LSTM stock prediction model based on blockchain

The stock market is a vital component of the financial sector. Due to the inherent uncertainty and volatility of the stock market, stock price prediction has always been both intriguing and challenging. To improve the accuracy of stock predictions, we construct a model that integrates investor sentiment with Long Short-Term Memory (LSTM) networks. By extracting sentiment data from the “Financial Post” and quantifying it with the Vader sentiment lexicon, we add a sentiment index to improve stock price forecasting. We combine sentiment factors with traditional trading indicators, making predictions more accurate. Furthermore, we deploy our system on the blockchain to enhance data security, reduce the risk of malicious attacks, and improve system robustness. This integration of sentiment analysis and blockchain offers a novel approach to stock market predictions, providing secure and reliable decision support for investors and financial institutions. We deploy our system and demonstrate that our system is both efficient and practical. For 312 bytes of stock data, we achieve a latency of 434.42 ms with one node and 565.69 ms with five nodes. For 1700 bytes of sentiment data, we achieve a latency of 1405.25 ms with one node and 1750.25 ms with five nodes. • We incorporated investor sentiment factors in our prediction model and innovatively used news from the Financial Post website as our dataset. By analyzing sentiment data along with stock trading indicators, we enhanced the model’s sensitivity to market fluctuations and predictive accuracy. • We employed blockchain to ensure data security. The decentralized nature and immutability of blockchain effectively prevent data tampering or loss, ensuring data integrity during transmission and storage. This provides a higher level of security for financial management. • By deploying our stock prediction system on the blockchain, we improved system security. Its distributed nature enables stability against attacks. We also ensure that the system is transparent and tamper-proof. Blockchain’s transparency makes data tamper-proof and enhances system credibility. • Through sentiment analysis, LSTM networks, and blockchain, we have achieved an accurate and efficient online stock prediction model. The successful implementation of this model provides more precise and secure decision support for investors and financial institutions.