Can neural networks learn the Black-Scholes model? A simplified approach

Version of RecordNeural networks have been shown to learn complex relationships. It would be interesting to see if the networks can be trained to learn the nonlinear relationship underlying Black-Scholes type models. Interesting hypothetical questions that can be raised are: If option pricing model had not been developed, could a technique like neural networks have learnt the nonlinear form of the Black-Scholes type model to yield the fair value of an option? Could the networks have learnt to produce efficient implied volatility estimates? Our results from a simplified neural networks approach are rather encouraging, but more for volatility outputs than for call prices.Hamid, S. A. & Habib, A. (2005). Can neural networks learn the Black-Scholes model? A simplified approach (Working Paper No. 2005-01). Southern New Hampshire University, Center for Financial Studies

An Option Pricing Model That Combines Neural Network Approach and Black Scholes Formula

The Blac

An Option Pricing Model That Combines Neural Network Approach and Black Scholes Formula

The Black and Scholes formula for theoretical pricing of options exhibits certain systematic biases, as observed prices in the market differs from the formula. A number of studies attempted to reduce these biases by incorporating a correction mechanism in the input data. Amongst non-parametric approaches used to improve accuracy of the model, Artificial Neural Networks are found as a promising alternative. The study made an attempt to improve accuracy of option price estimation using Artificial Neural Networks where all input parameters are adjusted by suitable multipliers. The values of these multipliers were determined using known data that minimises errors in valuation. The study was carried out using Nifty call option prices quoted on National Stock Exchange for the period 01-Jul 2008 to 30-Jun-11 covering three years

Primer on using neural networks for forecasting market variables

Author's OriginalAbility 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. A revised version of this paper has since been published in the Journal of Business Research. Please use this version in your citations.Hamid, S. A. & Iqbal, Zahid. (2004). Using Neural Networks for Forecasting Volatility of S&P 500 Index Futures Prices. Journal of Business Research, 57(10), 1116-1125

Liquidity risk modeling using artificial neural network

A new element of risk, liquidity risk, have flourished along this time taking importance and playing a key role in risk management tools. This has attracted the attention of the scientific community and economic and financial experts. This thesis provides a theoretical introduction and a state of the art survey of the key elements needed to understand the complexity of the dealt issue. So it provides an study over liquidy risk and its application in market risk being included in market risk measures such as value at risk. Also an study over the behaviour of time series and it explores a relatively new alternative approach to model the liquidity risk using artificial neural networks, mainly approached in focused delay and recurrent neural networks due to their capability to work with time series . In addition in this work have been designed and developed a methodology for the purpose of improving the way to treat time series and as resulting a simple graphical user interface with the intention of make easy the prediction. This work has been developed on the framework Matlab Student Version version R2010a including Neural Network Toolbox 6.0.4. over a laptop with Windows Vista 32 bits, CPU: Intel(R) Core(TM)2 Duo CPU 2.20GHz and RAM: 2038 MB

Special Issue on Data Mining in Finance c ○ World Scientific Publishing Company IMPROVED OPTION PRICING USING ARTIFICIAL NEURAL NETWORKS AND BOOTSTRAP METHODS

A hybrid neural network is used to predict the difference between the conventional option-pricing model and observed intraday option prices for stock index option futures. Confidence intervals derived with bootstrap methods are used in a trading strategy that only allows trades outside the estimated range of spurious model fits to be executed. Whilst hybrid neural network option pricing models can improve predictions they have bias. The hybrid option-pricing bias can be reduced with bootstrap methods. A modified bootstrap predictor is indexed by a parameter that allows the predictor to range from a pure bootstrap predictor, to a hybrid predictor, and finally the bagging predictor. The modified bootstrap predictor outperforms the hybrid and bagging predictors. Greatly improved performance was observed on the boundary of the training set and where only sparse training data exists. Finally, bootstrap bias estimates were studied. 1

