Abstract
In the last few decades, techniques such as Artificial Neural Networks and Fuzzy Inference Systems were used for developing predictive models to estimate the required parameters. Since the recent past Soft Computing techniques are being used as alternate statistical tool. Determination of nature of financial time series data is difficult, expensive, time consuming and involves complex tests. In this paper, we use Multi Layer Perception and Radial Basis Functions of Artificial Neural Networks, Adaptive Neuro Fuzzy Inference System for prediction of S% (Financial Stress percent) of financial time series data and compare it with traditional statistical tool of Multiple Regression. The accuracies of Artificial Neural Network and Adaptive Neuro Fuzzy Inference System techniques are evaluated as relatively similar. It is found that Radial Basis Functions constructed exhibit high performance than Multi Layer Perception, Adaptive Neuro Fuzzy Inference System and Multiple Regression for predicting S%. The performance comparison shows that Soft Computing paradigm is a promising tool for minimizing uncertainties in financial time series data. Further Soft Computing also minimizes the potential inconsistency of correlations.
Highlights
Forecasting (Armstrong, 2001; Chen, 2002) and predicting financial time series (Box, 2008; Brockwell et al, 2009; Chatfield, 2000; Fuller et al, 1999) has been a topic of active research since past few decades
The study aims to determine comparative empirical relationships for estimation of Financial Stress percent of time series data by using Multiple Regression (Fuller et al, 1999; Lund et al, 2002) Artificial Neural Network (ANN) models such as Multi Layer Perception (MLP) and Radial Basis Functions (RBF) (Kenneth et al, 2001) and Adaptive Neuro Fuzzy Inference System (ANFIS) (Jang, 1993). 239 time series data samples are tested for determination of Financial Stress percent (S%) (Bordo et al, 2001; Kindleberger, 2005) in terms of four major explanatory variables viz. Credit Measures (CM), Asset Prices (AP), Macroeconomic Variables (MV) and Foreign Variables (FV) to establish predictive models using Statistical and Machine Learning and Soft Computing techniques (Kosko, 2008; Zadeh, 1994)
Experimental Results This section illustrates the results obtained towards prediction of financial stress percent of financial time series data using Multiple Regression, MLP, RBF and ANFIS models
Summary
Forecasting (Armstrong, 2001; Chen, 2002) and predicting financial time series (Box, 2008; Brockwell et al, 2009; Chatfield, 2000; Fuller et al, 1999) has been a topic of active research since past few decades. The study aims to determine comparative empirical relationships for estimation of Financial Stress percent of time series data by using Multiple Regression (Fuller et al, 1999; Lund et al, 2002) Artificial Neural Network (ANN) models such as Multi Layer Perception (MLP) and Radial Basis Functions (RBF) (Kenneth et al, 2001) and Adaptive Neuro Fuzzy Inference System (ANFIS) (Jang, 1993). 239 time series data samples are tested for determination of Financial Stress percent (S%) (Bordo et al, 2001; Kindleberger, 2005) in terms of four major explanatory variables viz. Credit Measures (CM), Asset Prices (AP), Macroeconomic Variables (MV) and Foreign Variables (FV) to establish predictive models using Statistical and Machine Learning and Soft Computing techniques (Kosko, 2008; Zadeh, 1994).
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