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Issue 5(1), October 2010 -- Paper Abstracts

Girard  (p. 9-22)

Cooper (p. 23-32)

Kunz-Osborne (p. 33-41)

Coulmas-Law (p.42-46)

Stasio (p. 47-56)

Albert-Valette-Florence (p.57-63)

Zhang-Rauch (p. 64-70)

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Mattare-Monahan-Shah (p. 79-94)

Nonis-Hudson-Hunt (p. 95-106)

JOURNAL OF APPLIED BUSINESS AND ECONOMICS

Announcement Effects of Macro Economic Variables on Stock Market Returns and Volatility- A Neural Network Approach

Author(s): Ohaness Paskelian, Stephen Bell, Julia Creek

Citation: Ohaness Paskelian, Stephen Bell, Julia Creek, (2018) "Announcement Effects of Macro Economic Variables on Stock Market Returns and Volatility- A Neural Network Approach," Journal of Applied Business and Economics, Vol. 20, Iss.1,  pp. 10-18

Article Type: Research paper

Publisher: North American Business Press

Abstract:

We study the effects of macroeconomic variables on stock returns and volatility using a Neural Network approach. Neural network models can discover nonlinear complex patterns with the ability to process high levels of data. Neural networks can be used for any type of similar data sets with the ability to process and uncover similar data patterns, and provide a result. In this paper, we use a learning neural network model to find the relationship and strength of six widely used macroeconomic variables on stock market returns and volatility. Results indicate the three most influential variables are announcements of inflation, unemployment, and national income.