BERT를 활용한 뉴스 기사 감성분석과 블랙-리터만 모형을 결합한 자산 배분 전략 제안 = Asset Allocation Strategy based on News Article Sentiment Analysis using BERT and Black-Litterman Model

다국어 초록 (Multilingual Abstract)

This study introduces an asset allocation strategy that utilize sentiment analysis from news articles through a deep learning model. The derived sentiments are then integrated into the Black-Litterman model, offering a systematic approach to mitigate the inherent subjectivity in investor decisions. Empirical findings from this study reveal the superiority of this proposed approach compared with conventional benchmark portfolios such as market, equal-weighted, and mean-variance portfolios. In particular, the exclusion of articles generating neutral sentiment (neither positive nor negative) further enhances the profitability of the portfolio. In addition, the study shows that constructing portfolios based on the polarity of sentiment, rather than considering its positive or negative intensity , improves profitability. The significance of this study lies in its introduction of a novel framework for constructing asset allocation strategies. By utilizing objective information from publicly available news articles, it effectively circumvents the limitations tied to subjective investor judgment in predicting expected returns. The demonstrated feasibility and superiority of this data-driven approach in asset allocation strategies underscore its potential to revolutionize current practices.

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