Abstract
Patterns of stock market are non-linear and hence it becomes difficult to forecast the future trends of the stock market. There are many economic and non-economic factors which involves the price movements of the stock market. We used various macroeconomic factors of the Indian stock market in this paper. Technical indicators are macroeconomic factors. These technical indicators aid in determining market patterns at any given time. There are hundreds of technical indicators available, but not all of them are useful. So, we tried to find out the 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 (HMM) is a popular statistical stochastic model for forecasting the market. Therefore, in this research, we thought of using this model for predicting market trends. In literature survey it was found that HMM gives better accuracy than other statistical models. Based on the results of the experiments, it was discovered that HMM with PCA performed well, with a mean absolute percentage error (MAPE) of 1.77%.
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