Utilized sentiment-based features to predict cryptocurrency returns, models used: Random Forest Classifier, Random Forest Regressor, and VAR time-series model
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Updated
Jan 1, 2021 - Python
Utilized sentiment-based features to predict cryptocurrency returns, models used: Random Forest Classifier, Random Forest Regressor, and VAR time-series model
CryptoGAT: Are Time Series Models Effective for Cryptocurrency Forecasting?
Cross-sectional Transformer and FFN for stock return prediction and alpha generation. Implements GKX (2020) NN5 replication and MSRR loss (Kelly et al. 2025) for direct portfolio Sharpe optimization. Avg SDF Sharpe 2.05, significant alpha (t=5.34) unexplained by FF5+Momentum.
Prediction of Order Returns of an Online Clothing Retailer (Real-World Data) With XGBoost and Random Forest
Statistical learning workflow for stock-level return prediction using linear models, LightGBM, and neural networks on equity factor data.
Forecast Monthly Excess Returns of the CRSP Value-Weighted Market Index
Developed a machine learning system capable of identifying high-risk return orders before fulfillment, enabling retailers to potentially reduce logistics costs, inventory disruptions, and refund processing overhead.
Penentuan Peluang Naik Turun Harga Saham Harian 1 Interval dengan Simulasi Markov Chain
RetireRich is a python based application to calculate and analyze the risk-return of the investment funds as compared to the S&P 500 Index.
P.A.P.E.R. Platform for Asset-Pricing Experimentation and Research
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