed area compared to traditional approaches. With the explosion of social media, advances in computing resources, and AI technology, sentiment-based trading is making progress. In this post, I will explore some aspects of sentiment trading.

Using ChatGPT to Extract Market Sentiment for Commodity Trading

A Large Language Model (LLM) is an advanced AI system trained on vast amounts of text data to understand, generate, and analyze human language. In finance, LLMs are used for tasks like analyzing earnings reports, generating market sentiment analysis, automating financial research, and enhancing algorithmic trading strategies.

Reference [1] examines the effectiveness of ChatGPT in predicting commodity returns. Specifically, it extracts commodity news information and forecasts commodity futures returns. The study gathers over 2.5 million articles related to the commodity market from nine international newspapers across three countries, covering a diverse set of 18 commodities.

Findings

-A novel Commodity News Ratio Index (CNRI) was developed using ChatGPT, derived from the analysis of more than 2.5 million news articles from nine international newspapers across 18 commodities.

-The CNRI effectively forecasts commodity futures excess returns over 1- to 12-month periods, demonstrating significant predictive power in both in-sample and out-of-sample regression analyses.

-ChatGPT was used to classify sentiment in commodity-related news as either positive or negative, based on headlines, abstracts, or full article content.

-The CNRI shows stronger forecasting accuracy during specific macroeconomic conditions—particularly economic expansions, contango market phases, and periods of declining inflation.

-This ChatGPT-based approach outperforms traditional text analysis methods, including BERT and Bag-of-Words, in predicting future returns in commodity markets.

-The study controlled for various business variables and economic indicators, confirming the independent predictive significance of the CNRI.

-Results indicate that the CNRI also holds macroeconomic insight, offering valuable signals on broader economic performance beyond commodity markets.

-Findings affirm the utility of ChatGPT in financial forecasting, showcasing the broader potential of LLMs in understanding and extracting actionable intelligence from complex financial text data.

-This research highlights the growing role of AI in finance, illustrating how LLMs can enhance decision-making for investors, analysts, and risk managers alike.

In short, ChatGPT proves useful in forecasting commodity market dynamics and provides valuable insights for investors and risk managers.

Reference

[1]Shen Gao, Shijie Wang, Yuanzhi Wang, Qunzi Zhang, ChatGPT and Commodity Return, Journal of Futures Market, 2025; 1–15

Using the Number of Confirmed Covid Cases as a Sentiment Indicator

COVID-19, the novel coronavirus, was a source of anxiety for markets and individuals around the world since its outbreak in December 2019. Many traders looked for ways to use the information on the spread of the virus to predict market movements.

In Reference [2], the authors established an intraday algorithmic trading system that would open a short position in the Eurostoxx 50 futures market if the number of new confirmed cases of Covid-19 increased in the previous day (suggesting that fear of the epidemic rises), and close by afternoon. The system will open a long position if the new confirmed cases of Covid-19 have decreased from the previous day. The trading system achieved an annual return of 423% and a Sharpe ratio of 4.74.

Findings

-Daily confirmed COVID-19 cases were used as a sentiment proxy, reflecting public fear and uncertainty in financial markets during the pandemic.

-Researchers built an intraday trading system for Eurostoxx 50 futures, responding to increases or decreases in new Covid-19 cases reported the previous day.

-The system opened short positions after rising case counts and long positions after declines, closing trades by the afternoon to reduce overnight exposure.

-This simple rule-based strategy delivered an annual return of 423% and a Sharpe ratio of 4.74, suggesting strong performance under extreme market stress.

-The study demonstrated that pandemic-related health data could serve as a reliable short-term predictor of market direction, especially during crisis periods.

-Results reinforce the idea that emotional triggers—like health fears—can impact trading behavior just as much as traditional economic indicators or financial models.

-During high-uncertainty environments, metrics that reflect collective anxiety, such as COVID-19 cases, can outperform classic sentiment tools like the VIX index.

-The strategy showed how non-financial data can be directly translated into market actions, offering practical tools for risk-aware investors and quant traders.

-Overall, the research contributes to behavioral finance by quantifying the influence of fear on asset prices in moments of extreme public concern.

The article presented new evidence that emotions have an impact on financial markets, especially in situations of extreme uncertainty. In these situations, investors may utilize a variety of investment techniques based on metrics reflecting the progression of fear.

Reference

[2] Gómez Martínez, R., Prado Román, C., &Cachón Rodríguez , G. (2021). Algorithmic trading based on the fear of Covid-19 in Europe, Harvard Deusto Business Research 10(2), 295-304.

