"A Comprehensive Approach to Financial Market Forecasting: Integrating Fundamental and Derivative Analysis and Prediction"

This paper shows how an integrating approach by combining fundamental analysis with derivative market data can be used to forecast the financial market trends by using the algorithms of LSTM (Long Short-Term Memory). The framework that has been suggested in this paper successfully leverages the important financial indicators like P/E ratios, earning reports and the macroeconomic factors. It also options the market signals so as to forecast the market trends. LSTM algorithm, which is a deep learning algorithm has been used that captures complex time ordered dependencies and even observes the non-linear patterns that appear within the system's data. Many accuracy metrices have also been brought into use. They include RMSE (Root Mean Squared error), MSE (Mean Squared Error) and MAE (Mean Absolute Error). They have been utilized to assess the presented model's performance. The potential of this approach has been demonstrated through the empirical results which successfully achieve the MAE of 0.048, MSE of 0.004 and, RMSE of 0.063, which altogether generate an overall prediction accuracy up to 96%. The achieved results clearly demonstrate that the accuracy in financial market trend forecasting can be greatly enhanced by an integration of the machine learning techniques along with a combination of fundamental and derivative analysis.