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

Vidya Moni
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引用次数: 1

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 14%.
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预测年度股票市场指数的机器学习-一种遗传规划方法
这项研究的目的是通过预测年度标准普尔500股票市场指数,得出一个全球政治稳定的指标。这是通过机器学习,使用遗传编程方法,创建了一个模板算法,该模板考虑了前几年的标准普尔500指数、黄金价格、美国战争中的伤亡人数、原油价格、道琼斯工业平均指数和美国通货膨胀率的数据,该算法的预测精度很高,在14%以内。
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