29 papers in the archive are tagged XGBoost, each distilled into a research flowchart and scored on two axes — mathematical complexity and empirical rigor (how scoring works). They are ranked below so the most evidence-backed work appears first.
- Scaling Conditional Autoencoders for Portfolio Optimization via Uncertainty-Aware Factor Selection Holy Grail Rigor 9 Math 8.5
- Hybrid Models for Financial Forecasting: Combining Econometric, Machine Learning, and Deep Learning Models Holy Grail Rigor 9 Math 7
- Improving S&P 500 Volatility Forecasting through Regime-Switching Methods Holy Grail Rigor 8 Math 7
- Forecasting Liquidity Withdraw with Machine Learning Models Holy Grail Rigor 8.5 Math 6
- CBDC Stress Test in a Dual-Currency Setting Holy Grail Rigor 7 Math 8
- Benchmarking Classical and Quantum Models for DeFi Yield Prediction on Curve Finance Holy Grail Rigor 8 Math 6.5
- Fairness-Aware Insurance Pricing: A Multi-Objective Optimization Approach Holy Grail Rigor 7.5 Math 6.5
- A Novel Decision Ensemble Framework: Customized Attention-BiLSTM and XGBoost for Speculative Stock Price Forecasting Holy Grail Rigor 7.5 Math 6
- XGBoost Forecasting of NEPSE Index Log Returns with Walk Forward Validation Street Traders Rigor 8.5 Math 4
- Machine and Deep Learning for Credit Scoring: A compliant approach Holy Grail Rigor 7.5 Math 5.5
- Interpretable Machine Learning for Macro Alpha: A News Sentiment Case Study Street Traders Rigor 8 Math 4.5
- Predicting and Explaining Customer Data Sharing in the Open Banking Street Traders Rigor 8.5 Math 3.5
- Can We Reliably Predict the Fed's Next Move? A Multi-Modal Approach to U.S. Monetary Policy Forecasting Holy Grail Rigor 7 Math 5.5
- Short-Term Stock Price Forecasting using exogenous variables and Machine Learning Algorithms Street Traders Rigor 7.5 Math 4
- Forecasting Credit Ratings: A Case Study where Traditional Methods Outperform Generative LLMs Street Traders Rigor 8 Math 3
- Optimizing Fintech Marketing: A Comparative Study of Logistic Regression and XGBoost Street Traders Rigor 7.5 Math 3.5
- Deep incremental learning models for financial temporal tabular datasets with distribution shifts Street Traders Rigor 7.5 Math 3.5
- Feature Selection with Annealing for Forecasting Financial Time Series Street Traders Rigor 6.5 Math 4.5
- S&P 500 Trend Prediction Street Traders Rigor 6.5 Math 4
- Exploring the Interpretability of Forecasting Models for Energy Balancing Market Street Traders Rigor 6.5 Math 3.5
- Enhancing ML Models Interpretability for Credit Scoring Street Traders Rigor 6.5 Math 3.5
- Improving Realized LGD Approximation: A Novel Framework with XGBoost for Handling Missing Cash-Flow Data Street Traders Rigor 6.5 Math 3.5
- Explainable AI in Request-for-Quote Lab Rats Rigor 4 Math 7
- Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending Street Traders Rigor 6.5 Math 3
- Improving Cryptocurrency Pump-and-Dump Detection through Ensemble-Based Models and Synthetic Oversampling Techniques Street Traders Rigor 6.5 Math 2.5
- Desenvolvimento de modelo para predição de cotações de ação baseada em análise de sentimentos de tweets Street Traders Rigor 6 Math 3
- Generating Alpha: A Hybrid AI-Driven Trading System Integrating Technical Analysis, Machine Learning and Financial Sentiment for Regime-Adaptive Equity Strategies Street Traders Rigor 6 Math 2.5
- A comparative analysis of machine learning algorithms for predicting probabilities of default Street Traders Rigor 5.5 Math 2.5
- An extreme Gradient Boosting (XGBoost) Trees approach to Detect and Identify Unlawful Insider Trading (UIT) Transactions Philosophers Rigor 4 Math 2.5
Broader area: Machine Learning · All topics: research topics → · Full archive: every paper →