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Computational Intelligence for Financial Engineering and Economics (CIFEr), 2011 IEEE Symposium on

Date 11-15 April 2011

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  • [Front cover]

    Publication Year: 2011 , Page(s): c1
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  • [Copyright notice]

    Publication Year: 2011 , Page(s): 1
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  • Table of contents

    Publication Year: 2011 , Page(s): iii - vi
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  • [Front matter]

    Publication Year: 2011 , Page(s): vii - viii
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  • Out-of-equilibrium price dynamics and the inflationary process

    Publication Year: 2011 , Page(s): 1 - 8
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    Out-of-equilibrium price dynamics are studied using agent-based computational models. We examine how agents with bounded rationality act in an environment in which they do not know precisely both relative prices and the level of the prices. We model imprecision and uncertainty with fuzzy numbers and use the theory of probabilistic sets as part of the simulation model. Our results explain both positive and negative correlations between output and inflation based on consumer behavior. View full abstract»

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  • Intelligent financial warning model using Fuzzy Neural Network and case-based reasoning

    Publication Year: 2011 , Page(s): 1 - 6
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    Creating an applicable and precise financial early warning model is highly desirable for decision makers and regulators in the financial industry. Although Business Failure Prediction (BFP) especially banks has been extensively a researched area since late 1960s, the next critical step which is the decision making support scheme has been ignored. This paper presents a novel model for financial warning which combines a fuzzy inference system with the learning ability of neural network as a Fuzzy Neural Network (FNN) to predict organizational financial status and also applies reasoning capability of Fuzzy Case-Based Reasoning (FCBR) to support decision makers measuring appropriate solutions. The proposed financial warning model generates an adaptive fuzzy rule base to predict financial status of target case and then if it is predicted to fail, the FCBR is used to find similar survived cases. Finally according similar cases and a fuzzy rule base, the model provides financial decisions to change particular features as company goals in upcoming year to avoid future financial distress. View full abstract»

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  • Forecast combination with optimized SVM based on quantum-inspired hybrid evolutionary method for complex systems prediction

    Publication Year: 2011 , Page(s): 1 - 6
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1061 KB) |  | HTML iconHTML  

    Complex systems are complex, evolutionary, and dynamical system. One general method to predict such systems is use the previous and most recently behavior of a system to predict its future changes. The main advantage of this method is the ability to predict the behavior of systems without analytical prediction rules. In this situation, decision makers are often presented with several competing forecasts produced by different forecasting methods. A decision maker who needs a predict could choose a combined forecast that is generally more precise than any of the individual forecasts, for the combined forecast gets more information into consideration and the preciseness of the combined forecast improves as more methods are included in the combination. This article proposes a new forecast combination strategy, by using support vector machines that improve the forecasting capability of the model. Finally the results of using this method on two sample datasets are presented and the superiority of this method is demonstrated. View full abstract»

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  • Unified knowledge economy competitiveness index using fuzzy clustering model

    Publication Year: 2011 , Page(s): 1 - 6
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    The aim of this paper is to design a unified knowledge economy competitiveness index using fuzzy clustering, to aggregate four of the most reputable and famous knowledge economy indicators into a unified index that reflects the overall rate of knowledge in an economy, to serve many purposes for the decision makers and foreign investors interested in such economy. The four selected indices are: Knowledge Economy Index (KEI) from World Bank, Information and Communication Technologies Development Index (IDI) from United Nations agency for information and communication technology issues (ITU), Global Competitiveness Index (GCI) from the World Economic Forum, and World Competitiveness Yearbook (WCY) from Institute for Management Development (IMD). To achieve this unified index, a four steps framework is proposed. The first step utilizes a Correlation analysis, the second step is to carry a Principle Component Analysis (PCA) analysis and the third step employs training an Adaptive Neural Fuzzy Inference Systems (ANFIS) and the forth step is to create a unified index based on all existing indices. The purpose of the first step was to test the relationship between the selected indices and how strong it is. The PCA is employed to test the similarity amongst existing indices and whether they can be reduced in any form. ANFIS was used to generate rules to create trained submodel that determine which of the input indices make efficient contribution to the new unified knowledge indicator. Then, the fuzzy c-means clustering technique is used to construct the new Unified Knowledge Competitiveness and Progress Indicator (UKPI) which combines the four selected aggregate indices into a new single meaningful index that reflects the overall rate of Knowledge competitiveness and progress in a nation. View full abstract»