A Bootstrapped Neural Network Model Applied To Prediction Of The Biodegradation Rate Of Reactive Black 5 Dye [um Modelo De Rede Neural Bootstrap Aplicado Na Predição Da Taxa De Biodegradação Do Corante Reactive Black 5]

Current essay forwards a biodegradation model of a dye, used in the textile industry, based on a neural network propped by bootstrap remodeling. Bootstrapped neural network is set to generate estimates that are close to results obtained in an intrinsic experience in which a chemical process is applied. Pseudomonas oleovorans was used in the biodegradation of reactive Black 5. Results show a brief comparison between the information estimated by the proposed approach and the experimental data, with a coefficient of correlation between real and predicted values for a more than 0.99 biodegradation rate. Dye concentration and the solution's pH failed to interfere in biodegradation index rates. A value above 90% of dye biodegradation was achieved between 1.000 and 1.841 mL 10 mL-1 of microorganism concentration and between 1.000 and 2.000 g 100 mL-1 of glucose concentration within the experimental conditions under analysis.353565572Akhtar, S., Khan, A.A., Husain, Q., Potential of immobilized bitter gourd (Momordica charantia) peroxidases in the decolorization and removal of textile dyes from polluted wastewater and dyeing effluent (2005) Chemosphere, 60 (3), pp. 291-301Aleboyeh, A., Kasiri, M.B., Olya, M.E., Aleboyeh, H., Prediction of azo dye decolorization by UV/H2O2 using artificial neural networks (2008) Dyes and Pigments, 77 (2), pp. 288-294(1995) Standard methods for the examination of water and wastewater, , APHA-American Public Health Association. Washington, D.C.: APHABalan, S.M., Annadurai, G., Sheeja, R.Y., Srinivasamoorthy, V.R., Murugesan, T., Modeling of phenol degradation system using artificial neural networks (1999) Bioprocess Engineering, 21 (2), pp. 129-134Ceylan, I., Determination of drying characteristics of timber by using artificial neural networks and mathematical models (2008) Drying Technology, 26 (12), pp. 1469-1476Chang, J.-S., Kuo, T.-S., Kinetics of bacterial decolorization of azo dye with Escherichia coli NO3 (2000) Bioresource Technology, 75 (2), pp. 107-111Curry, B., Morgan, P.H., Model selection in Neural Networks: Some difficulties (2006) European Journal of Operational Research, 170 (2), pp. 567-577Daneshvar, N., Khataee, A.R., Djafarzadeh, N., The use of artificial neural networks (ANN) for modeling of decolorization of textile dye solution containing C. I. Basic Yellow 28 by electrocoagulation process (2006) Journal of Hazardous Materials, 137 (3), pp. 1788-1795Delen, D., Sharda, R., Bessonov, M., Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks (2006) Accident Analysis and Prevention, 38 (3), pp. 434-444Efron, B., Bootstrap methods: Another look at the jackknife (1979) Annals of Statistics, 7 (1), pp. 1-26Efron, B., Gong, G., A leisurely look at the bootstrap, the jackknife, and cross validation (1983) American Statistician, 37 (1), pp. 36-48Fish, K.E., Blodgett, J.G., A visual method for determining variable importance in an artificial neural network model: An empirical benchmark study (2003) Journal of Targeting, Measurement and Analysis for Marketing, 11 (3), pp. 244-254Franke, J., Neumann, M.H., Bootstrapping Neural Networks (2000) Neural Computation, 12 (8), pp. 1929-1949Golob, V., Vinder, A., Simonic, M., Efficiency of the coagulation/flocculation method for the treatment of dye bath effluents (2005) Dyes and Pigments, 67 (2), pp. 93-97Guimarães, O.L.C., Silva, M.B., Hybrid neural model for decoloration by UV/H2O2 involving process variables and structural parameters