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Financial Trends prediction using the Back Propagation Neural Network and YQL

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Trapti Mishra, V. Kapoor

Trapti Mishra and V Kapoor. Financial Trends prediction using the Back Propagation Neural Network and YQL. International Journal of Computer Applications 144(10):20-25, June 2016. BibTeX

	author = {Trapti Mishra and V. Kapoor},
	title = {Financial Trends prediction using the Back Propagation Neural Network and YQL},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2016},
	volume = {144},
	number = {10},
	month = {Jun},
	year = {2016},
	issn = {0975-8887},
	pages = {20-25},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2016910451},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In this world a amount of things and procedures are impulsive, among the stock market value is an entity which is impulsive. The stock market and their prices are haphazard in impulsive manner that can be depends on amount of external associations such as politics, terror, social and others. But a number of researchers are building efforts to forecast the stock market values. To forecast the upcoming stock market trends the data mining and machine intelligence based techniques are employed to forecast precisely the worth. It works too precisely estimate the stock value a back propagation neural network based system is used. The proposed system is an enhanced version of the traditionally available back propagation neural network. The back propagation neural network is a supervised learning advance therefore the training and testing both are required. To prepare the proposed data model the financial records of the companies are required. Thus for finding the historical financial market trends the YQL (yahoo query language) is proposed for use. This YQL make available the historical records of the target company between two dates. Using the acquired data from YQL is used to train the BPN network. After training the implemented classifier is used for forecasting the upcoming stock value. The implementation of the proposed system is performed using the JAVA technology. Additionally to justify the obtained results the performance of the proposed system is compared with the traditional BPN model. The relative performance study is performed using the accuracy, error rate, and memory and time consumption. According to the obtained results the performance of the proposed system is found improved and adoptable.


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Stock market prediction, Neural network, YQL, data mining, machine learning.