Month: June 2025

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.

The ratio of gold prices to other asset classes has been shown to be a useful predictor of stock market returns. In this post, we discussed several gold-based ratios and how they can be used to forecast equity market performance.

Gold Oil Price Ratio As a Predictor of Stock Market Returns

Analyzing intermarket relationships between assets can help identify trends and predict returns. Traditionally, analysts use commodity, currency, and interest-rate data to predict the direction of the stock market. In this regard, Reference [1] brings a fresh new perspective. It utilizes price ratios of gold over other assets in order to forecast stock market returns.

Findings

-The gold-oil price ratio (GO) is shown to be a strong predictor of future stock market returns.

-Researchers created ten different gold price ratios by comparing gold to various assets like oil, silver, CPI, corn, copper, and several financial indicators.

-They used statistical models (univariate and bivariate regressions) to test how well these ratios could predict U.S. stock returns.

-Among all the ratios tested, the gold-oil ratio (GO) had the highest predictive power.

-A one standard deviation increase in the GO ratio is linked to a 6.60% rise in annual excess stock returns for the following month.

-The GO ratio performs better than traditional forecasting methods, including the historical average model.

-It also offers meaningful economic benefits for investors who use mean-variance strategies.

-The study concludes that the predictive ability of the GO ratio is both statistically reliable and economically useful.

In summary, the gold-oil price ratio is identified as a robust predictor of stock market returns, outperforming traditional predictors and other gold price ratios. A one standard deviation increase in GO is associated with a significant 6.60% increase in annual excess returns for the next month.

Reference

[1] T. Fang, Z. Su, and L Yin, Gold price ratios and aggregate stock returns, SSRN 3950940

The Bitcoin-Gold Ratio as a Predictor of Stock Market Returns

The ratio of gold prices to other asset classes has been shown to be a useful predictor of stock market returns. The previous article discussed how the gold-oil ratio serves as one such indicator.

Continuing this line of inquiry, Reference [2] examines the informational value of the Bitcoin-gold (BG) price ratio. The logic behind this metric is that Bitcoin represents a high-risk asset, whereas gold is traditionally viewed as a safe haven. Therefore, a rising BG ratio may signal increased investor risk appetite. It may also reflect growing optimism and interest in technological innovation, which boosts demand for Bitcoin. As a result, a higher BG ratio can indicate a tech-driven risk appetite that translates into stronger stock returns.

Findings

-The Bitcoin-Gold (BG) ratio is positively linked to U.S. stock market returns, especially during and after the COVID-19 pandemic.

-A rising BG ratio suggests increased investor risk appetite, as Bitcoin is seen as high-risk and gold as a safe haven.

-The effect of the BG ratio on stock returns remains strong even when using Ethereum instead of Bitcoin, showing broader crypto-gold relevance.

-The positive impact of the BG ratio also applies to the European stock market, not just the U.S., indicating global relevance.

-The main channel through which the BG ratio affects stock returns is investor risk aversion or appetite.

-The study uses various economic controls, like volatility, inflation, and liquidity, and still finds the results hold strong.

-There was no significant impact of the BG ratio on stock returns before the pandemic, suggesting this relationship is more recent.

-The BG ratio reflects shifts in market sentiment and offers a new tool for gauging investor behavior.

-Investors can use the BG ratio as a signal to adjust their equity exposure based on prevailing market conditions.

In summary, the paper makes a novel contribution by introducing crypto-gold ratios as reliable indicators of stock market direction across multiple regions.

Reference

[2] Elie Bouri, Ender Demir, Bitcoin-to-gold ratio and stock market returns, Finance Research Letters (2025) 107456

Closing Thoughts

Both studies show that gold price ratios can offer valuable insights into stock market returns. The gold-oil ratio (GO) stands out as a strong, traditional predictor, while the Bitcoin-gold ratio (BG) brings a modern twist by capturing shifts in investor risk appetite. Together, these findings suggest that combining safe-haven and risk assets in a ratio form can help investors better understand and respond to changing market conditions.