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Intelligent portfolio construction via news sentiment analysis

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  • Hung, Ming-Chin
  • Hsia, Ping-Hung
  • Kuang, Xian-Ji
  • Lin, Shih-Kuei

Abstract

In this study, we apply deep learning and natural language processing methods to construct the view distribution in the Black–Litterman model. We implement this approach for portfolio allocation and perform statistical analysis to assess portfolio performance. The empirical analysis yields two main results. For the three deep learning models, we use mean square error to compare the model prediction results. The gated recurrent unit (GRU) model outperforms the other two models in the price prediction of seven stock assets. Moreover, it is more effective in capturing future trends and stock prices. The long short-term memory (LSTM) model outperforms the recurrent neural network (RNN) model. Moreover, in the comparison of the portfolio models, the Black–Litterman model, constructed by using Google’s Bidirectional Encoder Representations from Transformers (BERT) to measure news sentiment and by using the GRU model to predict stock prices, yields the highest annualized return rate of 46.6%. In addition, it has the highest Sharpe and Sortino ratios of 13.0% and 17.9%, respectively, which means that under a certain degree of risk, the Black–Litterman model still outperforms other constructed portfolios.

Suggested Citation

  • Hung, Ming-Chin & Hsia, Ping-Hung & Kuang, Xian-Ji & Lin, Shih-Kuei, 2024. "Intelligent portfolio construction via news sentiment analysis," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 605-617.
  • Handle: RePEc:eee:reveco:v:89:y:2024:i:pa:p:605-617
    DOI: 10.1016/j.iref.2023.07.103
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    References listed on IDEAS

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    1. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    2. Ellie Birbeck & Dave Cliff, 2018. "Using Stock Prices as Ground Truth in Sentiment Analysis to Generate Profitable Trading Signals," Papers 1811.02886, arXiv.org.
    3. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    4. Doron Avramov & Guofu Zhou, 2010. "Bayesian Portfolio Analysis," Annual Review of Financial Economics, Annual Reviews, vol. 2(1), pages 25-47, December.
    5. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
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