Closing Thoughts

Together, these studies highlight the growing role of alternative data and AI-driven sentiment analysis in financial forecasting. From pandemic case counts to millions of news articles, both fear and information flow can shape markets in measurable ways. Whether through rule-based trading or LLM-powered indices, the findings underscore how emotion, uncertainty, and unstructured data are becoming key inputs in modern investment strategies.

Machine learning has many applications in finance, such as asset pricing, risk management, portfolio optimization, and fraud detection. In this post, I discuss the use of machine learning in forecasting volatility.

Using Machine Learning to Predict Market Volatility

The unpredictability of the markets is a well-known fact. Despite this, many traders and portfolio managers continue to try to predict market volatility and manage their risks accordingly. Usually, econometric models such as GARCH are used to forecast market volatility.

In recent years, machine learning has been shown to be capable of predicting market volatility with accuracy. Reference [1] explored how machine learning can be used in this context.

Findings

-Machine learning models can accurately forecast stock return volatility using a small set of key predictors: realized volatility, idiosyncratic volatility, bid-ask spread, and returns.

-These predictors align with existing empirical findings, reinforcing the traditional risk-return trade-off in finance.

-ML methods effectively capture both the magnitude and direction of predictor impacts, along with their interactions, without requiring pre-specified model assumptions.

-Large current-period volatility values strongly predict higher future volatility; small values have a muted or negative impact.

-LSTM models outperform feedforward neural networks and regression trees by leveraging temporal patterns in historical data.

-An LSTM using only volatility and return history over one year performs comparably to more complex models with additional predictors.

-LSTM models function as distribution-free alternatives to traditional econometric models like GARCH.

-Optimal lag length remains critical in LSTM performance and must be selected through model training.

-The study reports an average predicted realized volatility of 44.1%, closely matching the actual value of 43.8%.

-Out-of-sample R² values achieved are significantly higher than those typically reported in related volatility forecasting literature.

In short, the paper aimed to demonstrate the potential of machine learning for modeling market volatility. In particular, the authors have shown how the LSTM model can be used to predict market volatility and manage risks. The results suggest that this is a promising alternative approach to traditional econometric models like GARCH.

Reference

[1] Filipovic, Damir and Khalilzadeh, Amir, Machine Learning for Predicting Stock Return Volatility (2021). Swiss Finance Institute Research Paper No. 21-95

Machine Learning Models for Predicting Implied Volatility Surfaces

The Implied Volatility Surface (IVS) represents the variation of implied volatility across different strike prices and maturities for options on the same underlying asset. It provides a three-dimensional view where implied volatility is plotted against strike price (moneyness) and time to expiration, capturing market sentiment about expected future volatility.

Reference [2] examines five methods for forecasting the Implied Volatility Surface of short-dated options. These methods are applied to forecast the level, slope, and curvature of the IVS.

Findings

-The study evaluates five methods—OLS, AR(1), Elastic Net, Random Forest, and Neural Network—to forecast the implied volatility surface (IVS) of weekly S&P 500 options.

-Forecasts focus on three IVS characteristics: level, slope, and curvature.

-Random Forest consistently outperforms all other models across these three IVS dimensions.

-Non-learning-based models (OLS, AR(1)) perform comparably to some machine learning methods, highlighting their continued relevance.

-Neural Networks forecast the IVS level reasonably well but perform poorly in predicting slope and curvature.

-Elastic Net, a linear machine learning model, is consistently outperformed by the non-linear models (Random Forest and Neural Network) for the level characteristic.

-The study emphasizes the importance of model selection based on the specific IVS characteristic being forecasted.

-Performance evaluation is supported using the cumulative sum of squared error difference (CSSED) and permutation variable importance (VI) metrics.

-The research highlights the utility of Random Forest in capturing complex, non-linear patterns in IVS dynamics.

-Accurate IVS forecasting is valuable for derivative pricing, hedging, and risk management strategies.

This research highlights the potential of machine learning in forecasting the implied volatility surface, a key element in options pricing and risk management. Among the five methods studied, Random Forest stands out as the most consistent and accurate across multiple IVS features.

Reference

[2] Tim van de Noort, Forecasting the Characteristics of the Implied Volatility Surface for Weekly Options: How do Machine Learning Methods Perform? Erasmus University, 2024

Closing Thoughts

These studies highlight the growing effectiveness of machine learning in financial forecasting, particularly for market volatility and implied volatility surfaces. Models like LSTM and Random Forest demonstrate clear advantages over traditional methods by capturing complex patterns and dependencies. As financial markets evolve, leveraging such tools offers a promising path for enhancing predictive accuracy and risk management.

