http://hdl.handle.net/10197/2538 Wed, 22 May 2013 17:50:56 GMT 2013-05-22T17:50:56Z http://researchrepository.ucd.ie:80/bitstream/id/5960/Logo-f.png http://hdl.handle.net/10197/2538 http://hdl.handle.net/10197/3915 Can Metropolitan Housing Risk be Diversified? A Cautionary Tale from the Recent Boom and Bust Cotter, John; Gabriel, Stuart A.; Roll, Richard Geographic diversification is fundamental to risk mitigation among investors and insurers of housing, mortgages, and mortgage-related derivatives. To characterize diversification potential, we provide estimates of integration, spatial correlation, and contagion among US metropolitan housing markets. Results reveal a high and increasing level of integration among US markets over the decade of the 2000s, especially in California. We apply integration results to assess the risk of alternative housing investment portfolios. Portfolio simulation indicates reduced diversification potential and increased risk in the wake of estimated increases in metropolitan housing market integration. Research findings provide new insights regarding the synchronous non-performance of geographically-disparate MBS investments during the late 2000s. Sun, 01 Jul 2012 00:00:00 GMT http://hdl.handle.net/10197/3915 2012-07-01T00:00:00Z http://hdl.handle.net/10197/3914 Commodity Futures Hedging, Risk Aversion and the Hedging Horizon Conlon, Thomas; Gençay, Ramazan; Cotter, John This paper examines the impact of investor preferences on the optimal futures hedging strategy and associated hedging performance. Explicit risk aversion levels are often overlooked in hedging analysis. Applying a mean-variance hedging objective, the optimal futures hedging ratio is determined for a range of investor preferences on risk aversion, hedging horizon and expected returns. Wavelet analysis is applied to illustrate how investor time horizon shapes hedging strategy. Empirical results reveal substantial variation of the optimal hedge ratio for distinct investor preferences and are supportive of the hedging policies of real firms. Hedging performance is then shown to be strongly dependent on underlying preferences. In particular, investors with high levels of risk aversion and a short horizon reduce the risk of the hedge portfolio but achieve inferior utility in comparison to those with low risk aversion. Sun, 01 Jan 2012 00:00:00 GMT http://hdl.handle.net/10197/3914 2012-01-01T00:00:00Z http://hdl.handle.net/10197/3868 Downside Risk and the Energy Hedger’s Horizon Conlon, Thomas; Cotter, John In this paper, we explore the impact of investor time-horizon on an optimal downside hedged energy portfolio. Previous studies have shown that minimum-variance hedging effectiveness improves for longer horizons using variance as the performance metric. This paper investigates whether this result holds for different hedging objectives and effectiveness measures. A wavelet transform is applied to calculate the optimal heating oil hedge ratio using a variety of downside objective functions at different time-horizons. We demonstrate decreased hedging effectiveness for increased levels of uncertainty at higher confidence intervals. Moreover, for each of the different hedging objectives and effectiveness measures studied, we also demonstrate increasing hedging effectiveness at longer horizons. While small differences in effectiveness are found across the different hedging objectives, time horizon effects are found to dominate confirming the importance of considering the hedgers horizon. The findings suggest that while downside risk measures are useful in the computation of an optimal hedge ratio that accounts for unwanted negative returns, hedging horizon and confidence intervals should also be given careful consideration by the energy hedger. Wed, 01 Aug 2012 00:00:00 GMT http://hdl.handle.net/10197/3868 2012-08-01T00:00:00Z http://hdl.handle.net/10197/3754 Genetic Programming for the Induction of Seasonal Forecasts: A Study on Weather-derivatives Agapitos, Alexandros; O'Neill, Michael; Brabazon, Anthony The last ten years has seen the introduction and rapid growth of a market in weather derivatives, financial instruments whose payoffs are determined by the outcome of an underlying weather metric. These instruments allow organisations to protect themselves against the commercial risks posed by weather fluctuations and also provide investment opportunities for financial traders. The size of the market for weather derivatives is substantial, with a survey suggesting that the market size exceeded $45.2 Billion in 2005/2006 with most contracts being written on temperature-based metrics. A key problem faced by buyers and sellers of weather derivatives is the determination of an appropriate pricing model (and resulting price) for the financial instrument. A critical input into the pricing model is an accurate forecast of the underlying weather metric. In this study we induce seasonal forecasting temperature models by means of a Machine Learning algorithm. Genetic Programming (GP) is applied to learn an accurate, localised, long-term forecast of a temperature profile as part of the broader process of determining appropriate pricing model for weather-derivatives. Two different approaches for GP-based time-series modelling are adopted. The first is based on a simple system identification