Quant Mashup - Unexpected Correlations Cross-Sectional Alpha Factors in Crypto: 2+ Sharpe Ratio Without Overfitting [Unexpected Correlations]In the early ’90s, the quant forefathers (Fama and French) introduced their now-canonical factor models: first three, then five, and eventually seven, explaining much of the variation in US equity returns. Today, these models are used to understand what easy-to-replicate risk factors managers are(...) How Speculative Money Flows into Crypto [Unexpected Correlations]Compared to traditional futures or equities, crypto markets offer greater transparency—thanks primarily to the public blockchain and also to the unique culture that shaped the industry. This opens up new opportunities for investors and traders to monitor and measure liquidity dynamics that are(...) The unreasonable effectiveness of volatility targeting - and where it falls short [Unexpected Correlations]This is part 1 of our in-depth investigation of how quantitative risk management could help improve risk-adjusted returns: I'll explain what volatility targeting is, explore a seemingly paradoxical phenomenon, and highlight its blindspots. Volatility targeting’s goal is to keep an asset or(...) The Least-Amount of Assumptions Backtest [Unexpected Correlations]There’s this Neumann quote: "With four parameters I can fit an elephant, and with five I can make him wiggle his trunk." Funny, but also true. It’s very fitting (haha) to our job at Unravel where we scan tens of thousands of time series in order to identify the ones that can be used as(...)