1,721,584 research outputs found
A conceptual model of investor behavior
No full textBehavioral finance is a subdiscipline of finance that uses insights from cogni tive and social psychology to enrich our knowledge of how investors make their financial decisions. Agent-based artificial financial markets are bottomup models of financial markets that start from the micro level of individual investor behavior and map it into the macro level of aggregate market phenomena. It has been recognized in the literature, yet not fully explored, that such agent-based models are very suitable tool to generate or test various behavioral hypotheses. To pursue this research idea, first we develop a con ceptual model of individual investor that consists of a cognitive model of the investor and a description of the investment environment. In the modeling tradition of cognitive science and intelligent systems, the investor is seen as learning, adapting, and evolving entity that perceives the environment, pro cesses information, acts upon it, and updates its internal states. This con ceptual model can be used to build stylized representations of (classes of) individual investors, and further studied within the paradigm of agent-based artificial financial markets
Modeling loss aversion and biased self-attribution using fuzzy aggregation
No full textIn this paper we use an agent-based stock market to study how investor performance and market predictions influence investor sentiment and confidence. Investor sentiment is modeled using a generalized average operator, which has been proposed in the fuzzy literature as an index of optimism. Our simulations show the impact of loss aversion on investor optimism, and the emergence of investor overconfidence through biased self-attribution. Computational models of financial markets show potential for studying the dynamics of investor psychology with respect to various market feedbacks, while the fuzzy aggregation operator used provides a convenient way of modeling those psychological effect
Feature selection using fuzzy objective functions
No full textOne of the most important stages in data preprocessing for data mining is feature selection. Real-world data analysis, data mining, classification and modeling problems usually involve a large number of candidate inputs or features. Less relevant or highly correlated features decrease, in general, the classification accuracy, and enlarge the complexity of the classifier. Feature selection is a multicriteria optimization problem, with contradictory objectives, which are difficult to properly describe by conventional cost functions. The use of fuzzy decision making may improve the performance of this type of systems, since it allows an easier and transparent description of the different criteria used in the feature selection process. In previous work an ant colony optimization algorithm for feature selection was presented, which minimizes two objectives: number of features and classification error. Two pheromone matrices and two different heuristics are used for each objective. In this paper, a fuzzy objective function is proposed to cope with the difficulty of weighting the different criteria involved in the optimization algorithm
Overconfident investors in the LLS agent-based artificial financial market
No full textAgent-based artificial financial markets are bottom-up models of financial markets which explore the mapping from the micro level of individual investor behavior into the macro level of aggregate market phenomena. It has been recently recognized in the literature that such (agentbased) models are potentially a very suitable tool to generate or test various behavioral hypotheses. One of the psychological biases that received a lot of attention in financial studies, both mainstream and behavioral, is the phenomena of investor overconfidence. This paper studies overconfident investors in the agent-based artificial financial market based on the Levy, Levy, Solomon (2000) model. Overconfidence is modeled as miscalibration, i.e. as underestimated risk of expected returns. We find that overconfident investors create less frequent but more extreme bubbles and crashes when compared to the unbiased efficient market believers of the original model. When investors are modeled to exhibit a biased self-attribution, they quickly move to the state of high overconfidence and remain there. With an unbiased self-attribution, on the other hand, investor overconfidence varies greatly, but around a moderate level of overconfidenc
Just-in-time production and delivery in supply chains : a hybrid evolutionary approach
No full textThe timely production and distribution of rapidly perishable goods is one of the most challenging logistic problems in the context of supply chain operation. The problem involves several tightly interrelated planning, scheduling and routing problems, each with large combinatorial complexity. From a more practical perspective, the problem calls for a trade-off between risks and returns. To effectively deal with these considerable difficulties, we propose a novel meta-heuristic approach based on a hybrid evolutionary algorithm combined with constructive heuristics for addressing just-in-time production and delivery with time constraints on both the earliness and the lateness of supply. Distribution of ready-made concrete is used as a practical example. A case study based on industrial data illustrates the potential of the proposed approach
Local models for the analysis of spatially varying relationships in a lignite deposit
No full textRelationships between geographically referenced variables are usually spatially heterogeneous and, to account for such variations, local models are necessary. This paper compares the Geographically Weighted Regression (GWR) model, usually used to integrate and examine the spatial heterogeneity of a relationship, and the Fuzzy Clustering-Based Least Squares (FCBLS) model for the analysis of spatially varying relationships. Both models use the same model parameters and bandwidth values derived from the Akaike Information Criterion. The results show that FCBLS outperforms the GWR model
Modeling investor sentiment and overconfidence in an agent-based stock market
No full textAgent-based stock markets as bottom-up models of financial markets allow us to study the link between individual investor behavior and aggregate market phenomena, and as such are a useful tool for investigating the implications of behavioral finance and investor psychology. In this paper we want to disentangle between the effects of investor sentiment and investor overconfidence. While investor optimism or pessimism influences the expectations of future returns, overconfidence is related to the precision of those expectations and is modeled as miscalibration. In an artificial stock market based on the LLS model, we find that more optimistic investors create more pronounced booms and crashes in the market, when compared to the unbiased efficient market believers of the original model. In the case of extreme optimism, the optimistic investors end up dominating the market, while in the case of extreme pessimism, the market reduces to the benchmark model of rational informed investors. The overconfidence of investors is found to exacerbate the effects of investor sentimen
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