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Articles

Stock market prediction using weighted inter-transaction class association rule mining and evolutionary algorithm

ORCID Icon, &
Pages 5971-5996 | Received 28 Jun 2021, Accepted 11 Feb 2022, Published online: 08 Apr 2022

Figures & data

Figure 1. Structures of GNP individual and GNP node.

Source: drawn by authors with the help of R software.

Figure 1. Structures of GNP individual and GNP node.Source: drawn by authors with the help of R software.

Table 1. Price return of transaction.

Table 2. Database after transformation.

Table 3. Example of the weight database of a transaction.

Figure 2. Judgment node chains.

Source: drawn by authors with the help of R software.

Figure 2. Judgment node chains.Source: drawn by authors with the help of R software.

Figure 3. Rule extraction of the proposed method.

Source: drawn by authors with the help of R software.

Figure 3. Rule extraction of the proposed method.Source: drawn by authors with the help of R software.

Table 4. Parameters of the class association rule mining method.

Table 5. Six kinds of classifiers.

Table 6. Average profits for 30 stocks of the 6 classifier models (%).

Table 7. Max profits for 30 stocks of the 6 classifier models (%).

Figure 4. Profit fluctuation for exxon mobil corp.

Source: drawn by authors with the help of R software.

Figure 4. Profit fluctuation for exxon mobil corp.Source: drawn by authors with the help of R software.

Figure 5. Profit fluctuation for J.P. Morgan Chase & Co.

Source: drawn by authors with the help of R software.

Figure 5. Profit fluctuation for J.P. Morgan Chase & Co.Source: drawn by authors with the help of R software.

Figure 6. Profit fluctuation for Coca-Cola Company.

Source: drawn by authors with the help of R software.

Figure 6. Profit fluctuation for Coca-Cola Company.Source: drawn by authors with the help of R software.

Table 8. Accuracy of prediction.