Papers for

financial risk managers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Audit method clarifies when not trading is a mistake

When Is Inaction a Mistake? Continuation-Aware Auditing of PPO Trading Policies

Abstract: An optimal reference may recommend trading when a learned policy chooses inaction, but the recommendation depends on information and future decisions. We introduce a four-stage audit for frozen proximal policy optimization policies without retraining. It examines deployment occupancy, matches current information, tests isolated deviations under incumbent continuation, and evaluates repeated deployment of observation-based alternatives. In controlled linear-Gaussian simulations, information matching explains part of the disagreement, while continuation changes its interpretation. At unit observation noise, incumbent continuation reverses 99.3% of projected-hard missed-advantage mass; repeated projected-rule deployment improves all 50 policies. These comparisons distinguish isolated action changes from policy replacement. Historical Bitcoin/Tether (BTCUSDT) replay applies this deployment perspective to a hand-specified intervention selected using 2024 data and frozen for 2025. Daily net reward improves by 135.03 basis points, with gains in 46 of 50 policies, primarily through lower turnover costs. The audit clarifies what oracle-flagged inaction implies for deployed decision making.

Fri 11 SeptComputational Engineering, Finance, and Science
The gist
Sometimes a trading strategy learned by a computer decides not to trade, but another ideal choice might have suggested trading. The authors created a way to check these situations without training new strategies. Their audit looks at whether the decision to stay put matches the current information and what would happen if the decision changed. They tested this on simulated data and real Bitcoin trading history, showing that adjusting certain decisions can make trading more profitable by reducing costs.
Open → 2609.12536v1

Feature-based explanations capture time-dependent model outputs better

A Hilbert-Valued Functional Decomposition Framework for Explaining Time-Dependent Outputs

Abstract: Feature-based explanations quantify features' influence on model predictions, but are primarily designed for scalar outputs. In many applications, however, outputs are functional or multivariate, such as time-dependent trajectories in demand forecasting. Consequently, existing approaches typically explain each output location independently, ignoring dependencies across the output components. We address this limitation by developing a unified framework for feature-based explanations of time-dependent outputs. Specifically, we generalize functional decomposition to Hilbert-valued prediction functions and extend an existing feature-based explanation framework to this setting. Our framework introduces kernel-based output representations that enable time-dependency-aware explanations at multiple levels of temporal granularity, including time-specific, time-resolved, and time-aggregated, while providing a unified view in which existing methods arise as special cases. We validate our framework on synthetic and real-world data, including intraday financial market volatility prediction and energy demand forecasting.

Thu 10 SeptMachine Learning
The gist
Many computer models produce results that change over time, like forecasting energy use or stock volatility, but current explanation methods treat each time point separately. The authors developed a new technique that explains how input features affect the entire time-dependent output as a whole, considering how different moments in time relate to each other. Their method uses math tools called Hilbert spaces and kernels to provide explanations that work at different time scales, from specific moments to overall trends. They tested their approach with simulated data and real-world cases to show it works.
Open → 2609.11295v1