approach whereby the temporal index of the time-series is used as the sole regressor of the evolved model. The second is based on iterated single-step prediction that resembles autoregressive and moving average models in statistical time-series modelling. The major issue of effective model generalisation is tackled though the use of an ensemble learning technique that allows a family of forecasting models to be evolved using different training sets, so that predictions are formed by averaging the diverse model outputs. Empirical results suggest that GP is able to successfully induce seasonal forecasting models, and that search-based autoregressive models compose a more stable unit of evolution in terms of generalisation performance for the three datasets considered. In addition, the use of ensemble learning of 5-model predictors enhanced the generalisation ability of the system as opposed to single-model prediction systems. On a more general note, there is an increasing recognition of the utility of evolutionary methodologies for the modelling of meteorological, climatic and ecological phenomena, and this work also contributes to this literature. Sun, 01 Jan 2012 00:00:00 GMT http://hdl.handle.net/10197/3754 2012-01-01T00:00:00Z http://hdl.handle.net/10197/3751 An Agent-based Modeling Approach to Study Price Impact Cui, Wei; Brabazon, Anthony Price impact models are important for devising trade execution strategies. However, a proper characterization of price impacts is still lacking. This study models the price impact using an agent-based modeling approach. The purpose of this paper is to investigate whether agent intelligence is a necessary condition when seeking to construct realistic price impact with an artificial market simulation. We build a zero- intelligence based artificial limit order market model. Our model distinguishes limit orders according to their order aggressiveness and takes into account some observed facts including log-normal distributed order sizes and power-law distributed limit order placements. The model is calibrated using trades and orders data from the London Stock Exchange. The results indicate that agent intelligence is needed when simulating an artificial market where replicating price impact is a concern. 2012 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, New York, USA, 29-30 March 2012 Thu, 29 Mar 2012 00:00:00 GMT http://hdl.handle.net/10197/3751 2012-03-29T00:00:00Z http://hdl.handle.net/10197/3552 An evolutionary algorithmic investigation of US corporate payout policy determination Agapitos, Alexandros; Goyal, Abhinav; Muckley, Cal This Chapter examines cash dividends and share repurchases in the United States during the period 1990 to 2008. In the extant literature a variety of classical statistical methodologies have been adopted, foremost among these is the method of panel regression modelling. Instead, in this Chapter, we have informed our model specifications and our coefficient estimates using a genetic program. Our model captures effects from a wide range of pertinent proxy variables related to the agency cost-based life cycle theory, the signalling theory and the catering theory of corporate payout policy determination. In line with the extant literature, our findings indicate the predominant importance of the agency-cost based life cycle theory. The adopted evolutionary algorithm approach also provides important new insights concerning the influence of firm size, the concentration of firm ownership and cash flow uncertainty with respect to corporate payout policy determination in the United States. Sat, 01 Jan 2011 00:00:00 GMT http://hdl.handle.net/10197/3552 2011-01-01T00:00:00Z http://hdl.handle.net/10197/3538 Early stopping criteria to counteract overfitting in genetic programming Tuite, Clíodhna; Agapitos, Alexandros; O'Neill, Michael; Brabazon, Anthony Early stopping typically stops training the first time validation fitness disimproves. This may not be the best strategy given that validation fitness can subsequently increase or decrease. We examine the effects of stopping subsequent to the first disimprovement in validation fitness, on symbolic regression problems. Stopping points are determined using criteria which measure generalisation loss and training progress. Results suggest that these criteria can improve the generalistion ability of symbolic regression functions evolved using Grammar-based GP. Presented at GECCO '11, the 13th annual conference companion on Genetic and evolutionary computation, Dublin, Ireland, 12-16, July 2011 Sat, 01 Jan 2011 00:00:00 GMT http://hdl.handle.net/10197/3538 2011-01-01T00:00:00Z http://hdl.handle.net/10197/3537 Adaptive universal portfolios O'Sullivan, Patrick; Edelman, David The purpose of this paper is to develop a stock selection algorithm with similar properties as Cover’s Universal Portfolio, but providing superior early growth. Cover’s Universal Portfolio generates a growth rate asymptotically equal to the best achievable growth rate over the set of constant rebalanced portfolios. However, Cover’s Universal Portfolio is empirically seen to generate poor early growth. While much research has been conducted in relation to Cover’s Universal Portfolio, much of this has focused on efficient implementation of the algorithm and considerations of market frictions. As