Micro-Financial Analysis And A Schematic View of Ai, Machine Learning And Big Data Analytics On Financial Markets
Keywords:
Machine Learning, Artificial Intelligence, Financial Markets, Big Data AnalyticsAbstract
Forecast of financial time series is of the most critical issues in making financial decisions. In this regard, the Tehran Stock Exchange is of great importance for domestic and international financial markets. Based on the economic events and data of the past, it provides a profitable method for the future. This research is related to the usefulness of different machine learning methods in forecasting time series on financial markets. The main issue in this field is that economic managers and scientific society are still longing for more accurate forecasting algorithms. Fulfilling this request leads to an increase in forecasting quality and, therefore, more profitability and efficiency. In this paper, while we introduce the most efficient features, we will show how valuable results could be achieved by the use of a financial time series technical variables that exist on the Tehran stock market. This paper provides an analysis and schematic view of AI, ML and BDA on financial markets.
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