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 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 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 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.

LITERATURE SURVEY ON STOCK PRICE PREDICTION USING MACHINE LEARNING

2020 ◽  
Vol 5 (8) ◽  
Author(s):  
Anusha J Adhikar ◽  
Apeksha K Jadhav ◽  
Charitha G ◽  
Karishma KH ◽  
Supriya HS
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Learning Technologies ◽  
Research Knowledge ◽  
Market Values ◽  
Stock Price Prediction ◽  
Price Prediction

In today’s financial world stock exchange has become one of the most significant events. The world’s economy today is widely dependent on the stock market prices. The Stock Market has been very successful in attracting people from various backgrounds be it educational or business .The nonlinear nature of the Stock Market has made its research one of the most trending and crucial topics all around the world.. People decide to invest in the stock market on the basis of some prior research knowledge or some prediction. In terms of prediction people often look for tools or methods that would minimize their risks and maximize their profits and hence the stock price prediction takes on an influential role in the ever challenging stock market business. Adopting traditional methodologies such as fundamental and technical analysis doesn’t seem to ensure the consistency and accuracy in the prediction. As a result the machine learning technologies have become the recent trend in the stock market prediction whose prediction is based on the existing stock market values eventually as an outcome of training on their previous values. This paper focuses on RNN (Recurrent Neural Networks) and LSTM (Long Short term memory) technologies in predicting the ongoing trend of the stock market.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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 A Machine Learning View on Momentum and Reversal Trading

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 170 ◽  
Author(s):  
Zhixi Li ◽  
Vincent Tam
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Stock Market ◽  
Short Term Memory ◽  
Predictive Ability ◽  
Trading Strategies ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Learning Techniques

Momentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it is very difficult to transfer the findings of these studies to real-world investment trading strategies due to the lack of predictive ability. This paper represents the first attempt to adopt machine learning techniques for investigating the momentum and reversal effects occurring in any stock market. In the study, various machine learning techniques, including the Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), and Long Short-Term Memory Neural Network (LSTM) were explored and compared carefully. Several models built on these machine learning approaches were used to predict the momentum or reversal effect on the stock market of mainland China, thus allowing investors to build corresponding trading strategies. The experimental results demonstrated that these machine learning approaches, especially the SVM, are beneficial for capturing the relevant momentum and reversal effects, and possibly building profitable trading strategies. Moreover, we propose the corresponding trading strategies in terms of market states to acquire the best investment returns.

scholarly journals Prediction of Stock Prices using Random Forest and Support Vector Machines

2019 ◽  
Vol 8 (4) ◽  
pp. 473-477
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Random Forest ◽  
Stock Prices ◽  
Support Vector ◽  
Future Cost ◽  
Financial Instrument ◽  
Vector Machines ◽  
The Future ◽  
Future Value

Prediction of stock markets is the act of attempting to determine the future value of an inventory of a business or other financial instrument traded on an economic exchange.Effectively foreseeing the future cost of a stock will amplify the benefits of the financial specialist.This article suggests a model of machine learning to forecast the price of the stock market.During the way toward considering various techniques and factors that should be considered, we found that strategy, for example, random forest, support vector machines were not completely used in past structures. In this article, we will present and audit an increasingly suitable strategy for anticipating more prominent exactness stock oscillations.The primary thing we thought about was the securities exchange estimating informational index from yahoo stocks. We will audit the utilization of random forest after pre-handling the data, help the vector machine on the informational index and the outcomes it produces.The powerful stock gauge will be a superb resource for financial exchange associations and will give genuine options in contrast to the difficulties confronting the stock speculator.

scholarly journals Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xingyu Zhou ◽  
Zhisong Pan ◽  
Guyu Hu ◽  
Siqi Tang ◽  
Cheng Zhao
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Stock Price ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Forecast Error ◽  
Effective Strategies ◽  
Stock Price Prediction ◽  
Adversarial Training ◽  
Theory Research

Stock price prediction is an important issue in the financial world, as it contributes to the development of effective strategies for stock exchange transactions. In this paper, we propose a generic framework employing Long Short-Term Memory (LSTM) and convolutional neural network (CNN) for adversarial training to forecast high-frequency stock market. This model takes the publicly available index provided by trading software as input to avoid complex financial theory research and difficult technical analysis, which provides the convenience for the ordinary trader of nonfinancial specialty. Our study simulates the trading mode of the actual trader and uses the method of rolling partition training set and testing set to analyze the effect of the model update cycle on the prediction performance. Extensive experiments show that our proposed approach can effectively improve stock price direction prediction accuracy and reduce forecast error.

scholarly journals Stock Market Price Forecasting by Using Deep Learning

2018 ◽  
Vol 7 (3.12) ◽  
pp. 627
Author(s):  
Madhusudan Reddy ◽  
Arun Gade ◽  
Sreekarreddy . ◽  
P Prabhu
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Market Price ◽  
Price Forecasting ◽  
Price Forecast ◽  
Corporate Capital ◽  
Financial Products ◽  
New Information ◽  
The Future ◽  
Future Value

Stock market forecasts are an attempt to determine the future value of corporate capital or other financial products consumed in the stock market. If the future stock price forecast succeeds, you can gain great profit. The efficient market presents all the current stock price information, which shows that price fluctuations are not the basis for unnecessary new information. Others disagree that people who have these ideas have many methods and techniques to help them get future information. [1]  

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