scholarly journals Ensemble Stock Market Prediction using SVM,LSTM, and Linear Regression

2021 ◽  
Author(s):  
Amila Indika ◽  
Nethmal Warusamana ◽  
Erantha Welikala ◽  
Sampath Deegalla
Keyword(s):  
Linear Regression ◽  
Stock Market ◽  
Historical Data ◽  
Stock Exchange ◽  
Stock Market Prediction ◽  
Data Points

Data for this research was gathered from online available sources from the NASDAQ American stock exchange.<div>We gathered data for most active 20 companies and 10 years of historical data from 21st September 2019 backwards. We used 40044 data points in total.</div>

STOCK MARKET PREDICTION IN BRICS COUNTRIES USING LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORK HYBRID MODELS

2021 ◽  
pp. 1-19
Author(s):  
GÖRKEM ATAMAN ◽  
SERPIL KAHRAMAN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Stock Market ◽  
Stock Markets ◽  
Hybrid Models ◽  
Stock Market Prediction ◽  
Macroeconomic Variables ◽  
Brics Countries ◽  
Artificial Neural

The BRICS (Brazil, Russia, India, China and South Africa) acronym was created by the International Monetary Foundation (IMF)–Group of Seven (G7) to represent the bloc of developing economies which crucially impact on the global economy by their potential economic growth. Most of the foreign direct investment are considering the stock markets of BRICS as the most attractive destination for foreign portfolio investment. This study aims to identify the relationship between macroeconomic variables and the stock market index values of BRICS and generate accurate predictions for index values by performing linear regression and artificial neural network hybrid models. Monthly data from January 2003 to December 2019 are used for the empirical study. The results indicate that a strong correlation exists between the stock market and macroeconomic variables in BRICS over time. The hybrid model is observed very accurate for index value prediction where the mean absolute percentage error (MAPE) value is 0.714% for the whole data set covering all BRICS countries data during the study period. Additionally, MAPE values for each of the BRICS countries are, respectively, obtained as 0.083%, 2.316%, 0.116%, 0.962% and 0.092%. Thus, the main findings of this study show that while neural network-integrated models have high performances for volatile stock market prediction, macroeconomic stabilization should be the priority of monetary policy to prevent the high volatility of stock markets.

Analysis on Indian Stock Market Prediction Using Deep Learning Models

2021 ◽  
pp. 80-90
Author(s):  
Kalaivani Karuppiah ◽  
Umamaheswari N. ◽  
Venkatesh R.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Stock Market ◽  
Multilayer Perceptron ◽  
Stock Exchange ◽  
Market Price ◽  
Learning Models ◽  
Stock Market Prediction ◽  
Indian Stock Market

The neural network is one of the best data mining techniques that have been used by researchers in different areas for the past 10 years. Analysis on Indian stock market prediction using deep learning models plays a very important role in today's economy. In this chapter, various deep learning architectures such as multilayer perceptron, recurrent neural networks, long short -term memory, and convolutional neural network help to predict the stock market prediction. There are two different stock market price companies, namely National Stock Exchange and New York Stock Exchange, are used for analyzing the day-wise closing price used for comparing different techniques such as neural network, multilayer perceptron, and so on. Both the NSE and NYSE share their common details, and they are compared with various existing models. When compared with the previous existing models, neural networks obtain higher accuracy, and their experimental result is shown in betterment compared with existing techniques.

scholarly journals An enhanced feature representation based on linear regression model for stock market prediction

2018 ◽  
Vol 22 (1) ◽  
pp. 45-76 ◽  
Author(s):  
Hani A.K. Ihlayyel ◽  
Nurfadhlina Mohd Sharef ◽  
Mohd Zakree Ahmed Nazri ◽  
Azuraliza Abu bakar
Keyword(s):  
Linear Regression ◽  
Stock Market ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feature Representation ◽  
Stock Market Prediction

scholarly journals Future Stock Market Prediction Using Machine Learning

2021 ◽  
pp. 553-557
Author(s):  
Raghavendra Likhite ◽  
Gowardhan Mahajan ◽  
Samadhan Padulkar ◽  
Suraj Kakani ◽  
P. T. Suradkar
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Stock Market ◽  
Support Vector ◽  
Stock Market Prediction ◽  
Highly Effective ◽  
The Future

Stock market prediction using machine learning is highly effective to predict the future prices of the stock with minimum investment. This paper proposes the system that will predict the future prices of the stock of different companies this prediction will help a investor to take decisions to maximize profits. This paper shows that by using different techniques like support vector, LSTM, linear regression future prices of the stock can be effectively predicted.

scholarly journals Indian Stock-Market Prediction using Stacked LSTM AND Multi-Layered Perceptron

2020 ◽  
Vol 9 (3) ◽  
pp. 1051-1055
Keyword(s):  
Stock Market ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Future Research ◽  
Future Price ◽  
Data Set ◽  
Stock Market Prediction ◽  
The Future ◽  
Future Value ◽  
Indian Stock Market

We aim to construe the Stacked Long–Short term memory (LSTM) and Multi-layered perceptron intended for the NSE-Stock Market prediction. Stock market prediction can be instrumental in determining the future value of a company stock.It is imperative to say that a successful prediction of a stock's future price could yield significant profit which would be beneficial for those who invested in the pipeline of things including stock market prediction. The model uses the information pertaining to the stocks and contemplates the previous model accuracy to innovate the approach used in our paper. The experimental evaluation is based on the historical data set of National Stock Exchange (NSE). The proposed approach aims to provide models like Stacked LSTM and MLP which perform better than its contemporaries which have been achieved to a certain extent. This can be verified by the results embedded in the paper . The future research can be focused on adding more variables to the model by fetching live data from the internet as well as improving model by selecting more critical factors by ensemble learning.

scholarly journals Machine Learning Model for Stock Market Prediction

2021 ◽  
Vol 9 (VI) ◽  
pp. 4057-4062
Author(s):  
Dr. S. T. Patil
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Prediction Model ◽  
Stock Price ◽  
Stock Exchange ◽  
Learning Model ◽  
Stock Market Prediction ◽  
Machine Learning Model

: In recent time’s stock market predictions is gaining more attention, maybe due to the fact that if the trend of the market is successfully predicted, the investors may be better guided. A stock exchange is a system where you can buy and sell stocks. By stock we mean the share in the ownership of the company. Companies buy stocks to get the money they need to grow. Whereas people buy the stocks, also called as securities as investment or ways of possibly earning money. A stock Market Prediction model will help people to predict particular company’s stock price before they want to invest. This system will help people to invest wisely.

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