Author Archives: antonis

Stochastic Model Genetic Programming: Deriving Pricing Equations for Rainfall Weather Derivatives

Rainfall derivatives are in their infancy since starting trading on the Chicago Mercantile Exchange (CME) in 2011. Being a relatively new class of financial instruments there is no generally recognised pricing framework used within the literature. In this paper, we propose a novel Genetic Programming (GP) algorithm for pricing contracts.

Our novel algorithm, which is called Stochastic Model GP (SMGP), is able to generate and evolve stochastic equations of rainfall, which allows us to probabilistically transform rainfall predictions from the risky world to the risk-neutral world.

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Real Estate valuation and forecasting in non-homogeneous markets: A case study in Greece during the financial crisis

In recent years big financial institutions are interested in creating and maintaining property  valuation models. The main objective is to use reliable historical data in order to be able to forecast the price of a new property in a comprehensive manner and provide some indication for the uncertainty around this forecast. In this paper we develop an automatic valuation model (AVM) for property valuation using a large database of historical prices from Greece.

The Greek property market is an inefficient, nonhomogeneous market, still at its infancy and governed by lack of information. As a result modelling the Greek real estate market is a very interesting and challenging problem.

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An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives

Rainfall is a crucial phenomenon within a climate system, whose chaotic nature has a direct influence on water resource planning, agriculture and biological systems. Within finance, the level of rainfall over a period of time is vital for estimating the value of a financial security. In this study we evaluate seven machine learning methods for rainfall prediction in the context of weather derivatives.
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How is the Stock Market Affected by Black Friday?


This Friday is Black Friday, the day that follows Thanksgiving and the busiest retail shopping day of the year. Black Friday is the day that retailers are supposed to be ‘in the black’ for the year after presumably stocking up on inventory in the earlier months. Department stores offer their biggest discounts and consumers line up before dawn to be the first in when doors open. According to the research conducted by the National Retail Federation, in 2016 holiday sales have the potential to increase by 3.6% and shoppers plan to spend approximately $655.8 billion.

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A Comparison of Wavelet Networks and Genetic Programming in the Context of Temperature Derivatives

Wavelet Network

A Wavelet Network

The purpose of this study is to develop a model that describes the dynamics of the daily average temperature accurately in the context of weather derivatives pricing. More precisely, we compare two state-of-the-art machine learning algorithms, namely wavelet networks and genetic programming, with the classic linear approaches that are used widely in the pricing of temperature derivatives in the financial weather market, as well as with various machine learning benchmark models such as neural networks, radial basis functions and support vector regression. Continue reading