Quant Mashup - Aligrithm

Can Machines Learn Weak Signals? Ridge > Zero > Lasso [Aligrithm]

Feed 920 firm characteristics into a Lasso to predict next month's stock returns and it will do something that should stop you cold: it loses to a model that predicts zero for every stock. Not "underperforms a good benchmark." Loses to the number 0. Shen and Xiu prove this is not bad(...)

Percentile-Rank Momentum With Hysteresis: Low-Churn Signals [Aligrithm]

Momentum is the oldest anomaly in the book, and a new momentum paper has to justify why it exists. Landolfi's percentile-rank framework does not sell you the momentum. It sells the plumbing around it: rank each move against its own sign-consistent history instead of a raw threshold, gate(...)

State-Space Models for Price: CryptoMamba vs Transformers (Skeptical) [Aligrithm]

Every few years a new architecture gets pointed at Bitcoin and a paper announces it won. This round it is Mamba, the selective state-space model that is genuinely reshaping language and vision. Sepehri, Mehradfar, Soltanolkotabi, and Avestimehr at USC built CryptoMamba, a compact Mamba network that(...)

Network Momentum as a Cross-Asset Factor [Aligrithm]

Momentum is the one factor nobody argues about. Winners keep winning, losers keep losing, and the effect shows up in stocks, bonds, commodities, and currencies across a century of data. The old article "From Intermarket Analysis to Network Momentum" pushed a harder claim: an asset's(...)

Trend-Following P&L Is a Function of Autocorrelation (Closed Form) [Aligrithm]

Ask a CTA salesperson why their fund makes money and you get a story: markets trend, we ride the trend, we cut losers and let winners run. That story is untestable. Sepp and Lucic did something the industry rarely does. They wrote down the exact profit-and-loss of a standard European trend-follower(...)