A Review on Machine Learning and Deep Learning Models for Forecasting Stock Market Trends

A stock market, sometimes referred to as an equity market, is a gathering of buyers and sellers of stocks that represent company ownership. In this market, various investors sell and acquire shares based on stock availability. Stock trading is an important practice in the world of finance, and it is the cornerstone of many enterprises. A developing country’s rapid economic development, such as India’s, is dependent on its stock market. It is crucial in today’s economic and social environment. The stock market’s ups and downs have an impact on stakeholders’ benefits. Stock market value prediction has long captivated the interest of investors and researchers because of its complexity, inherent ambiguity, and ever-changing nature. “Stock market prediction” is a method of trying to anticipate the worth of a given “stock” in the coming days. This is performed by considering historical stock values as well as price variances throughout the previous days. Due to market volatility, forecasting stock indices is definitely tough, necessitating an accurate forecast model. Recent advancement in stock market prediction technology is machine learning, which produces forecasts based on the values of current stock market indices by training on their prior values. The term “machine learning” (ML) refers to a subdivision of “artificial intelligence” (AI) in which we train machines with data and use test data to forecast the future. This study presents an overview of deep learning techniques that are currently being used to anticipate stock market movements and predictions.KeywordsMachine learning (ML)Deep learning (DL)Artificial neural networks (ANN)Recurrent neural networks (RNN)LSTM (Long Short-Term Memory) Model

The integration of Artificial Intelligence (AI) into financial forecasting has transformed traditional stock market prediction methods. This research paper explores the effectiveness of AI techniques in forecasting stock trends within the Indian stock market over the last few years. We examine AI methodologies, including machine learning (ML), deep learning (DL), and hybrid models applied to the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE). By comparing historical data with AI-predicted trends, this study evaluates prediction accuracy and market relevance (Patel et al., 2015). Furthermore, the research outlines existing study gaps and proposes a future scope of integrating AI with behavioral finance and real-time analytics (Chen et al., 2022). The prediction of stock market trends remains a significant challenge due to the stochastic and non-linear nature of financial time series data (Zhang & Zhou, 2020). With the proliferation of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL) techniques, there has been a paradigm shift in the modeling and forecasting of stock price movements (Fischer & Krauss, 2018). This paper presents a comprehensive study on the effectiveness of AI in predicting stock market trends within the Indian financial ecosystem, focusing on a comparative analysis of AI models implemented over the last five years (2018–2023) on major indices and stocks listed on the NSE and BSE. This research evaluates the performance of multiple AI algorithms—including Support Vector Machines (SVM), Random Forests (RF), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and hybrid ensemble models—in forecasting short- and medium-term price trends using historical stock data (Krauss et al., 2017; Chen & He, 2021). Standard evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and classification accuracy are employed to assess model efficacy. The results indicate that while traditional machine learning algorithms offer moderate predictive power, advanced deep learning and hybrid models significantly outperform them, particularly during periods of high market volatility, such as the COVID-19 pandemic and post-lockdown recovery phases (Weng et al., 2018; Jain & Jain, 2021). The study identifies key research gaps, including limited sectoral diversification in datasets, inadequate integration of sentiment and behavioral data, overfitting issues in complex models, and the lack of real-time prediction systems in the Indian context (Gupta & Pathak, 2022). Furthermore, current models often ignore nonquantitative factors such as investor sentiment, macroeconomic indicators, and global events, which can critically impact prediction accuracy (Nassirtoussi et al., 2014). These limitations suggest the need for a more holistic and interdisciplinary approach to AI-driven financial forecasting. The scope for future research includes the development of real-time, adaptive AI systems using high-frequency trading data, the incorporation of behavioral finance through social media and news analytics, and the exploration of quantum computing-based AI models (Liu et al., 2023). From a practical standpoint, the findings of this study offer valuable insights for institutional investors, financial analysts, regulatory bodies, and developers of AIpowered trading platforms. The study concludes that while AI is not a definitive solution for market prediction, it provides a powerful augmentative tool that, when designed with robustness, transparency, and adaptability, can significantly enhance decision-making in India’s fastevolving financial markets.

The investment banking and financial sectors have undergone significant transformation from the 19th century to the present, driven by technological advancements and evolving trading strategies. Traditional methods of buying and selling stocks have been replaced or supplemented by automated algorithms and machine learning techniques, enabling more precise stock market predictions. This review paper consolidates historical and contemporary strategies employed by traders and investors to maximize profits in the stock market. It emphasizes the application of machine learning in predicting stock market trends and decision-making for buying and selling securities. A systematic review of journal articles published between 2016 and 2022 was conducted to identify the primary markets, stock indices, and trading strategies utilized in stock market predictions. The paper contributes to the literature by providing (1) a detailed analysis of trading strategies and market indicators, and (2) a comprehensive review of machine learning techniques employed in stock market forecasting. Furthermore, a bibliometric analysis highlights the most influential studies in this domain. Technical analysis tools, such as moving averages and candlestick patterns, are explored alongside modern methodologies. The study also identifies existing gaps in trading strategy research, offering insights for future advancements in this field. This review serves as a valuable resource for understanding the intersection of traditional trading methods, machine learning algorithms, and stock market prediction.