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  • Coevolutionary particle swarm optimization for evolving trend reversal indicators

    Publication Year: 2011 , Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (269 KB) |  | HTML iconHTML  

    A competitive coevolutionary particle swarm optimization approach is proposed in this paper to train neural networks from zero knowledge to act as security trading agents. The coevolved neural networks are used for timing buying and short selling securities to maximize net profit and minimize risk over time. The proposed model attempts to identify security trend reversals using technical market indicators. No expert trading knowledge is presented to the model, only the technical market indicator data. A competitive fitness function is defined that allows the evaluation of each solution relative to other solutions, based on predefined performance metric objectives. The relative fitness function in this study considers net profit and the Sharpe ratio as a risk measure. For the purposes of this study, the stock prices of eight large market capitalisation companies were chosen. Two benchmarks were used to evaluate the discovered trading agents, consisting of a Bollinger Bands/Relative Strength Index rule-based strategy and the popular buy-and-hold strategy. The agents that were discovered from the proposed model outperformed both benchmarks by producing higher returns for in-sample and out-sample data at a low risk. View full abstract»

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  • GP-based rebalancing triggers for the CPPI

    Publication Year: 2011 , Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (631 KB) |  | HTML iconHTML  

    The Constant Proportion Portfolio Insurance (CPPI) technique is a dynamic capital-protection strategy that aims at providing investors with a guaranteed minimum level of wealth at the end of a specified time horizon. A pertinent concern of issuers of CPPI products is when to perform portfolio readjustments. One way of achieving this is through the use of rebalancing triggers; this constitutes the main focus of this paper. We propose a genetic programming (GP) approach to evolve trigger-based rebalancing strategies that rely on some tolerance bounds around the CPPI multiplier, as well as on the time-dependent implied multiplier, to determine the timing sequence of the portfolio readjustments. We carry out experiments using GARCH datasets, and use two different types of fitness functions, namely variants of Tracking Error and Sortino ratio, for multiple scenarios involving different data and/or CPPI settings. We find that the GP-CPPI strategies yield better results than calendar-based rebalancing strategies in general, both in terms of expected returns and shortfall probability, despite the fitness measures having no special functionality that explicitly penalises floor violations. Since the results support the viability and feasibility of the proposed approach, potential extensions and ameliorations of the GP framework are also discussed. View full abstract»

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  • Investigating the effect of different GP algorithms on the non-stationary behavior of financial markets

    Publication Year: 2011 , Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (597 KB) |  | HTML iconHTML  

    This paper extends a previous market microstructure model, where we used Genetic Programming (GP) as an inference engine for trading rules, and Self Organizing Maps as a clustering machine for those rules. Experiments in that work took place under a single financial market and investigated whether its behavior is non-stationary or cyclic. Results showed that the market's behavior was constantly changing and strategies that would not adapt to these changes, would become obsolete, and their performance would thus decrease over time. However, because experiments in that work were based on a specific GP algorithm, we are interested in this paper to prove that those results are independent of the choice of such algorithms. We thus repeat our previous tests under two more GP frameworks. In addition, while our previous work surveyed only a single market, in this paper we run tests under 10 markets, for generalization purposes. Finally, we deepen our analysis and investigate whether the performance of strategies, which have not co-evolved with the market, follows a continuous decrease, as it has been previously suggested in the agent-based artificial stock market literature. Results show that our previous results are not sensitive to the choice of GP. Strategies that do not co-evolve with the market, become ineffective. However, we do not find evidence for a continuous performance decrease of these strategies. View full abstract»

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  • Comparison of trade decision strategies in an equity market GA trader

    Publication Year: 2011 , Page(s): 1 - 8
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (396 KB) |  | HTML iconHTML  

