Stock Price Forecasting on Telecommunication Sector Companies in Indonesia Stock Exchange Using Machine Learning Algorithms

2020 ◽  
Author(s):  
Jimmy H. Moedjahedy ◽  
Reymon Rotikan ◽  
Wien Fitrian Roshandi ◽  
Joe Yuan Mambu
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Stock Exchange ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Price Forecasting ◽  
Telecommunication Sector ◽  
Stock Price Forecasting

scholarly journals Stock Market Prediction using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 3628-3632
Author(s):  
Prof. Gowrishankar B S
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Stock Market Prediction ◽  
The Future ◽  
Complicated Model

Stock market is one of the most complicated and sophisticated ways to do business. Small ownerships, brokerage corporations, banking sectors, all depend on this very body to make revenue and divide risks; a very complicated model. However, this paper proposes to use machine learning algorithms to predict the future stock price for exchange by using pre-existing algorithms to help make this unpredictable format of business a little more predictable. The use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. The data has to be cleansed before it can be used for predictions. This paper focuses on categorizing various methods used for predictive analytics in different domains to date, their shortcomings.

scholarly journals Unsupervised Learning Based Stock Price Recommendation using Collaborative Filtering

2019 ◽  
Vol 8 (11) ◽  
pp. 2051-2055
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Collaborative Filtering ◽  
Portfolio Management ◽  
Recommender System ◽  
Stock Price ◽  
Stock Exchange ◽  
Performance Measure ◽  
Machine Learning Algorithms ◽  
Market Data

In this study, 17 stock market data were adopted for long term Prediction of stock price. Now days, Stock market data have got a significant role for invest finance in portfolio management. The various non-linear algorithms and statistical models are used for forecasting of financial data. In this article, we have used application of recommender system for this purpose. We primarily focused on use of machine learning algorithms for developing a stock market data recommender system. Machine learning has become a widely operational tool in financial recommendation systems. Here we considered the daily wise equity trading of Nifty 50 from National Stock Exchange (NSE) of 50 companies in 10 different sectors around 5986 days’ transactions as data. We adopted k-Nearest Neighbors classification algorithm to classify users based recommender system. Collaborative filtering method uses for recommend the stock, the performance measure through RMSE, and R2. The result also reveals that k-NN algorithm shown more accuracy as compare to other existing methods

scholarly journals Stock Price Prediction

2019 ◽  
Vol 9 (2S) ◽  
pp. 425-429
Keyword(s):  
Machine Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Company Stock ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Technical And Fundamental Analysis ◽  
Future Value

Stock trading is a very crucial activity in the world of Finance and is a supporting structure for many companies. Predicting the future value of a stock is the main goal of stock price prediction project. In this paper, we have used machine learning algorithms to predict future stock prices of a company. Stock prediction by the stock brokers is mainly done using the time series or the technical and fundamental analysis but as these techniques are very unreliable and limited, we propose making use of intelligent techniques such as machine learning. Python is a programming language which can be used to implement machine learning algorithms with its numerous inbuilt libraries. We propose an approach that uses machine learning algorithms and will be trained on the historical stock data that is available and gain intelligence, later it uses the knowledge acquired for predicting the stock prices accurately. Random Forest Regression is one of the machine learning technique that is used for stock price prediction for small and large capitalizations also in different markets employing both up-to-minute and daily frequencies.

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.

Fake News, Investor Attention, and Market Reaction

2020 ◽  
Author(s):  
Jonathan Clarke ◽  
Hailiang Chen ◽  
Ding Du ◽  
Yu Jeffrey Hu
Keyword(s):  
Machine Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Learning Algorithms ◽  
Control Sample ◽  
Machine Learning Algorithms ◽  
Fake News ◽  
Investor Attention ◽  
Price Reaction ◽  
Stock Price Reaction

Does fake news in financial markets attract more investor attention and have a significant impact on stock prices? The authors use the SEC crackdown of stock promotion schemes in April 2017 to examine investor attention and the stock price reaction to fake news articles. Using data from Seeking Alpha, the authors find that fake news stories generate significantly more attention than a control sample of legitimate articles. The authors find no evidence that article commenters can detect fake news, and they find that Seeking Alpha editors have only modest ability to detect fake news. However, the authors implement several well-known machine learning algorithms based on linguistic characteristics and show that machine learning algorithms can successfully identify fake news. In addition, the stock market appears to price fake news correctly. While abnormal trading volume increases around the release of fake news, the increase is less than that observed for legitimate news. The stock price reaction to fake news is discounted when compared with legitimate news articles.

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