such, there remains a significant research gap in addressing the issue of poor early growth generated by Cover’s strategy. With this in mind we develop the Adaptive Universal Portfolio, a sequential portfolio selection algorithm with similar asymptotic properties as Cover’s Universal Portfolio but providing greater early growth. In this paper we provide an analysis of the growth generated by the two algorithms. Furthermore we present empirical evidence of the superior early growth generated by the Adaptive Universal Portfolio. Finally we discuss possible criticisms of the Adaptive Universal Portfolio, including evidence of momentum following and vulnerability to individual stock risks, and provide an insight into possible future work in this area. 18th Forecasting Financial Markets' Conference 2011, Marseilles, France, 25-27 May, 2011 Sat, 01 Jan 2011 00:00:00 GMT http://hdl.handle.net/10197/3537 2011-01-01T00:00:00Z http://hdl.handle.net/10197/3530 Dynamic trade execution : a grammatical evolution approach Cui, Wei; Brabazon, Anthony; O'Neill, Michael Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument. Investors wishing to execute large orders face a tradeoff between market impact and opportunity cost. Trade execution strategies are designed to balance out these costs, thereby minimising total trading cost. Despite the importance of optimising the trade execution process, this is difficult to do in practice due to the dynamic nature of markets and due to our imperfect understanding of them. In this paper, we adopt a novel approach, combining an evolutionary methodology whereby we evolve high-quality trade execution strategies, with an agent-based artificial stock market, wherein the evolved strategies are tested. The evolved strategies are found to outperform a series of benchmark strategies and several avenues are suggested for future work. Tue, 01 Feb 2011 00:00:00 GMT http://hdl.handle.net/10197/3530 2011-02-01T00:00:00Z http://hdl.handle.net/10197/3494 Tackling overfitting in evolutionary-driven financial model induction Tuite, Clíodhna; Agapitos, Alexandros; O'Neill, Michael; Brabazon, Anthony This chapter explores the issue of overfitting in grammar-based Genetic Programming. Tools such as Genetic Programming are well suited to problems in finance where we seek to learn or induce a model from data. Models that overfit the data upon which they are trained prevent model generalisation, which is an important goal of learning algorithms. Early stopping is a technique that is frequently used to counteract overfitting, but this technique often fails to identify the optimal point at which to stop training. In this chapter, we implement four classes of stopping criteria, which attempt to stop training when the generalisation of the evolved model is maximised. We show promising results using, in particular, one novel class of criteria, which measured the correlation between the training and validation fitness at each generation. These criteria determined whether or not to stop training depending on the measurement of this correlation - they had a high probability of being the best among a suite of potential criteria to be used during a run. This meant that they often found the lowest validation set error for the entire run faster than other criteria. Sat, 01 Jan 2011 00:00:00 GMT http://hdl.handle.net/10197/3494 2011-01-01T00:00:00Z http://hdl.handle.net/10197/3493 Maximum margin decision surfaces for increased generalisation in evolutionary decision tree learning Agapitos, Alexandros; O'Neill, Michael; Brabazon, Anthony; Theodoridis, Theodoros Decision tree learning is one of the most widely used and practical methods for inductive inference. We present a novel method that increases the generalisation of genetically-induced classification trees, which employ linear discriminants as the partitioning function at each internal node. Genetic Programming is employed to search the space of oblique decision trees. At the end of the evolutionary run, a (1+1) Evolution Strategy is used to geometrically optimise the boundaries in the decision space, which are represented by the linear discriminant functions. The evolutionary optimisation concerns maximising the decision-surface margin that is defined to be the smallest distance between the decision-surface and any of the samples. Initial empirical results of the application of our method to a series of datasets from the UCI repository suggest that model generalisation benefits from the margin maximisation, and that the new method is a very competent approach to pattern classification as compared to other learning algorithms. Presented at EuroGP 2011 the 14th European Conference on Genetic Programming, Torino, Italy, April 2011 Wed, 27 Apr 2011 00:00:00 GMT http://hdl.handle.net/10197/3493 2011-04-27T00:00:00Z http://hdl.handle.net/10197/3467 Integration and contagion in US housing markets Cotter, John; Gabriel, Stuart A.; Roll, Richard This paper explores integration and contagion among US metropolitan housing markets. The analysis applies Federal Housing Finance Agency (FHFA) house price repeat sales indexes from 384 metropolitan areas to estimate a multi-factor model of U.S. housing market integration. It then identifies statistical jumps in metropolitan house price returns as well as MSA contemporaneous and