Forecasting Stock Index Returns: A Hybrid ARIMA and Neural Network Approach
Coblenz, Maximilian (2012) Forecasting Stock Index Returns: A Hybrid ARIMA and Neural Network Approach. [Dissertation (University of Nottingham only)] (Unpublished)
Forecasting of stock indices has become a crucial task for investors and other market participants due to the increasing use of stock index related instruments for hedging and trading. A forecasting model proposed by Zhang (2003) is used on return data from the ATX, AEX, CAC 40, DAX, FTSE 100, Hang Seng, KOSPI 200, NIKKEI 225, RTS and S&P 500 indices. The model combines linear predictions from an ARIMA model and nonlinear predictions from a neural network to yield higher accuracy. At first glance the model seems to outperform an ARIMA model and a simple random walk. However, in the majority of the cases the results are statistically not significant. Hence, the accuracy improvement is poor in relation to the increased estimation effort. Also, a trading simulation is conducted. Yet, statistical tests do not support the superior profits generated from using the forecasts. Additionally, in a realistic setting the trading simulation results are unlikely to be reproducible. Therefore, the results do not challenge the Efficient Market Hypothesis.
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