Machine Learning to Predict Annual Stock Market Index - a Genetic Programming Approach

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@InProceedings{Moni:2019:ICIICT,

• author = "Vidya Moni",
• title = "Machine Learning to Predict Annual Stock Market Index - a Genetic Programming Approach",
• booktitle = "2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)",
• year = "2019",
• address = "Chennai, India",
• month = "25-26 " # apr,
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ICIICT1.2019.8741439",
• isbn13 = "978-1-7281-1604-4",
• abstract = "The objective of this research was to generate an indicator of global political stability, by predicting the annual S&P 500 stock market index. This was done through machine learning, using a genetic programming approach, creating an algorithm with a template that takes into account the previous years' data of S&P 500 stock index, gold prices, the number of casualties in U.S. wars, crude oil prices, Dow Jones Industrial Average and rates of inflation in U.S. The prediction of this algorithm was highly accurate, within 1percent.",
• notes = "Also known as \cite{8741439}",

}

Genetic Programming entries for Vidya Moni

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