AI-SMIS: An Explainable, Evidence-Grounded Framework for Stock Market Intelligence — System Architecture and Pre-Specified Evaluation Protocol
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Investors and researchers must combine prices, company results, filings, news and economic data that arrive in different forms and at different speeds. Simple screeners apply a few fixed filters, and many machine-learning tools predict prices without explaining their answers. This paper describes the architecture of AI-SMIS, an explainable framework for researching Indian listed companies, and fixes in advance the protocol by which it will be tested. The framework combines a point-in-time data pipeline that records the source and public-release date of every value; nine analysis modules (fundamental, technical, valuation, sector, market regime, sentiment, events, risk and anomaly); four machine-learning tasks explained with SHAP; an additive intelligence score whose changes can be traced exactly to its components; and a tool-grounded research assistant that answers only from retrieved evidence. For reproducibility, the paper specifies the study period (April 2011 to March 2026), nine expanding-window walk-forward folds with purging and embargo, label definitions, default score weights, rule-based market-regime labelling, hyper-parameter search ranges, and the models used for sentiment, embeddings and the language model. It also pre-specifies a six-step feature-ablation ladder, four hypotheses with quantitative decision rules and named statistical tests, and an annotated evaluation of the research assistant against a no-retrieval baseline. No empirical results are reported here; all example values are illustrative and are labelled as such. The framework supports research and decision making; it is not a trading system and does not give investment advice.
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