# A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction

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## Abstract

**:**

## 1. Introduction

## 2. Neural Networks and Saudi Stock Market

## 3. Bees-Inspired Learning Algorithms

#### 3.1. Artificial Bee Colony (ABC) Algorithm

_{ij}represents the number of new solutions in the neighbourhood of x

_{ij}for the employed bees, k is a solution in the neighbourhood of i, and $\theta $ is a random number in the range [−1, 1].

#### 3.2. Gbest Guided Artificial Bee Colony (GGABC) Algorithm

_{j}is the gbest solution of the population, ψ

_{ij}is a uniform random number in [0, C], C > 0. The value of C can balance the exploitation ability. For more details, we refer to Ref. [24].

#### 3.3. Quick Artificial Bee Colony (QABC) Algorithm

## 4. The Proposed: Quick Gbest Guided Artificial Bee Colony Algorithm

- Step 1:
- Initialize the population x
_{i}, where $i=1,2,3,\dots ,SN$. - Step 2:
- Compute the fitness values.
- Step 3:
- Cycle = 1.
- Step 4:
- Repeat from step 2 to step 14.
- Step 5:
- Produce new solutions ${V}_{ij}$ in the neighbourhood of ${x}_{ij}$ using Equation (7).
- Step 6:
- Apply the Greedy Selection process.
- Step 7:
- Calculate the probability values p
_{i}for the solutions x_{i}using Equation (10).$${p}_{i}=\frac{fi{t}_{i}}{{\displaystyle \sum _{k=1}^{n}fi{t}_{k}}}$$$$fi{t}_{i}=\{\begin{array}{ll}\frac{1}{1+{f}_{i}},& \mathrm{for}\text{}{f}_{i}\ge 0\\ 1+\mathrm{abs}\left({f}_{i}\right),& \mathrm{for}\text{}{f}_{i}0\end{array}$$ - Step 8:
- Apply greedy selection.
- Step 9:
- Select the solution x
_{i}based on p_{i}and generate the new solutions ${V}_{{N}_{m,i}}^{best}$ for the quick onlookers by Equation (8). - Step 10:
- For an abandoned source, if it exists, and replaces it with a new solution using Equation (9).
- Step 11:
- Memorize the best solution achieved so far.
- Step 12:
- Cycle = cycle + 1
- Step 13:
- Until cycle = Maximum Cycle Number (MCN)

## 5. Experimental Evaluation and Analysis

^{−4}, 2.01 × 10

^{−4}, 1.99 × 10

^{−4}, 1.90 × 10

^{−5}, and 7.99 × 10

^{−5}for STC, Zain, Almarai, SAPCO, and Al Rajhi prices, respectively. The improved GGABC-FFNN, QABC-FFNN MSE testing reached 9.89 × 10

^{−5}, 1.52 × 10

^{−5}, 1.11 × 10

^{−4}, 9.30 × 10

^{−5}, 1.12 × 10

^{−6}and 9.00 × 10

^{−5}, 9.99 × 10

^{−6}, 9.20 × 10

^{−6}, 1.00 × 10

^{−7}, 9.99 × 10

^{−7}, respectively. Thus, we conclude that the proposed QGGABC-FFNN obtained the minimum MSE for STC, Zain, Almarai, SAPCO stock prices. On the other hand, the average SNR, NMSE, accuracy, and success rates are given in Table 5, Table 6, Table 7 and Table 8, respectively. From this table, it is seen that in terms of SNR and NMSE, the best values were obtained by the proposed QGGABC for STC, Zain, Almarai, SAPCO dataset, while QABC outperformed better for Al Rajhi dataset. The GGABC and ABC obtained enough SNR and NMSE values for all dataset prices predictions. However, in term of accuracy, the proposed QGGABC got higher accuracy than other algorithms except on Al Rajhi dataset where QABC reached a high accuracy of 98.81. Through the quick and gbest guided exploitation and exploration strategies, the proposed method achieved the highest accuracy compared to QABC and GGABC algorithms. Furthermore, the average success rate of 10 runs, ABC, QABC, and QGGABC got 100% success rate for STC dataset, while QGGABC and QABC got 100% on Almarai prices prediction as given in Table 8.

