Recent Academic Research
Leveraged ETF outperformance, investor attention and crypto volatility, and the impact of tariffs
Leveraged ETF Myths
The paper challenges the widespread belief that leveraged ETFs (LETFs) always lose value over time due to volatility drag, showing instead that market trends and autocorrelation can significantly boost or hurt their long-term performance.
This study argues that volatility drag alone does not explain LETF performance. Instead, the compounding effects depend on the underlying market’s return dynamics, such as whether returns are independent, trend-following, or mean-reverting. The authors use a unified framework combining AR(1), AR-GARCH, and regime-switching models to explore how LETFs behave under different conditions.
They find that in momentum-driven markets, daily-rebalanced LETFs tend to outperform, while in mean-reverting or oscillating markets, frequent rebalancing can amplify losses.
For example, empirical tests using 20 years of SPY and QQQ data confirm that LETFs benefited during bull markets like the post-2009 recovery, but underperformed in sideways markets where rebalancing systematically hurt returns.
The key takeaway is that investors should consider not just volatility but also return autocorrelation when using LETFs, and they might adjust rebalancing frequency depending on the prevailing market environment.
Hsieh, Chung-Han, Jow-Ran Chang, and Hui Hsiang Chen. "Compounding Effects in Leveraged ETFs: Beyond the Volatility Drag Paradigm." arXiv preprint arXiv:2504.20116 (2025).
Investor Attention and Cryptocurrency Volatility
Institutional investor attention is a stronger and more consistent driver of cryptocurrency volatility than individual investor chatter, with machine learning models revealing these patterns better than traditional econometric tools.
This paper investigates how investor attention affects the realized volatility of Bitcoin, Ethereum, and XRP by separating the influence of institutional players (measured using Bloomberg news) and retail investors (measured using Twitter data).
The authors apply both GARCH-X econometric models and advanced machine learning methods like neural networks, random forests, and support vector regression.
They find that institutional attention consistently predicts volatility across cryptocurrencies, while individual attention shows mixed or weaker effects. Notably, the role of “whales” (large holders controlling significant portions of supply) amplifies volatility in Bitcoin and Ethereum but not in XRP, where centralized control dampens such effects.
The bar plots rank features by their average SHAP value, revealing that institutional attention is the dominant driver of volatility for Bitcoin and Ethereum, while individual attention plays a larger role in predicting XRP volatility.
These findings suggest that while retail noise matters, institutional actors, armed with capital and better information, play the central role in shaping crypto market turbulence, and machine learning tools are better equipped to capture these complex, nonlinear relationships.
Gadirli, Farid and Chen, Minjia and Haile, Getinet Astatike, Investor Attention and Cryptocurrency Volatility: A Machine Learning and Econometric Analysis. Available at SSRN: https://ssrn.com/abstract=5230632 or http://dx.doi.org/10.2139/ssrn.5230632
Recessionary Risks from Tariffs
Temporary import tariffs can trigger recessions when the contractionary forces of falling consumption and exports outweigh the benefits of shifting demand toward domestic goods, especially when trading partners retaliate.
This paper builds a New Keynesian model that tracks how sudden tariff shocks ripple through GDP, the trade balance, prices, and employment. The authors find that when households sharply cut back on spending (due to rising prices) and exports lose competitiveness, the economy shrinks even before considering retaliation.
They show that the key tipping point depends on how easily consumers postpone purchases and how sensitive foreign demand is to price changes. When other countries impose retaliatory tariffs, the recession deepens and the trade balance can actually worsen, a result that sharply contrasts with long-run trade models predicting balanced flows.
The authors further test the model under different monetary policy responses, inventories, and anticipated tariff effects, finding that recessions are hard to avoid even when policymakers try to offset shocks. Overall, the paper argues that short-run macroeconomic risks make optimal tariffs far lower than standard trade theory would suggest, especially when accounting for the pain of a tariff-induced downturn.
Ataei, Masoud. "Modeling Regime Structure and Informational Drivers of Stock Market Volatility via the Financial Chaos Index." arXiv preprint arXiv:2504.18958 (2025).
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