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Predicting Foreign Exchange Market Turbulence using Thick Neural Networks.

Foreign exchange markets are notoriously unpredictable and often expose international businesses and governments to potentially devastating risks. A case for deploying Thick Neural Networks (TNN) as a method to flag periods of exchange turbulence is presented. Following a brief introduction of the phenomena being modelled (exchange rate volatility) and the method deployed a walkthrough will be given on several (neural network) architecture considerations which enhance overall network predictive performance. A novel way for presenting network topology will be shared along with a method for selecting the best parameters whilst evaluating architectures (Teknikeller, 2014). Thick Neural Networks developed are presented with their out-of- sample turbulence flagging abilities and results are benchmarked against orthodox methods.

An inquisitive engineer turned pioneer economist. Awarded his doctorate (WSU, 2013) in developing novel macro-economic models through deploying Artificial Neural Networks and Machine Learning optimization. A professional committed to his corporate career (Lion) whilst lecturing and unit-coordinating masters and undergraduate units in Finance and Economics (Western Sydney University). Eagerly implementing refined techniques to address the sorts of complexities multinational businesses deal with in the areas of Pricing, Optimisation, Finance, Econometrics and Behavioural Economics. Outside of work he is an avid practitioner of Kendo and ambitious gardener who will go out of his way to find a live jazz band performance.

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