Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets with Sentiment Measurements

• URL: http://arxiv.org/abs/2202.03158v1
• Date: Thu, 27 Jan 2022 20:32:46 GMT
• Title: Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets with Sentiment Measurements
• Authors: Jia Wang, Hongwei Zhu, Jiancheng Shen, Yu Cao, Benyuan Liu
• Abstract summary: We propose a novel deep learning approach, named dual-CLVSA, to predict financial market movement with both trading data and the corresponding social sentiment measurements, each through a separate sequence-to-sequence channel. The experiment results show that dual-CLVSA can effectively fuse the two types of data, and verify that sentiment measurements are not only informative for financial market predictions, but they also contain extra profitable features to boost the performance of our predicting system.
• Score: 11.97251638872227
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: It is a challenging task to predict financial markets. The complexity of this task is mainly due to the interaction between financial markets and market participants, who are not able to keep rational all the time, and often affected by emotions such as fear and ecstasy. Based on the state-of-the-art approach particularly for financial market predictions, a hybrid convolutional LSTM Based variational sequence-to-sequence model with attention (CLVSA), we propose a novel deep learning approach, named dual-CLVSA, to predict financial market movement with both trading data and the corresponding social sentiment measurements, each through a separate sequence-to-sequence channel. We evaluate the performance of our approach with backtesting on historical trading data of SPDR SP 500 Trust ETF over eight years. The experiment results show that dual-CLVSA can effectively fuse the two types of data, and verify that sentiment measurements are not only informative for financial market predictions, but they also contain extra profitable features to boost the performance of our predicting system.

Related papers

• FinBERT-BiLSTM: A Deep Learning Model for Predicting Volatile Cryptocurrency Market Prices Using Market Sentiment Dynamics [3.6423651166048874]
  We propose a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) networks with FinBERT to enhance forecasting accuracy for cryptocurrencies. This approach fills a key gap in forecasting volatile financial markets by blending advanced time series models with sentiment analysis.
  arXiv  Detail & Related papers  (2024-11-02T14:43:06Z)
• 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)
• 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)
• Stock Market Price Prediction: A Hybrid LSTM and Sequential Self-Attention based Approach [3.8154633976469086]
  We propose a new model named Long Short-Term Memory (LSTM) with Sequential Self-Attention Mechanism (LSTM-SSAM) We conduct extensive experiments on the three stock datasets: SBIN,BANK, and BANKBARODA. The experimental results prove the effectiveness and feasibility of the proposed model compared to existing models.
  arXiv  Detail & Related papers  (2023-08-07T14:21:05Z)
• Joint Latent Topic Discovery and Expectation Modeling for Financial Markets [45.758436505779386]
  We present a groundbreaking framework for financial market analysis. This approach is the first to jointly model investor expectations and automatically mine latent stock relationships. Our model consistently achieves an annual return exceeding 10%.
  arXiv  Detail & Related papers  (2023-06-01T01:36:51Z)
• 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)
• Univariate and Multivariate LSTM Model for Short-Term Stock Market Prediction [1.6114012813668934]
  This paper presents an LSTM model with two different input approaches for predicting the short-term stock prices of two Indian companies. Ten years of historic data (2012-2021) is taken from the yahoo finance website to carry out analysis of proposed approaches.
  arXiv  Detail & Related papers  (2022-05-08T07:01:12Z)
• 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)
• Financial Markets Prediction with Deep Learning [11.26482563151052]
  We propose a novel one-dimensional convolutional neural networks (CNN) model to predict financial market movement. The customized one-dimensional convolutional layers scan financial trading data through time, while different types of data, such as prices and volume, share parameters ( Kernels) with each other. Our model automatically extracts features instead of using traditional technical indicators.
  arXiv  Detail & Related papers  (2021-04-05T19:36:48Z)
• 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)
• 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)

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.