Multifactor prediction model for stock market analysis based on deep learning techniques
In addition to the case study, this section describes certain metrics associated with the proposed model’s processing outcomes. This includes precision prediction, change detection, stability matching, range error, and detection time. These metrics are compared under the variables: time between 2/2023 and 5/2023, selecting 4 dates from the given dataset. Another variable is the influencing factors listed in Table 2. In this comparison, the existing SMP-DL27, HDFM34, and PPO-TLSTM20 methods/ techniques are used along the proposed model.
Table 4 summarizes key acronyms of the baseline methods with their full forms and specific uses in the research; it highlights innovative models and methodologies for enhanced stock market forecasting.
