This is a summary of links recently featured on Quantocracy as of Saturday, 10/24/2026. To see our most recent links, visit the Quant Mashup. Read on readers!
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Shorting Leveraged ETFs: Role of Borrowing Costs and Rebalancing in Volatility-Decay Strategies [Quantpedia]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
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The Log-Linear Regression: Usage for Long-Term Financial Asset Analysis [Portfolio Optimizer]In finance, various studies have explored the use of regression models in forecasting stock prices [] based on historical data and other relevant variables1. Among these models, the log-linear regression model is of particular importance due to its relationship with the geometric Brownian motion (GBM) asset pricing model introduced by Paul Samuelson2 and at the heart of the BlackScholes
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Long Volatility Is the Wrong Hedge for a Risk-Parity Portfolio [Beyond Passive]A common piece of advice for any leveraged portfolio is to hold some long volatility on the side, a small position in VIX futures that pays when the stock market crashes. This article tests that advice on a risk-parity portfolio of stocks, bonds and gold and finds it wrong: VIX insures stocks, and this portfolios worst days are mostly not stock-market days. What protects it is a limit on how
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Do round numbers matter for gold? [Quantex]Do round prices such as US$2,000 act as barriers for gold? Using 5,138 daily XAUUSD bars from 2005 to 2024, we test whether gold closes back on its side after touching a US$100 level and compare the round grid with 80 placebo grids of the same spacing, using rank-based tests, a Holm correction and block bootstraps. On 232 first touches gold held 45.0% of the time against 49.2% for ordinary levels;
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The Part Time Trader Part 4: Building the Lab [Algorithmic Advantage]Most back-tests are wrong before the first trade is placed; heres everything I settled before I let a single result count. Apologies for some of this content being dry, I just want to make sure that if you take this series in its entirety, you basically have a mini-course in building momentum strategies, and these foundations count. The payload in the article is the full factor bank of 34
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Ornstein-Uhlenbeck (OU) mean reversion on S&P 500 point-in-time constituents [Quanter Lab]The Ornstein-Uhlenbeck process is the textbook model of mean reversion: a price that wanders away from its usual level is pulled back toward it, like a ball on a spring. Traders use it to buy a stock when its price is unusually far below its estimated level, and to sell when the price has come back. One number in the model matters most for a trader: the half-life, how many days a gap between the