STOCK PRICE FORECAST IN VIETNAM
4. EXPERIMENTAL RESULTS AND DISCUSSION
100 200 300 400 500 600 700 800
60 80 100 120 140 160 180 200 220
Data points (Jan 2015 - Dec 2017)
Daily closing price (1000 VN dong)
Learning data Test data
100 200 300 400 500 600 700 800
20 25 30 35 40 45 50 55
Data points (Jan 2015 - Dec 2017)
Daily closing price (1000 VN dong)
Learning data Test data
Fig. 3. Daily closing prices of VNM. Fig. 4. Daily closing prices of VCB.
In this section, the performances of the ARIMA, LSSVR, and FA-LSSVR models are compared with each other using the VNM and VCB datasets. The initial settings of the proposed FA-LSSVR is presented in Table 2. Two hyperparameters of the LSSVR model are set to their default values (i.e., C = 10 and ơ = 0.1).
Table 2. Parameter settings of the FA-LSSVR.
Components Name Settings
Learning data Training data 70%
Validation data 30%
LSSVR’s parameters C [10-3; 1012]
ơ [10-3; 1012]
FA’s parameters Number of fireflies 60
Max. of generation 30
As mentioned in Section 2.3, the embedding dimension or lag must be defined before the prediction is made. In this study, the optimal lag is determined by a sensitivity analysis. In each dataset, a subset in the year 2017 is used to validate the FA-LSSVR model when lag ranges from 3 to 10 days. The result indicates that the optimal lag of VNM dataset and VCB dataset is 3 days and 4 days, respectively. The performance of predictive models are then compared by adopting the optimal lag. Table 3 and 4 respectively compare the performance of predictive models in forecasting daily closing stock prices of VNM and VCB. The average performance measures and improvement rates are showed in Table 5.
Table 3. Performance measures of predictive models using the VNM dataset.
ARIMA LSSVR FA-LSSVR
RMSE MAE MAPE RMSE MAE MAPE RMSE MAE MAPE
1000 VN
dong 1000 VN
dong (%) 1000 VN
dong 1000 VN
dong (%) 1000 VN
dong
1000
dongVN (%)
2015 6.552 4.952 4.60 23.399 23.117 21.87 2.081 1.552 1.46
2016 11.877 9.927 7.64 3.162 2.424 1.84 2.193 1.747 1.32
2017 38.252 35.101 18.17 45.462 41.578 21.50 4.395 3.292 1.75
Table 4. Performance measures of predictive models using the VCB dataset.
ARIMA LSSVR FA-LSSVR
RMSE MAE MAPE RMSE MAE MAPE RMSE MAE MAPE
1000 VN dong
1000
VN dong (%) 1000 VN
dong
1000
dongVN (%) 1000 VN dong
1000
dongVN (%)
2015 2.193 1.858 5.81 0.972 0.720 2.23 0.653 0.423 1.31
2016 0.872 0.741 2.11 0.436 0.348 0.98 0.357 0.263 0.74
2017 7.560 6.613 13.69 9.176 8.574 17.96 1.116 0.856 1.80
Table 5. Average performance measures and error rates improvement by the FA-LSSVR.
Average performance measures Improved by the FA-LSSVR
RMSE MAE MAPE RMSE MAE MAPE
1000 VN dong 1000 VN dong (%) (%) (%) (%)
VNM dataset
ARIMA 18.894 16.660 10.14 84.71 86.81 85.11
LSSVR 24.008 22.373 15.07 87.96 90.18 89.98
FA-LSSVR 2.890 2.197 1.51 - -
-196 HỘI THẢO KHOA HỌC QUỐC TẾ KHỞI NGHIỆP ĐỔI MỚI SÁNG TẠO QUỐC GIA VCB dataset
ARIMA 3.542 3.071 7.20 79.99 83.26 82.18
LSSVR 3.528 3.214 7.06 79.91 84.01 81.81
FA-LSSVR 0.709 0.514 1.28 - -
-Table 3 shows that the FA-LSSVR outperformed the LSSVR and ARIMA models in predicting the daily closing price of the VNM. The FA-LSSVR obtained the significant lower values of RMSE, MAE, and MAPE over a period of 3 years compared to the ARIMA and the LSSVR. The lowest MAPE obtained by the proposed MFA-LSSVR was 1.32% while those of the ARIMA and the LSSVR were 4.6% (in 2015) and 1.84% (in 2016), respectively. In addition, the ARIMA showed a better predictive ability than the LSSVR.
