scholarly journals Stock Market Prices Prediction using Random Forest and Extra Tree Regression

2019 ◽  
Vol 8 (3) ◽  
pp. 1224-1228
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Research Area ◽  
Support Vector ◽  
Market Prices ◽  
Machine Learning Approach ◽  
Innovative Work

Prediction of Stock price is now a day’s an existing and interesting research area in financial and academic sectors to know the scale of economies. There did not exists any significant set of rules to estimate and predict the scale of share in the stock exchange. Many evolutionary technologies are existing such as technical, fundamental, time, statistical and series analysis which help us to attempt the prediction process, but none of the methods are proved as reliable and accurate tool to the society in the estimation of stock exchange or share market scales. Here in this paper we attempted to do innovative work through Machine Learning approach to predict or sense the behaviour tracking of the stock market sensex. Linear regression, Support Vector regression, Decision Tree, Ramdom Forest Regressor and Extra Tree Regressor are the Machine Learning models implemented effectively in predicting the stock prices and define the activity between the exchanges the securities between the buyers and sellers. We predicted the price of the stock based on the closing value and stock price. An algorithm with high accuracy we do the process of comparison for the accuracy of each of the model and finally is considered as better algorithm for predicting stock price. As share market is a vague domain we cannot predict the conditions occur, and also share market can never be predicted, this job can be done easily and technically through this work and the main aim of this paper is to apply algorithms in Machine Learning in predicting the stock prices.

scholarly journals APPLYING MACHINE LEARNING MODELS IN STOCK MARKET PREDICTION

2020 ◽  
pp. 395-398
Author(s):  
Vignesh CK
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Support Vector ◽  
Company Stock ◽  
The Future ◽  
Future Value ◽  
Past Data

This paper deals with the techniques of attempting to calculate the future value of a company stock or any other financial instrument which is being traded in a stock exchange. This prediction plays a great role in many financing and investing decisions. This calculation can be done by Machine learning by training a model to identify the trend from past data in order to predict the future. The main topic of study here will be the comparative analysis of the SVM and LTSM algorithms. KEYWORDS: Machine learning, Stock price, Stock market, Support vector machine, neural network, long short term memory.

scholarly journals ANALYSIS OF MACHINE LEARNING METHODS FOR PREDICTIONS OF STOCK EXCHANGE SHARE PRICES

2021 ◽  
pp. 94-100
Author(s):  
V. Serbin ◽  
U. Zhenisserov
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Financial Markets ◽  
Stock Prices ◽  
Stock Price ◽  
Learning Algorithms ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Price Patterns

Since the stock market is one of the most important areas for investors, stock market price trend prediction is still a hot subject for researchers in both financial and technical fields. Lately, a lot of work has been analyzed and done in the field of machine learning algorithms for analyzing price patterns and predicting stock prices and index changes. Currently, machine-learning methods are receiving a lot of attention for predicting prices in financial markets. The main goal of current research is to improve and develop a system for predicting future prices in financial markets with higher accuracy using machine-learning methods. Precise predicting stock market returns is a very difficult task due to the volatile and non-linear nature of financial stock markets. With the advent of artificial intelligence and machine learning, forecasting methods have become more effective at predicting stock prices. In this article, we looked at the machine learning techniques that have been used to trade stocks to predict price changes before an actual rise or fall in the stock price occurs. In particular, the article discusses in detail the use of support vector machines, linear regression, and prediction using decision stumps, classification using the nearest neighbor algorithm, and the advantages and disadvantages of each method. The paper introduces parameters and variables that can be used to recognize stock price patterns that might be useful in future stock forecasting, and how the boost can be combined with other learning algorithms to improve the accuracy of such forecasting systems.

scholarly journals How Does Stock Prices Respond to Various Macroeconomic Factors? A Case Study of Pakistan

2014 ◽  
Vol 1 (4) ◽  
pp. 25-30
Author(s):  
Ayaz Khan
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Correction Term ◽  
Macroeconomic Factors ◽  
Market Prices ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Long Run

Over the time everything flourished, at the same token the interrelationship among the stock market prices, returns and macroeconomic factors got attendance of the researchers in the field of finance and economics around the world. In this respect current study is an attempt to investigate the response of various macroeconomic factors (GDP, Money Supply, inflation, exchange rate and Size of firm) toward stock market prices in case of Karachi stock exchange over a period of 1971 to 2012. The study utilizes Autoregressive Distributed lag model (ARDL) technique. The results shows that in long run each factor significantly contribute to the stock price while in shot run some factors were significant while some were not but the error correction term shows significant convergence toward equilibrium. The findings of study suggest that for smoothness of stock market the current factors must be targeted.

