Stock Market Analysis: A Review and Classification of Estimate Performances
Abstract
In the World of big data and deep learning, predicting the price and trends of the stock price is more crowd-pleasing than ever. We can collect one, two, more years of data from the Stock market and propose key feature engineering models and deep learning models for predicting the stock market price fluctuations. The suggested solution is far-reaching because it involves the concept of feature engineering techniques that are combined with the stock market dataset pre- processing and a custom-made deep learning-based stock market price forecasting system. It controlled an extensive evaluation of commonly utilized machine learning models that concluded the expected resolution was admirable for the extensive feature engineering we are created. This technique provides an up-to-date level of overall accuracy which is helpful in predicting and analyzing any stock trend in the share market. At the same time, the length of the forecast period, feature engineering, detailed design and evaluation of the data preprocessing mechanism, that task provide to the equity research group with a pair of financial and technical fields.
How to cite this article:
Soni G, Chadha R, Singh R et al. Ankit Jha, Paras Singh. Stock Market Analysis: A Review and Classification of Estimate Performances. J Engr Desg Anal 2022; 5(1): 6-8.
References
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Ariyo, Adebiyi A., Adewumi O. Adewumi, Charles K. Ayo. 2014. Stock Price Prediction Using the Arima Model. Paper presented at the 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation (UKSim), Cambridge, UK, March 26–28.
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https://www.sciencedirect.com/science/article/pii/S187705091503207X?via%3Dihub
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