Research on the correlation between text emotion mining and stock market based on deep learning

• URL: http://arxiv.org/abs/2205.06675v1
• Date: Mon, 9 May 2022 12:51:16 GMT
• Title: Research on the correlation between text emotion mining and stock market based on deep learning
• Authors: Chenrui Zhang
• Abstract summary: 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.
• Score: 6.000327333763521
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper discusses how to crawl the data of financial forums such as stock bar, and conduct emotional analysis combined with the in-depth learning model. This paper will use the Bert model to train the financial corpus and predict the Shenzhen stock index. Through the comparative study of the maximal information coefficient (MIC), 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, which is conducive to effectively improve the prediction accuracy. At the same time, this paper combines in-depth learning with financial texts to further explore the impact mechanism of investor sentiment on the stock market through in-depth learning, which will help the national regulatory authorities and policy departments to formulate more reasonable policies and guidelines for maintaining the stability of the stock market.

Related papers

• Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach [6.112119533910774]
  This paper introduces an advanced approach by employing Large Language Models (LLMs) instruction fine-tuned with a novel combination of instruction-based techniques and quantized low-rank adaptation (QLoRA) compression. Our methodology integrates 'base factors', such as financial metric growth and earnings transcripts, with 'external factors', including recent market indices performances and analyst grades, to create a rich, supervised dataset. This study not only demonstrates the power of integrating cutting-edge AI with fine-tuned financial data but also paves the way for future research in enhancing AI-driven financial analysis tools.
  arXiv  Detail & Related papers  (2024-08-13T04:53:31Z)
• Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis [2.7921137693344384]
  We use deep learning networks, based on the history of stock prices and articles of financial, business, technical news that introduce market information to predict stock prices. We developed a pre-trained NLP model known as FinBERT, designed to discern the sentiments within financial texts. This model utilizes news categories related to the stock market structure hierarchy, namely market, industry, and stock related news categories, combined with the stock market's stock price situation in the previous week for prediction.
  arXiv  Detail & Related papers  (2024-07-23T03:26:07Z)
• 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)
• HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE [113.47287249524008]
  It is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting. We propose the first deep learning based online and adaptive factor model, HireVAE, at the core of which is a hierarchical latent space that embeds the relationship between the market situation and stock-wise latent factors. Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over previous methods.
  arXiv  Detail & Related papers  (2023-06-05T12:58:13Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Deep learning based Chinese text sentiment mining and stock market correlation research [6.000327333763521]
  We explore how to crawl financial forum data such as stock bars and combine them with deep learning models for sentiment analysis. In this paper, we will use the BERT model to train against the financial corpus and predict the SZSE Component Index. The obtained sentiment features will be able to reflect the fluctuations in the stock market and help to improve the prediction accuracy effectively.
  arXiv  Detail & Related papers  (2022-05-10T08:35:33Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• 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)
• Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning [2.741266294612776]
  We apply deep learning to learn and exploit lagged correlations among S&P 500 stocks to compare model behaviour in stable and volatile markets. Our findings show that accuracies, while remaining significant, decrease with shorter prediction horizons. We discuss implications for modern finance theory and our work's applicability as an investigative tool for portfolio managers.
  arXiv  Detail & Related papers  (2020-02-24T17:19:54Z)
• 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.