Stock market prediction is important topic in economics and finance which has garnered the interest of researchers. This paper attempts to explore the usage of hybrid model to predict stock market movements. The data under consideration was sourced from Quandl, a repository that provides data related to the stock market for a wide variety of companies, and in order to forecast the prices of said stocks in the future, ensemble machine learning methods together with ARIMA for feature prediction have been employed. Hybrid approach for prediction stock price is proposed. Comparisons among ensemble learning algorithms are discussed, and interesting results has been obtained and future possibilities are touched upon in this paper. Intensive testing was done by gathering various stock market data from various sectors to explore robustness of the proposed model.
susululu/Stock-Market-Prediction-using-a-Hybrid-Model
Stock market prediction is important topic in economics and finance which has garnered the interest of researchers. This paper attempts to explore the usage of hybrid model to predict stock market movements. The data under con- sideration was sourced from Quandl, a repository that provides data related to the stock market for a wide variety of companies, and in order to forecast the prices of said stocks in the future, ensemble machine learning methods together with ARIMA for feature prediction have been employed. Hybrid approach for predic- tion stock price is proposed. Comparisons among ensemble learning algorithms are discussed, and interesting results has been obtained and future possibilities are touched upon in this paper. Intensive testing was done by gathering various stock market data from various sectors to explore robustness of the proposed model.
Python