scholarly journals Prediction of Stock Index of Tata Steel using Hybrid Machine Learning based Optimization Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 3186-3193
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Prediction Models ◽  
Market Price ◽  
Optimization Techniques ◽  
Stock Index ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Stock Price Prediction ◽  
Hybrid Machine

The trend of stock price prediction has always been in the focal point of analytical activity in financial domain for both the researchers and investors. Prediction with accuracy is very essential for improved investment decisions that imbibe minimum risk factors. Due to this, majority of investors depend upon that intelligent trading system which generates better forecasting results. As forecasting stock market price with high accuracy is quite a challenging task for the analysts, machine learning has been adopted as one of the popular techniques to predict future trends. Even if there are many recognized analytical time series analysis that are categorized either under soft computing or under conventional statistical techniques like fuzzy logic, artificial neural networks and genetic algorithms, researchers have been looking for more appropriate techniques which can exhibit improved results. In this paper, we developed different hybrid machine learning based prediction models and compared their efficiency. Dimension reduction techniques such as orthogonal forward selection (OFS) and kernel principal component analysis (KPCA) are used separately with support vector regression (SVR) and teaching learning based optimization (TLBO) to predict the stock price of Tata Steel. The performance of both the proposed approach is evaluated with 4143days daily transactional data of Tata steels stocks price, which was collected from Bombay Stock Exchange (BSE). We compared the results of both OFS-SVR-TLBO and KPCA-SVR-TLBO hybrid models and concludes that by incorporating KPCA is more practicable and performs better results than OFS

scholarly journals Predicting Nigerian Stock Returns using Technical Analysis and Machine Learning

2019 ◽  
Vol 3 (2) ◽  
Author(s):  
D. O. Oyewola ◽  
Emmanuel Gbenga Gbenga Dada ◽  
Omole Ezekiel Olaoluwa ◽  
K.A. Al-Mustapha
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Stock Returns ◽  
Stock Price ◽  
Prediction Models ◽  
Technical Analysis ◽  
Classification Problem ◽  
Support Vector ◽  
Stock Market Returns ◽  
Stock Price Prediction

Models of stock price prediction have customarily utilized technical indicators alone to produce trading signals. In this paper, we construct trading techniques by applying machine-learning methods to technical analysis indicators and stock market returns data. The resulting prediction models can be utilized as an artificial trader used to trade on any given stock trade. Here the issue of stock trading decision prediction is enunciated as a classification problem with two class values representing the buy and sell signals. The stacking technique utilized in this paper is to assist trader with applying the proposed algorithms in their trading using random forest which was staked with different algorithms which incorporates Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN). The experimental results indicated that Top Layer of Random Forest (TRF) produced the best performance among all the algorithms compared. This is an indication that it is a promising strategy for forecasting Nigerian stock returns.

scholarly journals ℓp-Norm Multikernel Learning Approach for Stock Market Price Forecasting

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Xigao Shao ◽  
Kun Wu ◽  
Bifeng Liao
Keyword(s):  
Regression Model ◽  
Support Vector Regression ◽  
Market Price ◽  
Stock Index ◽  
Support Vector ◽  
Optimization Strategy ◽  
Support Vector Regression Model ◽  
Stock Price Prediction ◽  
Multiple Kernel ◽  
Multiple Support

Linear multiple kernel learning model has been used for predicting financial time series. However,ℓ1-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adoptℓp-norm multiple kernel support vector regression (1≤p<∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better thanℓ1-norm multiple support vector regression model.

scholarly journals Applying of a Company’s Stock Price Prediction Using Data Mining

2019 ◽  
Vol 8 (2) ◽  
pp. 2847-2850
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Stock Prices ◽  
Stock Price ◽  
Mining Machine ◽  
Support Vector ◽  
Product Reviews ◽  
Stock Price Prediction

Stock market analysis is a common economic activity that has been an attractive topic to research and used in different forms of day-to-day life in order to predict the stock prices. Techniques like major analysis, Statistical investigation, Time arrangement analysis and so on are reliably worthy forecast device. In this paper, Data mining, Machine learning (ML) and Sentiment analysis are techniques used for analyzing public emotions in order predict the future stock prices. The goal of a project is to review totally different techniques to predict stock worth movement victimization the sentiment analysis from social media, data processing. Sentiment classifiers are designed for social media text like product reviews, blog posts, and email corpus messages. In the company’s communication network, information mining calculation is utilized as to mine email correspondence records and verifiable stock costs. Implementing various Machine learning and Classification models such as Deep Neural network, Random forests, Support Vector Machine, the company can successfully implemented a company-specific model capable of predicting stock price movement with efficient accuracy

scholarly journals Performance Evaluation of Machine Learning Algorithms for Stock Price and Stock Index Movement Prediction Using Trend Deterministic Data Prediction

