Each stock market prediction model comprises two primary components: the prediction method, and the features used by the model. While some researchers aim to enhance prediction accuracy by employing more advanced techniques, others focus on obtaining informative feature sets from various information sources. There are studies that report advancements in both aspects.
2. Prediction Methods
Regarding the first category of related works that focus on improving the prediction method, there is a wide range of algorithms and tools available for stock market prediction, such as neural networks, deep learning, support vector machines (SVMs), and random forests.
Machine learning methods have predominantly been used for technical analysis in this field, with various studies comparing various types of algorithms. Ensemble approaches such as random forests and AdaBoost, as well as single classifier models such as neural networks and logistic regression, were compared using data from 5767 businesses 
. Sharma et al. 
proposed using LSboost to aggregate the predictions of an ensemble of trees in a random forest (referred to as LS-RF). Each prediction model defines a set of technical indicators as inputs. The performance of the suggested model was compared to that of the well-known support vector regression model. Picasso et al. 
incorporated technical and fundamental analysis parameters to evaluate the performance of several machine learning methods, including SVM and random forest. Various other supervised approaches, such as support vector regression (SVR) 
, multiple linear regression (LMLR) 
, and the j48 algorithm 
, have been investigated within the field of stock market prediction.
Artificial neural networks (ANN), based on several studies, have emerged as popular tools for financial prediction 
. Most of these studies have utilized historical market data as input features. Among ANNs, the multilayer perceptron (MLP) network is widely employed for stock forecasts. MLP is a feed-forward network comprising one or more hidden layers: an input layer, and an output layer. Each layer incorporates non-linear learning capabilities. Previous studies 
have proposed MLP networks for stock market prediction tasks. Deep ANNs are also extensively used in this field. To predict NASDAQ prices, ref. 
tested ANNs with various structures using historical prices on four- and nine-day timeframes. Their findings indicated that deep ANNs outperformed shallow networks. Arévalo et al. 
applied a deep ANN with five hidden layers to forecast Apple’s stock in the NASDAQ exchange, and they achieved approximately 65% directional accuracy. Chong et al. 
explored different data representation methods, including auto-encoder, RBM, and PCA, using raw data with 380 variables. These representations were employed as input for a deep ANN in stock prediction tasks. The results indicated no significant superiority of one method over the others. Hoseinzade et al. 
employed combinations of historical prices, technical indicators, and macroeconomic data as features. They used a CNN model to train the model in 3D spaces. The proposed model was compared to a PCA+ANN technique, and the results showed the CNN outperformed the other methods. Gao et al. 
and Wang et al. 
integrated attention layers with CNNs to forecast the following day’s index price based on past data. The findings suggested that the attention-based approach yielded the best results among the tested models.
Recurrent neural networks (RNNs) incorporate an internal memory, thereby enabling them to capture historical information and generate predictions 
. Among RNNs, LSTM is a widely used type that has also been applied to stock market prediction. Nelson et al. 
fed technical indicators into an LSTM to forecast price trends in the Brazilian stock exchange. The results showed the superior performance of LSTM compared to MLP. In another work, 
introduced a recursive network, the Echo State Network (ESN), to predict S&P 500 stocks. They used various stock market features, including price, volume, and the moving average. The ESN was applied to 50 stocks and achieved an error rate of 0.0027. Ding and Qin 
proposed an LSTM-based network with multiple inputs and outputs—specifically, the opening price, lowest price, and highest price of a stock. Their investigations revealed that the suggested model outperformed the LSTM network model and other deep recurrent neural networks in predicting multiple values simultaneously, with a prediction accuracy exceeding 95%. Jin et al. 
incorporated investors’ sentiment into stock prediction by utilizing empirical modal decomposition (EMD) to fail the complex sequence of stock prices. They also employed an LSTM network with attention mechanisms to focus on the most relevant data. The revised LSTM model not only improved prediction accuracy, but also reduced time delay according to the study’s findings. Liu et al. 
proposed a two-component multi-element hierarchical attention capsule network. The first component, multi-element hierarchical attention, assigned weights to valuable information from various news and social media sources. The capsule network component captured additional context information from events. Their model enhanced prediction accuracy by quantifying the diverse influences of events.