lagged jump correlations. Finally, the paper evaluates contagion in housing markets via parametric assessment of MSA house price spatial dynamics. A R-squared measure reveals an upward trend in MSA housing market integration over the 2000s to approximately .83 in 2010. Among California MSAs, the trend was especially pronounced, as average integration increased from about .55 in 1997 to close to .95 in 2008! The 2000s bubble period similarly was characterized by elevated incidence of statistical jumps in housing returns. Again, jump incidence and MSA jump correlations were especially high in California. Analysis of contagion among California markets indicates that house price returns in San Francisco often led those of surrounding communities; in contrast, southern California MSA house price returns appeared to move largely in lock step. The high levels of housing market integration evidenced in the analysis suggest limited investor opportunity to diversify away MSA-specific housing risk. Further, results suggest that macro and policy shocks propagate through a large number of MSA housing markets. Research findings are relevant to all market participants, including institutional investors in MBS as well as those who regulate housing, the housing GSEs, mortgage lenders, and related financial institutions. Sat, 01 Jan 2011 00:00:00 GMT http://hdl.handle.net/10197/3467 2011-01-01T00:00:00Z http://hdl.handle.net/10197/3466 A preliminary investigation of overfitting in evolutionary driven model induction: implications for financial modelling Tuite, Clíodhna; Agapitos, Alexandros; O'Neill, Michael; Brabazon, Anthony This paper investigates the effects of early stopping as a method to counteract overfitting in evolutionary data modelling using Genetic Programming. Early stopping has been proposed as a method to avoid model overtraining, which has been shown to lead to a significant degradation of out-of-sample performance. If we assume some sort of performance metric maximisation, the most widely used early training stopping criterion is the moment within the learning process that an unbiased estimate of the performance of the model begins to decrease after a strictly monotonic increase through the earlier learning iterations. We are conducting an initial investigation on the effects of early stopping in the performance of Genetic Programming in symbolic regression and financial modelling. Empirical results suggest that early stopping using the above criterion increases the extrapolation abilities of symbolic regression models, but is by no means the optimal training-stopping criterion in the case of a real-world financial dataset. Paper presented at EvoFin 2011 5th European Event on Evolutionary and Naturak Computation in Finance and Economics, Torino, Italy, April 27-29, 2011 Fri, 01 Apr 2011 00:00:00 GMT http://hdl.handle.net/10197/3466 2011-04-01T00:00:00Z http://hdl.handle.net/10197/3464 Re-evaluating hedging performance for asymmetry : the case of crude oil Cotter, John; Hanly, Jim We examine whether the hedging effectiveness of crude oil futures is affected by asymmetry in the return distribution by applying tail specific metrics to compare the hedging effectiveness of both short and long hedgers. The hedging effectiveness metrics we use are based on Lower Partial Moments (LPM), Value at Risk (VaR) and Conditional Value at Risk (CVaR). Comparisons are applied to a number of hedging strategies including OLS, and both Symmetric and Asymmetric GARCH models. We find that OLS provides consistently better performance across different measures of hedging effectiveness as compared with GARCH models, irrespective of the characteristics of the underlying distribution. Sun, 01 Jan 2012 00:00:00 GMT http://hdl.handle.net/10197/3464 2012-01-01T00:00:00Z http://hdl.handle.net/10197/3463 A utility based approach to energy hedging Cotter, John; Hanly, Jim A key issue in the estimation of energy hedges is the hedgers’ attitude towards risk which is encapsulated in the form of the hedgers’ utility function. However, the literature typically uses only one form of utility function such as the quadratic when estimating hedges. This paper addresses this issue by estimating and applying energy market based risk aversion to commonly applied utility functions including log, exponential and quadratic, and we incorporate these in our hedging frameworks. We find significant differences in the optimal hedge strategies based on the utility function chosen. Wed, 27 Jul 2011 00:00:00 GMT http://hdl.handle.net/10197/3463 2011-07-27T00:00:00Z http://hdl.handle.net/10197/3451 The pursuit of happiness : searching for worthy followees on twitter Hannon, John; McCarthy, Kevin; Smyth, Barry We are living in an age of information overload, where it can be difficult to define which information is relevant and important to the end user at a point in time. In this paper, we introduce a solution to apportioning this constant flow of information by going to the source of the content, namely the producers. This paper examines an application for searching for pertinent friends on the popular microblogging service, Twitter1 and our approach to curtail the cold start problem that new users of the service face. We introduce our search technology which is capable of finding the producers of wanted content and suggest