scholarly journals Analytics of stock market prices based on machine learning algorithms

2019 ◽  
Vol 16 (2) ◽  
pp. 1050
Author(s):  
Puteri Hasya Damia Abd Samad ◽  
Sofianita Mutalib ◽  
Shuzlina Abdul-Rahman
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Data Types ◽  
Market Prices ◽  
Financial News ◽  
Svm Algorithm

This study focuses on the use of machine learning algorithms to analyse financial news on stock market prices. Stock market prediction is a challenging task because the market is known to be very volatile and dynamic. Investors face these kinds of problems as they do not properly understand which stock product to subscribe or when to sell the product with an optimum profit. Analyzing the information individually or manually is a tedious task as many aspects have to be considered. Five different companies from Bursa Malaysia namely CIMB, Sime Darby, Axiata, Maybank and Petronas were chosen in this study. Two sets of experiments were performed based on different data types. The first experiment employs textual data involving 6368 articles, extracted from financial news that have been classified into positive or negative using Support Vector Machine (SVM) algorithm. Bags of words and bags of combination words are extracted as the features for the first experiment. The second experiment employs the numeric data type extracted from historical data involving 5321 records to predict whether the stock price is going up (positive) or down (negative) using Random Forest algorithm. The Rain Forest algorithm gives better accuracy in comparison with SVM algorithm with 99% and 68% accuracy respectively. The results demonstrate the complexities of the textual-based data and demand better feature extraction technique.

scholarly journals Efficient Stock-Market Prediction Using Ensemble Support Vector Machine

2020 ◽  
Vol 10 (1) ◽  
pp. 153-163
Author(s):  
Isaac Kofi Nti ◽  
Adebayo Felix Adekoya ◽  
Benjamin Asubam Weyori
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Stock Market ◽  
Stock Price ◽  
Stock Exchange ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Parameter Optimisation ◽  
Kernel Parameter

AbstractPredicting stock-price remains an important subject of discussion among financial analysts and researchers. However, the advancement in technologies such as artificial intelligence and machine learning techniques has paved the way for better and accurate prediction of stock-price in recent years. Of late, Support Vector Machines (SVM) have earned popularity among Machine Learning (ML) algorithms used for predicting stock price. However, a high percentage of studies in algorithmic investments based on SVM overlooked the overfitting nature of SVM when the input dataset is of high-noise and high-dimension. Therefore, this study proposes a novel homogeneous ensemble classifier called GASVM based on support vector machine enhanced with Genetic Algorithm (GA) for feature-selection and SVM kernel parameter optimisation for predicting the stock market. The GA was introduced in this study to achieve a simultaneous optimal of the diverse design factors of the SVM. Experiments carried out with over eleven (11) years’ stock data from the Ghana Stock Exchange (GSE) yielded compelling results. The outcome shows that the proposed model (named GASVM) outperformed other classical ML algorithms (Decision Tree (DT), Random Forest (RF) and Neural Network (NN)) in predicting a 10-day-ahead stock price movement. The proposed (GASVM) showed a better prediction accuracy of 93.7% compared with 82.3% (RF), 75.3% (DT), and 80.1% (NN). It can, therefore, be deduced from the fallouts that the proposed (GASVM) technique puts-up a practical approach feature-selection and parameter optimisation of the different design features of the SVM and thus remove the need for the labour-intensive parameter optimisation.

scholarly journals IntelliFin: Advanced Stock Prediction using Hybrid ML and LSTM Model with Financial Indicators powered by Sentiment Determination using NLP

2020 ◽  
Vol 9 (5) ◽  
pp. 428-433
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Majority Voting ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Financial History ◽  
The Right

Stock Trading has been one of the most important parts of the financial world for decades. People investing in the share market analyze the financial history of a corporation, the news related to it and study huge amounts of data so as to predict its stock price trend. The right investment i.e. buying and selling a company stock at the right time leads to monetary benefits and can make one a millionaire overnight. The stock market is an extremely fluctuating platform wherein data is produced in humongous quantities and is influenced by numerous disparate factors such as socio-political issues, financial activities like splits and dividends, news as well as rumors. This work proposes a novel system “IntelliFin” to predict the share market trend. The system uses the various stock market technical indicators along with the company's historical market data trends to predict the share prices. The system employs the sentiment determination of a company's financial and socio-political news for a more accurate prediction. This system is implemented using two models. The first is a hybrid LSTM model optimized by an ADAM optimizer. The other is a hybrid ML model which integrates a Support Vector Regressor, K-Nearest Neighbor classifier, an RF classifier and a Linear Regressor using a Majority Voting algorithm. Both models employ a sentiment analyzer to account for the news impacting the stock prices which is powered by NLP. The models are trained continuously using Reinforcement Learning implemented by the Q-Learning Algorithm to increase the consistency and accuracy. The project aims to support the inexperienced investors, who don't have enough experience in investing in the stock market and help them maximize their profit and minimize or eliminate the losses. The developed system will also serve as a tool for professional investors to help and aid their decision making.

