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All Journal The Journal of Artificial Intelligence and Development
Kangming Xu
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Computer Science & IT Environmental Science Mathematics Social Sciences Other

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Enhancing Stock Price Prediction through Attention-BiLSTM and Investor Sentiment Analysis Kangming Xu; Biswajit Purkayastha
Journal of Artificial Intelligence and Development Vol. 3 No. 2 (2024): AI Development
Publisher : Edujavare Publishing

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Abstract

The change of stock price is the focus of investors in the stock market, so stock price trend prediction has always been a hot topic in quantitative investment research. Traditional machine learning prediction model is difficult to deal with nonlinear, high frequency and high noise stock price time series, which makes the prediction accuracy of stock price trend low. In order to improve the forecasting accuracy, the temporal characteristics of stock price data are studied. A bidirectional long short-term memory neural network combining empirical mode decomposition (EMD), investor sentiment and attention mechanism is proposed to predict the rise and fall of stock prices. First, the empirical mode decomposition algorithm is used to extract the characteristics of stock price time series on different time scales, and the investor complex index of the text from the close of the last trading day to the opening of the next trading day is extracted by constructing the all-inclusive sentiment dictionary.The realization of a stock price trend prediction model based on Attention-BiLSTM involves combining the Bidirectional Long Short-Term Memory (BiLSTM) network with an attention mechanism. The BiLSTM processes data points from both past and future for better context understanding, while the attention mechanism selectively focuses on crucial information, improving the model's predictive accuracy in capturing and utilizing patterns in stock price movements. This sophisticated approach enhances the model's ability to forecast stock trends effectively.
Integrating Artificial Intelligence with KMV Models for Comprehensive Credit Risk Assessment Kangming Xu; Vishal Jangir
Journal of Artificial Intelligence and Development Vol. 3 No. 2 (2024): AI Development
Publisher : Edujavare Publishing

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Abstract

With the continuous development of artificial intelligence and various new intelligent algorithm technologies, the business contacts between various institutions within financial enterprises are gradually increasing, and traditional financial risk management can no longer adapt to the current status quo in the era of big data. The lack of information sharing among institutions can reduce the efficiency of financial management and adversely affect the operation of enterprises. At present, financial credit risk mainly includes credit risk, market risk and operational risk. Credit risk relates to the possibility that a borrower will not be able to repay loans or debts on time, market risk covers potential losses caused by market volatility, price changes and adverse events, while operational risk includes risks such as internal operational errors, technical failures and fraud, which may adversely affect the normal operations and financial condition of a financial institution. These risk factors need to be integrated and managed in the financial sector to ensure financial stability and customer trust. Therefore, this paper aims to establish a KMV financial credit risk model, continuously strengthen the internal risk management of enterprises, achieve management modeling and a good KMV algorithm mechanism, and realize the cooperation and stickiness between customers and enterprises, so as to avoid unnecessary financial risks
Co-Authors Biswajit Purkayastha Vishal Jangir