Abstract
Artificial intelligence (AI) methods are interesting alternatives to classical approaches for modeling financial time series since they relax the assumptions imposed on the data generating process by the parametric models and do not impose any constraint on the model’s functional form. Even if many studies employed these techniques for modeling financial time series, the connection of the models’ performances with the statistical characteristics of the data series has not yet been investigated. Therefore, this research aims to study the performances of Gene Expression Programming (GEP) for modeling monthly and weekly financial series that present trend and/or seasonality and after the removal of each component. It is shown that series normality and homoskedasticity do not influence the models’ quality. The trend removal increases the models’ performance, whereas the seasonality elimination results in diminishing the goodness of fit. Comparisons with ARIMA models built are also provided.
Highlights
Financial time series analysis and forecasting have been interesting research fields for decades
In the context of time series modeling, a candidate solution is a mathematical expression formed by functions, variables, and constants, and as a consequence, it is represented as a parse tree of the mathematical expression [20,23]
After taking the first-order difference, the Augmented Dickey-Fuller test (ADF) and PP tests rejected the existence of the unit root, while the KPSS test did not reject the hypothesis of the stationarity in level and around a deterministic trend
Summary
Financial time series analysis and forecasting have been interesting research fields for decades. Time series analysis may explain the law governing the data generating process, while good models may provide accurate predictions of its future behavior, supporting the decisions for profitable trading strategies [1,2]. In 1989, Hamilton [6] introduced the Markov Switching Model, which became one of the most popular models used for the nonlinear series, involving multiple equations that characterize the series behavior in different regimes [7]. Recent studies suggest that GARCH [10,11] can be a promising alternative to the traditional SARIMA method in forecasting problems, especially for nonlinear data. It was shown that it is difficult to find such models with very high accuracy for long series with high variability
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