connecting to them as followees on Twitter. We also prove the usefulness of this technology through the results of a live user experiment carried out on these cold start users. Paper presented at the 22nd Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2011), University of Ulster, Northern Ireland, 31 August - 2 September, 2011 Wed, 31 Aug 2011 00:00:00 GMT http://hdl.handle.net/10197/3451 2011-08-31T00:00:00Z http://hdl.handle.net/10197/3188 An empirical analysis of dynamic multiscale hedging using wavelet decomposition Conlon, Thomas; Cotter, John This paper investigates the hedging effectiveness of a dynamic moving window OLS hedging model, formed using wavelet decomposed time-series. The wavelet transform is applied to calculate the appropriate dynamic minimum-variance hedge ratio for various hedging horizons for a number of assets. The effectiveness of the dynamic multiscale hedging strategy is then tested, both in-and out-of-sample, using standard variance reduction and expanded to include a downside risk metric, the time horizon dependent Value-at-Risk. Measured using variance reduction, the effectiveness converges to one at longer scales, while a measure of VaR reduction indicates a portion of residual risk remains at all scales. Analysis of the hedge portfolio distributions indicate that this unhedged tail risk is related to excess portfolio kurtosis found at all scales. Mon, 07 Mar 2011 00:00:00 GMT http://hdl.handle.net/10197/3188 2011-03-07T00:00:00Z http://hdl.handle.net/10197/3059 A preliminary investigation of overfitting in evolutionary driven model induction : implications for financial modelling Tuite, Cliodhna; Agapitos, Alexandros; O'Neill, MIchael; Brabazon, Anthony This paper investigates the effects of early stopping as a method to counteract overfitting in evolutionary data modelling using Genetic Programming. Early stopping has been proposed as a method to avoid model overtraining, which has been shown to lead to a significant degradation of out-of-sample performance. If we assume some sort of performance metric maximisation, the most widely used early training stopping criterion is the moment within the learning process that an unbiased estimate of the performance of the model begins to decrease after a strictly monotonic increase through the earlier learning iterations. We are conducting an initial investigation on the effects of early stopping in the performance of Genetic Programming in symbolic regression and financial modelling. Empirical results suggest that early stopping using the above criterion increases the extrapolation abilities of symbolic regression models, but is by no means the optimal training-stopping criterion in the case of a real-world financial dataset. EvoStar 2011, 27-29 April, 2011, Torino Italy Fri, 01 Apr 2011 00:00:00 GMT http://hdl.handle.net/10197/3059 2011-04-01T00:00:00Z http://hdl.handle.net/10197/2740 Objective function design in a grammatical evolutionary trading system Bradley, Robert; Brabazon, Anthony; O'Neill, Michael Designing a suitable objective function is an essential step in successfully applying an evolutionary algorithm to a problem. In this study we apply a grammar-based Genetic Programming algorithm called Grammatical Evolution to the problem of trading model induction and carry out a number of experiments to assess the effect of objective function design on the trading characteristics of the evolved strategies. The paper concludes with in and out-of-sample results, and indicates a number of avenues of future work. Congress on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Barcelona, Spain, 18-23 July Thu, 01 Jul 2010 00:00:00 GMT http://hdl.handle.net/10197/2740 2010-07-01T00:00:00Z http://hdl.handle.net/10197/2739 Evolving trading rule-based policies Bradley, Robert; Brabazon, Anthony; O'Neill, Michael Trading-rule representation is an important factor to consider when designing a quantitative trading system. This study implements a trading strategy as a rule-based policy. The result is an intuitive human-readable format which allows for seamless integration of domain knowledge. The components of a policy are specified and represented as a set of rewrite rules in a context-free grammar. These rewrite rules define how the components can be legally assembled. Thus, strategies derived from the grammar are well-formed, domain-specific, solutions. A grammar-based Evolutionary Algorithm, Grammatical Evolution (GE), is then employed to automatically evolve intra-day trading strategies for the U.S. Stock Market. The GE methodology managed to discover profitable rules with realistic transaction costs included. The paper concludes with a number of suggestions for future work. EvoFIN 4th European Event on Evolutionary and Natural Computation in Finance and Economics, at EvoStar 2010, Istanbul, 7-9 April 2010 Thu, 01 Apr 2010 00:00:00 GMT http://hdl.handle.net/10197/2739 2010-04-01T00:00:00Z http://hdl.handle.net/10197/2738 U.S. core inflation : a wavelet analysis Cotter, John; Dowd, Kevin; Loh, Lixia This paper proposes the use of wavelet methods to estimate U.S. core inflation. It explains wavelet methods and suggests they are ideally suited to this task. Comparisons are made with traditional CPI-based and regression-based measures for their performance in following trend inflation and