Stock Market Price Forecasting using ARIMA vs ANN; A Case study from CSE

2020 ◽  
Author(s):  
G.W.R.I. Wijesinghe ◽  
R.M.K.T. Rathnayaka
Keyword(s):  
Stock Market ◽  
Market Price ◽  
Price Forecasting

scholarly journals FOURIER ANALYSIS FOR STOCK PRICE FORECASTING: ASSUMPTION AND EVIDENCE

2016 ◽  
Vol 17 (3) ◽  
pp. 365-380 ◽  
Author(s):  
Bohumil STÁDNÍK ◽  
Jurgita RAUDELIŪNIENĖ ◽  
Vida DAVIDAVIČIENĖ
Keyword(s):  
Stock Market ◽  
Fourier Analysis ◽  
Stock Price ◽  
Market Price ◽  
Theoretical Background ◽  
Data Series ◽  
Price Forecasting ◽  
Similar Data ◽  
Price Series ◽  
Momentum Trading

The research addressed the relevant question whether the Fourier analysis really provides practical value for investors forecasting stock market price. To answer this question, the significant cycles were discovered using the Fourier analysis inside the price series of US stocks; then, the simulation of an agent buying and selling on minima and maxima of these cycles was made. The results were then compared to those of an agent operating chaotically. Moreover, the existing significant cycles were found using more precise methods, suggested in the research, and based on the results of an agent buying and selling on all possible periods and phases. It has been analysed whether these really existing cycles were in accordance with the significant cycles resulting from the Fourier analysis. It has been concluded that the Fourier analysis basically failed. Suchlike failures are expected on similar data series. In addition, momentum and level trading backtests have been used in a similar way. It has been found that the level trading does provide a certain practical value in comparison to the momentum trading method. The research also simplifies the complicated theoretical background for practitioners.

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]  

Support vector regression with chaos-based firefly algorithm for stock market price forecasting

2013 ◽  
Vol 13 (2) ◽  
pp. 947-958 ◽  
Author(s):  
Ahmad Kazem ◽  
Ebrahim Sharifi ◽  
Farookh Khadeer Hussain ◽  
Morteza Saberi ◽  
Omar Khadeer Hussain
Keyword(s):  
Stock Market ◽  
Support Vector Regression ◽  
Firefly Algorithm ◽  
Market Price ◽  
Support Vector ◽  
Price Forecasting

scholarly journals A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting

2017 ◽  
Vol 81 ◽  
pp. 177-192 ◽  
Author(s):  
Rubén Arévalo ◽  
Jorge García ◽  
Francisco Guijarro ◽  
Alfred Peris
Keyword(s):  
Pattern Recognition ◽  
Stock Market ◽  
Market Price ◽  
Price Forecasting ◽  
Trading Rule ◽  
Rule Based ◽  
Dynamic Trading

Share Market Data Prediction Strategies using Deep Learning Algorithm

2019 ◽  
Vol 13 ◽  
Author(s):  
A John. ◽  
D. Praveen Dominic ◽  
M. Adimoolam ◽  
N. M. Balamurugan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Stock Market ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Market Price ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Mining Machine ◽  
Gradient Descent Algorithm

Background:: Predictive analytics has a multiplicity of statistical schemes from predictive modelling, data mining, machine learning. It scrutinizes present and chronological data to make predictions about expectations or if not unexplained measures. Most predictive models are used for business analytics to overcome loses and profit gaining. Predictive analytics is used to exploit the pattern in old and historical data. Objective: People used to follow some strategies for predicting stock value to invest in the more profit-gaining stocks and those strategies to search the stock market prices which are incorporated in some intelligent methods and tools. Such strategies will increase the investor’s profits and also minimize their risks. So prediction plays a vital role in stock market gaining and is also a very intricate and challenging process. Method: The proposed optimized strategies are the Deep Neural Network with Stochastic Gradient for stock prediction. The Neural Network is trained using Back-propagation neural networks algorithm and stochastic gradient descent algorithm as optimal strategies. Results: The experiment is conducted for stock market price prediction using python language with the visual package. In this experiment RELIANCE.NS, TATAMOTORS.NS, and TATAGLOBAL.NS dataset are taken as input dataset and it is downloaded from National Stock Exchange site. The artificial neural network component including Deep Learning model is most effective for more than 100,000 data points to train this model. This proposed model is developed on daily prices of stock market price to understand how to build model with better performance than existing national exchange method.

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