Stock Value Prediction System

2015 ◽  
Vol 3 (4) ◽  
pp. 2217-2219 ◽  
Author(s):  
Dhiraj Mundada ◽  
Keyword(s):  
Prediction System ◽  
Value Prediction ◽  
Stock Value

Reducing Errors During Stock Value Prediction Q-Learning-Based Generic Algorithm

2021 ◽  
pp. 321-332
Author(s):  
Rachna Yogesh Sable ◽  
Shivani Goel ◽  
Pradeep Chatterjee
Keyword(s):  
Generic Algorithm ◽  
Value Prediction ◽  
Q Learning ◽  
Stock Value

Comparative Study of Stock Value Prediction

2021 ◽  
Author(s):  
Bharat Udawat ◽  
Jatin Begani ◽  
Mudit Mansinghka Mudit Mansinghka ◽  
Mahesh Maurya
Keyword(s):  
Comparative Study ◽  
Value Prediction ◽  
Stock Value

scholarly journals Advanced Deep Learning Framework for Stock Value Prediction

2019 ◽  
Vol 8 (10) ◽  
pp. 2358-2367 ◽  
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Prediction Error ◽  
Economic Value ◽  
Attractive Feature ◽  
Data Set ◽  
Value Prediction ◽  
Learning Framework ◽  
Stock Value ◽  
Deep Learning Neural Network

The main attractive feature to stock market is speedy growth of stock economic value in short yoke of time. The investor analyses the demonstration, estimated value and growth of organizations before investing money in market. The analysis may not be enough by using conventional process or some available methods suggested by different researches. In present days large number of stocks are available in market it is very difficult to study each stock by help of very few suggested foretelling methods. To know the anticipated stock value we need some advanced prediction technology for stock market. This paper introduce an advanced skillful method to plan and analyze the different organizers stock execution in market and prognosticate best suitable stock by predicting close price of stock. The projected arrangement is based on multilayer deep learning neural Network optimized by Adam optimizer. Recent 6 years (2010-2016) data of different organizations are applied to the model to demonstrate the skillfulness of the projected proficient method. From result it has been ascertained that the projected framework is best suited to all different data set of various sectors. The prediction error is very minimal as visible from outcome graph of framework

scholarly journals Fuzzy Logic based Stock Value Prediction using Fundamental Analysis

2017 ◽  
Vol 10 (1) ◽  
pp. 120-126
Author(s):  
Chittaranjan Mangale ◽  
Shyam Meena ◽  
Preetesh Purohit
Keyword(s):  
Fuzzy Logic ◽  
Intrinsic Value ◽  
Fuzzy Model ◽  
Model Accuracy ◽  
Value Prediction ◽  
The Past ◽  
Feature Processing ◽  
Stock Value ◽  
Proposed Model

Stock market is very versatile and fluctuates with time. For the same way it becomes difficult to predict movement of the stock, there are various approaches and tools through which the price of the stock is determined by the past patterns. Mostly the approaches are in terms of fundamental approach and technical approach. For the long-term valuation fundamental approach is used. Every stock is having its own value that does not depend on the price of the stock that is known as Intrinsic value. The proposed model works through phases of data collection, feature processing, fuzzy logic mapping and stock value calculation. Fuzzy logic is used to map the quality as well as quantity valuation factors. The IF THEN rules are applied on the linguistic variable. The fuzzy model outcomes the stock value which is used to provide stock worth. The stock value is calculated by Dividend discount model. Accuracy of the system is 0.77. The results offer the backbone for the value and not the price.

Deep convolutional fuzzy systems of stock value prediction based on AFS theory

2021 ◽  
Author(s):  
Mingxiang Guo ◽  
Xuejun Pan ◽  
Shifan Song ◽  
Wenjuan Jia ◽  
Xiaodong Liu
Keyword(s):  
Fuzzy Systems ◽  
Value Prediction ◽  
Stock Value ◽  
Afs Theory

PENGARUH FREKUENSI PERDAGANGAN PADA SEKTOR PROPERTY DAN REAL ESTATE TERHADAP HARGA SAHAM

2019 ◽  
Vol 7 (02) ◽  
pp. 51
Author(s):  
Adri Wihananto
Keyword(s):  
Real Estate ◽  
Stock Price ◽  
Stock Exchange ◽  
Stock Trading ◽  
Trading Frequency ◽  
Important Thing ◽  
Know How ◽  
Stock Value ◽  
Measuring Frequency ◽  
Case Based

Trading frequency can be said as the implementation from trader of commerce. This case based on positive or negative trader reaction given by trader information.  Stock trading in BEI always fluctuate with price of volume value and frequency particularly. Frequency itself shows the company  involved or not. In trading frequency, if the indicator frequency it self shown the higher point, it means better. In spite of the most important thing is how the fluctuation or value conversion itself. On the frequencies we also could see which stocks is interested by the investor. When trading frequency high, it  may be create sense of interest from investors.The aim of this research, in order to know how far the effect of trading frequency (X) with stock value (Y) using cover stock value. The information used is begin 2008 with sample from twelve property and real estate companies. According to the research can be conclude from twelve companies in Indonesia Stock Exchange in 2008, 75 % of trading frequency samples doesn’t have signification degree between trading frequency and stock value. This case can be explained count on smaller than t tableEvaluation of this research is the trading measuring frequency at property sector and real estate not influence to stock priceKeywords : Trading Frequency, Stock Price 

Application of the Boundary Element Method and Modal Analysis to Tire Acoustics Problems

1993 ◽  
Vol 21 (2) ◽  
pp. 66-90 ◽  
Author(s):  
Y. Nakajima ◽  
Y. Inoue ◽  
H. Ogawa
Keyword(s):  
Finite Element ◽  
Boundary Element Method ◽  
Boundary Element ◽  
Acoustic Radiation ◽  
Traffic Noise ◽  
Noise Prediction ◽  
Prediction System ◽  
Tire Noise ◽  
Acoustic Contribution ◽  
Element Method

Abstract Road traffic noise needs to be reduced, because traffic volume is increasing every year. The noise generated from a tire is becoming one of the dominant sources in the total traffic noise because the engine noise is constantly being reduced by the vehicle manufacturers. Although the acoustic intensity measurement technology has been enhanced by the recent developments in digital measurement techniques, repetitive measurements are necessary to find effective ways for noise control. Hence, a simulation method to predict generated noise is required to replace the time-consuming experiments. The boundary element method (BEM) is applied to predict the acoustic radiation caused by the vibration of a tire sidewall and a tire noise prediction system is developed. The BEM requires the geometry and the modal characteristics of a tire which are provided by an experiment or the finite element method (FEM). Since the finite element procedure is applied to the prediction of modal characteristics in a tire noise prediction system, the acoustic pressure can be predicted without any measurements. Furthermore, the acoustic contribution analysis obtained from the post-processing of the predicted results is very helpful to know where and how the design change affects the acoustic radiation. The predictability of this system is verified by measurements and the acoustic contribution analysis is applied to tire noise control.

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