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This book examines the prediction of stock market movements of India using big data analytics. Stock markets have shifted from the guiding principle of standard finance into behavioral finance. Forecasting is one of the classic issues since the stock markets are volatile, stochastic and non-linear in nature. The values of Momentum, Relative Strength Index, Williams %R and Commodity Channel Index indicated both bullish and bearish trends for BSE-Sensex and NSE-Nifty stock indices which were rampant and robust during the study period. This phenomenon negates the Efficient Market Hypothesis, but it confirmed the existence of Random Walk Theory in the realm of capital market movements. One of the neural network methods, the k-nn algorithm exhibited a higher predictive accuracy than the logistic regression approach. The business architecture and market value of company stocks are changing in every millisecond. The close correlation between the predicted and the actual values indicated that deep learning methods such as Machine Learning and Artificial Neural Networks were more powerful tools in the stock price prediction and helped the investors to make intelligent investment decisions.
| Autorius: | Sigo Marxia Oli, Murugesan Selvam, |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2020 |
| Knygos puslapių skaičius: | 232 |
| ISBN-10: | 6200586349 |
| ISBN-13: | 9786200586346 |
| Formatas: | 220 x 150 x 14 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Big Data Analytics and Business Architecture of Indian Stock Market: Predictive Analytics of the Indian Stock Market Movements ¿ Applications of Machine Learning and Artificial Intelligence“