    This paper investigates different trade decision strategies under different market conditions so that a genetic algorithm could be designed to use the appropriate decision strategy. A trade decision strategy defines how a single action is decided upon based on a number of signals where each signal is a result of a technical analysis function. Using historical market data, a population is trained using a simple genetic algorithm employing crossover and mutation. Four genetic algorithms are used to evolve agents to trade, where each genetic algorithm uses a different trade decision strategy. The best individual from each evolved population is compared using an out-of-sample data set. Results show a significant difference in performance between the four decision strategies especially within bearish to moderately bullish stocks. Populations evolved using a weighted decision strategy performs better than strategies that are not weighted when trading bearish to moderately bullish stocks. Non-weighted decision strategies appear to out-perform weighted strategies when used on extremely bullish stock. This out-performance could be attributed to fewer trades made by non-weighted strategies compared to weighted ones. View full abstract»

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  • Evolutionary optimization of Risk Budgeted long-short portfolios

    Publication Year: 2011 , Page(s): 1 - 8
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (859 KB) |  | HTML iconHTML  

    Risk Budgeting is a relatively recent investment strategy instrumental in building long-short portfolios and notionally expected to enhance investment exposure and market protection. However, the inclusion of the strategy in the Portfolio Optimization problem model yields a complex constraint that is difficult to handle using traditional methods, justifying a compelling need to look for heuristic solutions. In this paper we discuss an Evolutionary Computation (EC) based solution for an integrated optimization of long-short portfolios, when the Risk Budgeting strategy is incorporated in the problem model, besides inclusion of constraints reflective of investor preferences. Two EC based strategies viz., Evolution Strategy with Hall of Fame and Differential Evolution (rand/1/bin) with Hall of Fame have been evolved to solve the complex problem and compare the quality of the solutions obtained. The experimental studies have been undertaken on the Bombay Stock Exchange (BSE200) and Tokyo Stock Exchange (Nikkei 225) data sets and specifically for the period March 1999-March 2009 which included both upturns and downturns in the markets. The efficiency of the portfolios obtained by the two EC based methods have been analyzed using a portfolio productivity indicator employing the efficiency improvement possibility function which is a variant of Luenberger's shortage function. View full abstract»

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  • Sovereign debt monitor: A visual Self-organizing maps approach

    Publication Year: 2011 , Page(s): 1 - 8
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (820 KB) |  | HTML iconHTML  

    In the 1980s and at the turn of last century, severe global waves of sovereign defaults occurred in less developed countries. To date, the forecasting and monitoring results of debt crises are still at a preliminary stage, while the issue is at present highly topical. This paper explores whether the application of the Self-organizing map (SOM), a neural network-based visualization tool, facilitates the monitoring of multidimensional financial data. First, this paper presents a SOM model for visual benchmarking and for visual analysis of the evolution of debt crisis indicators. Second, the method pairs the SOM with a geospatial dimension by mapping the `probability' of a crisis on a geographic map. This paper demonstrates that the SOM is a feasible tool for monitoring indicators of sovereign defaults. View full abstract»

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  • Fuzzy present value

    Publication Year: 2011 , Page(s): 1 - 6
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    Investors are constantly confronted with deciding between a multitude of different investments. The characteristics, especially the estimated return, of each alternative is never precisely known. In this paper, we propose to use fuzzy present values to model this uncertainty. We extend previous work with the possibility to account for uncertain project durations, which become increasingly important for long-term projects. The results allowed a detailed assessment of the cost of hydrogen production using a thermo-chemical cycle which is still in the early phase of research. On the theoretical side, we propose a sound fuzzification over crisp domains, avoiding in particular the unsteady behaviour of existing approaches. View full abstract»

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  • Financial contagion simulation through modelling behavioural characteristics of market participants and capturing cross-market linkages

