There is a quiet confidence in submitting a paper that knows exactly what it is not claiming. The researcher here understands something that many seasoned academics still wrestle with: predictive power and scientific value are not the same thing. Forecasting a stock index with macroeconomic variables, addressing non-stationarity, and then using SHAP to expose a model's blind spot around regime shifts is not a confession of failure. It is a precise, honest piece of diagnostic work. That matters.
The instinct to frame this as an interpretability study rather than a prediction breakthrough is not just wise, it is the correct reading of the contribution. The model struggled with oil becoming a liability instead of an asset depending on the period, and the explanation is straightforward: the model never learned the inverted relationship because the regime shift was not represented in the training data. That is not a flaw to hide. It is a finding. It tells the community something about the limits of tree-based models in non-stationary financial environments, and it opens a clear path for future work, whether that involves regime-switching models, feature engineering, or more dynamic interpretability tools.
For a local conference, the bar is not whether the results will move markets. The bar is whether the research question is coherent, the methodology is sound, and the discussion advances understanding. This submission meets that bar. The researcher is not overpromising. They are not claiming a very accurate predictor. They are offering a diagnostic lens, one that is open for interpretability and further work. That is a legitimate contribution, and it will read as such if presented with the same clarity used in the framing.
The practical takeaway is this: do not bury the lede in caveats. Lead with the diagnostic insight. State plainly that the model's predictive power is modest, then pivot to what the SHAP analysis reveals about regime sensitivity. That is the story. That is the publishable insight. A local conference is not asking for a Nobel Prize. It is asking for a sound, honest, and useful piece of research. This qualifies. Bring that framing to the presentation, and the discussion will follow.