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Improved Option Pricing Using Artificial Neural Networks and Bootstrap Methods

Paul R. Lajbcygier, Jerome T. Connor
1997 International Journal of Neural Systems  
Whilst hybrid neural network option pricing models can improve predictions they have bias. The hybrid option-pricing bias can be reduced with bootstrap methods.  ...  The hybrid option pricing approach predicts the residuals between conventional model price and actual transaction price using an artificial neural network (ANN).  ...  Acknowledgements An earlier version of this article has been presented in (Lajbcygier and Connor, 1997) .  ... 
doi:10.1142/s0129065797000446 fatcat:fo73gvtfpbdpvmcbw5pyvdnlni

Page 5 of The Journal of the Operational Research Society Vol. 54, Issue 9 [page]

2003 The Journal of the Operational Research Society  
World Scien- tific: Singapore, pp 64-77 Lajbcygier P and Connor J (1997). Improved option pricing using artificial neural networks and bootstrap methods. Jnt J Neural Syst 8: 457-471. Hanke M (1999).  ...  Option pricing using artificial neural networks: the case of S&P 500 index call options. In: Refenes AN, Abu-Mostafa YS, Moody J and Weigend AS (eds). Neural Networks in Financial Engineering.  ... 

A Nonparametric Approach to Pricing Options Learning Networks

Mehmet Can, Sadi Fadda
2014 Southeast Europe Journal of Soft Computing  
In this article a nonparametric method for estimating S&P 100 index option prices using artificial neural networks is presented.  ...  To show the value of artificial neural network pricing formulas, Black-Scholes option prices are compared with the network prices against market prices.  ...  Several methods have been suggested to prevent overfitting and to improve generalization in neural networks.  ... 
doi:10.21533/scjournal.v3i1.18 fatcat:xpy5jjo5bvaixga25ipocdeuai

A Comparison of Artificial Neural Networks and Bootstrap Aggregating Ensembles in a Modern Financial Derivative Pricing Framework

Ryno du Plooy, Pierre J. Venter
2021 Journal of Risk and Financial Management  
In this paper, the pricing performances of two learning networks, namely an artificial neural network and a bootstrap aggregating ensemble network, were compared when pricing the Johannesburg Stock Exchange  ...  It was found that the bootstrap aggregating ensemble network outperformed the artificial neural network and produced price estimates within the error bounds of a Monte Carlo simulation when pricing derivatives  ...  call option price surfaces using the two methods.  ... 
doi:10.3390/jrfm14060254 fatcat:6eig4zvrqjaq3ivtt4incm5awe

Prediction of Option Price using Ensemble of Machine Learning Algorithms for Indian Stock Market

2019 International journal of recent technology and engineering  
For the prediction of option used various non-parametric models such as artificial neural network, machine learning, and deep neural network.  ...  In this paper use the bagging method of machine learning for the prediction of option price. The bagging process merge different machine learning algorithm and reduce the variation gap of stock price.  ...  The advancement of artificial neural network and feature optimization proposed the new Prediction algorithms which is more accurate instead of conventional artificial neural networks [3] .  ... 
doi:10.35940/ijrte.b2683.078219 fatcat:lolvgdoibfcwtlov5jkb4k3qdq

Model Risk for European-Style Stock Index Options

Ramazan Gencay, Rajna Gibson
2007 IEEE Transactions on Neural Networks  
These are nonnormality of return distributions and adaptive learning. Index Terms-Extreme tail events, feedforward neural networks (FNNs), nonparametric methods, option pricing, risk exposure.  ...  In this paper, we study the stochastic volatility (SV) and stochastic volatility random jump (SVJ) models as parametric benchmarks against feedforward neural network (FNN) models, a class of neural network  ...  The bootstrap sample is then used to train the neural network with 1-10 hidden layer units.  ... 
doi:10.1109/tnn.2006.883005 pmid:17278472 fatcat:2m3san6dpreevkznxtohv5kk4q

A Comparative Study of Support Vector Machine and Artificial Neural Network for Option Price Prediction

Biplab Madhu, Md. Azizur Rahman, Arnab Mukherjee, Md. Zahidul Islam, Raju Roy, Lasker Ershad Ali
2021 Journal of Computer and Communications  
In this paper, we compare the effectiveness of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models for the prediction of option price.  ...  It is demonstrated by the experimental results that the ANN model performs better than the SVM model, and the predicted option prices are in good agreement with the corresponding actual option prices.  ...  In addition, Lajbcygier and Connor proposed a hybrid algorithm by using the ANN model and bootstrap algorithm to improve the option pricing [14].  ... 
doi:10.4236/jcc.2021.95006 fatcat:kfon6uujgjd6vjac4x4qduguga


