Year of Graduation
Forecast of Dynamics of Stock Market Based on Economic Indicators
This thesis contains research of influence of macroeconomic factors on the dynamics of Russian stock market and a forecast on real data (RTS index values during 2017 year). The methods used in the thesis are linear regression and artificial neural network. The metric to choose the best of the selected models is RMSE (root mean square error). The results of the thesis say that nonlinear model (ANN) outperforms linear one on chosen data and time horizon.