Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

Abstract

The creation of trustworthy models of the equities market enables investors to make better-informed choices. A trading model may lessen the risks that are connected with investing and make it possible for traders to choose companies that offer the highest dividends. However, due to the high degree of correlation between stock prices, analysis of the stock market is made more difficult by batch processing approaches. The prediction of the stock market has entered a technologically advanced era with the advent of technological marvels such as global digitization. For this reason, artificial intelligence models have become very important due to the continuous increase in market capitalization. The novelty of the proposed study is the development of the robustness time series model based on deep leaning for forecasting future values of stock marketing. The primary purpose of this study was to develop an intelligent framework with the capability of predicting the direction in which stock market prices will move based on financial time series as inputs. Among the cutting-edge technologies, artificial intelligence has become the backbone of many different models that predict the direction of markets. In particular, deep learning strategies have been effective at forecasting market behavior. In this article, we propose a framework based on long short-term memory (LSTM) and a hybrid of a convolutional neural network (CNN-LSTM) with LSTM to predict the closing prices of Tesla, Inc. and Apple, Inc. These predictions were made using data collected over the past two years. The mean squared error (MSE), root mean squared error (RMSE), normalization root mean squared error (NRMSE), and Pearson’s correlation (R) measures were used in the computation of the findings of the deep learning stock prediction models. Between the two deep learning models, the CNN-LSTM model scored slightly better (Tesla: R-squared = 98.37%; Apple: R-squared = 99.48%). The CNN-LSTM model showed a superior performance compared with the single deep learning LSTM and existing systems in predicting stock market prices.

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

“…Hybrid DL approaches frequently combine DL techniques with traditional methods [71][72][73][74][75] or DL architectures with each other, such as CNN-LSTM, LSTM or BiLSTM with attention mechanisms (AMs), transformer models, and graph convolutional neural network (GraphCNN). [76][77][78][79][80][81][82][83][84][85][86][87][88][89][90] These hybrid DL models prove to be efficient in identifying complex patterns and relationships in data due to the high capacity and adaptability of DL architectures, especially in applications like SPF. Chandar 71 proposed a new method for stock trading by combining technical indicators and CNNs, termed TI-CNN.…”

Enhancing Exchange-Traded Fund Price Predictions: Insights from Information-Theoretic Networks and Node Embeddings

“…Althelaya, Mohammed, and El-Alfy (2021) enhanced stock market forecasting by integrating deep learning with a multiresolution analysis, demonstrating the efficacy of combining varied analytical methods [17]. Aldhyani and Alzahrani (2022) crafted a novel framework using deep learning techniques, marking a significant stride in computational finance [18].…”

Application of Artificial Intelligence for Predicting Real Estate Prices: The Case of Saudi Arabia

“…Since their introduction in the 1940s, ANNs have been recognized as a class of neural network models [41]. An ANN is a system for parallel information processing that comprises a network of hidden layers of neurons [42][43][44][45]. It is a two-layer neuronal framework comprising an input section (where data are fed into the primary predictive model), a hidden layer (where data features are extracted to construct a predictive model), and an output layer.…”