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Stock Price ◽  
Prediction Models ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Stock Index ◽  
Support Vector ◽  
Technical Indicators ◽  
Data Prediction ◽  
Insight Into

This experimental study addresses the problem of predicting the direction of stocks and the movement of stock price indices for three major stocks and stock indices. The proposed approach for processing input data involves the computation of ten technical indicators using stock trading data. The dataset used for the evaluation of all the prediction models consists of 11 years of historical data from January 2007 to December 2017. The study comprises four prediction models which are Long Short-Term Memory, XGBoost, Support Vector Machine ( and Random forests. Accuracy scores and F1 scores for each of the prediction models have been evaluated using this input approach. Experimental results reveal that a continuous data approach using ten technical indicators gives the best performance in the case of the Random Forest classifier model with the highest accuracy of 84.89% (average wise 83.74%) and highest F1 score of 89.33% (average wise 83.74%). The experiments also give us an insight into why a Naïve Bayes Classification model is not a suitable prediction model for the above task.

scholarly journals Application of Support Vector Regression in Indonesian Stock Price Prediction with Feature Selection Using Particle Swarm Optimisation

2019 ◽  
Vol 2019 ◽  
pp. 1-5 ◽  
Author(s):  
Zuherman Rustam ◽  
Puteri Kintandani
Keyword(s):  
Support Vector Regression ◽  
Stock Prices ◽  
Stock Price ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Training Data ◽  
Stock Index ◽  
Support Vector ◽  
Stock Price Prediction ◽  
Price Prediction

Stock investing is one of the most popular types of investments since it provides the highest return among all investment types; however, it is also associated with considerable risk. Fluctuating stock prices provide an opportunity for investors to make a high profit. We can see the movement of groups of stock prices from the stock index, which is called Jakarta Composite Index (JKSE) in Indonesia. Several studies have focused on the prediction of stock prices using machine learning, while one uses support vector regression (SVR). Therefore, this study examines the application of SVR and particle swarm optimisation (PSO) in predicting stock prices using stock historical data and several technical indicators, which are selected using PSO. Subsequently, a support vector machine (SVM) was applied to predict stock prices with the technical indicator selected by PSO as the predictor. The study found that stock price prediction using SVR and PSO shows good performances for all data, and many features and training data used by the study have relatively low error probabilities. Thereby, an accurate model was obtained to predict stock prices in Indonesia.

scholarly journals Stock Market Prediction Using Machine Learning

2019 ◽  
pp. 206-210
Author(s):  
Prof. Shailendra Gaur ◽  
Rishabh Bhardwaj ◽  
Vinay Bansal ◽  
Nidhi Kumari ◽  
Shalley Gupta
Keyword(s):  
Machine Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Supply And Demand ◽  
Feature Space ◽  
Support Vector ◽  
Data Sets ◽  
Stock Price Prediction ◽  
Number Of Factors ◽  
Highly Correlated

Stock price prediction is one of the most complex machine learning problems. It depends on a large number of factors which contribute to changes in the supply and demand. In this paper, we propose a stock prediction analysis using machine learning based on support vector machines (SVM), linear regression and reinforcement learning. SVM are favored in applications where text mining is used for market prediction. SVMs can be used for both linearly and non-linearly separable data sets. when the data is linearly separable, SVMs construct a hyperplane on the feature space to distinguish the training tuples in the data such that the margin between the support vectors is maximized. Correlation is used between stock prices of different companies to predict the price of a stock by using technical indicator of highly correlated stocks, not only stock to be predicted.

scholarly journals Applications of neural network based methods on stock market prediction: survey

2018 ◽  
Vol 7 (2.6) ◽  
pp. 71 ◽  
Author(s):  
Avilasa Mohapatra ◽  
Smruti Rekha Das ◽  
Kaberi Das ◽  
Debahuti Mishra
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Prediction Models ◽  
Optimization Techniques ◽  
Stock Market Prediction ◽  
Stock Price Prediction ◽  
Statistical Measures ◽  
Financial Application

Financial forecasting is one of the domineering fields of research, where investor’s money is at stake due to the rise or fall of the stock prices which unpredictable and fluctuating. Basically as the demand for stock markets has been rising at an unprecedented rate so its prediction becomes all the more exciting and challenging. Prediction of the forthcoming stock prices mostly Artificial Neural Network (ANN) based models are taken into account. The other models such as Bio-inspired Computing, Fuzzy network model etc., considering statistical measures, technical indicators and fundamental indicators are also explored by the researchers in the field of financial application. Ann’s development has led the investors for hoping the best prediction because networks included great capability of machine learning such as classification and prediction. Most optimization techniques are being used for training the weights of prediction models. Currently, various models of ANN-based stock price prediction have been presented and successfully being carried to many fields of Financial Engineering. This survey aims to study the mostly used ANN and related representations on Stock Market Prediction and make a proportional analysis between them.

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