    Publication Year: 2011 , Page(s): 1 - 6
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    Over the past two decades, financial market crises with similar features have occurred in different regions of the world. Unstable cross-market linkages during financial crises are referred to as financial contagion. We simulate the transmission of financial crises in the context of a model of market participants adopting various strategies; this allows testing for financial contagion under alternative scenarios. Using a comprehensive approach, we develop an agent-based multinational model and identify factors contributing to contagion. Although contagion has been investigated in the financial literature, it has not yet been studied extensively through computational intelligence techniques. The first steps in that direction are taken in. We extend these studies and introduce GARCH model and Clayton copula to better capture markets interdependence and to improve the evolutionary optimization technique. Our model further comprises four rather than three types of traders: technical, game, herd, and noise traders, respectively. The different types of traders use different strategies to make now three rather than two kinds of decisions: “buy”, “sell”, or ”hold”. Our simulations shed light on parameter values and characteristics which can be exploited in further research to detect contagion at an earlier stage, hence recognizing financial crises with the potential to destabilize cross-market linkages. View full abstract»

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  • Minimal agent-based model for the origin of trading activity in Foreign exchange market

    Publication Year: 2011 , Page(s): 1 - 8
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (333 KB) |  | HTML iconHTML  

    In this paper, we show that a minimal agent-based model for the Foreign Exchange (FX) market is capable of reproducing, to a certain extent, FX market trading activity. The model is minimal in that it has the advantage of having the minimum set of elements necessary for modelling the FX market in order to reproduce the FX market trading activity. The key elements are zero-Intelligence directional-change events traders, historical prices, actual FX traders' behaviour, limit orders, FX market trading sessions, market holidays, and the activation of the initial condition. All of these play a fundamental role. Most importantly, the simulation output is evaluated by contrast against the microscopic behavioural analysis of high-frequency data of individual traders' transactions on an account level provided by OANDA LTD. The results of this comparison prove that the trading agents' behaviour reproduces the FX market trading activity. Overall, the model leads to the identification of the key elements that may be responsible for the emergence of FX market trading activity in an agent-based model. View full abstract»

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  • Modeling moneyness volatility in measuring exchange rate volatility

    Publication Year: 2011 , Page(s): 1 - 6
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    The implied volatility (IV) is widely believed to be the best measure of exchange rate volatility. Despite its widespread usage, the IV approach suffers from an obvious chicken-egg problem: obtaining an unbiased IV requires the options to be priced correctly and calculating option prices accurately requires an unbiased IV. We contribute to this literature by developing a new model for exchange rate volatility which we term as the “moneyness volatility (MV)”. Besides eliminating the chicken-egg problem of IV, the MV approach outperforms the IV in forecasting ability in both in-sample and out-of-sample tests. The F-test, Granger-Newbold test and Diebold-Mariano test results consistently reveal that MV outperforms IV in estimating as well as forecasting exchange rate volatility. Furthermore, test results reveal that our approach works well for the six major currency options. Our pioneering approach in modeling exchange rate volatility has far-reaching implications for academicians, professional traders and risk managers. View full abstract»

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  • An experimental study of Multi-Objective Evolutionary Algorithms for balancing interpretability and accuracy in fuzzy rulebase classifiers for financial prediction

    Publication Year: 2011 , Page(s): 1 - 6
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1007 KB) |  | HTML iconHTML  

    This paper examines the advantages of simple models over more complex ones for financial prediction. This premise is examined using a genetic fuzzy framework. The interpretability of fuzzy systems is oftentimes put forward as a unique advantageous feature, sometimes to justify effort associated with using fuzzy classifiers instead of alternatives that can be more readily implemented using existing tools. Here we investigate if model interpretability can provide further benefits by realizing useful properties in computationally intelligent systems for financial modeling. We test an approach for learning momentum based strategies that predict price movements of the Bombay Stock Exchange (BSE). The paper contributes an experimental evaluation of the relationship between the predictive capability and interpretability of fuzzy rule based systems obtained using Multi-Objective Evolutionary Algorithms (MOEA). View full abstract»

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  • Fundamental analysis powered by Semantic Web

