Comparing the market risk premia forecasts in JSE and NYSE equity markets

Arbeitspapier

This paper examines the evidence regarding predictability in the market risk premium using artificial neural networks (ANNs), namely the Elman Network (EN) and the Higher Order Neural network (HONN), univariate ARMA and exponential smoothing techniques, such as Single Exponential Smoothing (SES) and Exponentially Weighted Moving Average (EWMA). The contribution of this paper is the inclusion of the South African market risk premium to the forecasting exercise and its direct comparison with US forecasting results. The market risk premium is defined as the expected rate of return on the market portfolio in excess of the shortterm interest rate for each market. All data are taken from January 2007 till December 2014 on a daily basis. Elman networks provide superior results among the tested models in both insample and out-of sample periods as well as among the tested markets. In general, neural networks beat the naive benchmark model and achieve to perform better than the rest of their linear tested counterparts. The forecasting models successfully capture patterns in the data that improve the forecasting accuracy of the tested models. Therefore, they can be applied to trading and investment purposes.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Discussion Papers ; No. 203

Classification and Topics

• Classification: Wirtschaft ; Neural Networks and Related Topics ; Model Evaluation, Validation, and Selection ; International Financial Markets ; Financial Forecasting and Simulation
• Subject: forecasting performance ; market risk premium ; South African stock market ; US stock market

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Oikonomikou, Leoni Eleni
• Event: Veröffentlichung
• (who): Georg-August-Universität Göttingen, Courant Research Centre - Poverty, Equity and Growth (CRC-PEG)
• (where): Göttingen
• (when): 2016

Further information

• Handle: http://hdl.handle.net/10419/130594
• Last update: 10.03.2025, 11:42 AM CET