Deep Learning for Financial Markets: A Case-Based Analysis of BRICS Nations in the Era of Intelligent Forecasting

Journal of Economics Finance and Accounting Studies, Journal Year: 2025, Volume and Issue: 7(1), P. 01 - 15

Published: Jan. 5, 2025

In this paper, we develop a method based on deep learning in financial market prediction, which includes BRICS economies as the test cases. Financial markets are rife with volatility that is affected by "bed of complexity," coddled local and distal factors. To leverage these vast datasets both models such Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) networks well hybrid architectures used study. The paper evaluates predictive accuracy models, so doing, identifies their strengths predicting temporal dependencies intricate patterns. particular, techniques applied to case studies individual countries highlight application disparate country specific problems, liquidity crises shocks. These findings show classical statistical methods outperformed systems precise reliable forecasting. This research highlights ability AI driven change decision making processes, improving investor confidence economic stability nations. study also readers value analysis, especially developing countries. Application e.g. (CNNs) excel at identifying spatial patterns, Short-Term renowned for prowess sequential time series data, real world prediction explained. addition, discusses extend knowledge, fusing improve how develops solve particular challenges. Through reading notes get exposed data preprocessing normalization feature selection important boosting performance. an introduction evaluation using MSE R-squared values validating them terms outputs. combines theory practical offer useful educational resource students, researchers, practitioners who want apply forecasting complex dynamic global markets.

Language: Английский

Comparative Analysis of Currency Exchange and Stock Markets in BRICS Using Machine Learning to Forecast Optimal Trends for Data-Driven Decision Making

Journal of Economics Finance and Accounting Studies, Journal Year: 2025, Volume and Issue: 7(1), P. 26 - 48

Published: Jan. 8, 2025

The BRICS nations’ economies show that the countries are global financial powerhouses whose currency exchange rates and stock markets have influence globally. In this paper, analysis of forecast trends in both Currency Exchange Stock Markets using a dual layered machine learning approach exposing models such as Long Short Term Memory (LSTM), Random Forest, Gradient Boosting Support vector machines (SVM) is conducted. Their performance tested twice, first on then market data, to compare them basis predictive power deliver actionable insights. Each model applied separately, study mainly uses extensive historical datasets from economies. Benchmarking done metrics Mean Absolute Error (MAE), Root Square (RMSE) R-squared values. For exchange, LSTM turned out be most effective it can handle sequence time series data. best for forecasting was achieved by Boosting, which adept at finding complex nonlinear relationships. Forest proved consistent across Datasets but SVM found challenged Scalability Data Complexity, with relatively lower accuracy. research goes repeat comparative each different models, illustrate subtle differences between techniques their capacity effectively process all varieties. Predictive accuracy reliability further enhanced reconcile conflicting creating an ensemble algorithms. These findings provide robust framework informed decision making stakeholders identify more stable hence profitable context. results add expansion application finance demonstrating how tailored algorithms offer significant economic planning investment strategy plans.

Language: Английский

Accelerating BRICS Economic Growth: AI-Driven Data Analytics for Informed Policy and Decision Making

Journal of Economics Finance and Accounting Studies, Journal Year: 2024, Volume and Issue: 6(6), P. 102 - 115

Published: Dec. 30, 2024

This paper analyzes how the Artificial Intelligence (AI) and Machine Learning (ML) are bridging gap between economic growth in BRICS countries. countries emerging economies that challenged by increasing income inequality, industrial transformation need for infrastructure development. Driven AI, this study applies data analytics to macroeconomic datasets, tracking down patterns functional takeaways regarding policy formulation strategic decision making. The research employs techniques, including predictive modeling, clustering, natural language processing (NLP), areas such as trade optimization, resource allocation labour market analysis. Case examples document successful introduction of AI systems solve critical problems, from healthcare access raising productivity agriculture. findings illustrate role ML helping policymakers an informed, driven puts core process advancement, a solution developmental gaps driver growth. contributes both its practical outcomes providing insights into can complex problems markets. introduces which anticipates trends based on past clustering groups similar behaviors find tools important Further, Natural Language Processing (NLP) is covered highly effective approach understand documents, news, unstructured improve ability make decisions. By students, researchers, these powered techniques optimize trade, management labor, scalable solutions sustainable development available. touts innovation means global challenges, well-equipped readers with skills knowledge leverage progress geography dynamic connected.

Language: Английский