Deep learning based Chinese text sentiment mining and stock market
correlation research
- URL: http://arxiv.org/abs/2205.04743v1
- Date: Tue, 10 May 2022 08:35:33 GMT
- Title: Deep learning based Chinese text sentiment mining and stock market
correlation research
- Authors: Chenrui Zhang
- Abstract summary: 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.
- Score: 6.000327333763521
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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, and find that applying the BERT model to the financial corpus
through the maximum information coefficient comparison study. The obtained
sentiment features will be able to reflect the fluctuations in the stock market
and help to improve the prediction accuracy effectively. Meanwhile, this paper
combines deep learning with financial text, in further exploring the mechanism
of investor sentiment on stock market through deep learning method, which will
be beneficial for national regulators and policy departments to develop more
reasonable policy guidelines for maintaining the stability of stock market.
Related papers
- 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) - Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning [0.0]
The study builds upon existing literature on stock price prediction methods, emphasizing the shift toward machine learning and deep learning approaches.
Using historical stock prices of 180 stocks across 18 sectors listed on the NSE, India, the LSTM model predicts future prices.
Results demonstrate the efficacy of the LSTM model in accurately predicting stock prices and informing investment decisions.
arXiv Detail & Related papers (2024-05-28T17:55:54Z) - 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) - Forecasting Cryptocurrency Prices Using Deep Learning: Integrating
Financial, Blockchain, and Text Data [3.8443430569753025]
We analyse the influence of public sentiment on cryptocurrency valuations using advanced deep learning NLP methods.
We compare the performance of various ML models, both with and without NLP data integration.
We discover that pre-trained models, such as Twitter-RoBERTa and BART MNLI, are highly effective in capturing market sentiment.
arXiv Detail & Related papers (2023-11-23T16:14:44Z) - Diffusion Variational Autoencoder for Tackling Stochasticity in
Multi-Step Regression Stock Price Prediction [54.21695754082441]
Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility.
Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions.
We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction.
Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
arXiv Detail & Related papers (2023-08-18T16:21:15Z) - 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 [57.70351255180495]
We use ChatGPT to assess whether each headline is good, bad, or neutral for firms' stock prices.
We find that ChatGPT outperforms traditional sentiment analysis methods.
Long-short strategies based on ChatGPT-4 deliver the highest Sharpe ratio.
arXiv Detail & Related papers (2023-04-15T19:22:37Z) - 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) - 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) - 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.