Enhancing Stock Market Forecasting Through Deep Learning and Decentralized Data Integrity: A Blockchain-Integrated Framework

The reliability and precision of stock market forecasting are of paramount importance to investors, regulatory authorities, and financial institutions.Traditional centralized systems for data processing and model deployment have been found to suffer from critical vulnerabilities, including susceptibility to tampering, single points of failure, and a lack of verifiability.To address these limitations, a novel hybrid framework has been developed that integrates advanced deep learning models with decentralized blockchain infrastructure to ensure both predictive accuracy and data integrity in financial time series forecasting.Temporal dependencies in market dynamics are captured through the use of recurrent neural networks (RNNs) and long short-term memory (LSTM) architectures, which have been extensively trained to model non-linear and non-stationary behaviors in high-frequency financial data.In parallel, a private Ethereum-based blockchain has been deployed to record cryptographic hashes of input datasets, model parameters, and forecasting outputs, thereby ensuring transparency, auditability, and immutability across the data lifecycle.To enable computational scalability, deep learning operations have been executed off-chain, while on-chain mechanisms are utilized for secure checkpointing and traceability.Empirical validation has been conducted using real-time data from the Borsa stanbul (BIST), demonstrating significant improvements in forecasting accuracy when compared with baseline statistical and machine learning (ML) models.Moreover, the integration of blockchain technology has enabled a verifiable audit trail for all predictive operations, enhancing trust in the data pipeline without compromising computational efficiency.The proposed framework represents a significant advancement towards secure, transparent, and trustworthy artificial intelligence (AI) in financial forecasting, with potential implications for the broader decentralized finance (DeFi) ecosystem and regulatory-compliant AI deployments in capital markets.