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- W2590141440 abstract "Time series for forecasting stock market prices has become extremely popular over the last few decades. Both stand-alone time series algorithms and hybrids have been successfully implemented, with several papers documenting their positive results. Correlation and volatility across the major markets have also seen quite a number of works conducted with positive results being documented. This research has examined the existence of a correlation between major stock market prices and the effectiveness of time series for forecasting future prices. A cross-correlation matrix and ARIMA time series algorithm for forecasting were implemented. The results, while proving correlation exists between the major stock markets, was inconclusive when examining the percentage change in US GDP growth and its possible effect on the change in correlation. Time series results were unexpected, however, this researcher strongly believes it is indeed effective in making stock market price predictions." @default.
- W2590141440 created "2017-03-03" @default.
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- W2590141440 date "2016-09-15" @default.
- W2590141440 modified "2023-09-27" @default.
- W2590141440 title "Analyzing Historical Stock Market Data to Determine if a Correlation Exists Between Major Stock Market Indexes and if Time Series is Sufficient to Make Predictions" @default.
- W2590141440 hasPublicationYear "2016" @default.
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