In this paper, we propose a new procedure for unconditional and conditional forecasting in agent-based models. The proposed algorithm is based on the application of amortized neural networks and consists of two steps. The first step simulates artificial datasets from the model. In the second...

The prediction of financial distress has emerged as a significant concern over a prolonged period spanning more than half a century. This subject has garnered considerable attention owing to the precise outcomes derived from its predictive models. The main objective of this study is to predict...

This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the period between 1982:1 and 2009:12. We find that at...

This paper presents a study of Artificial Neural Network (ANN) and Bayesian Network (BN) for use in stock index prediction. The data from Nigerian Stock Exchange (NSE) market are applied as a case study. Based on the rescaled range analysis, the neural network was used to capture the...

In this paper we design a simple trading strategy to exploit the hypothesized distinct informational content of the arithmetic and geometric mean. The rejection of cointegration between the two stock market indicators supports this conjecture. The profits generated by this cheaply replicable...

In most of the empirical research on capital markets, stock market indexes are used as proxies for the aggregate market development. In previous work we found that a particular market segment might be less efficient than the whole market and hence easier to forecast. In this paper we extend the...

We explore convenient analytic properties of distributions constructed as mixtures of scaled and shifted t-distributions. A feature that makes this family particularly desirable for econometric applications is that it possesses closed-form expressions for its anti-derivatives (e.g., the...

Recurrent Support Vector Regression for a Nonlinear ARMA Model with Applications to Forecasting Financial Returns Abstract: Motivated by the recurrent Neural Networks, this paper proposes a recurrent Support Vector Regression (SVR) procedure to forecast nonlinear ARMA model based simulated data...

Die Prognose der Insolvenzgefährdung von Unternehmen anhand statistischer Methodik war und ist eine bedeutende Aufgabe empirischer Forschung. Eine Möglichkeit der Beurteilung der finanziellen bzw. wirtschaftlichen Verfassung von Unternehmen stellt die sog. externe Bilanzanalyse anhand...