Abstract
A stock market is a collection of buyers and sellers of equity that are ownership statements for firms that have publicly listed stocks on a stock exchange. In the past few years, we have seen huge losses caused by the destruction of lives and hence there is a requirement for a life cycle prediction strategy that can be reliable and precise. Forecasting market trends, which are crucial for creditors, asset retailers, and critical researchers, are an important part of financial time series projections. Investors must correctly predict equities to make large gains. However, due to market volatility, this form of prediction is highly challenging. We suggest that an investor might utilize deep learning to make decisions. The main goal is to create a model utilizing deep learning techniques. In this regard, we have put forth a concept. In this regard, a component of our work focuses on integrating suitable techniques to combine financial data from multiple sources. Here, we proposed a concept by integrating deep learning with technological research. Technical analysis examines historical market trends using stock charts to forecast potential future market directions for that industry. In other words, the technical analysis uses OHLC (close, open, low, high) prices in conjunction with historical experience to produce a market map that predicts an asset’s direction. Data from the KSE Stock Exchange is used to evaluate the analysis’s success. There are other unconventional learning techniques, but XGBoost is one of the most popular. The Term XGBoost refers to the engineering objective of increasing the computational capital limitations for expanded tree algorithms. In this research, we use XGBoost to forecast valuations. We have obtained a historical data frame of more than 16 years of daily collected data for our selected stock which we will investigate.
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Hussain, N. et al. (2024). Stock Market Performance Analytics Using XGBoost. In: Calvo, H., Martínez-Villaseñor, L., Ponce, H. (eds) Advances in Computational Intelligence. MICAI 2023. Lecture Notes in Computer Science(), vol 14391. Springer, Cham. https://doi.org/10.1007/978-3-031-47765-2_1
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DOI: https://doi.org/10.1007/978-3-031-47765-2_1
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