financial-distress

🏢 Modern employer review platform with advanced features: AI-powered insights, web scraping infrastructure, financial distress tracking, rising startups monitoring, and enterprise-grade security. Built with Next.js, Supabase, and TypeScript.

Streamlit app: CatBoost + SHAP early-warning scores for corporate financial distress

Hybrid network + ML framework for corporate financial-distress prediction using network features and classic financial indicators.

This is a scientific research recognized by the Scholar Committee of UEH University

Public corporate distress prediction benchmark over Visegrád Group economies (2006–2021). Reference evaluations for tabular baselines, TabPFN, and Llama-3 QLoRA.

This repository contains my Master Thesis for the Master's in Computational Social Sciences (UC3M): Financial distress prediction: a data-driven approach for Spanish companies.

Research project for course of Quantitative Asset Allocation. Performed investigatory analysis on comparison between Batch and Online learning classes for portfolio selection on ETF US market over financial distress periods.

AI-powered financial distress prediction with interactive Streamlit dashboard