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Forecasting of Indian Stock Market by Effective Macro- Economic Factors and Stochastic Model

机译:有效宏观经济因素和随机模型对印度股市的预测

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The stock market patterns are non-linear in nature therefore it is difficult to forecast the future trends of the market. In this paper we have used different macro-economic factors of Indian stock market. Macro-economic factors include technical indicators. These technical indicators help to decide the patterns of the market at a particular time. There are hundreds of technical indicators are available, but all technical indicators are not useful. So we have obtained most effective technical indicators by applying Principal Component Analysis (PCA). Selected technical indicators are taken as input variable. Future prices are found through Hidden Markov Model (HMM). Hidden Markov Model is a very powerful stochastic model. In literature survey it was found that HMM gives better accuracy than other models. On the basis of experiment it was found that HMM with PCA performed well and gives Mean Absolute Percentage Error (MAPE) 1.77%.
机译:股市模式本质上是非线性的,因此很难预测市场的未来趋势。在本文中,我们使用了印度股票市场的各种宏观经济因素。宏观经济因素包括技术指标。这些技术指标有助于确定特定时间的市场格局。有数百种技术指标可用,但所有技术指标均无用。因此,我们通过应用主成分分析(PCA)获得了最有效的技术指标。选定的技术指标作为输入变量。未来价格可通过隐马尔可夫模型(HMM)找到。隐马尔可夫模型是一种非常强大的随机模型。在文献调查中发现,HMM比其他模型具有更高的准确性。在实验的基础上,发现带有PCA的HMM表现良好,并且给出的平均绝对百分比误差(MAPE)为1.77%。

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