GNBLGRN: Enhancing Stock Value Prediction Accuracy Through Iterative Graph Networks and a Hybrid BiLSTM and BiGRU Recurrent Neural Network Approach
Keywords:
Stock Value Prediction, Graph Networks, BiLSTM, BiGRU, Technical IndicatorsAbstract
Stock price prediction remains an important problem in stock exchanges hence, when the market experiences volatility in its shares. Mainly due to these shortcomings of present methods, the latter often gives inadequate account of the intricate and dynamic nature of stock market data samples. This paper provides a framework for short-term and long-term stock value forecast based on graph-based BiLSTM & BiGRU recurrent neural network (GBRNN). Before a set of technical indicators is ranked based on how accurately they predict stock prices in the future, our method derives twenty distinct signals for particular stocks. Thus, we input these rated indicators into the GBRNN model that employs iterative graph networks, BiLSTM, and BiGRU to estimate how much a certain stock can be worth in the future. They are used to enhance the model’s prediction capability since the model has the ability to capture short term and long term dependency patterns in stock data. We see that improving prediction specifics increases specifications of the model by 3.9 % while shortening latency by 8.3 %, and significantly raises precision of a prediction by 2.5 %; accuracy of a prediction – by 4.9%, recall – by 3.5% as well as AUC – by 1.9%. These results show how effectively our model entails complex relationships of stock markets that let us build more accurate tools for stock prediction. This work provides good foundation for further research on this field, and clearly has been a valuable contribution to the attempt to overcome the challenges derived from the uncertainty of financial markets.
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