TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data

• URL: http://arxiv.org/abs/2304.02094v1
• Date: Mon, 13 Mar 2023 18:55:41 GMT
• Title: TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data
• Authors: Faraz Sasani, Ramin Mousa, Ali Karkehabadi, Samin Dehbashi, Ali Mohammadi
• Abstract summary: We will introduce TM-vector and then use this vector to train an IndRNN and ultimately model the market users' behaviour. In the proposed model, TM-vector is simultaneously trained with both the extracted Twitter features and market information. Various factors have been used for the effectiveness of the proposed forecasting approach.
• Score: 1.5749416770494706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Stock market forecasting has been a challenging part for many analysts and researchers. Trend analysis, statistical techniques, and movement indicators have traditionally been used to predict stock price movements, but text extraction has emerged as a promising method in recent years. The use of neural networks, especially recurrent neural networks, is abundant in the literature. In most studies, the impact of different users was considered equal or ignored, whereas users can have other effects. In the current study, we will introduce TM-vector and then use this vector to train an IndRNN and ultimately model the market users' behaviour. In the proposed model, TM-vector is simultaneously trained with both the extracted Twitter features and market information. Various factors have been used for the effectiveness of the proposed forecasting approach, including the characteristics of each individual user, their impact on each other, and their impact on the market, to predict market direction more accurately. Dow Jones 30 index has been used in current work. The accuracy obtained for predicting daily stock changes of Apple is based on various models, closed to over 95\% and for the other stocks is significant. Our results indicate the effectiveness of TM-vector in predicting stock market direction.

Related papers

• Combining supervised and unsupervised learning methods to predict financial market movements [0.0]
  We identify novel features extracted from emergent and well-established financial markets. We used approximately six months of data consisting of minute candles from the Bitcoin, Pepecoin, and Nasdaq markets. We explored the performance of various machine learning strategies, such as Random Forests (RF) and K-Nearest Neighbours (KNN) to classify market movements.
  arXiv  Detail & Related papers  (2024-08-19T13:17:36Z)
• GraphCNNpred: A stock market indices prediction using a Graph based deep learning system [0.0]
  We give a graph neural network based convolutional neural network (CNN) model, that can be applied on diverse source of data, in the attempt to extract features to predict the trends of indices of textS&textP 500, NASDAQ, DJI, NYSE, and RUSSEL. Experiments show that the associated models improve the performance of prediction in all indices over the baseline algorithms by about $4% text to 15%$, in terms of F-measure.
  arXiv  Detail & Related papers  (2024-07-04T09:14:24Z)
• Beyond Trend Following: Deep Learning for Market Trend Prediction [49.89480853499917]
  We advocate for the use of Artificial Intelligence and Machine Learning techniques to predict future market trends. These predictions, when done properly, can improve the performance of asset managers by increasing returns and reducing drawdowns.
  arXiv  Detail & Related papers  (2024-06-10T11:42:30Z)
• Cryptocurrency Portfolio Optimization by Neural Networks [81.20955733184398]
  This paper proposes an effective algorithm based on neural networks to take advantage of these investment products. A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio. A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy.
  arXiv  Detail & Related papers  (2023-10-02T12:33:28Z)
• 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)
• Support for Stock Trend Prediction Using Transformers and Sentiment Analysis [3.147603836269998]
  We develop a Transformer based model that uses technical stock data and sentiment analysis to conduct accurate stock trend prediction over long time windows. This paper also introduces a novel dataset containing daily technical stock data and top news headline data spanning almost three years.
  arXiv  Detail & Related papers  (2023-05-18T03:26:39Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• Predicting The Stock Trend Using News Sentiment Analysis and Technical Indicators in Spark [0.0]
  Different features are given to help the machine learning model predict the label of a given day. The overall sentiment score on a given day is created from all news released on that day. Random Forest was the best performing model with a 63.58% test accuracy.
  arXiv  Detail & Related papers  (2022-01-19T10:22:33Z)
• 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)
• Capturing dynamics of post-earnings-announcement drift using genetic algorithm-optimised supervised learning [3.42658286826597]
  Post-Earnings-Announcement Drift (PEAD) is one of the most studied stock market anomalies. We use a machine learning based approach instead, and aim to capture the PEAD dynamics using data from a large group of stocks.
  arXiv  Detail & Related papers  (2020-09-07T13:27:06Z)
• TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation [66.04457457299218]
  We propose a novel target attentive graph neural network (TAGNN) model for session-based recommendation. In TAGNN, target-aware attention adaptively activates different user interests with respect to varied target items. The learned interest representation vector varies with different target items, greatly improving the expressiveness of the model.
  arXiv  Detail & Related papers  (2020-05-06T14:17:05Z)

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.