Being able to anticipate a market correction or an economic recession is important for managing risk and positioning your portfolio ahead of major shifts. In this post, we feature two articles: one that analyzes indicators signaling a potential market correction, and another that examines recession forecasting models based on macroeconomic data.

Predicting Recessions Using The Volatility Index And The Yield Curve

The yield curve is a graphical representation of the relationship between the yields of bonds with different maturities. The yield curve has been inverted before every recession in the United States since 1971, so it is often used as a predictor of recessions.

A study [1] shows that the co-movement between the yield-curve spread and the VIX index, a measure of implied volatility in S&P500 index options, offers improvements in predicting U.S. recessions over the information in the yield-curve spread alone.

Findings

-The VIX index measures implied volatility in S&P 500 index options and reflects investor sentiment and market uncertainty.

-A counterclockwise pattern (cycle) between the VIX index and the yield-curve spread aligns closely with the business cycle.

-A cycle indicator based on the VIX-yield curve co-movement significantly outperforms the yield-curve spread alone in predicting recessions.

-This improved forecasting performance holds true for both in-sample and out-of-sample data using static and dynamic probit models.

-The predictive strength comes from the interaction between monetary policy and financial market corrections, not from economic policy uncertainty.

-Shadow rate analysis confirms the cycle indicator’s effectiveness, even during periods of unconventional monetary policy and flattened yield curves.

-The findings suggest a new framework for macroeconomic forecasting, with the potential to enhance early detection of financial instability.

-The VIX-yield curve cycle adds value beyond existing leading indicators and may help in anticipating major economic disruptions like the subprime crisis.

In short, the study concludes that the co-movement between the yield curve spread and the VIX index, which is a measure of implied volatility in S&P 500 index options, provides an improved prediction for U.S. recessions over any information available from just considering the yield-curve spreads alone.

This new research will have implications for how macroeconomists forecast future economic conditions and could even change how we predict periods of high financial instability like the subprime crisis.

Reference

[1] Hansen, Anne Lundgaard, Predicting Recessions Using VIX-Yield-Curve Cycles (2021). SSRN 3943982

Can We Predict a Market Correction?

A correction in the equity market refers to a downward movement in stock prices after a sustained period of growth. Market corrections can be triggered by various factors such as economic conditions, changes in investor sentiment, or geopolitical events. During a correction, stock prices may decline by a certain percentage from their recent peak, signaling a temporary pause or reversal in the upward trend.

Reference [2] examines whether a correction in the equity market can be predicted. It defines a correction as a 4% decrease in the SP500 index. It utilizes logistic regression to examine the predictability of several technical and macroeconomic indicators.

Findings

-Eight technical, macroeconomic, and options-based indicators were selected based on prior research.

-Volatility Smirk (skew), Open Interest Difference, and Bond-Stock Earnings Yield Differential (BSEYD) are statistically significant predictors of market corrections.

-These three predictors were significant at the 1% level, indicating strong reliability in forecasting corrections.

-TED Spread, Bid-Offer Spread, Term Spread, Baltic Dry Index, and S&P GSCI Commodity Index did not show consistent predictive power.

-The best-performing model used a 3% correction threshold and achieved 77% accuracy in in-sample prediction.

-Out-of-sample testing showed 59% precision in identifying correction events, offering an advantage over random prediction.

-The results highlight inefficiencies in the market and support the presence of a lead-lag effect between option and equity markets.

-The research provides valuable tools for risk management and identifying early signs of downturns in equity markets.

In short, the following indicators are good predictors of a market correction,

-Volatility Smirk (i.e. skew),

-Open Interest Difference, and

-Bond-Stock Earnings Yield Differential (BSEYD)

The following indicators are not good predictors,

-The TED Spread,

-Bid-Offer Spread,

-Term Spread,

-Baltic Dry Index, and

-S&P GSCI Commodity Index

This is an important research subject, as it allows investors to manage risks effectively and take advantage of market corrections.

Reference

[2] Elias Keskinen, Predicting a Stock Market Correction, Evidence from the S&P 500 Index, University of VAASA

Closing Thoughts

This research underscores the growing value of combining traditional financial indicators with options market metrics to improve market correction and recession forecasts. Tools like the VIX-yield curve cycle, Volatility Smirk, and BSEYD offer a more refined understanding of market risks. As financial markets evolve, integrating diverse data sources will be key to staying ahead of economic and market shifts.

