Next-Gen Market Predictor: Transformed Moving Average Fast-RNN Hybrid with Advanced CNNS

The performance of a country's financial market is an essential factor in determining its overall economic status, allowing economists and financial specialists to assess the country's present economic health. The stock market is a crucial contributor among the many financial markets. A country's economic status impacts various industries, including finance, agriculture, metals, and investment banking. These sectors' development depends on their volatility, which adheres to the fundamental principle of supply and demand. Increased supply prompts traders and financial institutions to invest in that sector or stock, causing prices to rise [1]. Regular dividend payments also help to generate profits and returns on invested capital. Investors must find the best time to sell shares and obtain their desired profits. Financial markets include stocks, derivatives, bonds, and commodities [2]. The stock market allows investors to invest in companies and possess a portion or percentage. As a company grows, it frequently requires additional cash to finance its future operations. Companies can sell these shares to investors after receiving approval from present shareholders, who will erode their shareholding due to the issuance of additional shares. Successful outcomes lead to enhanced stock market value for the shares.

Stock price indicators serve as fundamental benchmarks in financial markets, shaping investment strategies and market dynamics worldwide. These indicators aggregate the intricate movements of individual company stocks or specific market segments on exchanges, reflecting the collective sentiment and underlying economic conditions [3]. Forecasting these indicators, especially stock price movements, poses a formidable challenge for stakeholders and researchers, given their pivotal role in investment decision-making. Accurate forecasts of stock price changes have become essential for investors trying to maximise their portfolio performance [4]. The ability to anticipate whether a stock's price will rise or fall informs decisions to buy, sell, or hold securities, influencing profitability and risk management strategies. At its core, stock price prediction aims to identify stocks with a high likelihood of price appreciation for purchase and those with a high probability of price depreciation for sale, aligning investment decisions with anticipated market trends.

Two primary approaches to stock market forecasting have emerged: fundamental and technical analysis. Fundamental analysis delves into an organisation's intrinsic value by examining market position, financial performance, costs, revenue, and growth prospects. By assessing these essential data points, investors may determine an organisation's inventory's underlying strength and probable future trajectory, guiding their investing decisions accordingly. In contrast, technical analysis examines past price movements and market trends to forecast potential price behaviour [5]. This method is based on the notion that past price patterns repeat themselves and can be used to predict price movements in the future [6, 7, 8]. Technical analysts use charts, graphs, and indicators of trends, such as moving averages and trade volumes, to discover patterns and trends that indicate probable buy or sell signals, which aids in developing trading strategies.

Time series decomposition is a frequent technique used in stock price forecasting. This entails breaking down past stock price data into essential components, such as trend, seasonality, and random variations [9]. By isolating these components, analysts can identify underlying patterns and trends that inform future price movements, facilitating more accurate predictions and informed decision-making. Despite the advancements in forecasting techniques, stock price prediction remains challenging due to financial markets' inherent uncertainty and complexity [10]. Factors such as market sentiment, geopolitical events, and macroeconomic trends introduce volatility and unpredictability, complicating accurately forecasting price movements.

Machine learning has also significantly affected stock price prediction, as it allows for analysing large datasets and discovering patterns that are difficult to detect using traditional methods. Stock prices have been predicted using techniques such as SVM, decision trees, and ensemble learning approaches, which learn from previous data and recognise complicated patterns [11]. ML models may process various inputs, including stock prices, technical indications, and economic considerations, to create predictive models that help investors make better decisions [12]. However, these models frequently necessitate substantial feature engineering and need to be improved because of their inability to learn hierarchical representations of data automatically.

Deep learning has become a potent instrument in stock price prediction, providing benefits above conventional statistical techniques and fundamental analysis [13]. Unlike traditional models, which often require manual feature engineering and assumptions about underlying data distribution, DL models can learn complicated patterns and representations from the raw data. One of its main advantages is deep learning's capacity to manage high-dimensional data well. Deep learning models can process large amounts of information regarding stock price prediction, including historical price data, trade volumes, news mood, macroeconomic indicators, and more [14]. By ingesting such diverse sources of information, deep learning models can capture subtle relationships and interactions that may not be apparent to human analysts or traditional statistical models. Hybrid deep learning techniques combine the strengths of deep learning models with other methodologies, such as conventional statistical methods or expert knowledge, to improve prediction accuracy further. These hybrid techniques combine the interpretability of classical models with the tremendous feature extraction capabilities of DL systems.