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Comparative analysis of Stock Market Prediction Algorithms based on Twitter Data
Abstract
Stock market prediction is considered as one of the most promising research area that is attainning the attention of various researchers. The vital information which is available for access is assumed to have predictive relationships to the future stock returns. The present work gives information to the investors so that the decision could be made better during the purchase of stocks. The factors that contribute towards the decision are the historical prices of stocks and tweet comments regarding the same. The proposed method uses four methods for predicting the stock market status, namely, Linear Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), and Random Forest (RF) approaches. When evaluated with standard datasets, experimental results concluded that the SVM based prediction has significant predicting performance than the other methods. The proposed work gives a comparison of factors in order to decide the purchase.
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Predicting stock exchange index is an attractive research topic in the field of machine learning. Numerous studies have been conducted using various techniques to predict stock market volume. This paper presents first detailed study on data of Karachi Stock Exchange (KSE) and Saudi Stock Exchange (SSE) to predict the stock market volume of ten different companies. In this study, we have applied and compared salient machine learning algorithms to predict stock exchange volume. The performance of these algorithms have been compared using accuracy metrics on the dataset, collected over the period of six months, by crawling the KSE and SSE website.
We offer a systematic analysis of the use of deep learning networks for stock market analysis and prediction. Its ability to extract features from a large set of raw data without relying on prior knowledge of predictors makes deep learning potentially attractive for stock market prediction at high frequencies. Deep learning algorithms vary considerably in the choice of network structure, activation function, and other model parameters, and their performance is known to depend heavily on the method of data representation. Our study attempts to provides a comprehensive and objective assessment of both the advantages and drawbacks of deep learning algorithms for stock market analysis and prediction. Using high-frequency intraday stock returns as input data, we examine the effects of three unsupervised feature extraction methods—principal component analysis, autoencoder, and the restricted Boltzmann machine—on the network’s overall ability to predict future market behavior. Empirical results suggest that deep neural networks can extract additional information from the residuals of the autoregressive model and improve prediction performance; the same cannot be said when the autoregressive model is applied to the residuals of the network. Covariance estimation is also noticeably improved when the predictive network is applied to covariance-based market structure analysis. Our study offers practical insights and potentially useful directions for further investigation into how deep learning networks can be effectively used for stock market analysis and prediction.
Every day more than 5000 trade companies enlisted in Bombay stock Exchange (BSE) offer an average of 24,00,00,000+ stocks, making an approximate of 2000Cr+ Indian rupees in investments. Thus analyzing such a huge market will prove beneficial to all stakeholders of the system. An application which focuses on the patterns generated in this stock trade over the period of time, and extracting the knowledge from those patterns to predict future behavior of the BSE stock market is essential. An application representing the information in visual form for user interpretation to buy and to sell a specific company‟s stock is a key requirement.
Such an application based on machine learning algorithms is the right choice in current scenario. This paper surveys the machine learning algorithms suitable for such an application; as well it discusses what are the current tools and techniques appropriate for its implementation.
Forecasting stock return is an important financial subject that has attracted researchers' attention for many years. It involves an assumption that fundamental information publicly available in the past has some predictive relationships to the future stock returns. This study tries to help the investors in the stock market to decide the better timing for buying or selling stocks based on the knowledge extracted from the historical prices of such stocks. The decision taken will be based on decision tree classifier which is one of the data mining techniques. To build the proposed model, the CRISP-DM methodology is used over real historical data of three major companies listed in Amman Stock Exchange (ASE).
Stock market prediction is forever important issue for investor. Computer science plays vital role to solve this problem. From the evolution of machine learning, people from this area are busy to solve this problem effectively. Many different techniques are used to build predicting system. This research describes different state of the art techniques used for stock forecasting and compare them w.r.t. their pros and cons. We have classified different techniques categorically; Time Series, Neural Network and its different variation (RNN, ESN, MLP, LRNN etc.) and different hybrid techniques (combination of neural network with different machine learning techniques) (ANFIS, GA/ATNN, GA/TDNN, ICA-BPN). By extensive study of different techniques, it was analyzed that Neural Network is the best technique till time to predict stock prices especially when some de- noising schemes are applied with neural network. We, also, have implemented and compared different neural network techniques like Layered Recurrent Neural Network (LRNN), Wsmpca-NN and Feed forward Neural Network (NN). By comparing said techniques, it was observed that LRNN performs better than feed forward NN and Wsmpca-NN performs better than LRNN and NN. We have applied said techniques on PSO (Pakistan State Oil), S&P500 data sets.
Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price will maximize investor's gains. This paper proposes a machine learning model to predict stock market price. The proposed algorithm integrates Particle swarm optimization (PSO) and least square support vector machine (LS-SVM). The PSO algorithm is employed to optimize LS-SVM to predict the daily stock prices. Proposed model is based on the study of stocks historical data and technical indicators. PSO algorithm selects best free parameters combination for LS-SVM to avoid over-fitting and local minima problems and improve prediction accuracy. The proposed model was applied and evaluated using thirteen benchmark financials datasets and compared with artificial neural network with Levenberg-Marquardt (LM) algorithm. The obtained results showed that the proposed model has better prediction accuracy and the potential of PSO algorithm in optimizing LS-SVM.
Prediction for Stock Marketing Using Machine Learning
- Shubham Jain
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Shubham Jain, Mark Kain, "Prediction for Stock
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