Table 5 shows that the error rates of the proposed model were 84.71-86.81% and 87.96-90.18% lower than those of the ARIMA and the LSSVR, respectively.
Table 4 and 5 indicates that performance measures of the FA-LSSVR were superior to those of other models when using VCB dataset. The average MAE value yielded by the FA-LSSVR was 514 VND which was significantly lower than that yielded by the ARIMA (3,071 VND) and the LSSVR (3,214 VND). Similar to the VNM dataset, prediction errors obtained by all models in 2017 were higher than those obtained in 2015 and in 2016. This confirmed a strong fluctuation of Vietnam stock market in 2017. Comparing to the ARIMA, the LSSVR had lower values of RMSE, MAE, and MAPE, indicating its better predictive ability. The overall percentage improvement in error rates for the FA-LSSVR were 79.99-83.26% and 79.91-84.01% better than those of the ARIMA and the LSSVR, respectively.
10 20 30 40 45
80 85 90 95 100 105 110 115 120
Number of observations
Daily closing price (1000 VND)
Actual value Predicted value by ARIMA Predicted value by LSSVR Predicted value by FA-LSSVR
10 20 30 40 45
29 30 31 32 33 34 35 36 37
Number of observations
Daily closing price (1000 VND)
Actual value Predicted value by ARIMA Predicted value by LSSVR Predicted value by FA-LSSVR
(a) VNM dataset - 2015 (d) VCB dataset - 2015
10 20 30 40 45
120 125 130 135 140 145 150
Number of observations
Daily closing price (1000 VND)
Actual value Predicted value by ARIMA Predicted value by LSSVR Predicted value by FA-LSSVR
10 20 30 40 45
34 34.5 35 35.5 36 36.5 37
Number of observations
Daily closing price (1000 VND)
Actual value Predicted value by ARIMA Predicted value by LSSVR Predicted value by FA-LSSVR
(b) VNM dataset - 2016 (e) VCB dataset - 2016
10 20 30 40 45 140
150 160 170 180 190 200 210
Number of observations
Daily closing price (1000 VND)
Actual value Predicted value by ARIMA Predicted value by LSSVR Predicted value by FA-LSSVR
10 20 30 40 45
38 40 42 44 46 48 50 52 54 56
Number of observations
Daily closing price (1000 VND)
Actual value Predicted value by ARIMA Predicted value by LSSVR Predicted value by FA-LSSVR
(c) VNM - 2017 (f) VCB dataset - 2017
Fig. 5. The comparison of actual values and predicted values of the VNM and VCB datasets.
Fig. 5 displays actual values and predicted values when using VNM dataset and VCB dataset, respectively. It is clear that predicted values achieved by the FA-LSSVR model were closer to actual values than those achieved by the ARIMA and the LSSVR models. This confirmed the efficiency of the proposed model in predicting stock prices.
CONCLUSIONS
This study proposes a hybrid model of least squares support vector regression and a firefly algorithm to forecast financial time series data. The FA was utilized to automatically optimize the LSSVR’s parameters, which is aimed to improve the forecast accuracy. The proposed FA-LSSVR model was validated using two daily stock price datasets namely VNM and VCB.
The performance of the FA-LSSVR was compared with that of ARIMA and the LSSVR. Experimental results show that the predictive ability of the FA-LSSVR was superior to that of the ARIMA and the LSSVR in both datasets.
In practice, the stock price is affected by some factors like rates, political events that were not considered in this study. Thus, a novel model which predicts multivariate time series data should be developed. In addition, the proposed model needs to be confirmed by using other financial datasets like exchange rates.
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