scholarly journals Stock Prediction Model Using TensorFlow

2021 ◽  
Vol 9 (12) ◽  
pp. 99-103
Author(s):  
Gourav Jaiswal
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Data Visualization ◽  
Recent Trend ◽  
Stock Exchange ◽  
Stock Prediction ◽  
Future Value ◽  
A Company ◽  
Stock Values ◽  
Learning Data

Abstract: In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend available in market prediction technologies is that the use of machine learning that makes predictions on the basis of values of current stock exchange indices by training on their previous values. Machine learning itself employs completely different models to create prediction easier and authentic. The paper focuses on the use of Regression and LSTM based Machine learning to predict stock values. Considering the factors are open, close, low, high and volume. Keywords: Stock Prediction, Machine Learning, Data Visualization, Yahoo Finance Dataset

scholarly journals Stock Price Prediction Using Machine Learning

2021 ◽  
pp. 386-389
Author(s):  
Warade Kalyani Gopal ◽  
Jawale Mamta Pandurang ◽  
Tayade Pratiksha Devaram ◽  
Dr. Dinesh D. Patil
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Mathematical Functions ◽  
Stock Market Prediction ◽  
Stock Price Prediction ◽  
Future Value ◽  
A Company ◽  
Stock Values

In Stock Market Prediction, the aim is to predict for future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market by training on their previous values. Machine learning itself employs different models to make prediction easier. The paper focuses on Regression and LSTM based Machine learning to predict stock values. Factors considered are open, close, low, high and volume. In order to predict market movement, the stock prices and stock indicators in addition to the news related to these stocks. Most of the previous work in this industry focused on either classifying the released market news and demonstrating their effect on the stock price or focused on the historical price movement and predicted their future movement. In this work, we propose an automated trading system that integrates mathematical functions, machine learning, and other external factors such as news’ sentiments for the purpose of a better stock prediction accuracy and issuing profitable trades. The aim to determine the price of a certain stock for the coming end-of-day considering the first several trading hours of the day.

scholarly journals Bidders Recommender for Public Procurement Auctions Using Machine Learning: Data Analysis, Algorithm, and Case Study with Tenders from Spain

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Manuel J. García Rodríguez ◽  
Vicente Rodríguez Montequín ◽  
Francisco Ortega Fernández ◽  
Joaquín M. Villanueva Balsera
Keyword(s):  
Machine Learning ◽  
Public Procurement ◽  
Public Information ◽  
Random Forest Classifier ◽  
Procurement Auctions ◽  
Machine Learning Method ◽  
Test Conditions ◽  
A Company ◽  
Learning Data

Recommending the identity of bidders in public procurement auctions (tenders) has a significant impact in many areas of public procurement, but it has not yet been studied in depth. A bidders recommender would be a very beneficial tool because a supplier (company) can search appropriate tenders and, vice versa, a public procurement agency can discover automatically unknown companies which are suitable for its tender. This paper develops a pioneering algorithm to recommend potential bidders using a machine learning method, particularly a random forest classifier. The bidders recommender is described theoretically, so it can be implemented or adapted to any particular situation. It has been successfully validated with a case study: an actual Spanish tender dataset (free public information) which has 102,087 tenders from 2014 to 2020 and a company dataset (nonfree public information) which has 1,353,213 Spanish companies. Quantitative, graphical, and statistical descriptions of both datasets are presented. The results of the case study were satisfactory: the winning bidding company is within the recommended companies group, from 24% to 38% of the tenders, according to different test conditions and scenarios.

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 Auditor Choice Prediction Model Using Corporate Governance and Ownership Attributes: Machine Learning Approach

2021 ◽  
Vol 11 (7) ◽  
pp. 87-94
Author(s):  
Rahayu Abdul Rahman ◽  
◽  
Suraya Masrom ◽  
Nor Balkish Zakaria ◽  
Sunarti Halid
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Financial Reporting ◽  
Learning Algorithm ◽  
Stock Exchange ◽  
Features Selection ◽  
Firm Characteristics ◽  
Auditor Choice ◽  
Machine Learning Model ◽  
Bayes Algorithm

-External auditor is one of the governance mechanisms in mitigating corporate managerial misconduct and thereby enhance the credibility of accounting information. Thus, the main objective of this study is to develop machine learning prediction model on auditor choice of the firm which signal the quality of auditing and financial reporting processes.This paper presents the fundamental knowledge on the design and implementation of machine learning model based on four selected algorithms tested on the real dataset of 2,262 firm-year observations of companies listed on Malaysian stock exchange from 2000 to 2007. The performance of each machine learning algorithm on the auditor choice dataset has been observed based on three groups of features selection namely firm characteristics, governance and ownership. The findings indicated that the machine learning models present better accuracy performance with ownership features selection mainly with the Naïve Bayes algorithm. Keywords-Auditor Choice, Machine Learning, Prediction

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.

scholarly journals Analisis Perbandingan Model Prediksi Kebangkrutan Altman Z-Score dan Zmijewski di BEI Periode 2011 - 2015

2019 ◽  
Vol 6 (2) ◽  
pp. 171
Author(s):  
Nindya Ayu Damayanti ◽  
N. Nurhayati ◽  
Susanti Prasetyaningtyas
Keyword(s):  
Data Analysis ◽  
Prediction Model ◽  
Stock Exchange ◽  
Secondary Data ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Z Score ◽  
Study Population ◽  
A Company

This study aimed to compare the use of bankruptcy prediction model Altman Z-Score and Zmijewski on delisting companies on the Stock Exchange the period 2011 - 2015. The study population is a company delisting from the Stock Exchange in the period 2011-2015. The sample consists of 7 companies using method. purposive sampling Secondary data used in the form of financial statements of companies that issued from stock for bankruptcy in the period 2011-2015. The data analysis in this research is to perform the calculation of financial ratios in each sample, according to the variables of bankruptcy prediction model were compared to the model of Altman Z-Score and Zmijewski. Furthermore, the company classifies conditions appropriate point cut-off of each model and did calculations the accuracy of each model. Keywords: Altman Z-Score, Delisting, Bankruptcy, Zmijewski.

Sign in / Sign up

Export Citation Format

Share Document