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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SSM Dataset | Year | Total Days | Training 75% | Testing 25% |
---|---|---|---|---|

STC | 2015–2016 | 250 | 175 | 75 |

Zain | 2015–2016 | 250 | 175 | 75 |

Almarai | 2015–2016 | 250 | 175 | 75 |

SAPCO | 2015–2016 | 250 | 175 | 75 |

Al Rajhi | 2015–2016 | 250 | 175 | 75 |

Dataset | No of Inputs | Hidden Nodes | CS | Upper Bound | Lower Bound | MCN |
---|---|---|---|---|---|---|

STC | 5 | 2–7 | 40 | 5 | −5 | 2000 |

Zain | 5 | 3–7 | 40 | 10 | −10 | 2000 |

Almarai | 5 | 3–9 | 40 | 20 | −20 | 2000 |

SAPCO | 5 | 3–9 | 40 | 15 | −15 | 2000 |

Al Rajhi | 5 | 3–7 | 40 | 20 | −20 | 2000 |

Data Set | NN Structure | ABC | GGABC | QABC | QGGABC |
---|---|---|---|---|---|

STC | 5-2-1 | 1.20 × 10^{−3} | 8.27 × 10^{−4} | 7.88 × 10^{−5} | 9.28373 × 10^{−7} |

5-3-1 | 1.10 × 10^{−3} | 8.29 × 10^{−4} | 2.96 × 10^{−5} | 8.24877 × 10^{−7} | |

5-5-1 | 1.10 × 10^{−3} | 8.50 × 10^{−4} | 1.06 × 10^{−5} | 7.10192 × 10^{−7} | |

5-7-1 | 1.07 × 10^{−3} | 7.09 × 10^{−4} | 1.91 × 10^{−6} | 1.01188 × 10^{−8} | |

Zain | 5-3-1 | 1.09 × 10^{−3} | 7.00 × 10^{−3} | 1.01 × 10^{−5} | 1.29192 × 10^{−8} |

5-4-1 | 2.28 × 10^{−3} | 1.09 × 10^{−3} | 1.93 × 10^{−5} | 1.21211 × 10^{−8} | |

5-6-1 | 3.98 × 10^{−3} | 9.21 × 10^{−3} | 1.33 × 10^{−5} | 1.20927 × 10^{−8} | |

5-7-1 | 2.21 × 10^{−3} | 4.19 × 10^{−3} | 1.01 × 10^{−5} | 1.27644 × 10^{−8} | |

Almarai | 5-3-1 | 1.90 × 10^{−3} | 5.95 × 10^{−3} | 1.01 × 10^{−6} | 1.20544 × 10^{−8} |

5-6-1 | 1.20 × 10^{−3} | 5.12 × 10^{−3} | 1.10 × 10^{−5} | 1.28948 × 10^{−8} | |

5-8-1 | 1.10 × 10^{−3} | 4.02 × 10^{−3} | 1.02 × 10^{−5} | 1.20992 × 10^{−8} | |

5-9-1 | 1.10 × 10^{−3} | 1.08 × 10^{−3} | 1.91 × 10^{−5} | 1.28974 × 10^{−9} | |

SAPCO | 5-3-1 | 9.20 × 10^{−5} | 9.29 × 10^{−4} | 2.01 × 10^{−5} | 1.0924 × 10^{−8} |

5-5-1 | 8.93 × 10^{−5} | 1.92 × 10^{−4} | 1.31 × 10^{−5} | 1.01101 × 10^{−8} | |

5-6-1 | 1.09 × 10^{−4} | 9.22 × 10^{−4} | 1.11 × 10^{−5} | 1.23323 × 10^{−8} | |

5-7-1 | 9.21 × 10^{−4} | 2.07 × 10^{−5} | 2.01 × 10^{−5} | 1.0092 × 10^{−8} | |

5-9-1 | 2.92 × 10^{−5} | 5.52 × 10^{−4} | 1.01 × 10^{−8} | 1.0009 × 10^{−9} | |

Al Rajhi | 5-2-1 | 6.00 × 10^{−4} | 5.24 × 10^{−4} | 2.01 × 10^{−5} | 9.28687 × 10^{−5} |

