Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Nagarathinam, Aishwarya; Padhi, Pritesh Kumar; Rangasamy, Sangeetha; Hemalatha, M.; Chellasamy, Aarthy Title A name given to the resource Predicting financial asset prices with neural network: a comparative study of neural networks effectiveness in financial decision-making Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Accounting and Economics Studies;Volume;12;Issue;1;pp.131-145 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14419/f221hb82" target="_blank" rel="noreferrer noopener">https://doi.org/10.14419/f221hb82</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105007191899?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105007191899?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Nagarathinam A., Christ University, Bangalore, India; Padhi P.K., Christ University, Bangalore, India; Rangasamy S., Christ University, Bangalore, India; Hemalatha M., Christ University, Bangalore, India, Model Institute of Engineering and Technology, Jammu, India; Chellasamy A., Christ University, Bangalore, India Description An account of the resource Investing requires deep knowledge of complex financial markets, making it incredibly tedious to predict inflation and deflation. Predictive conventional models like ARIMA and GARCH do not accurately capture the non-linearity and volatility presented in financial datasets. This research examines the different forms of predictive assets, real estate, stocks, commodities, bonds, and cryptocurrency using Long Short-Term Memory (LSTM) Neural networks. The primary focus of this research is to assess the valuable prediction capabilities of LSTM across assets and its integration with financial decision-making. According to the empirical results, deep learning LSTM models give better outcomes with equities and gold, with the R2 indicator reaching over 99% alongside a low RMSE. LSTMs had an over 100% MPE prediction error rate for other assets during the test phase, making it harder to predict intensely volatile assets. The model's verification transfers residual autocorrelation, showing that it can enhance forecasting performance with detailed macroeconomic indicators and sentiment analysis data. Studies show that LSTMs are effective in high-frequency markets with non-linear price changes, but they require special attention to balance interpretability and overfitting. Despite the progress that has been achieved in utilizing neural networks for financial forecasting, hybrid models integrated with XAI are recommended to improve efficiency and real-world applicability. These results contribute to the growing domain of AI-powered finance by offering additional means for many investors, analysts, and decision-makers who wish to utilize data for market speculation. Dr. Aishwarya Nagarathinam et al. Subject The topic of the resource Financial Decision Making; GARCH; LSTM; Neural Networks: ARIMA; Prediction Publisher An entity responsible for making the resource available Science Publishing Corporation Inc. Relation A related resource ISSN: 23094508; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Bronze Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online