| 研究実績の概要 |
In recent years, the financial markets have faced significant challenges such as the 2018 bitcoin price crashes and the 2020 US stock market declines. While previous research has largely focused on analyzing price data, this researcher sought to take a novel approach by incorporating natural language data such as news articles. Deep learning techniques were employed to process both price and language data within a single computational framework, establishing a connection between these two complex social systems and ultimately enhancing our understanding of financial markets.
In the past year, the researcher proposed a generalized model for stock portfolio optimization that integrates natural language data. This model represented a stock with a vector obtained from news articles and identified extreme risk correlations between stocks from these articles, effectively diversifying the risks. This work was accepted for publication in "Knowledge-Based Systems."
Additionally, the researcher investigated the limitations of vector representations of stocks in describing complex phenomena like polysemy. To address these limitations, a new representation method using functions instead of vectors was proposed and validated on language data. The method will be further validated on financial markets and has been accepted for publication in "Advances in Neural Information Processing Systems 2022."
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