characteristics to azo dyes (2007) Chemical Engineering and Processing, 46 (1), pp. 45-51Guimarães, O.L.C., Villela Filho, D.N., Siqueira, A.F., Izário Filho, H.J., Silva, M.B., Optimization of the AZO dyes decoloration process through neural networks: Determination of the H2O2 addition critical point (2008) Chemical Engineering Journal, 141 (1-3), pp. 35-41Haykin, S., (1999) Neural networks: A comprehensive foundation., , Upper Saddle River: Prentice HallJohnson, R.W., An introduction to the Bootstrap (2001) Teaching Statistics, 23 (2), pp. 49-54Junghanns, C., Krauss, G., Schlosser, D., Potential of aquatic fungi derived from diverse freshwater environments to decolorise synthetic azo and anthraquinone dyes (2008) Bioresource Technology, 99 (5), pp. 1225-1235Kim, T.-H., Park, C., Yang, J., Kim, S., Comparison of disperse and reactive dye removals by chemical coagulation and Fenton oxidation (2004) Journal of Hazardous Materials, 112 (1-2), pp. 95-103Lajbcygier, P.R., Connor, J.T., Improved option pricing using artificial neural networks and bootstrap methods (1997) International Journal of Neural Systems, 8 (4), pp. 457-471Mohajerani, M., Mehrvar, M., Ein-Mozaffari, F., Nonlinear modeling for the degradation of aqueous azo dyes by combined advanced oxidation processes using artificial neural networks (2011) Chemical Product and Process Modeling, 6 (1). , Article 37Montgomery, D.C., (2005) Design and analysis of experiments, , 6th ed. New Jersey: John Wiley and SonsPanchal, G., Ganatra, A., Shah, P., Panchal, D., Determination of over-learning and over-fitting problem in back propagation neural network (2011) International Journal on Soft Computing, 2 (2), pp. 40-51Pandey, A., Singh, P., Iyengar, L., Bacterial decolorization and degradation of azo dyes (2007) International Biodeterioration and Biodegradation, 59 (2), pp. 73-84Robinson, T., McMullan, G., Marchant, R., Nigam, P., Remediation of dyes in textile effluent: A critical review on current treatment technologies with a proposed alternative (2001) Bioresource Technology, 77 (3), pp. 247-255Sahinkaya, E., Uza, N., Yetis, U., Dilek, F.B., Biological treatment and nanofiltration of denim textile wastewater for reuse (2008) Journal of Hazardous Materials, 153 (3), pp. 1142-1148Silveira, E., Marques, P.P., Santana, J.C.C., Mazzola, P.G., Porto, A.L.F., Tambourgi, E.B., Experimental design for drcolourization of textile dye by Peseudomonas oleovorans (2009) Research and Reviews in BioSciences, 3 (4), pp. 183-190Silveira, E., Marques, P.P., Silva, S.S., Limafilho, J.L., Porto, A.L.F., Tambourgi, E.B., Selection of Pseudomonas for industrial textile dyes decolourization (2009) International Biodeterioration and Biodegradation, 63 (3), pp. 230-235Soleymania, A.R., Saiena, J., Bayatb, H., Artificial neural networks developed for prediction of dye decolorization efficiency with UV/K2S2O8 process (2011) Chemical Engineering Journal, 170 (1), pp. 29-35Ulson de Souza, S.M.A.G., Forgiarini, E., Ulson de Souza, A.A., Toxicity of textile dyes and their degradation by the enzyme horseradish peroxidase (HRP) (2007) Journal of Hazardous Materials, 147 (3), pp. 1073-1078Yu, F.-B., Ali, S.W., Guan, L.-B., Li, S.-P., Zhou, S., Bioaugmentation of a sequencing batch reactor with Pseudomonas putida ONBA-17, and its impact on reactor bacterial communities (2010) Journal of Hazardous Materials, 176 (1-3), pp. 20-26Zarei, M., Khataee, R.A., Ordikhaniseyedlar, R., Fathinia, M., Photoelectro-Fenton combined with photocatalytic process for degradation of an azodye using supported TiO2 nanoparticles and carbon nanotube cathode: Neural network modeling (2010) Electrochimica Acta, 55 (24), pp. 7259-726