5-3-1 | 5.20 × 10^{−3} | 1.07 × 10^{−4} | 1.09 × 10^{−5} | 8.21217 × 10^{−5} | |

5-5-1 | 4.02 × 10^{−3} | 1.86 × 10^{−4} | 1.82 × 10^{−5} | 5.8793 × 10^{−5} | |

5-6-1 | 1.20 × 10^{−3} | 1.77 × 10^{−4} | 1.91 × 10^{−6} | 2.90333 × 10^{−5} | |

5-7-1 | 9.13 × 10^{−4} | 1.71 × 10^{−5} | 1.01 × 10^{−7} | 2.29997 × 10^{−5} |

Data Set | ABC | GGABC | QABC | QGGABC |
---|---|---|---|---|

STC | 3.20 × 10^{−4} | 1.83 × 10^{−5} | 9.99 × 10^{−5} | 9.99 × 10^{−7} |

3.10 × 10^{−4} | 9.98 × 10^{−4} | 9.43 × 10^{−5} | 8.25 × 10^{−7} | |

2.01 × 10^{−4} | 8.50 × 10^{−4} | 1.99 × 10^{−5} | 8.00 × 10^{−7} | |

1.10 × 10^{−4} | 9.89 × 10^{−5} | 9.99 × 10^{−6} | 1.00 × 10^{−7} | |

Zain | 1.09 × 10^{−4} | 9.12 × 10^{−4} | 9.78 × 10^{−5} | 2.00 × 10^{−8} |

2.76 × 10^{−4} | 1.91 × 10^{−4} | 1.91 × 10^{−6} | 1.99 × 10^{−8} | |

3.12 × 10^{−4} | 2.92 × 10^{−4} | 7.99 × 10^{−5} | 9.01 × 10^{−8} | |

2.01 × 10^{−4} | 1.52 × 10^{−5} | 9.00 × 10^{−5} | 1.92 × 10^{−8} | |

Almarai | 2.00 × 10^{−4} | 2.36 × 10^{−4} | 9.99 × 10^{−6} | 7.12 × 10^{−9} |

1.90 × 10^{−4} | 1.51 × 10^{−5} | 9.90 × 10^{−5} | 9.93 × 10^{−8} | |

1.99 × 10^{−4} | 4.02 × 10^{−3} | 1.21 × 10^{−6} | 9.90 × 10^{−8} | |

1.99 × 10^{−4} | 1.11 × 10^{−4} | 9.20 × 10^{−6} | 1.29 × 10^{−10} | |

SAPCO | 9.22 × 10^{−5} | 9.29 × 10^{−4} | 2.99 × 10^{−5} | 1.45 × 10^{−8} |

8.10 × 10^{−6} | 2.00 × 10^{−4} | 7.30 × 10^{−5} | 1.90 × 10^{−8} | |

1.01 × 10^{−5} | 9.22 × 10^{−4} | 7.98 × 10^{−5} | 1.99 × 10^{−8} | |

9.22 × 10^{−5} | 7.21 × 10^{−6} | 8.90 × 10^{−5} | 6.79 × 10^{−8} | |

1.90 × 10^{−5} | 9.30 × 10^{−5} | 1.00 × 10^{−7} | 2.00 × 10^{−10} | |

Al Rajhi | 7.01 × 10^{−5} | 3.45 × 10^{−5} | 3.00 × 10^{−6} | 2.00 × 10^{−5} |

8.00 × 10^{−5} | 9.56 × 10^{−5} | 1.99 × 10^{−6} | 9.99 × 10^{−5} | |

9.14 × 10^{−4} | 3.42 × 10^{−5} | 2.00 × 10^{−6} | 9.43 × 10^{−5} | |

3.99 × 10^{−4} | 8.97 × 10^{−5} | 9.12 × 10^{−6} | 7.90 × 10^{−5} | |

7.99 × 10^{−5} | 1.12 × 10^{−6} | 9.99 × 10^{−7} | 9.00 × 10^{−5} |

Dataset | ABC | GGABC | QABC | QGGABC |
---|---|---|---|---|

STC | 33.23 | 36.56 | 35.11 | 38.12 |

Zain | 36.34 | 37.24 | 36.08 | 39.35 |

Almarai | 36.56 | 37.28 | 38.61 | 40.14 |

SAPCO | 35.93 | 37.19 | 38.04 | 39.93 |

Al Rajhi | 37.34 | 37.45 | 39.11 | 37.41 |

Dataset | ABC | GGABC | QABC | QGGABC |
---|---|---|---|---|

STC | 3.09 × 10^{−4} | 6.83 × 10^{−5} | 9.19 × 10^{−5} | 9.99 × 10^{−7} |

4.00 × 10^{−4} | 9.99 × 10^{−4} | 9.94 × 10^{−5} | 8.87 × 10^{−7} | |

6.07 × 10^{−4} | 9.00 × 10^{−4} | 7.99 × 10^{−5} | 8.00 × 10^{−7} | |

