Model-Risk

TL;DR for operators Models rarely fail because nobody ran a sensitivity analysis. They fail because the sensitivity analysis answered the convenient question instead of the relevant one. The paper behind gsaot introduces an R package for Optimal Transport-based global sensitivity analysis.1 Its practical value is not that it makes Sobol’ indices obsolete. It does not. The useful shift is narrower and more interesting: gsaot estimates how much the entire output distribution changes when an input is known, rather than asking only how much of the output variance can be attributed to that input. ...

TL;DR for operators A model score is not a certificate. It is a timestamp. That is the operational message of D. Sculley and co-authors’ position paper on GenAI evaluation.1 Their argument is not that every static benchmark is useless, nor that competitions are magical truth machines with leaderboards attached. The argument is sharper: GenAI has broken the old bargain behind machine-learning evaluation. ...

TL;DR for operators QuantBench is not another paper asking investors to believe that the newest neural architecture will finally decode markets because it has more layers and a nicer diagram. Mercifully. It is a benchmark platform for quantitative investment that tries to evaluate AI methods across the full quant workflow: factor mining, modelling, end-to-end position generation, portfolio optimisation, and order execution.1 ...