During periods of market stress, assets that normally behave differently often begin to move together. Correlations rise, diversification weakens, and different parts of the equity market increasingly respond to the same underlying risk factors. This raises a practical portfolio-management question: can a sufficiently strong increase in short-term correlations help identify periods when temporarily hedging equity exposure becomes useful?

Our experiment follows the same general research direction but asks a different question. Instead of using correlation primarily as a momentum filter, we test whether an acceleration in correlations across US equity ETFs can act as a practical trigger for temporarily hedging SPY.

A trading strategy’s value goes beyond returns—it also depends on what it adds to a portfolio. Our latest case study combines Quantpedia’s Strategy Grading report with a custom API workflow to compare WTMF against 44 peer strategies. The results highlight a useful trade-off: modest risk-adjusted performance, but lower correlations that suggest potential diversification value. Explore how custom peer groups can bring more context to strategy evaluation.

Leveraged ETFs provide amplified exposure to an underlying index, but their daily reset mechanism creates volatility drag, which can substantially reduce their long-term value. This raises the possibility of exploiting the effect by simultaneously shorting leveraged ETFs with opposite exposures. We examine a 50/50 short portfolio of TQQQ and SQQQ, which provides approximately neutral exposure to the Nasdaq-100 while seeking to capture volatility decay. We analyze the strategy explicitly incorporate borrowing costs. We further investigate daily and weekly rebalancing, execution timing, and tranche-based rebalancing over five-, ten-, and twenty-day horizons. The results show that borrowing costs materially reduce the apparent profitability of the strategy. Moreover, after costs, returns are concentrated in a small number of high-volatility periods, while the strategy is largely neutral during ordinary market conditions. The findings demonstrate that ignoring short-selling costs and implementation details can substantially overstate the attractiveness and robustness of volatility-decay strategies.

For anyone trading or investing in Bitcoin, a familiar indicator can become a dangerous comfort blanket. Josh Molnar’s Bitcoin Runs on a Clock argues that classic cycle signals—such as MVRV, the Mayer Multiple and Pi Cycle Top—have weakened as Bitcoin’s price swings become less extreme. Thresholds calibrated to earlier cycles may consequently stop triggering, leaving investors waiting for a sell signal that never arrives. The practical takeaway is that a strong historical record does not make an indicator’s settings permanent. If the market changes, the dashboard needs to change with it.

This paper investigates whether longer historical training periods improve the out-of-sample performance of a Hidden Markov Model (HMM) used for identifying financial market regimes. Four four-state Gaussian HMMs are compared, differing only in the starting point of their training data (1970, 1980, 1990, and 2000), while being evaluated on the same test period. The model uses five market-based features capturing volatility, drawdowns, credit conditions, and sector rotation to identify latent risk regimes.

The study highlights that validation methodology and evaluation criteria are as important as model design and data length when assessing regime-based investment models.

Hello all,

– Screener and API extended with two new fields: Transaction Costs and Robustness
– 11 new Quantpedia Premium strategies
– 7 new related research papers
– 8 new backtests
– and finally, 6 new posts on our Quantpedia blog