Quant Beckman @quantbeckman
Quantitative Researcher & Dev. | Financial Data Scientist | Machine Learning Engineer | Mathematical Research | Algorithmic Trading Systems quantbeckman.com Fresh takes on Quant Research Joined July 2019-
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The paper uses random-maturity arbitrage, meaning the convergence time is uncertain. A trade can be theoretically positive at some future bounded time and still be liquidated first because of margin pressure, widening basis, collateral stress, or exchange constraints
Raw momentum often hides market, sector, and factor exposure. Strip those common components first, then measure momentum only on the idiosyncratic residual return. Go long names with strong positive residual momentum and short names with strong negative residual momentum.
This is a pretty common problem with complex models (overall ML models)
Month-end creates predictable portfolio rebalancing pressure as funds, pensions, and systematic allocators adjust exposures. Assets that outperformed may face selling pressure, while laggards may receive buying pressure.
@RebellioMarket I would say: traders VS gammers 🤭
The whole framework is built around the Numerai dataset. That makes the results hard to transfer directly to normal equity trading, futures, ETFs, intraday data.
An accurate signal and good timing in the market are very different from closing in positive territory.
Sector returns often show recurring seasonal patterns linked to fiscal cycles, earnings windows, policy calendars... The strategy ranks sectors by historical strength for the current month.
🟥For this one and more papers with code: quantbeckman.com
The paper shows that same-day co-trading networks are positively related to same-day realized covariance. That supports a structural association, but it does not prove that co-trading predicts future covariance. For trading or allocation, the key question is whether yesterday’s co-trading structure improves tomorrow’s risk estimate after costs and turnover.
Here another good trading principle in quantitative research
Here an interesting formal trading statement
Use publicly reported insider transactions as an alignment signal. Clustered open-market buys after a drawdown can indicate insider confidence when price is weak. Clustered insider sells into rallies can indicate reduced alignment after price strength.
It's not every day you see traders using time variables to exit a trade
@afekz This is the typical pattern of small caps
🟥For this one and more papers with code: quantbeckman.com
The empirical strength comes from the factor model inputs (IPCA, PCA, RP-PCA, and AP-Trees). In other words, the performance depends on those factor-return forecasts
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