Comparison of Stock Price Prediction Models using Pre-trained Neural Networks
Volume-3 | Issue-2
Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach
Volume-3 | Issue-2
Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division
Volume-3 | Issue-2
Blockchain Framework for Communication between Vehicle through IoT Devices and Sensors
Volume-3 | Issue-2
Gas Leakage Detection in Pipeline by SVM classifier with Automatic Eddy Current based Defect Recognition Method
Volume-3 | Issue-3
A Review on Data Securing Techniques using Internet of Medical Things
Volume-3 | Issue-3
Machine Learning Algorithms Performance Analysis for VLSI IC Design
Volume-3 | Issue-2
Maximizing the Prediction Accuracy in Tweet Sentiment Extraction using Tensor Flow based Deep Neural Networks
Volume-3 | Issue-2
Assimilation of IoT sensors for Data Visualization in a Smart Campus Environment
Volume-3 | Issue-4
DDOS ATTACK DETECTION IN TELECOMMUNICATION NETWORK USING MACHINE LEARNING
Volume-1 | Issue-1
Gas Leakage Detection in Pipeline by SVM classifier with Automatic Eddy Current based Defect Recognition Method
Volume-3 | Issue-3
Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division
Volume-3 | Issue-2
Comparison of Stock Price Prediction Models using Pre-trained Neural Networks
Volume-3 | Issue-2
Construction of a Framework for Selecting an Effective Learning Procedure in the School-Level Sector of Online Teaching Informatics
Volume-3 | Issue-4
Machine Learning Algorithms Performance Analysis for VLSI IC Design
Volume-3 | Issue-2
Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach
Volume-3 | Issue-2
Characterizing WDT subsystem of a Wi-Fi controller in an Automobile based on MIPS32 CPU platform across PVT
Volume-2 | Issue-4
Assimilation of IoT sensors for Data Visualization in a Smart Campus Environment
Volume-3 | Issue-4
Design of Data Mining Techniques for Online Blood Bank Management by CNN Model
Volume-3 | Issue-3
Ethereum and IOTA based Battery Management System with Internet of Vehicles
Volume-3 | Issue-3
Volume - 3 | Issue - 4 | december 2021
Published
20 January, 2022
Traders' tactics shift in response to the shifting market circumstances. The statistical features of price fluctuations may be significantly altered by the collective conduct of traders. When some changes in the market eventuate, a "regime shift" takes place. According to the observed directional shifts, this proposed study attempts to define what constitutes between normal and abnormal market regimes in the financial markets. The study begins by using data from ten financial marketplaces. For each call, a time frame in which major events may have led to regime change is chosen. Using the previous returns of all the companies in the index, this study investigates the usage of a CNN with SVM deep learning hybrid to anticipate the index's movement. The experiment findings reveal that this CNN model can successfully extract more generic and useful features than conventional technical indicators and produce more resilient and lucrative financial performance than earlier machine learning techniques. Most of the inability to forecast is due to randomness, and a small amount is due to non-stationarity. There is also a statistical correlation between the legal regimes of various marketplaces. Using this data, it is conceivable to tell the difference between normal regimes and lawful regimes. The results show that the stock market efficiency has never been tested before with such a large data set, and this is a significant step forward for weak-form market efficiency testing.
KeywordsConvolutional Neural Networks classification deep learning market prediction SVM statistical approach
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