scholarly journals Differences In Intrinsic Value With Stock Market Prices Using The Price Earning Ratio (Per) Approach As An Investment Decision Making Indicator (Case Study Of Manufacturing Companies In Indonesia Period 2016 - 2017)

2019 ◽  
Vol 1 (1) ◽  
pp. 82-92
Author(s):  
Ardy Indra Lekso Wibowo Putra ◽  
Aditya Dwiansyah Putra ◽  
Murni Sari Dewi ◽  
Denny Oktavina Radianto
Keyword(s):  
Decision Making ◽  
Stock Market ◽  
Stock Price ◽  
Stock Exchange ◽  
Intrinsic Value ◽  
Investment Decision ◽  
Manufacturing Companies ◽  
Market Prices ◽  
Investment Decision Making

An investor must be able to consider all kinds of steps that will be taken or that will be carried out, assessing stocks - shares that will provide optimal benefits in making an investment decision. By analyzing the intrinsic value of the price of a company's stock, investors can assess the fairness of the stock price. The method used to analize intrinsic value is fundamental analysis using the Price Earning Ratio (PER) approach. The samples to be taken in this research are manufacturing companies in Indonesia which are listed on the Indonesia Stock Exchange (IDX) for the period 2016 - 2017 with certain criteria. The results of this research will show that the shares of companies listed are in overvalued, undervalued or correctly valued conditions. So investors can decide to buy, hold or sell their shares.

scholarly journals Stock Picker using Machine Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 3912-3914
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Stock Market ◽  
Stock Price ◽  
Historical Data ◽  
Great Accuracy ◽  
Current Data ◽  
Random Forest Classifier ◽  
Market Prices ◽  
Market Institution

The main objective of this paper is to build a model to predict the value of stock market prices from the previous year's data. This project starts with collecting the stock price data and pre-processing the data. 12 years dataset is used to train the model by the Random Forest classifier algorithm. Backtesting is the most important part of the quantitative strategy by which the accuracy of the model is obtained. Then the current data is collected from yahoo finance and the data is fed to the model. Then the model will predict the stock that is going to perform well based on its learning from the historical data. This model predicted the stocks with great accuracy and it can be used in the stock market institution for finding the good stock in that index.

Performance and development of a thin stock market: the Stockholm Stock Exchange 1912–2017

2020 ◽  
pp. 1-19
Author(s):  
Kristian Rydqvist ◽  
Rong Guo
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Stock Selection ◽  
Data Set ◽  
Transaction Prices ◽  
Limit Orders ◽  
A Value

We estimate historical stock returns for Swedish listed companies in a newly constructed data set of daily stock prices that spans more than 100 years. Stock returns exhibit all the familiar characteristics. The growth of the public sector depressed the stock market, and the process of globalization revitalized it. Banks played an important role in the early development of the stock market. There was little trading in the past, and we examine the effects on return measurement from missing data. Stock selection and the replacement of missing transaction prices through search back procedures or limit orders make little difference to a value-weighted stock price index, while ignoring the price effects of capital operations makes a big difference.

Impact on Stock Price by the Inclusion to and Exclusion from CNX Nifty Index

2012 ◽  
Vol 13 (1) ◽  
pp. 39-50 ◽  
Author(s):  
M. Selvam ◽  
G. Indhumathi ◽  
J. Lydia
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Research Study ◽  
Stock Exchange ◽  
Valuation Effect ◽  
Index Maintenance ◽  
Indian Stock Market ◽  
The Impact ◽  
Indian Perspective

Changes in an index are a regular phenomenon and they take place due to the inclusion and exclusion of stocks from the index. The inclusion or exclusion of stocks creates great impact on the value of the firm. However, these changes are simply a short-lived event with no permanent valuation effect. The present research study analyzed the impact of the inclusion into and exclusion of certain stocks from National Stock Exchange (NSE) S&P CNX Nifty index with Indian perspective. The study provides evidence on whether the announcements of Nifty index maintenance committee have any information content. This will also demonstrate the efficiency of Indian stock market with particular reference to NSE. The study revealed that on an average, no permanent effects were observed on stock prices. It is also found from the study that the NSE reacted unfavourably to the inclusion and exclusion of stocks and it is impossible to earn any excess returns where the particular stocks are included or excluded from the index.

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