Abstract

As traders, investors, and analysts try to decide whether to buy, sell, or hold Apple Inc. shares, Apple Stock Prediction is a crucial component of the financial market. It is difficult to forecast the future value of Apple's stock due to market volatility and a variety of unknown events, including shifts in customer tastes, political unpredictability, and global economic trends. Therefore, it is essential to use a variety of techniques to find the most effective strategy for forecasting Apple's stock price. To ascertain the intrinsic value of a stock, fundamental analysis examines financial statements, market patterns, and economic conditions. This method looks at the sales, profit margins, and cash flow of Apple Inc. as well as its overall financial performance. On the other hand, technical analysis examines past market data, such as price and volume, to spot patterns and trends that can predict future price movements. Charts, graphs, and other visual aids are used in this strategy to pinpoint potential entry and exit positions for trading Apple's stock. Multi-Layer Perceptron is a kind of artificial neural network that mimics the actions of the human brain and has been successfully used to analyze large amounts of complex data. In contrast, XGBoost is a machine learning algorithm that makes predictions using previous data, making it perfect for predicting the future movement of Apple's stock.

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