Zobrazeno 1 - 10
of 704
pro vyhledávání: '"Nakagawa, Kei"'
This study aims to evaluate the sentiment of financial texts using large language models~(LLMs) and to empirically determine whether LLMs exhibit company-specific biases in sentiment analysis. Specifically, we examine the impact of general knowledge
Externí odkaz:
http://arxiv.org/abs/2411.00420
The AI traders in financial markets have sparked significant interest in their effects on price formation mechanisms and market volatility, raising important questions for market stability and regulation. Despite this interest, a comprehensive model
Externí odkaz:
http://arxiv.org/abs/2409.12516
In this paper, we propose the Continuous Time Fractional Topic Model (cFTM), a new method for dynamic topic modeling. This approach incorporates fractional Brownian motion~(fBm) to effectively identify positive or negative correlations in topic and w
Externí odkaz:
http://arxiv.org/abs/2402.01734
In this study, we address the challenge of portfolio optimization, a critical aspect of managing investment risks and maximizing returns. The mean-CVaR portfolio is considered a promising method due to today's unstable financial market crises like th
Externí odkaz:
http://arxiv.org/abs/2309.11693
Quantum computers are gaining attention for their ability to solve certain problems faster than classical computers, and one example is the quantum expectation estimation algorithm that accelerates the widely-used Monte Carlo method in fields such as
Externí odkaz:
http://arxiv.org/abs/2306.12303
Machine learning is an increasingly popular tool with some success in predicting stock prices. One promising method is the Trader-Company~(TC) method, which takes into account the dynamism of the stock market and has both high predictive power and in
Externí odkaz:
http://arxiv.org/abs/2210.17030
Autor:
Nakagawa, Kei, Sakemoto, Ryuta
Publikováno v:
In International Review of Financial Analysis October 2024 95 Part C
Autor:
Uchiyama, Yusuke, Nakagawa, Kei
The mean-variance portfolio that considers the trade-off between expected return and risk has been widely used in the problem of asset allocation for multi-asset portfolios. However, since it is difficult to estimate the expected return and the out-o
Externí odkaz:
http://arxiv.org/abs/2202.09939
Autor:
Hayashi, Kohei, Nakagawa, Kei
In this paper, we focus on the generation of time-series data using neural networks. It is often the case that input time-series data have only one realized (and usually irregularly sampled) path, which makes it difficult to extract time-series chara
Externí odkaz:
http://arxiv.org/abs/2201.05974
For supervised classification problems, this paper considers estimating the query's label probability through local regression using observed covariates. Well-known nonparametric kernel smoother and $k$-nearest neighbor ($k$-NN) estimator, which take
Externí odkaz:
http://arxiv.org/abs/2112.13951