# Hybrid Crow Search Algorithm-LSTM System for Enhanced Stock Price Forecasting
## 5 Conclusions  
This study presents a novel framework that employs three modules to predict stock prices. The model integrates CSA (crow search algorithm) to obtain the final prediction. This system uses multiple models and different data for prediction and integration of the predictions, and thus, the predicted values did not depend on a single data type or model but rather on multiple data and models. As a result, the predicted values were not limited to one answer but referred to multiple answers. An optimal algorithm was used to adjust the weights of the prediction of each model to achieve the best results within a limited range. The results indicated that the proposed model provided better predictions compared to standalone models for all five stocks employed in this study. Moreover, the individual models performed relatively poorly for stock data from the COVID-19 (Coronavirus disease 2019) period than for those outside this period. Additionally, Pearson's correlation coefficient was used for the feature selection of the technical indicator, which enhanced the performance of standalone models. Finally, the backtesting experiment showed that even simple operations might yield profits.  
This work will help guide future research in the following aspects: further optimization of hybrid models for financial forecasting; application of nature-inspired algorithms such as CSA in economic and financial problems; and integration of diverse datasets to improve prediction accuracy across various domains.  
Figure 17: Backtesting data schematic.  
Figure 18: Backtesting results for each stock.  
This study used only technical-analysis-related data; however, stock analysis is typically divided into fundamental and technical analyses. Therefore, information related to fundamental analysis, such as a company's financial statements and systems, should be incorporated to enhance the representation of the company's market value and better predict the potential of its stock. Additionally, combining the analysis of news or current affairs with stock-market data could allow the prediction model to consider more aspects, potentially improving prediction performance and immediacy.  
Limitations: The use of the information provided by the model in this paper is not optimal. Perhaps the prediction method of this model or the utility of the predicted information could be improved to enhance the backtesting process and its effectiveness.  
This study employed a linear method and combined the predicted values. However, a better integration method may yield more accurate predictions.  
Conceptualization, C.-L.J., Z.-E.S., Y.-K.T. and K.-W.H.; methodology, C.-L.J., Z.-E.S. and K.-W.H.; software, Z.-E.S., Y.-K.T. and K.-W.H.; validation, [PERSON]-E.S., Y.-K.T. and K.-W.H.; formal analysis, [PERSON]-L.J., [PERSON]-E.S., Y.-K.T., S.-H.L. and K.-W.H.; investigation, [PERSON]-E.S., Y.-K.T., S.-H.L. and K.-W.H.; resources, Z.-E.S. and C.-C.H.; writing--original draft preparation, [PERSON]-L.J., [PERSON]-E.S., Y.-K.T. and K.-W.H.; writing--review and editing, [PERSON]-L.J., S.-H.L. and K.-W.H.; visualization, C.-C.H.; supervision, S.-H.L. and K.-W.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Science and Technology of the Republic of China under grant 110-2222-E-992-006 and National Science and Technology Council of Taiwan under grant 113-2221-E-992-116.  
Not applicable.  
We collected data from the Taiwanese stock website ([[https://www.twse.com.tw/zh/index.html](https://www.twse.com.tw/zh/index.html)]([https://www.twse.com.tw/zh/index.html](https://www.twse.com.tw/zh/index.html)), accessed on 3 December 2024).  
Thanks to [PERSON]. [58] for giving comments on this article after graduating from the institute.  
The authors declare no competing interests directly or indirectly related to the work submitted for publication.