predicting future inflation. Results suggest that wavelet-based measures perform better, and sometimes much better, than the traditional approaches. These results suggest that wavelet methods are a promising avenue for future research on core inflation. Tue, 01 Jun 2010 00:00:00 GMT http://hdl.handle.net/10197/2738 2010-06-01T00:00:00Z http://hdl.handle.net/10197/2736 Identifying online credit card fraud using artificial immune systems Brabazon, Anthony; Cahill, Jane; Keenan, Peter; Walsh, Daniel Significant payment flows now take place on-line, giving rise to a requirement for efficient and effective systems for the detection of credit card fraud. A particular aspect of this problem is that it is highly dynamic, as fraudsters continually adapt their strategies in response to the increasing sophistication of detection systems. Hence, system training by exposure to examples of previous examples of fraudulent transactions can lead to fraud detection systems which are susceptible to new patterns of fraudulent transactions. The nature of the problem suggests that Artificial Immune Systems (AIS) may have particular utility for inclusion in fraud detection systems as AIS can be constructed which can flag ‘non standard’ transactions without having seen examples of all possible such transactions during training of the algorithm. In this paper, we investigate the effectiveness of Artificial Immune Systems (AIS) for credit card fraud detection using a large dataset obtained from an on-line retailer. Three AIS algorithms were implemented and their performance was benchmarked against a logistic regression model. The results suggest that AIS algorithms have potential for inclusion in fraud detection systems but that further work is required to realize their full potential in this domain. Congress on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Barcelona, Spain, 18-23 July Thu, 01 Jul 2010 00:00:00 GMT http://hdl.handle.net/10197/2736 2010-07-01T00:00:00Z http://hdl.handle.net/10197/2735 Evolutionary learning of technical trading rules without data-mining bias Agapitos, Alexandros; O'Neill, Michael; Brabazon, Anthony In this paper we investigate the profitability of evolved technical trading rules when controlling for data-mining bias. For the first time in the evolutionary computation literature, a comprehensive test for a rule’s statistical significance using Hansen’s Superior Predictive Ability is explicitly taken into account in the fitness function, and multi-objective evolutionary optimisation is employed to drive the search towards individual rules with better generalisation abilities. Empirical results on a spot foreign-exchange market index suggest that increased out-of-sample performance can be obtained after accounting for data-mining bias effects in a multi-objective fitness function, as compared to a single-criterion fitness measure that considers solely the average return. 11th International Conference on Parallel Problem Solving from Nature (PPSN 2010), Krakow, Poland, September 11-15, 2010 Wed, 01 Sep 2010 00:00:00 GMT http://hdl.handle.net/10197/2735 2010-09-01T00:00:00Z http://hdl.handle.net/10197/2734 Swarm intelligent optimisation based stochastic programming model for dynamic asset allocation Dang, Jing; Edelman, David; Hochreiter, Ronald; Brabazon, Anthony Asset allocation is critical for the portfolio management process. In this paper, we solve a dynamic asset allocation problem through a multiperiod stochastic programming model. The objective is to maximise the expected utility of wealth at the end of the planning periods. To improve the optimisation result of the model, we employ swarm intelligent optimisers, the Bacterial Foraging Optimisation (BFO) algorithm and the Particle Swarm Optimisation (PSO) algorithm. A hybrid optimiser using the Bacterial Foraging Optimisation algorithm for initialisation and the Sequential Quadratic Programming (SQP) for local search is also suggested. The results are compared with the standard-alone SQP and the canonical Genetic Algorithm. The numerical results suggest the hybrid method provides better result, with improved accuracy, stability and computing speed than using BFO, PSO, GA, or SQP alone. Congress on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Barcelona, Spain, 18-23 July Thu, 01 Jul 2010 00:00:00 GMT http://hdl.handle.net/10197/2734 2010-07-01T00:00:00Z http://hdl.handle.net/10197/2732 The syntax of stock selection : grammatical evolution of a stock picking model McGee, Richard; O'Neill, Michael; Brabazon, Anthony A significant problem in the area of stock selection is that of identifying the factors that affect a security’s return. While modern portfolio theory suggests a linear multi-factor model in the form of Arbitrage Pricing Theory it does not suggest the identity, or even the number, of risk factors in the model. Candidate factors for inclusion in a fundamental model can include hundreds of data points for each firm and with thousands of firms in the fund manager’s selection universe the model specification problem encompasses a large, computationally intense search space. Grammatical Evolution (GE) is a form of evolutionary computing that has been used successfully in model induction problems involving large search spaces. GE is applied to evolve a stock selection model with a customized