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Ma, X., Sha, J., Wang, D., Yu, Y., Yang, Q., & Niu, X. (2018). Study on a Prediction of P2P Network Loan Default Based on the Machine Learning LightGBM and XGboost Algorithms According to Different High Dimensional Data Cleaning. Electronic Commerce Research and Applications, 31, 24-39. https://doi.org/10.1016/j.elerap.2018.08.002

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• TITLE: Black-Litterman Based Portfolio Optimization: A Hybrid Approach

  AUTHORS: Jiaxin Ran, Ao Lv, Dechuan Li, Rui Liang, Yang Wang, Jerome Yen

  KEYWORDS: Black-Litterman, Hybrid Model, Machine Learning, Time-Series Forecasting

  JOURNAL NAME: Open Journal of Social Sciences, Vol.14 No.3, March 31, 2026

  ABSTRACT: We propose a hybrid approach that combines the time-series forecasting model and the ensemble learning algorithm to generate investor views in the Black-Litterman model. Specifically, we first use four time-series forecasting models, ARIMA, LSTM, Informer, and iTransformer, to forecast the dynamics of factors that affect the movements of assets, for example, those that are related to the market cap, trend, volatility, and momentum. Then, the ensemble learning algorithm, XGBoost, is used to integrate the results from time-series-based analyses to forecast stock returns as investor views in the Black-Litterman model for portfolio optimization. We tested the performance of our proposed hybrid model in the China A-share market, and the results indicated that the hybrid approach could significantly improve the performance of the Black-Litterman model. By comparison, the perfomance of different approaches, for example, a single time-series model and different hybrid models, the ARIMA-XGBoost and iTransformer-XGBoost performed much better.