23 citations · 38 across the 8 of their papers we have counts for
8 papers
SSAAM: Sentiment Signal-based Asset Allocation Method with Causality Information
Rei Taguchi, Hiroki Sakaji, Kiyoshi Izumi
This study demonstrates whether financial text is useful for tactical asset allocation using stocks by using natural language processing to create polarity indexes in financial new…
SETN: Stock Embedding Enhanced with Textual and Network Information
Takehiro Takayanagi, Hiroki Sakaji, Kiyoshi Izumi
Stock embedding is a method for vector representation of stocks. There is a growing demand for vector representations of stock, i.e., stock embedding, in wealth management sectors,…
Bilateral Trade Flow Prediction by Gravity-informed Graph Auto-encoder
Naoto Minakawa, Kiyoshi Izumi, Hiroki Sakaji
The gravity models has been studied to analyze interaction between two objects such as trade amount between a pair of countries, human migration between a pair of countries and tra…
Discovery of Rare Causal Knowledge from Financial Statement Summaries
Hiroki Sakaji, Jason Bennett, Risa Murono +2
What would happen if temperatures were subdued and result in a cool summer? One can easily imagine that air conditioner, ice cream or beer sales would be suppressed as a result of…
Summarization of Investment Reports Using Pre-trained Model
Hiroki Sakaji, Ryotaro Kobayashi, Kiyoshi Izumi +2
In this paper, we attempt to summarize monthly reports as investment reports. Fund managers have a wide range of tasks, one of which is the preparation of investment reports. In ad…
LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction
Meiyun Wang, Kiyoshi Izumi, Hiroki Sakaji
Recently, Large Language Models (LLMs) have attracted significant attention for their exceptional performance across a broad range of tasks, particularly in text analysis. However,…