mapping process developed specifically to enhance the performance of evolutionary operators for this problem. Stock selection models are rated using fitness functions commonly employed in asset management; the information coefficient and the inter-quantile return spread. The findings of the paper indicate that evolutionary computing is an excellent tool for the development of stock picking models. Congress on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Barcelona, Spain, 18-23 July Thu, 01 Jul 2010 00:00:00 GMT http://hdl.handle.net/10197/2732 2010-07-01T00:00:00Z http://hdl.handle.net/10197/2731 Intra-day seasonality in foreign exchange market transactions Cotter, John; Dowd, Kevin This paper examines the intra-day seasonality of transacted limit and market orders in the DEM/USD foreign exchange market. Empirical analysis of completed transactions data based on the Dealing 2000-2 electronic inter-dealer broking system indicates significant evidence of intraday seasonality in returns and return volatilities under usual market conditions. Moreover, analysis of realised tail outcomes supports seasonality for extraordinary market conditions across the trading day. Thu, 01 Apr 2010 00:00:00 GMT http://hdl.handle.net/10197/2731 2010-04-01T00:00:00Z http://hdl.handle.net/10197/2684 Housing risk and return : evidence from a housing asset-pricing model Case, Karl E.; Cotter, John; Gabriel, Stuart A. This paper investigates the risk-return relationship in determination of housing asset pricing. In so doing, the paper evaluates behavioral hypotheses advanced by Case and Shiller (1988, 2002, 2009) in studies of boom and post-boom housing markets. The paper specifies and tests a housing asset pricing model (H-CAPM), whereby expected returns of metropolitan-specific housing markets are equated to the market return, as represented by aggregate US house price time-series. We augment the model by examining the impact of additional risk factors including aggregate stock market returns, idiosyncratic risk, momentum, and Metropolitan Statistical Area (MSA) size effects. Further, we test the robustness of H-CAPM results to inclusion of controls for socioeconomic variables commonly represented in the house price literature, including changes in employment, affordability, and foreclosure incidence. Consistent with the traditional CAPM, we find a sizable and statistically significant influence of the market factor on MSA house price returns. Moreover we show that market betas have varied substantially over time. Also, we find the basic housing CAPM results are robust to the inclusion of other explanatory variables, including standard measures of risk and other housing market fundamentals. Additional tests of the validity of the model using the Fama-MacBeth framework offer further strong support of a positive risk and return relationship in housing. Our findings are supportive of the application of a housing investment risk-return framework in explanation of variation in metro-area cross-section and time-series US house price returns. Further, results strongly corroborate Case-Shiller behavioral research indicating the importance of speculative forces in the determination of U.S. housing returns. Sun, 01 Nov 2009 00:00:00 GMT http://hdl.handle.net/10197/2684 2009-11-01T00:00:00Z http://hdl.handle.net/10197/2168 Time varying risk aversion : an application to energy hedging Cotter, John; Hanly, Jim Risk aversion is a key element of utility maximizing hedge strategies; however, it has typically been assigned an arbitrary value in the literature. This paper instead applies a GARCH-in-Mean (GARCH-M) model to estimate a time-varying measure of risk aversion that is based on the observed risk preferences of energy hedging market participants. The resulting estimates are applied to derive explicit risk aversion based optimal hedge strategies for both short and long hedgers. Out-of-sample results are also presented based on a unique approach that allows us to forecast risk aversion, thereby estimating hedge strategies that address the potential future needs of energy hedgers. We find that the risk aversion based hedges differ significantly from simpler OLS hedges. When implemented in-sample, risk aversion hedges for short hedgers outperform the OLS hedge ratio in a utility based comparison. Annual meeting of the Financial Management Association International, October 21-24 2009, Reno, Nevada Thu, 01 Jan 2009 00:00:00 GMT http://hdl.handle.net/10197/2168 2009-01-01T00:00:00Z http://hdl.handle.net/10197/2161 Evolving dynamic trade execution strategies using grammatical evolution Cui, Wei; Brabazon, Anthony; O'Neill, Michael Although there is a plentiful literature on the use of evolutionary methodologies for the trading of financial assets, little attention has been paid to potential use of these methods for efficient trade execution. Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of interest. Grammatical Evolution (GE) is an evolutionary automatic programming methodology which can be used to evolve rule sets. In this paper we use a GE algorithm to discover dynamic, efficient, trade execution strategies which adapt to changing market conditions. The strategies are tested in an artificial limit order market. GE was found to be able to evolve quality trade execution strategies which are highly competitive with two benchmark trade execution strategies. Paper presented at EvoFin 2010, 4th European Event on Evolutionary and Natural Computation in Finance and Economics, as part of EvoStar 2010, 7-9 April 2010, Istanbul Fri, 01 Jan 2010 00:00:00 GMT http://hdl.handle.net/10197/2161 2010-01-01T00:00:00Z http://hdl.handle.net/10197/2160 Evolutionary computation and trade execution Cui, Wei; Brabazon, Anthony; O'Neill, Michael Although there is a plentiful literature on the use of evolutionary methodologies for the trading of Financial assets, little attention has been paid to the issue of efficient trade execution. Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of interest. This chapter introduces the concept of trade execution and outlines the limited prior work applying evolutionary computing methods for this task. Furthermore, we build an Agent-based Artificial Stock Market and apply a Genetic Algorithm to evolve an efficient trade execution strategy. Finally, we suggest a number of opportunities for future research. Fri, 01 Jan 2010 00:00:00 GMT http://hdl.handle.net/10197/2160 2010-01-01T00:00:00Z http://hdl.handle.net/10197/2142 Housing risk and return : evidence from a housing asset-pricing model Case, Karl E.; Cotter, John; Gabriel, Stuart A. This paper investigates the risk-return relationship in determination of housing asset pricing. In so doing, the paper evaluates behavioral hypotheses advanced by Case and Shiller (1988, 2002, 2009) in studies of boom and post-boom housing markets. The paper specifies and tests a housing asset pricing model (H-CAPM), whereby expected returns of metropolitan-specific housing markets are equated to the market return, as represented by aggregate US house price time-series. We augment the model by examining the impact of additional risk factors including aggregate stock market returns, idiosyncratic risk, momentum, and Metropolitan Statistical Area (MSA) size effects. Further, we test the robustness of H-CAPM results to inclusion of controls for socioeconomic variables commonly represented in the house price literature, including changes in employment, affordability, and foreclosure incidence. Consistent with the traditional CAPM, we find a sizable and statistically significant influence of the market factor on MSA house price returns. Moreover we show that market betas have varied substantially over time. Also, we find the basic housing CAPM results are robust to the inclusion of other explanatory variables, including standard measures of risk and other housing market fundamentals. Additional tests of the validity of the model using the Fama-MacBeth framework offer further strong support of a positive risk and return relationship in housing. Our findings are supportive of the application of a housing investment risk-return framework in explanation of variation in metro-area cross-section and time-series US house price returns. Further, results strongly corroborate Case-Shiller behavioral research indicating the importance of speculative forces in the determination of U.S. housing returns. 4th meeting of the Urban Economics Association (UEA) at the 56th Annual North American Meetings of the Regional Science Association International (RSAI), November 18-21 2009, San Francisco Sun, 01 Nov 2009 00:00:00 GMT http://hdl.handle.net/10197/2142 2009-11-01T00:00:00Z http://hdl.handle.net/10197/2140 Evolving efficient limit order strategy using grammatical evolution Cui, Wei; Brabazon, Anthony; O'Neill, Michael Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of interest. A practical problem in trade execution is how to trade a large order as efficiently as possible. A trade execution strategy is designed for this task to minimize total trade cost. Grammatical Evolution (GE) is an evolutionary automatic programming methodology which can be used to evolve rule sets. It has been proved successfully to be able to evolve quality trade execution strategies in our previous work. In this paper, the previous work is extended by adopting two different limit order lifetimes and three benchmark limit order strategies. GE is used to evolve efficient limit order strategies which can determine the aggressiveness levels of limit orders. We found that GE evolved limit order strategies were highly competitive against three benchmark strategies and the limit order strategies with long-term lifetime performed better than those with short-term lifetime. IEEE World Congress on Computational Intelligence, July 18-23 2010, Barcelona Fri, 01 Jan 2010 00:00:00 GMT http://hdl.handle.net/10197/2140 2010-01-01T00:00:00Z http://hdl.handle.net/10197/1159 U.S. core inflation : a wavelet analysis Dowd, Kevin; Cotter, John This paper proposes the use of wavelet methods to estimate U.S. core inflation. It explains wavelet methods and suggests they are ideally suited to this task. Comparisons are made with traditional CPI-based and regression-based measures for their performance in following trend inflation and predicting future inflation. Results suggest that wavelet-based measures perform better, and sometimes much better, than the Traditional approaches. These results suggest that wavelet methods are a promising avenue for future research on core inflation. Sun, 10 Sep 2006 00:00:00 GMT http://hdl.handle.net/10197/1159 2006-09-10T00:00:00Z