An application of a multiple neural network learning system to emulation of mortgage underwriting judgements

Collins, '; Ghosh,; Scofield,

doi:10.1109/icnn.1988.23960

Cited by 78 publications

(30 citation statements)

References 4 publications

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“…• Mortgage risk assessment : Collins, Ghosh and Scofield (1988), Reilly, Collins, Scofield and Ghosh. (1991), Grudnitski, Quang and Shilling (1995).…”

Section: Exhibit 1 Applications Of Neural Networkmentioning

confidence: 99%

Using neural networks for forecasting volatility of S&P 500 Index futures prices

Hamid

Iqbal

2004

Journal of Business Research

164

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Ability to forecast market variables is critical to analysts, economists and investors.Among other uses, neural networks are gaining in popularity in forecasting market variables.They are used in various disciplines and issues to map complex relationships.We present a primer for using neural networks for forecasting market variables in general, and in particular, forecasting volatility of the S&P 500 Index futures prices. We compare volatility forecasts from neural networks with implied volatility from S&P 500 Index futures options using the Barone-Adesi and Whaley (BAW) model for pricing American options on futures. Forecasts from neural networks outperform implied volatility forecasts. Volatility forecasts from neural networks are not found to be significantly different from realized volatility.Implied volatility forecasts are found to be significantly different from realized volatility in two of three cases.

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“…• Mortgage risk assessment : Collins, Ghosh and Scofield (1988), Reilly, Collins, Scofield and Ghosh. (1991), Grudnitski, Quang and Shilling (1995).…”

Section: Exhibit 1 Applications Of Neural Networkmentioning

confidence: 99%

Using neural networks for forecasting volatility of S&P 500 Index futures prices

Hamid

Iqbal

2004

Journal of Business Research

164

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“…This information can be used in the future to save money by only targeting groups that are likely to respond to the promotion. Further applications of classification are risk assessment for credit card loan 50 and mortgage applications 10 , as well as bankruptcy predictions. However, prediction comprises not only classification of unlabelled data, but also prediction of future trends.…”

Section: Data Miningmentioning

confidence: 99%

An overview of the use of neural networks for data mining tasks

Stahl

Jordanov

2012

WIREs Data Min & Knowl

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In the recent years, the area of data mining has been experiencing considerable demand for technologies that extract knowledge from large and complex data sources. There has been substantial commercial interest as well as active research in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from large datasets. Artificial neural networks (NNs) are popular biologically-inspired intelligent methodologies, whose classification, prediction, and pattern recognition capabilities have been utilized successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction, and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks.

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“…The dendrites are extensions of a neuron which extends to other parts of a neuron to form a neural network.A biological neuron can be connected to other neurons as well as accepting connections from other neurons, which serves as the basis the network.Though those connections, electrical pulses are transmitted, and information is carried in the timing and the frequency with which these pulses are emitted. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons [15,18].…”

Section: Stock Price Analysis With Neural Networkmentioning

confidence: 99%

Fluctuations in Stock Market Prices: What went wrong, its Implications to Nigerian Economy?

Vincent¹,

Bamiro²

2013

IJCA

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The Nigerian stock market recently witnessed a continuous drop in the All-Share Index and volume of traded securities. The stock market indices have moved far relative to their previous year's levels and banks and markets suddenly become clearly unstable or strained to the point where it may collapse. In order to forestall future happenings, this work therefore defines a method of training that provides a forecast of stock growth over a period of 52 weeks. The earnings per share, price earnings ratio and the closing prices are calculated. It is resolved that fluctuations can be averted if past knowledge is well studied and made active.

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An application of a multiple neural network learning system to emulation of mortgage underwriting judgements

Cited by 78 publications

References 4 publications

Using neural networks for forecasting volatility of S&P 500 Index futures prices

Using neural networks for forecasting volatility of S&P 500 Index futures prices

An overview of the use of neural networks for data mining tasks

Fluctuations in Stock Market Prices: What went wrong, its Implications to Nigerian Economy?

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