{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T10:05:57Z","timestamp":1771668357707,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>This paper examines how far financial crises can be anticipated using only publicly available macroeconomic, macro-financial and macro-demographic indicators. We construct a monthly US panel from OECD and Federal Reserve sources and transform standard aggregates into a rich feature set capturing volatility, momentum, higher-order moments, drawdowns and structural breaks. Crisis risk is modeled in a hazard-style early-warning framework: for each month in expansion, we define binary labels for crisis onsets within 6-, 12- and 18-month horizons, combined with lead-time weights that reward earlier, more operationally useful signals. Using this common information set, we compare three families of models: regularised logistic regressions, gradient-boosted decision trees (LightGBM and XGBoost) and a bidirectional LSTM with attention fed by fixed-length feature sequences. Models are evaluated with expanding-window cross-validation and strictly out-of-sample holdout tests. Across horizons, penalised logistic regressions deliver the most accurate and stable forecasts, achieving holdout ROC-AUCs up to 0.99 and F1 scores up to 0.86 at the 18-month horizon, while tree-based methods are competitive only at longer horizons and the Bi-LSTM substantially overfits, adding little incremental predictive power. These results suggest that, in small and highly imbalanced crisis datasets built from open macro-demographic indicators, well-regularised linear models can match or surpass more complex machine-learning and deep-learning approaches, and offer greater transparency for macroprudential policy use.<\/jats:p>","DOI":"10.31449\/inf.v50i6.12540","type":"journal-article","created":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T09:24:16Z","timestamp":1771665856000},"source":"Crossref","is-referenced-by-count":0,"title":["Forecasting Financial Crises with Public Macro-Demographic Indicators: A Comparison of Logistic, Tree-Based and LSTM Models"],"prefix":"10.31449","volume":"50","author":[{"given":"Dongsheng","family":"Bei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengwei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,2,21]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12540\/6475","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12540\/6475","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T09:24:16Z","timestamp":1771665856000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/12540"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,21]]},"references-count":0,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,2,21]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i6.12540","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2,21]]}}}