Univariate and Multivariate LSTM Model for Short-Term Stock Market Prediction

• URL: http://arxiv.org/abs/2205.06673v1
• Date: Sun, 8 May 2022 07:01:12 GMT
• Title: Univariate and Multivariate LSTM Model for Short-Term Stock Market Prediction
• Authors: Vishal Kuber, Divakar Yadav, Arun Kr Yadav
• Abstract summary: 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.
• Score: 1.6114012813668934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Designing robust and accurate prediction models has been a viable research area since a long time. While proponents of a well-functioning market predictors believe that it is difficult to accurately predict market prices but many scholars disagree. Robust and accurate prediction systems will not only be helpful to the businesses but also to the individuals in making their financial investments. This paper presents an LSTM model with two different input approaches for predicting the short-term stock prices of two Indian companies, Reliance Industries and Infosys Ltd. Ten years of historic data (2012-2021) is taken from the yahoo finance website to carry out analysis of proposed approaches. In the first approach, closing prices of two selected companies are directly applied on univariate LSTM model. For the approach second, technical indicators values are calculated from the closing prices and then collectively applied on Multivariate LSTM model. Short term market behaviour for upcoming days is evaluated. Experimental outcomes revel that approach one is useful to determine the future trend but multivariate LSTM model with technical indicators found to be useful in accurately predicting the future price behaviours.

Related papers

• Comparative Analysis of LSTM, GRU, and Transformer Models for Stock Price Prediction [0.9217021281095907]
  This paper takes AI driven stock price trend prediction as the core research. It makes a model training data set of famous Tesla cars from 2015 to 2024, and compares LSTM, GRU, and Transformer Models. The experimental results show that the accuracy of the LSTM model is 94%.
  arXiv  Detail & Related papers  (2024-10-20T14:00:58Z)
• Stock Price Prediction and Traditional Models: An Approach to Achieve Short-, Medium- and Long-Term Goals [0.0]
  A comparative analysis of deep learning models and traditional statistical methods for stock price prediction uses data from the Nigerian stock exchange. Deep learning models, particularly LSTM, outperform traditional methods by capturing complex, nonlinear patterns in the data. The findings highlight the potential of deep learning for improving financial forecasting and investment strategies.
  arXiv  Detail & Related papers  (2024-09-29T11:20:20Z)
• 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)
• 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)
• 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)
• 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)
• Design and Analysis of Robust Deep Learning Models for Stock Price Prediction [0.0]
  Building predictive models for robust and accurate prediction of stock prices and stock price movement is a challenging research problem to solve. This chapter proposes a collection of predictive regression models built on deep learning architecture for robust and precise prediction of the future prices of a stock listed in the diversified sectors in the National Stock Exchange (NSE) of India.
  arXiv  Detail & Related papers  (2021-06-17T17:15:02Z)
• Deep Stock Predictions [58.720142291102135]
  We consider the design of a trading strategy that performs portfolio optimization using Long Short Term Memory (LSTM) neural networks. We then customize the loss function used to train the LSTM to increase the profit earned. We find the LSTM model with the customized loss function to have an improved performance in the training bot over a regressive baseline such as ARIMA.
  arXiv  Detail & Related papers  (2020-06-08T23:37:47Z)

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.