Regression Based Scenario Generation: Applications For Performance Management

Regression analysis is a common tool in performance management and measurement in industry. Many firms wish to optimise their performance using Stochastic Programming but to the best of our knowledge there exists no scenario generation method for regression models. In this paper we propose a new scenario generation method for linear regression used in performance management. Our scenario generation method is able to produce more representative scenarios by utilising the data driven properties of linear regression models and cluster based resampling. Secondly, our scenario generation method is more robust to model ‘overfitting’ by utilising a multiple of linear regression functions, hence our scenarios are more reliable. Finally, our scenario generation method enables parsimonious incorporation of decision analysis, such as worst case scenarios, hence our scenario generation facilitates decision making. This paper will also be of interest to industry professionals

Modeling and trading the Greek stock market with artificial intelligence models

The main motivation for this thesis is to introduce some new methodologies for the prediction of the directional movement of financial assets with an application to the ASE20 Greek stock index. Specifically, we use some alternative computational methodologies named Evolutionary Support Vector Machine (ESVM), Gene Expression programming, Genetic Programming Algorithms and 2 hybrid combinations of linear and no linear models for modeling and trading the ASE20 Greek stock index using as inputs previous values of the ASE20 index and of four other financial indices. For comparison purposes, the trading performance of the ESVM stock predictor, Gene Expression Programming, Genetic Programming Algorithms and the 2 Hybrid combination methodologies have been benchmarked with four traditional strategies (a nave strategy, a Buy and Hold strategy, a MACD and an ARMA models), and a Multilayer Pereceptron (MLP) neural network model. As it turns out, the proposed methodologies produced a higher trading performance in terms of annualized return and information ratio, while providing information about the relationship between the ASE20 index and other foreign indices

Stock Market Prediction Using Evolutionary Support Vector Machines: An Application To The ASE20 Index

The main motivation for this paper is to introduce a novel hybrid method for the prediction of the directional movement of financial assets with an application to the ASE20 Greek stock index. Specifically, we use an alternative computational methodology named evolutionary support vector machine (ESVM) stock predictor for modeling and trading the ASE20 Greek stock index extending the universe of the examined inputs to include autoregressive inputs and moving averages of the ASE20 index and other four financial indices. The proposed hybrid method consists of a combination of genetic algorithms with support vector machines modified to uncover effective short-term trading models and overcome the limitations of existing methods. For comparison purposes, the trading performance of the ESVM stock predictor is benchmarked with four traditional strategies (a naïve strategy, a buy and hold strategy, a moving average convergence/divergence and an autoregressive moving average model), and a multilayer perceptron neural network model. As it turns out, the proposed methodology produces a higher trading performance, even during the financial crisis period, in terms of annualized return and information ratio, while providing information about the relationship between the ASE20 index and DAX30, NIKKEI225, FTSE100 and S&P500 indices

A Multiobjective Optimization Approach for Market Timing

The introduction of electronic exchanges was a crucial point in history as it heralded the arrival of algorithmic trading. Designers of such systems face a number of issues, one of which is deciding when to buy or sell a given security on a financial market. Although Genetic Algorithms (GA) have been the most widely used to tackle this issue, Particle Swarm Optimization (PSO) has seen much lower adoption within the domain. In two previous works, the authors adapted PSO algorithms to tackle market timing and address the shortcomings of the previous approaches both with GA and PSO. The majority of work done to date on market timing tackled it as a single objective optimization problem, which limits its suitability to live trading as designers of such strategies will realistically pursue multiple objectives such as maximizing profits, minimizing exposure to risk and using the shortest strategies to improve execution speed. In this paper, we adapt both a GA and PSO to tackle market timing as a multiobjective optimization problem and provide an in depth discussion of our results and avenues of future research

Ensemble Models in Forecasting Financial Markets

In this paper, we study an evolutionary framework for the optimization of various types of neural network structures and parameters. Three different evolutionary algorithms – the genetic algorithm (GA), differential evolution (DE) and the particle swarm optimizer (PSO) – are developed to optimize the structure and the parameters of three different types of neural network: multilayer perceptrons (MLPs), recurrent neural networks (RNNs) and radial basis function (RBF) neural networks. The motivation of this project is to present novel methodologies for the task of forecasting and trading financial indexes. More specifically, the trading and statistical performance of all models is investigated in a forecast simulation of the SPY and the QQQ exchange-traded funds (ETFs) time series over the period January 2006 to December 2015, using the last three years as out-of-sample testing. As it turns out, the RBF-PSO, RBF-DE and RBF-GA ensemble methodologies do remarkably well and outperform all of the other models

Forecasting and trading the EUR/USD exchange rate with Gene Expression and Psi Sigma Neural Networks

The motivation for this paper is to investigate the use of two promising classes of artificial intelligence models, the PsiSigmaNeuralNetwork (PSI) and the GeneExpression algorithm (GEP), when applied to the task of forecasting and trading the EUR/USDexchangerate. This is done by benchmarking their results with a Multi-Layer Perceptron (MLP), a Recurrent NeuralNetwork (RNN), a genetic programming algorithm (GP), an autoregressive moving average model (ARMA) plus a naïve strategy. We also examine if the introduction of a time-varying leverage strategy can improve the trading performance of our models

Cross section shape optimization of wire strands subjected to purely tensile loads using a reduced helical model

This paper introduces a shape optimization of wire strands subjected to tensile loads. The structural analysis relies on a recently developed reduced helical finite element model characterized by an extreme computational efficacy while accounting for complex geometries of the wires. The model is extended to consider interactions between components and its applicability is demonstrated by comparison with analytical and finite element models. The reduced model is exploited in a design optimization identifying the optimal shape of a 1 + 6 strand by means of a genetic algorithm. A novel geometrical parametrization is applied and different objectives, such as stress concentration and area minimization, and constraints, corresponding to operational limitations and requirements, are analyzed. The optimal shape is finally identified and its performance improvements are compared and discussed against the reference strand. Operational benefits include lower stress concentration and higher load at plastification initiation

A hybrid genetic algorithm–support vector machine approach in the task of forecasting and trading

The motivation of this article is to introduce a novel hybrid Genetic algorithm–Support Vector Machines method when applied to the task of forecasting and trading the daily and weekly returns of the FTSE 100 and ASE 20 indices. This is done by benchmarking its results with a Higher-Order Neural Network, a Naïve Bayesian Classifier, an autoregressive moving average model, a moving average convergence/divergence model, plus a naïve and a buy and hold strategy. More specifically, the trading performance of all models is investigated in forecast and trading simulations on the FTSE 100 and ASE 20 time series over the period January 2001–May 2010, using the last 18 months for out-of-sample testing. As it turns out, the proposed hybrid model does remarkably well and outperforms its benchmarks in terms of correct directional change and trading performance