**2. Related work**

The literature on systems and methods of stock price forecasting is quite rich. Numerous proposals exist on the mechanisms, approaches, and frameworks for predicting future stock prices and stock price movement patterns. At a broad level, these propositions can be classified into four categories. The proposals of the first category are based on different variants of univariate and multivariate regression models. Some of the notable approaches under this category are - *ordinary least square* (OLS) regression, *multivariate adaptive regression spline* (MARS), *penalty-based regression*, *polynomial regression*, etc. [1–4]. These approaches are not, in general, capable of handling the high degree of volatility in the stock price data. Hence, quite often, these models do not yield an acceptable level of accuracy in prediction. *Autoregressive integrated moving average* (ARIMA) and other approaches of econometrics such as *cointegration*, *vector autoregression* (VAR), *causality tests*, and *quantile regression* (QR), are some of the methods which fall under the second category of propositions [5–16]. The methods of this category are superior to the simple regression-based methods. However, if the stock price data are too volatile and exhibit strong randomness, the econometric methods also are found to be inadequate, yielding inaccurate forecasting results. The learning-based approach is the salient characteristic of the propositions of the third category. These proposals are based on various algorithms and architectures of machine learning, deep learning, and reinforcement learning [17–41]. Since the

### *Design and Analysis of Robust Deep Learning Models for Stock Price Prediction DOI: http://dx.doi.org/10.5772/intechopen.99982*

frameworks under this category use complex predictive models working on sophisticated algorithms and architectures, the prediction accuracies of these models are found to be quite accurate in real-world applications. The propositions of the fourth category are broadly based on hybrid models built of machine learning and deep learning algorithms and architectures and also on the relevant inputs of sentiment and news items extracted from the social web [42–47]. These models are found to yield the most accurate prediction of future stock prices and stock price movement patterns. The *information-theoretic* approach and the *wavelet* analysis have also been proposed in stock price prediction [48, 49]. Several *portfolio optimization* methods have also been presented in some works using forecasted stock returns and risks [50–55].

In the following, we briefly discuss the salient features of some of the works under each category. We start with the regression-based proposals.

Enke et al. propose a multi-step approach to stock price prediction using a multiple regression model [2]. The proposition is based on a *differential-evolution-based fuzzy clustering* model and a fuzzy neural network. Ivanovski et al. present a linear regression and correlation study on some important stock prices listed in the Macedonian Stock Exchange [3]. The results of the work indicate a strong relationship between the stock prices and the index values of the stock exchange. Sen and Datta Chaudhuri analyze the trend and the seasonal characteristics of the capital goods sector and the small-cap sector of India using a time series decomposition approach and a linear regression model [4].

Among the econometric approaches, Du proposes an integrated model combining an ARIMA and a backpropagation neural network for predicting the future index values of the Shanghai Stock Exchange [6]. Jarrett and Kyper present an ARIMAbased model for predicting future stock prices [7]. The study conducted by the authors reveals two significant findings: (i) higher accuracy is achieved by models involving fewer parameters, and (ii) the daily return values exhibit a strong autoregressive property. Sen and Datta Chaudhuri different sectors of the Indian stock market using a time series decomposition approach and predict the future stock prices using different types of ARIMA and regression models [9–14, 33]. Zhong and Enke present a gamut of econometric and statistical models, including ARIMA, *generalized autoregressive conditional heteroscedasticity* (GARCH), *smoothing transition autoregressive* (STAR), *linear* and *quadratic discriminant analysis* [16].

Machine learning and deep learning models have found widespread applications in designing predictive frameworks for stock prices. Baek and Kim propose a framework called ModAugNet, which is built on an LSTM deep learning model [17]. Chou and Nguyen preset a *sliding window metaheuristic optimization* method for stock price prediction [19]. Gocken et al. propose a hybrid artificial neural network using harmony search and genetic algorithms to analyze the relationship between various technical indicators of stocks and the index of the Turkish stock market [21]. Mehtab and Sen propose a gamut of models designed using machine learning and deep learning algorithms and architectures for accurate prediction of future stock prices and movement patterns [22–28, 34, 35]. The authors present several models which are built on several variants of *convolutional neural networks* (CNNs) and *long-and-shortterm memory networks* (LSTMs) that yield a very high level of prediction accuracy. Zhang et al. present a multi-layer perceptron for financial data mining that is capable of recommending buy or sell strategies based on forecasted prices of stocks [40].

The hybrid models use relevant information in the social web and exploit the power of machine learning and deep learning architectures and algorithms for making predictions with a high level of accuracy. Among some well-known hybrid models, Bollen et al. present a scheme for computing the mood states of the public from the Twitter feeds and

use the mood states information as an input to a nonlinear regression model built on a *selforganizing fuzzy neural network* [43]. The model is found to have yielded a prediction accuracy of 86%. Mehtab and Sen propose an LSTM-based predictive model with a sentiment analysis module that analyzes the public sentiment on Twitter and produces a highly accurate forecast of future stock prices [45]. Chen et al. present a scheme that collects relevant news articles from the web, converts the text corpus into a word feature set, and feeds the feature set of words into an LSTM regression model to achieve a highly accurate prediction of the future stock prices [44].

The most formidable challenge in designing a robust predictive model with a high level of precision for stock price forecasting is handling the randomness and the volatility exhibited by the time series. The current work utilizes the power of deep learning models in feature extraction and learning while exploiting their architectural diversity in achieving robustness and accuracy in stock price prediction on very granular time series data.
