Master's candidate · CUFE · Quant Investment & Asset Management

王佳晨 / Wang Jiachen

Financial Technology & Quantitative Research

Focused on quantitative research, machine learning, and empirical asset pricing in Chinese financial markets.

Quantitative ResearchMachine LearningFinancial Technology

About

Master's student in Quantitative Investment & Asset Management at Central University of Finance and Economics. Previously earned a Bachelor's degree in Financial Technology from Southwestern University of Finance and Economics. Experienced in alpha factor mining, index enhancement strategies, ETF rotation, and applying deep learning to financial prediction.

Featured Projects

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Quantitative Strategy2025.10 – 2026.01

LLM-Driven Factor Mining & ETF Rotation Strategy

End-to-end quant platform: LLM Agent factor discovery (GLM-4.5), multi-tier signal evaluation, rolling XGBoost/linear combination, and live ETF rotation trading.

PythonGLM-4.5pandasPolarsNumPyRead more

Academic Research2024.12 – 2025.04

Autoencoder-Driven Latent Factor Models for A-Share Market

Thesis: Built PCA, IPCA, and conditional autoencoder latent factor models on 1996–2023 A-share data with 91 firm characteristics.

PythonPyTorchpandasNumPyRead more

Deep Learning2023.12 – 2024.07

Candlestick Chart CNN Prediction for A-Share Market

Attention-CNN on A-share candlestick images for binary prediction; long-short Sharpe ratio of 3.30 (weekly) and 1.50 (monthly).

PythonPyTorchNumPyOpenCVRead more

Quantitative Research2023.11 – 2025.01

Index Enhancement Strategy with XGBoost

Full-stack index enhancement: factor engineering, XGBoost with linear leaf nodes, and adaptive market-style model fusion.