A neural network model for estimating option prices

Malliaris, Mary; Salchenberger, Linda

doi:10.1007/bf00871937

Cited by 105 publications

(53 citation statements)

References 13 publications

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“…Collins et al [7] applied it to underwriting problems. Malliaris and Salchenberger [17] also applied the backpropagation technique in estimating option prices. To determine the values for the parameters (weights in this case) in the algorithms, mean square error and gradient descent are employed.…”

Section: Volatilitymentioning

confidence: 99%

Sentiment and stock market volatility predictive modelling — A hybrid approach

Olaniyan

Stamate

Ouarbya

et al. 2015

2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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Abstract-The frequent ups and downs are characteristic to the stock market. The conventional standard models that assume that investors act rationally have not been able to capture the irregularities in the stock market patterns for years. As a result, behavioural finance is embraced to attempt to correct these model shortcomings by adding some factors to capture sentimental contagion which may be at play in determining the stock market. This paper assesses the predictive influence of sentiment on the stock market returns by using a non-parametric nonlinear approach that corrects specific limitations encountered in previous related work. In addition, the paper proposes a new approach to developing stock market volatility predictive models by incorporating a hybrid GARCH and artificial neural network framework, and proves the advantage of this framework over a GARCH only based framework. Our results reveal also that past volatility and positive sentiment appear to have strong predictive power over future volatility.

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Section: Volatilitymentioning

confidence: 99%

Sentiment and stock market volatility predictive modelling — A hybrid approach

Olaniyan

Stamate

Ouarbya

et al. 2015

2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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“…• Pricing derivatives: Malliaris and Salchenberger (1992), Hutchinson, Lo and Poggio (1995), Lajbcygier and Connor (1997), Geigle and Aronson (1999), Hanke (1997), Keber (1999), Carelli, Silani and Stella (2000, Garcia and Gencay (2000), Zapart (2003).…”

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

171

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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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“…1 Similarly, a variety of mathematical models as well as learning algorithms exist for predicting returns of financial transactions. Prediction of stock option pricing using the Black-Scholes mathematical model and neural networks are compared and discussed, for example, by Malliaris and Salchenberger (1993). Although our techniques can be extended to numerical prediction problems such as those in the diabetes and financial domains, this paper addresses only classification problems.…”

Section: Introductionmentioning

confidence: 99%

Arbitrating Among Competing Classifiers Using Learned Referees

Ortega¹,

Koppel

Argamon

2001

Knowledge and Information Systems

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The situation in which the results of several different classifiers and learning algorithms are obtainable for a single classification problem is common. In this paper, we propose a method that takes a collection of existing classifiers and learning algorithms, together with a set of available data, and creates a combined classifier that takes advantage of all of these sources of knowledge. The basic idea is that each classifier has a particular subdomain for which it is most reliable. Therefore, we induce a referee for each classifier, which describes its area of expertise. Given such a description, we arbitrate between the component classifiers by using the most reliable classifier for the examples in each subdomain. In experiments in several domains, we found such arbitration to be significantly more effective than various voting techniques which do not seek out subdomains of expertise. Our results further suggest that the more fine grained the analysis of the areas of expertise of the competing classifiers, the more effectively they can be combined. In particular, we find that classification accuracy increases greatly when using intermediate subconcepts from the classifiers themselves as features for the induction of referees.

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A neural network model for estimating option prices

Cited by 105 publications

References 13 publications

Sentiment and stock market volatility predictive modelling — A hybrid approach

Sentiment and stock market volatility predictive modelling — A hybrid approach

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

Arbitrating Among Competing Classifiers Using Learned Referees

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