Generating Financial News Articles from Factors of Stock Price Rise / Decline by LLM s (2025)

Abstract: In this paper, we study the task of generating financial news articles related to stock price fluctuations. Traditionally, reporters manually write these articles by identifying the causes behind significant stock price volatility. However, this process is time-consuming, limiting the number of articles produced. To address this, the study explores the use of generative AI to automatically generate such articles. The AI system, similar to human reporters, would analyze stock price volatility and determine the underlying factors contributing to these fluctuations. To support this approach, we introduces a Japanese dataset called JFinSR, which includes stock price fluctuation rankings from “Kabutan” and related financial information regarding factors of stock price rise / decline from “Nihon Keizai Shimbun (Nikkei).” Using this dataset, we implement the few-shot learning technique on large language models (LLMs) to enable automatic generation of high-quality articles from factors of stock price rise / decline that are available in Nikkei. In the evaluation, we compare zero-shot and few-shot learning approaches, where the few-shot learning achieved the higher F1 scores in terms of ROUGE-1/ROUGE-L metrics.

## introduction the utility of news articles in providing information about stock price fluctuations extends beyond merely indicating the magnitude of such fluctuations. they also offer insight into the underlying factors that drive these price fluctuations. typically, such articles are written manually for each stock. because of the time and effort required to perform these procedures manually, the top 50 rankings of daily stock price fluctuation such as"kabutan" 1 do not tend to include a sufficient number of articles. actually, as shown in the analysis in section 4 1 https://kabutan.jp/ based on however, it would be advantageous if these were generated automatically in large numbers. this objective can be achieved by instructing the generative ai to produce the articles as illustrated in figure 1 . the ai needs to identify information that may have contributed to the observed volatility in stock prices and write articles based on that information in a similar manner to that employed by reporters. the generation of articles may entail the synthesis of textual and non-textual information, such as figures and tables. among those issues on generating articles from textual and non-textual information, this paper concentrates on generating financial articles regarding stock price rise / decline from textual information on the factors of such stock price rise / decline. based on those observation, in this study, we first constructed a japanese dataset for generating financial articles that are directly related to daily stock price fluctuation rankings (jfinsr). jfinsr consists of rankings and articles on the top 50 stocks in terms of daily stock price fluctuation rate from "kabutan", and information on factors of stock price fluctuations from "nihon keizai shimbun (nikkei)" foot_0 . both are web media that primarily distribute financial news articles and information. in kabutan articles, technical terms in the stock domain are frequently used to precisely describe stock price fluctuations. in the process of generating the article, it is essential to select the most appropriate term to describe the stock price fluctuation. however, it should be noted that the term does not appear in the referenced nikkei information. the method of @xcite addresses this issue by training llm and lmm using time-series numerical data of stock prices foot_1 and stock chart images foot_2 . in addition, the method of nishida et al. ( 2023 ) can be employed to generate the most appropriate headlines from the text of the generated articles. using jfinsr, we implement the few-shot learning technique on large language models (llms) to enable automatic generation of high-quality articles from factors of stock price rise / decline that are available in nikkei. in the evaluation, we compare zero-shot and few-shot learning approaches, where the few-shot learning achieved the higher f1 scores in terms of rouge-1/rouge-l metrics. ## related work in the context of studies on news article headlines and stock prices, @xcite proposed the methods for the selection of technical terms in the stock domain that appropriately represent the characteristics of stock price fluctuations, where they conducted evaluation by feeding closing prices to large language models and a chart of stock price fluctuations over several days to large multimodal models. @xcite studied the task of generating headlines of stock price fluctuation articles from the article's content, where they solve three distinct tasks of generating article headlines, extracting the stock names, and ascertaining the trajectory of stock prices, whether they are rising or declining. tsutsumi and utsuro (2022) studied the issue of detecting causes of stock price rise and decline from the stock price fluctuation articles by machine reading comprehension models. in the context of stock price prediction using news headlines, @xcite studied to combine news headlines with technical indicators to predict stock prices. chen (2021) studied to predict the short-term movement of stock prices after financial news events using only the headlines of the news. @xcite proposed a method for stock trend prediction using news. two other approaches evaluate different machine learning and deep learning methods, such as support vector machines (svm) and long short-term memory (lstm), to predict stock price movement using financial news @xcite . ## data collection sources in order to construct jfinsr, we used "kabutan" and "nihon keizai shimbun (nikkei)", two web media that primarily distribute financial news articles and information. ## data analysis in utilizing jfinsr for the automated generation of articles, a series of analyses were conducted. ## evaluation of term selection for stock price fluctuation in the results of section 5.2, it was found that in many cases, terms which describe stock price fluctuations were not successfully generated from numerical data of stock prices. therefore, we considered applying the methods of @xcite for the selection of such terms. in their method, the llms were used for selecting terms from a list of ten terms foot_3 . ## conclusion in this paper, we studied the task of generating financial news articles related to stock price fluctuations. we first constructed a japanese dataset for generating financial articles using daily stock price fluctuation rankings (jfinsr). jfinsr consists of rankings and articles on the top 50 stocks in terms of daily stock price fluctuation rate from kabutan, and information on factors of stock price fluctuations from nikkei. we examined the correspondences between the kabutan articles and nikkei information presented in section 4.1 by classifying them according to the number of days that elapsed between the disclosure date of the nikkei information and the publication of the information in kabutan rankings. the results of those analyses indicate that the jfinsr is a sufficiently useful dataset for the automatic generation of financial articles in the kabutan rankings based on those factors of stock price rise / decline. we then implement the few-shot learning technique on llms to enable automatic generation of high-quality articles from factors of stock price rise / decline that are available in nikkei. in the evaluation, we compared zero-shot and few-shot learning approaches, where the few-shot learning achieved the higher f1 scores in terms of rouge-1/rouge-l metrics. our future work definitely includes generating financial articles not only from textual information but also from non-textual information such as figures and tables. another future work includes extending the approach of stock price fluctuation term selection employed in section 6. the approach employed in section 6 is limited to the 10 types of stock fluctuation terms examined in @xcite . beyond them, there exist other types of stock price term example sharp rise ishin co.,ltd. <id of tse: 143a> stock rose sharply in the afternoon session and was bought at a stop price of 1,007 yen. . . . continuous rise hotto link inc. <id of tse: 3680> stock has continued to rise substantially. continuous sharp rise gfa co., ltd. <id of tse: 8783> stock has continued to rise sharply. rebound exawizards inc. <id of tse: 4259> stock rebounded. sharp @xmath0: 186a> astroscale holdings inc. stock rebounded sharply. sharp decline daiichi sankyo co., ltd. <id of tse: 4568> stock is declining sharply. continuous sharp decline renova, inc. <id of tse: 9519> stock continued to decline sharply. sharp pullback hamamatsu photonics k.k. <id of tse: 6965> stock pulled back sharply. bid price a susmed, inc. <id of tse: 4263> stock is bid price. asked price b general oyster, inc. <id of tse: 3224> stock is asked price. steady yappli, inc. <id of tse: 4168> stock is steady. softness healios k.k. <id of tse: 4593> stock has softened.