Cross-Lingual News Event Correlation for Stock Market Trend Prediction

• URL: http://arxiv.org/abs/2410.00024v1
• Date: Mon, 16 Sep 2024 06:45:40 GMT
• Title: Cross-Lingual News Event Correlation for Stock Market Trend Prediction
• Authors: Sahar Arshad, Nikhar Azhar, Sana Sajid, Seemab Latif, Rabia Latif,
• Abstract summary: This study addresses the gap in comprehending financial dynamics across diverse global economies by creating a structured financial dataset. We conducted an analytical examination of news articles to extract, map, and visualize financial event timelines. Our method demonstrated a meaningful correlation between stock price movements and cross-linguistic news sentiments.
• Score: 0.1398098625978622
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the modern economic landscape, integrating financial services with Financial Technology (FinTech) has become essential, particularly in stock trend analysis. This study addresses the gap in comprehending financial dynamics across diverse global economies by creating a structured financial dataset and proposing a cross-lingual Natural Language-based Financial Forecasting (NLFF) pipeline for comprehensive financial analysis. Utilizing sentiment analysis, Named Entity Recognition (NER), and semantic textual similarity, we conducted an analytical examination of news articles to extract, map, and visualize financial event timelines, uncovering the correlation between news events and stock market trends. Our method demonstrated a meaningful correlation between stock price movements and cross-linguistic news sentiments, validated by processing two-year cross-lingual news data on two prominent sectors of the Pakistan Stock Exchange. This study offers significant insights into key events, ensuring a substantial decision margin for investors through effective visualization and providing optimal investment opportunities.

Related papers

• A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges [60.546677053091685]
  Large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. We explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive innovation. We highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications.
  arXiv  Detail & Related papers  (2024-06-15T16:11:35Z)
• BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights [0.0]
  We employ BERTopic, an advanced NLP technique, to analyze the sentiment of topics derived from stock market comments. Our methodology integrates this sentiment analysis with various deep learning models, renowned for their effectiveness in time series and stock prediction tasks. The results indicate that topics in stock market comments provide implicit, valuable insights into stock market volatility and price trends.
  arXiv  Detail & Related papers  (2024-04-02T15:50:10Z)
• AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework [48.3060010653088]
  We release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task.
  arXiv  Detail & Related papers  (2024-03-19T09:45:33Z)
• Research on the correlation between text emotion mining and stock market based on deep learning [6.000327333763521]
  This paper will use the Bert model to train the financial corpus and predict the Shenzhen stock index. It is found that the emotional characteristics obtained by applying the BERT model to the financial corpus can be reflected in the fluctuation of the stock market.
  arXiv  Detail & Related papers  (2022-05-09T12:51:16Z)
• Financial data analysis application via multi-strategy text processing [0.2741266294612776]
  This paper mainly focuses on the stock trading data and news about China A-share companies. We present our efforts and plans in deep learning financial text processing application scenarios using natural language processing (NLP) and knowledge graph (KG) technologies.
  arXiv  Detail & Related papers  (2022-04-25T01:56:36Z)
• FinEAS: Financial Embedding Analysis of Sentiment [0.0]
  We introduce a new language representation model in finance called Financial Embedding Analysis of Sentiment (FinEAS) In this work, we propose a new model for financial sentiment analysis based on supervised fine-tuned sentence embeddings from a standard BERT model.
  arXiv  Detail & Related papers  (2021-10-31T15:41:56Z)
• Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data [0.0]
  We propose a new stock movement prediction framework: Multi-Graph Recurrent Network for Stock Forecasting (MGRN) This architecture allows to combine the textual sentiment from financial news and multiple relational information extracted from other financial data. Through an accuracy test and a trading simulation on the stocks in the STOXX Europe 600 index, we demonstrate a better performance from our model than other benchmarks.
  arXiv  Detail & Related papers  (2021-07-22T21:57:18Z)
• REST: Relational Event-driven Stock Trend Forecasting [76.08435590771357]
  We propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods. To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts. To address the second shortcoming, we construct a stock graph and design a new propagation layer to propagate the effect of event information from related stocks.
  arXiv  Detail & Related papers  (2021-02-15T07:22:09Z)
• A Sentiment Analysis Approach to the Prediction of Market Volatility [62.997667081978825]
  We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. The sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information.
  arXiv  Detail & Related papers  (2020-12-10T01:15:48Z)
• Gaussian process imputation of multiple financial series [71.08576457371433]
  Multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state of the market. We focus on learning the relationships among financial time series by modelling them through a multi-output Gaussian process.
  arXiv  Detail & Related papers  (2020-02-11T19:18:18Z)
• Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States [71.54651874063865]
  Portfolio management (PM) aims to achieve investment goals such as maximal profits or minimal risks. In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data Heterogeneous data -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
  arXiv  Detail & Related papers  (2020-02-09T08:10:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.