Journal of Big Data (Dec 2018)
A method of trend forecasting for financial and geopolitical data: inferring the effects of unknown exogenous variables
Abstract
Abstract This paper intends to contribute to the field of trend forecasting by proposing a new forecasting approach for stock market prices and geopolitical time series data of economic, financial and geopolitical importance. Designing models which account for every possible exogenous variable of relevance to a time series in question can often be an onerous and impractical task. Instead, this paper explores a new method which uses periods of decreased significance in the variable of foremost importance as a window of opportunity to observe the possible effects other variables may be having in a general way for the purpose of trend forecasting. When the latter variables are too unquantifiable to be accounted for in a model, having the ability to nonetheless discern their overall influence can be useful for anticipating trend changes. The proposed method was used in conjunction with the existing method of exponential smoothing to generate forecasts. It was also applied alone and contrasted with the results of exponential smoothing when used separately. This paper specifically addresses the ability of the newly proposed method to forecast the upwards/downwards extrapolation of the weekly trend for 9 weeks on stock closing prices for five companies of interest (Apple Inc, Amazon.com Inc, General Electric Company, Intel Corporation, and Alcoa Corporation). It was also applied to forecasting the annual trend for 9 years of Afghan asylum seeker data. These differing areas were chosen in order to demonstrate applications in finance as well as international relations. The empirical results and 95% confidence intervals indicate a clear advantage when the newly proposed method is used both in conjunction with exponential smoothing and on its own.
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