Zuo Y, Kita E (2011) Stock price forecast using Bayesian network. Expert Syst Appl 39(8):6729–6737 CrossRef Ben-Gal I (2007) Bayesian network. In Ruggeri F, Ruggeri RS, Kennett, Faltin FW (eds.), Encyclopedia
The forecast algorithm of daily stock index fluctuation using Bayesian network is presented in this study.The up or down of the daily stock indexis taken as the random variable and then,Bayesian network is determined by using K2 algorithm and taking the K2 metric as the prediction accuracy of ...
For instance, Baek (2024) introduced a deep learning technique (CNN-LSTM) that is based on genetic algorithm optimization to forecast the closing stock price of the following day. This technique is designed to address the challenges of stock market volatility, the necessity of verified data, and...
Today, stock market has important function and it can be a place as a measure of economic position. People can earn a lot of money and return by investing their money in the stock exchange market. But it is not easy because many factors should be considered. So, there are many ways to...
Liu and Wang (2012) investigated and forecast the price fluctuation by an improved Legendre neural network by assuming that the investors decided their investing positions by analysing the historical data on the stock market. They also introduced a random time strength function in the forecasting ...
The nexus between stock price crash risk (NCSKEW) and investor attention (Attention) is comparatively complex. The lagged one-period effect of investor attention on stock price crash risk has a significantly negative impact, while the lagged two-period effect of investor attention on stock price cr...
Using the latest advancements in AI to predict stock market movements In this notebook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM...
Consequently, we do not need an extensive history of inputs to forecast the next trading day’s stock price. Figure 15. The predicted curves, along with the training and test data plots for the VN-Index (a) and HNX-Index (b), were generated using the RNN architecture. Figure 16. ...
The third graph (Figure 4c) below reveals the GRU’s forecast whereby it displays a better fit of the real stock prices compared to the RNN. Using GRU, the complex form of LSTM is reduced; the temporal dependencies can still not be ignored as in the case of applied financial time series...
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