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Top1. Introduction
Accurate stock price prediction is the major objectives of the investors from the starting of the stock market. The trading occurs for millions of dollars each day, and the profits are earned by the traders from their investments. The investors making the correct sell and buy decisions attain profits (Shi, et al., 2019). The right decisions are made by the investors through the judging process based on technical analysis like information from micro blogs as well as newspapers, stock market indices, and charts of the company. Yet, the investors find it difficult for predicting and investigating the market by going through the entire information (Lee, et al., 2019). In the earlier times, stock markets were forecasted normally through financial experts. Still, data scientists handle the problems of prediction via learning approaches. Further, the characteristics of the prediction methods as well as the prediction accuracy are enhanced by the computer scientists with the help of machine learning techniques (Chen, et al., 2019). The next phase in enhancing the models of prediction is the utilization of deep learning having superior performance. The stock market prediction is completely a challenging one, and few problems are solved by the data scientists while developing a predictive method (Wang, et al., 2019). The two major challenges created by the correlation among the market characteristics and the investment psychology as well as the stock market instability are nonlinearity and complexity.
Various techniques are enhanced for forecasting the stock market trends. The combination of “Hidden Markov Model (HMM), Artificial Neural Networks (ANNs), and Genetic Algorithms (GA)” is developed for the transformation of the daily stock values to the independent price groups (Lee and Kim, 2020; Hassan, et al., 2007). The base classifiers of the SVM ensemble were selected by the novel financial prediction algorithm based on the SVM ensemble. It deemed both the individual prediction as well as the diversity analysis (Huang, et al., 2005). The value trends associated with the Hang index were predicted by ten data mining techniques (Ou and Wang, 2009). The techniques included are the Neural Network (NN) (Manne, et al., 2020), SVM, Bayesian classification, K-Nearest Neighbor (KNN), and tree-oriented classification (Sun and Li, 2012). The outcomes showed that the SVM was better than various predictive methods. The value fluctuations through a Legendre NN were predicted with consideration of the position of the investor as well as their decisions by investigating the former data (Liu and Wang, 2012). A random function was also analyzed in the prediction method. The outcomes were compared by the morphological rank linear prediction technique with the multilayer perceptron networks, and time-delay included evolutionary prediction technique (Tsai, et al., 2011). Each of these algorithms handles the problems associated with the stock prediction. Yet, it is important to note that there are drawbacks to them.
The practical usage of deep learning in the prediction problems is successful since the output as well as the input relationship can be investigated by them if the dataset is assumed to be complex (Idrees, et al., 2019). These can recognize the novel test samples even if it is not utilized during the network training. Another important benefit of deep learning is automatic feature extraction (Zhang, et al., 2018). The network decides which dataset can be employed as the indicator for reliably labeling the particular data (Nabipour, et al., 2020; Baker and Belgorodskiy, 2007). Deep learning returns better accuracy with less human effort and minimal tuning. The usage of deep learning is enhanced because of the vast analytical power with the developing article count (Wen, et al., 2019). The CNN and LSTM are mostly used in financial research that in turn produces better outcomes (Wang, 2003; Kwon and Moon, 2007). Though these techniques are very supportive in the investigation of data’s, low time series are stochastic and volatile, and their predictions, as well as the analysis, exist to be a challenging one.