Stock market forecasting is becoming increasingly popular among academics. It is also an important topic in finance. Stock market forecasting and analysis assist investors in making educated judgements. To estimate the stock price, many prediction approaches such as technical analysis, fundamental analysis, time series analysis, and statistical analysis are all utilized, but none of these are proven to be efficient prediction methods. Stock prediction is considered to be incredibly difficult on account of the volatile nature of the stock market. The stock market works in cycles, what comes down goes up and vice versa. It is, therefore, considered unpredictable and uncontrollable. However, Machine learning is very effective in the field of stock market prediction as it contains a huge pool of data to be used. In the course of this paper, we aim to compare different Machine Learning algorithms used to predict stock prices and attempt to forecast stock prices using various prediction tools.

Financial stock market forecast using evaluated linear regression based machine learning technique

Abstract: Stock market forecasting remains a major concern to investors, financial institutions, and traders. Most classical forecasting techniques such as statistical modelling and technical analysis generally fail to account for the complexity and non-linearity of financial markets. Recently, neural networks, a new-age branch of machine learning, have emerged as a powerful weapon for better forecasting in the stock market. Neural networks, especially deep learning models such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM), are most suited to learning complex patterns from the historical data and reveal hidden correlations or relationships between different variables which have not been captured by the traditional models. The article discusses the role of neural networks in predicting stock market trends and the applications of these models in financial forecasting. The fundamentals of neural networks are explored, their special applications in stock market prediction, and the advantages they confer over traditional techniques. The objective of this article is to also provide insights into key obstacles, including data quality, overfitting, and interpretability, which can impinge upon the effective use of neural networks in finance. Case studies and simulated situations help present neural networks' use, where they are positioned to enhance stock market prediction output with accuracy and reliability. It seems that future directions that research in this field will take would involve integration with different alternative data sources, with quantum computing anticipated to change the landscape of financial prediction models. The insights provided... Keywords: Neural Networks, Stock Market Predictions, Machine Learning, Financial Forecasting, Trend Analysis, Deep Learning, Predictive Analytics, Data Sciences, and Algorithmic Trading.

The stock market prediction is a hot topic these days, and predicting the price of a stock is both difficult and important due to the numerous variables at play. There were numerous Machine Learning models submitted for Stock Market Prediction, but Hybrid Models were successful in making accurate predictions. The goal of this study is to create a hybrid Deep Learning model (Encoder-Decoder ConvLSTM) to anticipate stock market prices. We employed historical stock price data S and P500 (Daily Prices) from Yahoo's financial website and six months dataset of State Bank of Pakistan (Hourly values). Different prediction models have been tested for the S and P500 dataset which is publicly available and after finding out that the proposed model performed well it has been applied to the SBP dataset as well. The effectiveness of the proposed model has been calculated based on the following performance metrics, root means square error (RMSE), mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE). When compared to other comparable studies, the experimental findings indicate that the proposed model has the best performance metrics values. As a result, we can infer that our model is appropriate for accurate stock market time series prediction.

Stock market prediction is of great interest to stock traders and applied researchers. Main issues in developing a fully automated stock market prediction system are: feature extraction from the stock market data, feature selection for highest prediction accuracy, the dimensionality reduction of the selected feature set and the accuracy and robustness of the prediction system. In this paper, an automated decision tree-adaptive neuro-fuzzy hybrid automated stock market trend prediction system is proposed. The proposed system uses technical analysis (traditionally used by stock traders) for feature extraction and decision tree for feature selection. Selected features are then subjected to dimensionality reduction and the reduced dataset is then applied to the adaptive neuro-fuzzy system for the next-day stock market trend prediction. The proposed system is tested on four major international stock markets. The results show that the proposed hybrid system produces much higher accuracy when compared to stand-alone decision tree based system and ANFIS based system without feature selection and dimensionality reduction.

Stock market prediction is a challenging task in the machine learning research. The challenge is how to guide the investors when is the right time to buy or to sell. In the present day, there are numbers of machine learning techniques applied to predict the stock market such as Genetic Algorithm (GA), Support Vector Machines (SVM) and Artificial Neural Network (ANN). ANN is a major technique which is employed widely in this area. Therefore, in order to understand the trend of using ANN in the stock market prediction, the techniques to enhance the performance of ANN are reviewed. The period of the study is in the year between 2006 and 2013.

