MANA-Net: Mitigating Aggregated Sentiment Homogenization with News Weighting for Enhanced Market Prediction

• URL: http://arxiv.org/abs/2409.05698v1
• Date: Mon, 9 Sep 2024 15:12:24 GMT
• Title: MANA-Net: Mitigating Aggregated Sentiment Homogenization with News Weighting for Enhanced Market Prediction
• Authors: Mengyu Wang, Tiejun Ma,
• Abstract summary: We introduce the Market Attention-weighted News Aggregation Network (MANA-Net) MANA-Net learns the relevance of news sentiments to price changes and assigns varying weights to individual news items. We evaluate MANA-Net using the S&P 500 and NASDAQ 100 indices, along with financial news spanning from 2003 to 2018.
• Score: 3.456620177234167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is widely acknowledged that extracting market sentiments from news data benefits market predictions. However, existing methods of using financial sentiments remain simplistic, relying on equal-weight and static aggregation to manage sentiments from multiple news items. This leads to a critical issue termed ``Aggregated Sentiment Homogenization'', which has been explored through our analysis of a large financial news dataset from industry practice. This phenomenon occurs when aggregating numerous sentiments, causing representations to converge towards the mean values of sentiment distributions and thereby smoothing out unique and important information. Consequently, the aggregated sentiment representations lose much predictive value of news data. To address this problem, we introduce the Market Attention-weighted News Aggregation Network (MANA-Net), a novel method that leverages a dynamic market-news attention mechanism to aggregate news sentiments for market prediction. MANA-Net learns the relevance of news sentiments to price changes and assigns varying weights to individual news items. By integrating the news aggregation step into the networks for market prediction, MANA-Net allows for trainable sentiment representations that are optimized directly for prediction. We evaluate MANA-Net using the S&P 500 and NASDAQ 100 indices, along with financial news spanning from 2003 to 2018. Experimental results demonstrate that MANA-Net outperforms various recent market prediction methods, enhancing Profit & Loss by 1.1% and the daily Sharpe ratio by 0.252.

Related papers

• Modeling News Interactions and Influence for Financial Market Prediction [20.56956299552087]
  This paper introduces FININ, a novel market prediction model that captures the links between news and prices. We conduct extensive experiments on two datasets, encompassing the S&P 500 and NASDAQ 100 indices over a 15-year period. Our results reveal insights into the financial news, including the delayed market pricing of news, the long memory effect of news, and the limitations of financial sentiment analysis.
  arXiv  Detail & Related papers  (2024-10-14T15:19:49Z)
• 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)
• Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection [50.07850264495737]
  "Prompt-and-Align" (P&A) is a novel prompt-based paradigm for few-shot fake news detection. We show that P&A sets new states-of-the-art for few-shot fake news detection performance by significant margins.
  arXiv  Detail & Related papers  (2023-09-28T13:19:43Z)
• 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)
• ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media [74.93847489218008]
  We present a novel task, identifying manipulation of news on social media, which aims to detect manipulation in social media posts and identify manipulated or inserted information. To study this task, we have proposed a data collection schema and curated a dataset called ManiTweet, consisting of 3.6K pairs of tweets and corresponding articles. Our analysis demonstrates that this task is highly challenging, with large language models (LLMs) yielding unsatisfactory performance.
  arXiv  Detail & Related papers  (2023-05-23T16:40:07Z)
• 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)
• Towards systematic intraday news screening: a liquidity-focused approach [1.688090639493357]
  Given the huge amount of news articles published each day, most of which are neutral, we present a systematic news screening method to identify the true'' impactful ones. We show that the screened dataset leads to more effective feature capturing and thus superior performance on short-term asset return prediction.
  arXiv  Detail & Related papers  (2023-04-11T10:14:48Z)
• Stock Price Prediction Under Anomalous Circumstances [81.37657557441649]
  This paper aims to capture the movement pattern of stock prices under anomalous circumstances. We train ARIMA and LSTM models at the single-stock level, industry level, and general market level. Based on 100 companies' stock prices in the period of 2016 to 2020, the models achieve an average prediction accuracy of 98%.
  arXiv  Detail & Related papers  (2021-09-14T18:50:38Z)
• 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)
• Stock Movement Prediction with Financial News using Contextualized Embedding from BERT [0.0]
  We introduce a new text mining method called Fine-Tuned Contextualized-Embedding Recurrent Neural Network (FT-CE-RNN) Our model uses contextualized vector representations of the headlines (contextualized embeddings) generated from Bidirectional Representations from Transformers (BERT) It shows significant improvement compared with other baseline models, in both accuracy and trading simulations.
  arXiv  Detail & Related papers  (2021-07-19T09:47:28Z)
• 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)

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.