1.90 × 10^{−4} | 2.99 × 10^{−6} | 9.98 × 10^{−6} | 6.54 × 10^{−7} | |

Zain | 1.59 × 10^{−4} | 9.20 × 10^{−4} | 9.89 × 10^{−5} | 2.00 × 10^{−8} |

2.76 × 10^{−4} | 9.91 × 10^{−4} | 7.69 × 10^{−6} | 1.99 × 10^{−8} | |

3.90 × 10^{−4} | 2.92 × 10^{−4} | 7.99 × 10^{−5} | 9.88 × 10^{−8} | |

1.20 × 10^{−5} | 1.98 × 10^{−5} | 9.89 × 10^{−5} | 1.98 × 10^{−8} | |

Almarai | 3.00 × 10^{−4} | 2.98 × 10^{−4} | 9.21 × 10^{−6} | 7.12 × 10^{−9} |

1.92 × 10^{−4} | 1.92 × 10^{−5} | 1.02 × 10^{−6} | 9.93 × 10^{−8} | |

1.99 × 10^{−4} | 2.98 × 10^{−4} | 1.21 × 10^{−6} | 9.99 × 10^{−8} | |

8.00 × 10^{−4} | 6.11 × 10^{−5} | 1.23 × 10^{−7} | 9.98 × 10^{−10} | |

SAPCO | 9.81 × 10^{−5} | 1.00 × 10^{−3} | 2.99 × 10^{−5} | 1.98 × 10^{−8} |

9.99 × 10^{−6} | 9.98 × 10^{−4} | 7.30 × 10^{−5} | 6.88 × 10^{−8} | |

8.00 × 10^{−5} | 9.99 × 10^{−4} | 7.98 × 10^{−5} | 1.99 × 10^{−8} | |

7.92 × 10^{−6} | 7.22 × 10^{−6} | 8.90 × 10^{−5} | 8.79 × 10^{−8} | |

7.99 × 10^{−5} | 1.93 × 10^{−6} | 7.83 × 10^{−7} | 8.98 × 10^{−10} | |

Al Rajhi | 7.99 × 10^{−5} | 3.90 × 10^{−5} | 3.00 × 10^{−6} | 2.00 × 10^{−5} |

9.83 × 10^{−5} | 1.00 × 10^{−6} | 1.99 × 10^{−6} | 9.99 × 10^{−5} | |

9.90 × 10^{−4} | 3.95 × 10^{−5} | 2.00 × 10^{−6} | 9.74 × 10^{−5} | |

8.99 × 10^{−4} | 1.83 × 10^{−6} | 9.12 × 10^{−6} | 8.30 × 10^{−5} | |

9.10 × 10^{−5} | 1.80 × 10^{−6} | 1.10 × 10^{−8} | 9.00 × 10^{−6} |

Data Set | ABC | GGABC | QABC | QGGABC |
---|---|---|---|---|

STC | 91.31 | 92.72 | 95.18 | 98.77 |

Zain | 92.10 | 92.24 | 97.08 | 99.51 |

Almarai | 91.83 | 93.81 | 96.71 | 99.01 |

SAPCO | 93.36 | 95.78 | 96.04 | 99.78 |

Al Rajhi | 93.21 | 94.31 | 98.81 | 97.41 |

Data Set | ABC | GGABC | QABC | QGGABC |
---|---|---|---|---|

STC | 100% | 65% | 100% | 100% |

Zain | 95% | 60% | 98% | 100% |

Almarai | 89% | 85% | 100% | 100% |

SAPCO | 85% | 87% | 80% | 98% |

Al Rajhi | 80% | 69% | 100% | 100% |

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## Share and Cite

**MDPI and ACS Style**

Shah, H.; Tairan, N.; Garg, H.; Ghazali, R.
A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction. *Symmetry* **2018**, *10*, 292.
https://doi.org/10.3390/sym10070292

**AMA Style**

Shah H, Tairan N, Garg H, Ghazali R.
A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction. *Symmetry*. 2018; 10(7):292.
https://doi.org/10.3390/sym10070292

**Chicago/Turabian Style**

Shah, Habib, Nasser Tairan, Harish Garg, and Rozaida Ghazali.
2018. "A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction" *Symmetry* 10, no. 7: 292.
https://doi.org/10.3390/sym10070292