3. Feature-Based Methods
Market data, which encompasses the open/high/low/close (OHLC) prices of a share over a specific period, stands as the most prevalent feature employed in nearly all prediction algorithms in this field 
. The time of measurement (ranging from seconds to months) and the number of measurements used as inputs in the model may differ across various models.
In recent times, social networks have significantly influenced various aspects of human life, including financial markets. Social networks have a notable impact on financial markets through user interactions, opinion sharing, engaging in discussions, and following trusted individuals. Social trading is a specific form of this phenomenon, where investors observe and replicate the strategies of experts or peer traders. Within common social networks, two vital sources of information are the messages posted by users and the social information related to users themselves, such as following relationships. These aspects are further explored below. Concerning social network textual messages, sentiment analysis is a prevalent tool used to extract users’ opinions about shares, with the aim to classify the sentiment as positive, negative, or neutral 
. Notably, the study conducted by Nelson et al. 
represents one of the earliest attempts to forecast stock fluctuations using Twitter data.
To accurately assess stock market sentiment, the researchers evaluated a random subsample of tweets over six months and subsequently determined the correlation between this data and future stock market indicators. Baker et al. 
developed a sentiment index that captures changes in investors’ sentiments. They showed that fluctuations in this index impacted investors and stimulated changes in the overall stock market. Gilbert et al. 
suggested that an individual’s emotional state influences their decision making and confirmed that sentiment inferred from web content contained information that could forecast stock prices. While some studies have shown a correlation between emotional trends in internet comments and stock market movements, few have attempted to predict stock prices using sentiment analysis. For instance, Guo et al. 
proposed a technique based on the hot optimization route, which examined the relationship between user mood and the stock market by analyzing user review data from a stock review website. Zhou et al. 
achieved a stock market prediction accuracy of 64.15% using the SVM-ES model, wherein they incorporated social sentiments such as contempt, pleasure, melancholy, and fear. Picasso et al. 
employed data science and machine learning tools to combine technical and fundamental assessments. The result was a predictive model capable of forecasting the trajectory of a portfolio comprising the twenty most-capitalized enterprises in the NASDAQ100 index. Bouktif et al. 
employed improved sentiment analysis to assess the predictability of stock market directions. They delved deeper into stocks by examining various factors such as historical stock price, sentiment polarity, subjectivity, N-grams, custom text-based features, and feature delays. By employing advanced causality analysis, algorithmic feature selection, and machine learning techniques, including regularized model stacking, they collected and evaluated data from 10 major NASDAQ shares across diverse stock domains. Their method achieved a 60 percent accuracy rate, which surpassed existing sentiment-based stock market prediction algorithms, including deep learning. Alhamzeh et al. 
analyzed innovative data sources, specifically StockTwits paired with financial news, and tackled the problem as a binary classification task. They adopted a hybrid approach that combines sentiment and event-based features. The findings indicated that StockTwits data outperformed price data in predicting the closing prices of eight NASDAQ100 companies. Another valuable information source from social networks is user-related data, including the user’s influence within the network and the accuracy of their predictions. Kamkarhaghighi et al. 
examined the relationship between a Twitter user’s influential power in stock market prediction and their social network information, including details about their followers. They identified several active users in the stock exchange as valuable users and calculated a score for the accuracy of each user’s predictions. By setting a threshold to distinguish valuable and non-valuable users, they trained and reported the accuracy of a naive Bayes model using attributes such as the number of followers and the number of related followers of users. Ultimately, they concluded that users’ profile information could provide insights into their influence on the stock market. Bujari et al. 
explored the relationship between various social features, including the number of each user’s followers and the volume of tweets related to each stock, in relation to that stock’s market data. The results revealed that predictive features for each stock differ, and there is no general model that applies to all stocks.