The stock market’s dynamic nature has long attracted both experienced investors and newcomers, offering opportunities for significant gains alongside the risk of rapid losses. Traditionally, market predictions have relied on human expertise, where investors use knowledge, intuition, and awareness of economic and political trends to guide decisions. While effective in many cases, this approach is constrained by emotional bias and limited data-processing capabilities. Recent advances in Artificial Intelligence (AI), particularly in machine learning, have transformed market analysis by enabling the processing of vast datasets and the detection of patterns that may escape human observation. AI models, such as neural networks and decision trees, continuously refine their predictions without fatigue or distraction, providing data-driven insights with speed and precision. This study examines the comparative strengths and weaknesses of AI-driven forecasting and human expert analysis under similar conditions. Rather than seeking to establish superiority, the research aims to explore how human judgment and AI capabilities can work together to enhance accuracy and adaptability in financial forecasting. Keywords: Artificial intelligence, Human intelligence, Machine learning, Neural networks, Decision trees, Sentiment analysis, Financial forecasting, Hybrid prediction model, Market trends, Trading strategy,

The purpose of the study had been aimed to further the understanding in exploring the relevance performance of the monopoly stock of Tenaga Nasional Berhad (TNB) to assess the stock performance return in comparison against the Malaysian stock market with reference towards the measurement of the Kuala Lumpur Stock Exchange (KLSE) market index performance as the benchmark. With reference to the previous studies, there is relevance support to identify the tendency of the findings to suggest the higher performance for the major stocks like TNB stock where the business model of TNB being monopolizing the industry creating the upper hand for the stability in driving the revenue and profit leading to higher value in the stock price. The methodology of the research had further the quantitative analysis study using the historical data of 10years from 2014 to 2023 to identify the potential pattern and trend to assess the comparison for the performance and trading trend for both the TNB stock and market index of KLSE. The outcome of the research had suggested the sufficient evidence to identify the higher average return for the TNB stock over the negative return average being achieved by the KLSE market index putting clear picture on the higher performance of the monopoly TNB stock. In addition, the growth of the trading volume trend had suggested that the investors are being higher confidence towards the growth of the TNB stock where the growth of the trading volume for TNB stock is higher than the trading volume for KLSE market index and even exceeding the average return for the TNB stock. 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Stock market prediction using deep learning algorithms

The stock market is a highly complex and dynamic system, and forecasting stock is complicated and difficult. Successful prediction of stock prices may promise attractive benefits; therefore, stock market forecasting is important and of great interest. The economy of Taiwan relies on international trade deeply and the fluctuations of international stock markets impact Taiwan's stock market to certain degree. It is practical to use the fluctuations of other stock markets as forecasting factors for forecasting on the Taiwan stock market. Further, stock market investors usually make short-term decisions based on recent price fluctuations, but most time series models use only the last period of stock price in forecasting. In this article, the proposed model uses the fluctuations of other national stock markets as forecasting factors and employs an expectation equation method whose parameters are optimized by a genetic algorithm (GA) joined with an adaptive network–based fuzzy inference system (ANFIS) model to forecast the Taiwan stock index. To evaluate the forecasting performance, the proposed model is compared with Chen's model and Yu's model. The experimental results indicate that the proposed model is superior to the listing methods (Chen's model and Yu's model) in terms of root mean squared error (RMSE).

Forecast of financial exchange price and patterns is considered as a significant challenging task and is of extraordinary consideration as anticipating stock costs effectively may prompt alluring benefits by settling on appropriate choices. Because of non-stationary, blasting, and noisy information, stock market price prediction is one of the major challenges, and the expectation among financial specialists is therefore difficult to contribute to profit making. This work of literature review presents the survey of more than 100 research papers proposing the various philosophies of machine learning methods as well as SVM classifier, Random forest, Neural Network, Bayesian model, Fuzzy classifier, Artificial Neural Networks, etc., in view of stock market price prediction. Among the cutting-edge techniques, various machine learning approaches and models due to their ability to identify complex patterns in a number of applications are studied. The review works are investigated utilizing certain dataset parameters, forecast technique used, and results achieved by various procedures. It can be observed that the stock market forecast is a very complicated activity. Various parameters should be considered to predict the market more accurately and effectively. This review shows that in financial market forecasting, machine learning algorithms continue to outperform most conventional stochastic methods.

Stock market investment strategies are complex and rely on an evaluation of vast amounts of data. In recent years, machine learning techniques have increasingly been examined to assess whether they can improve market forecasting when compared with traditional approaches. The objective for this study is to identify directions for future machine learning stock market prediction research based upon a review of current literature. A systematic literature review methodology is used to identify relevant peerreviewed journal articles from the past twenty years and categorize studies that have similar methods and contexts. Four categories emerge: artificial neural network studies, support vector machine studies, studies using genetic algorithms combined with other techniques, and studies using hybrid or other artificial intelligence approaches. Studies in each category are reviewed to identify common findings, unique findings, limitations, and areas that need further investigation. The final section provides overall conclusions and directions for future research. Keywords: machine learning, stock market prediction, literature review, research taxonomy, artificial neural network, support vector machine, genetic algorithm, investment decision