    Publication Year: 2011 , Page(s): 1 - 8
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    Conducting fundamental analysis within subsets of comparable firms has been demonstrated to provide more reliable inferences and increase the prediction quality in equity research. However, incorporating and representing both firm-specific information and common economic determinants has been widely recognized as the key challenge. This paper investigates how to leverage Semantic Web technologies to assist fundamental analysis by generating flexible and meaningful selections of comparable firms at low costs. We approach the problem by proposing Linked Open Financial Data as the data organization model and ontology modeling for knowledge representation. Results are verified in terms of efficiency with examples of quick mashups, and feasibility by adapting to existing valuation models. View full abstract»

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  • Financial decision making with distance measures and induced probabilistic generalized aggregation operators

    Publication Year: 2011 , Page(s): 1 - 8
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (752 KB) |  | HTML iconHTML  

    We develop a new financial decision making approach by using induced and probabilistic generalized aggregation operators. We introduce the induced generalized probabilistic ordered weighted averaging distance (IGPOWAD) operator and some of its main properties. Its main advantage is that it uses distance measures in a unified framework between the probability and the OWA operator where we can consider the degree of importance of each concept in the aggregation. Moreover, it also uses order-inducing variables that represent complex reordering processes in the aggregation. We develop an application of this new approach in a financial multi-person decision making problem regarding the selection of financial strategies. We see that the opinion of several experts provides more robust information for the decision maker. View full abstract»

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  • The effects of variable stationarity in a financial time-series on Artificial Neural Networks

    Publication Year: 2011 , Page(s): 1 - 8
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    This study investigates the characteristic of non-stationarity in a financial time-series and its effect on the learning process for Artificial Neural Networks (ANN). It is motivated by previous work where it was shown that non-stationarity is not static within a financial time series but quite variable in nature. Initially unit-root tests were performed to isolate segments that were stationary or non-stationary at a pre-determined significance level and then various tests were conducted based on forecasting accuracy. The hypothesis of this research is that when using the de-trended/original observations from the time series the trend/level stationary segments should produce lower error measures and when the series are differenced the difference stationary (non-stationary) segments should have lower error. The results to date reveal that the effects of variable stationarity on learning with ANNs are a function of forecasting time-horizon, strength of the linear-time trend, sample size and persistence of the stationary process. View full abstract»

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  • Investment strategies based on supervised learning

    Publication Year: 2011 , Page(s): 1 - 8
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    The most common neurocomputational approaches to support trading decisions are based on price returns forecasting through supervised neural networks, followed by a decision (or prescriptive) model. Alternative approaches have been proposed, including reinforcement learning and neurodynamic programming, in which a unified system is directly optimised with respect to some trading performance measure. The first paradigm may lead to significantly suboptimal investment strategies, while in the latter the learning process can be very difficult to accomplish successfully and efficiently. In this paper, we seek to demonstrate that, while preserving computational efficiency, it is possible to improve the financial performance of the forecast-based approach through a better optimization of the trading module, and also by considering more appropriate neural forecasting models. In particular, we propose more adequate ways of designing the training patterns from nonstationary price data; new trading rules based on different forecast horizons; and, the use of adaptation rules able to cope with transaction costs. These ideas are then tested and compared to some of the alternatives proposed in the literature, under different criteria, for several price time series, as well as with artificial data generated according to different stochastic models. View full abstract»

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  • A fuzzy model of a European index based on automatically extracted content information

    Publication Year: 2011 , Page(s): 1 - 8
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (254 KB) |  | HTML iconHTML  

    In this paper we build on previous work related to predicting the MSCI EURO index based on content analysis of ECB statements. Our focus is on reducing the number of features employed for prediction through feature selection. For this purpose we rely on two methodologies: (stepwise) linear regression and greedy forward feature subset selection. The original dataset consists of 13 features (General Inquirer content categories). Both methodologies provide an improvement in the overall accuracy of the model, while reducing the number of features employed. Through linear regression we achieve an accuracy of 67.58% on the testing set by relying on six features, while greedy forward selection enables an accuracy on the test set of 69.50% while relying on eight features. View full abstract»

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  • Author index

    Publication Year: 2011 , Page(s): 148
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