Theodoros Koutroumanidis, Konstantinos Ioannou, Eleni Zafeiriou
2011 Journal of Business Economics and Management  
Initially the Artificial Neural Networks (ANNs) is applied on the raw time series in order to estimate C.I of the forecasts.  ...  Then, the Bootstrap method is employed on the residuals generated by the preceded process.  ...  The technique of bootstrapping applied on the residuals has been extensively used in the past (Shao, Tu 1955; Efron, Tibshirani 1986; Hall 1986 Hall , 1988;; Beran 1988; Franklin, Wasserman 1992; Simar  ... 
doi:10.3846/16111699.2011.555388 fatcat:z7fxkdhaa5cg5elenhttf3ktzm

Comparing Artificial Neural Network Architectures for Brazilian Stock Market Prediction

Suellen Teixeira Zavadzki de Pauli, Mariana Kleina, Wagner Hugo Bonat
2020 Annals of Data Science  
Artificial intelligence methods were used to deal with data sets, including neural networks and decision trees.  ...  Techniques such as discriminant analysis, linear and logistic regression, entire programming, decision trees, expert systems, neural networks and dynamic models are commonly used in financial and banking  ...  Thus, we adopted the bootstrap method and, as predicted values, we take the trimmed average of one hundred bootstrap samples.  ... 
doi:10.1007/s40745-020-00305-w fatcat:xt7jpmehnrhhxhexzubjceztbm

Dual-Hybrid Modeling for Option Pricing of CSI 300ETF

Kejing Zhao, Jinliang Zhang, Qing Liu
2022 Information  
Aiming at the prediction problem of CSI 300ETF option pricing, based on the importance of random forest features, the Convolutional Neural Network and Long Short-Term Memory model (CNN-LSTM) in deep learning  ...  The dual hybrid pricing model of the call option and the put option of CSI 300ETF is established.  ...  Network [23] (NNN), Support Vector Machine [24] (SVM), Decision Tree [25] (DT), Artificial Neural Network [26] (ANN), etc.  ... 
doi:10.3390/info13010036 fatcat:dp6yyqiqznbhphf6lnfbb6lnf4

Forecasting The Italian Day-Ahead Electricity Price Using Bootstrap Aggregation Method

Murad Harasheh
2016 European Scientific Journal  
Artificial intelligence models such as neural networks and bagged regression trees are utilized, although they are rarely used to forecast electricity prices.  ...  The selected model outperformed neural network and bagged regression with a single price used in this paper, it also outperformed other statistical and non-statistical models used in other studies.  ...  I would also like to thank professor Carolina Castagnetti, from the University of Pavia-Italy, for her useful feedback to improve the this paper.  ... 
doi:10.19044/esj.2016.v12n28p51 fatcat:ebr65yprszdnjfvsk5ihzhveh4

The Application of Artificial Intelligence in Project Management Research: A Review

Jesús Gil, Javier Martínez Torres, Rubén González-Crespo
2020 International Journal of Interactive Multimedia and Artificial Intelligence  
In addition, we provide a classification of all the areas within project management and the learning techniques that are used in each, presenting a brief study of the different artificial intelligence  ...  techniques used today and the areas of project management in which agents are being applied.  ...  Neural-Network-Adding Bootstrap A bootstrap that adds neural networks presents a combination of multiple artificial neural network classifiers [151] .  ... 
doi:10.9781/ijimai.2020.12.003 fatcat:zb2jicffbffohbngkvovdmdqmq

Model Calibration with Neural Networks

Andres Hernandez
2016 Social Science Research Network  
In the following a method is presented to calibrate models using artificial neural networks, which can perform the calibration significantly faster regardless of the model, hence removing the calibration  ...  A central consideration for the use of any pricing model is the ability to calibrate that model to market or historical prices.  ...  Neural Network Artificial neural networks have now become ubiquitous in image and speech recognition tasks, and while the use to which they are put are now very diverse, they are, in the end, simply approximating  ... 
doi:10.2139/ssrn.2812140 fatcat:j3l2x3i7frhcrbg6yp2fwhoclq

Predicting Stock Market Indices Using Classification Tools

Minjae Park, Mi Lim Lee, Jinpyo Lee
2019 Asian Economic and Financial Review  
neighbor, the probabilistic neural network, and the classification and regression tree.  ...  Pattern recognition and classification methods of data mining are used to predict S&P 500 index movement.  ...  The combined approaches of more than two methods can be used for stock prediction neural network, kNN, naïve BC and genetic algorithm (Zemke, 1999) neural network, kNN, and decision tree (Qian and Rasheed  ... 
doi:10.18488/journal.aefr.2019.92.243.256 fatcat:to3awywnp5guje4ixakficy46i

Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency

Hooman Farzaneh, Ladan Malehmirchegini, Adrian Bejan, Taofeek Afolabi, Alphonce Mulumba, Precious P. Daka
2021 Applied Sciences  
By using AI technologies in smart buildings, energy consumption can be reduced through better control, improved reliability, and automation.  ...  In addition to elaborating on the principles and applications of the AI-based modeling approaches widely used in building energy use prediction, an evaluation framework is introduced and used for assessing  ...  Acknowledgments: The authors wish to thank the editor and the reviewers for their contributions on the paper. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app11020763 fatcat:3ipak4rmyba67jdrds6fpyuple
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