A leveraged Exchanged Traded Fund (LETF) is a financial instrument designed to deliver a multiple of the daily return of an underlying index. Despite criticism, LETFs are frequently used by institutional investors. In this post, I discuss the practicality of LETFs and show that they are not as risky as they may seem.

Information Content of Leveraged ETFs Options

Leveraged ETFs, or exchange-traded funds, are investment funds designed to amplify the returns of an underlying index or asset class through the use of financial derivatives and debt. These ETFs aim to achieve returns that are a multiple of the performance of the index they track, typically two or three times (2x, 3x) the daily performance.

There is evidence that 1x ETF options provide an indication of the future return of the underlying 1x ETF. Reference [1] goes further and postulates that options on leveraged ETFs provide an even stronger indication of the 1x ETF future return.

Findings

-Options on leveraged ETFs provide stronger predictive signals for future ETF returns compared to standard ETF options, showing higher economic and statistical significance.

-The study uses unexpected changes in implied volatility from call and put options on leveraged ETFs to identify signals of informed trading activity.

-Leveraged ETF option signals consistently outperform unleveraged signals in predicting future returns of the underlying ETFs across various market conditions.

-Sophisticated investors often trade leveraged ETFs for exposure and rely on their options markets to hedge or speculate based on market expectations.

-A $1 investment in SPY based on leveraged option signals would have generated $27.59 in net returns from 2009 to 2021 after transaction costs.

-The predictive power of leveraged ETF option signals is especially strong during economic downturns, making them useful in volatile or declining markets.

-Inverse leveraged ETFs provide particularly strong predictive signals, especially when markets are trending downward or experiencing negative momentum.

-A trading strategy based on leveraged ETF option signals produced average abnormal returns of 1.13% per month, even after accounting for transaction costs.

-The findings suggest that options on leveraged ETFs play a key role in market efficiency and price discovery by reflecting informed investor activity.

-Both leveraged and unleveraged ETF options contain return-predictive information, but the economic impact is far greater when using leveraged ETF option signals.

In short, by using the difference in implied volatility innovations between calls and puts of leveraged ETFs as a trading signal, one can gain excess returns.

Reference

[1] Collin Gilstrap, Alex Petkevich, Pavel Teterin, Kainan Wang, Lever up! An analysis of options trading in leveraged ETFs, J Futures Markets. 2024, 1–17

Leveraged Exchange Traded Funds Revisited: Enhancing Returns or Adding Risk?

LETFs have received a lot of criticism. Despite the controversy, they remain popular among institutional investors. Reference [2] revisited the use of LETFs in portfolio allocation.

Findings

-LETFs aim to deliver amplified daily returns using derivatives and debt, making them suitable for short-term tactical strategies but requiring careful risk management.

-The study shows LETFs exhibit call option–like payoff characteristics, suggesting they can offer inexpensive leverage with built-in downside protection in certain scenarios.

-Under ideal conditions like continuous rebalancing and no constraints, the authors derived a closed-form information ratio–optimal strategy that followed a contrarian investment approach

-In realistic market conditions, including quarterly trading and margin constraints, a neural network approach was used to identify performance-optimized LETF allocation strategies.

-Results showed that unleveraged strategies using LETFs outperform benchmarks more frequently than leveraged strategies using standard (vanilla) ETFs on the same index.

-These unleveraged LETF strategies also showed partial stochastic dominance over both the benchmark and vanilla ETF-based strategies in terms of terminal wealth outcomes.

-The neural network–based strategy, trained on historical market data, further supports the practical value of including LETFs in actively managed portfolios.

-The findings challenge the common belief that LETFs only serve short-term speculation, revealing potential for long-term, dynamically optimized investment use.

-Overall, incorporating LETFs through informed strategies can enhance risk-adjusted returns, outperform traditional benchmarks, and improve the robustness of portfolio performance.

An interesting finding of this study is that, through a closed-form solution and numerical simulations, the authors demonstrated that LETFs behave like call options. Based on this, it is intuitive that if LETFs are part of a portfolio, they can enhance risk-adjusted returns.

Reference

[2] Pieter van Staden, Peter Forsyth, Yuying Li, Smart leverage? Rethinking the role of Leveraged Exchange Traded Funds in constructing portfolios to beat a benchmark, 2024, arXiv:2412.05431

Closing Thoughts

In conclusion, both studies provide compelling evidence that leveraged ETFs and their options hold significant value beyond short-term speculation. Leveraged ETF options offer strong predictive signals that can enhance trading strategies and market insight, while actively managed LETF allocations can improve long-term portfolio performance. When used thoughtfully, these instruments can deliver meaningful returns, manage risk, and contribute to price discovery.