IPO Performance Prediction During Covid-19 Pandemic in Indonesia Using Decision Tree Algorithm

Muditomo, Arianto; Broto, Ajar Susanto

doi:10.26905/jkdp.v25i1.5137

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…The purpose of J Keuangan's study was to find out whether a decision tree algorithm model can predict IPO performance during the COVID-19 pandemic in the Indonesian capital market due to the underpricing of IPOs due to COVID-19. The developed model used the IPO performance classification target variable, namely, overpriced, zero, underpriced one level, or underpriced level two [ 9 ]. In the case of the rapid development of the sports and fitness industry, in order to cope with the growing development of the sports and fitness industry, the management status of the members of the sports and fitness industry is out of sync with the development of the sports and fitness industry.…”

Section: Related Workmentioning

confidence: 99%

Identification Method of Influencing Factors of Hospital Catering Service Satisfaction Based on Decision Tree Algorithm

Liu

et al. 2022

Applied Bionics and Biomechanics

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Entering the 21st century, material abundance has been greatly enriched, and living standards have been continuously improved. Now society is gradually moving towards the era of experience economy. From the perspective of experience economy, patients’ demands for hospitals are not only the satisfaction of medical technology, but their catering consumption also has begun to change to the pursuit of higher requirements. Decision tree algorithm is a kind of data mining algorithm. Data mining technology is a young technology for data analysis. It can simulate mathematical models or algorithms through data analysis, which greatly improves the prediction accuracy. This paper aims to study how to identify the influencing factors of hospital catering service satisfaction, and proposes the application of decision tree algorithm to the hospital catering service satisfaction research, and proposes decision tree-related algorithms, such as ID3, C4.5, and C5.0. Based on the analysis of patients’ satisfaction with the hospital catering service in a certain hospital, the results of the model study based on the decision tree algorithm show that the risk estimation value of the training set is 0.064, and the total correct percentage is 93.6%. The risk estimate for the test set was 0.065, for a total correct percentage of 93.5%. It can be seen that the effect of the model is good and can be effectively predicted.

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Section: Related Workmentioning

confidence: 99%

Identification Method of Influencing Factors of Hospital Catering Service Satisfaction Based on Decision Tree Algorithm

Liu

et al. 2022

Applied Bionics and Biomechanics

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“…To explain the phenomenon of IPO underpricing, Keuangan proposed a decision tree algorithm and established a model according to the underpricing. The results from the experiments demonstrate that the decision tree algorithm can provide explanations within a specific classification range [8]. Additionally, the decision tree algorithm model can effectively replace the linear regression econometric model.…”

Section: Related Workmentioning

confidence: 91%

Discussion on the Enterprise Financial Risk Management Framework Based on AI Fintech

Liu

2023

Decis. Mak. Appl. Manag. Eng.

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Deep learning algorithms lack interpretability and interpretability in the decision-making process. This makes it difficult to understand the judgment basis and decision-making process of financial risks based on algorithms, which may reduce the trust and acceptance of risk decisions by enterprises. To address this issue, this study introduces the improved random forest algorithm based on the decision tree algorithm to discuss its framework. Through analysis of the PR curve in the experiment, it was determined that the AP value of the enhanced random forest algorithm is 0.9919, a significant improvement over the RF algorithm's previous value of 0.9237. It also has a good balance between the precision rate and the recall rate. By introducing the data set to compare and analyze the three algorithms of SVM, CRAT, and the improved random forest algorithm, it is found that the improved random forest algorithm has a higher test value. Through cluster analysis, it is found that the clustering accuracy of the improved random forest algorithm is about 81%. The final analysis of a company’s financial data sample showed an accuracy rate of 69.6% on the enterprise access index. The improved random forest algorithm achieves a good balance between accuracy and recall, resulting in the potential for high accuracy in the domain of risk assessment. In addition, compared with other algorithms such as SVM and CRAT, the improved random forest algorithm has higher test values, indicating its excellent performance in financial risk prediction. To sum up, this study firstly verified the feasibility and accuracy of the improved random forest algorithm for financial risk prediction. Additionally, it validated the method's predictive capability using factual enterprise data samples and ultimately established a foundation for the enterprise's financial risk management framework. Construction offers a new theoretical direction.

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“…We plan two types of tree-based machine learning models, namely, Random Forest Regressor (Baba and Sevil, 2020; Katsafados et al , 2020; Quintana et al , 2017) and Gradient Boosting Regressor (Muditomo and Broto, 2021). Both methods are very popular prediction algorithms.…”

Section: Methodsmentioning

confidence: 99%

COVID-19: the impact of the pandemic fear on IPO underpricing

Fedorova

Chertsov²,

Kuzmina³

2022

JFRA

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Purpose The purpose of this study is to assess how the information disclosed in prospectuses impacted the initial public offering (IPO) underpricing at a time of high government interference amid the ongoing pandemic. Design/methodology/approach The design of this study has several tracks, namely, a macro-level track, which is represented by the government measures to halt the pandemic; a micro-level track, which is followed by textual analysis of IPO prospectuses; and, finally, a machine learning track, in which the authors use state-of-the-art tools to improve their linear regression model. Findings The authors found that strict government anti-COVID-19 measures indeed contribute to the reduction of the IPO underpricing. Interestingly, the mere fact of such measures taking place is enough to take effect on financial markets, regardless of the resulting efficiency of such measures. At the micro-level, the authors show that prospectus sentiments and their significance differ across prospectus sections. Using linear regression and machine learning models, the authors find robust evidence that such sections as “Risk factors”, “Prospectus summary”, “Financial Information” and “Business” play a crucial role in explaining the underpricing. Their effect is different, namely, it turns out that the more negative “Risk factors” and “Financial Information” sentiment, the higher the resulting underpricing. Conversely, the more positive “Prospectus summary” and “Business” sentiments appear, the lower the resulting underpricing is. In addition, we used machine learning methods. Consisting of more than 580 IPO prospectuses, the study sample required modern and powerful machine learning tools like Isolation Forest for pre-processing or Random Forest Regressor and Light Gradient Boosting Model for modelling purposes, which enabled the authors to gain better results compared to the classic linear regression model. Originality/value At the micro level, this study is not confined to 2020, but also embraces 2021, the year of the record number of IPOs held. Moreover, in this paper, these were prospectuses that served as a source of management sentiment. In addition, the authors used a tailor-made government stringency index. At the micro level, basing the study on behavioural finance hypotheses, the authors conducted both separate and holistic analysis of prospectuses to assess investors’ reaction to different aspects of IPO companies as well as to the characteristics of the IPOs themselves. Lastly, the authors introduced a few innovations to the research methodology. Textual analysis was conducted on a corpus of prospectuses included in a study sample. However, the authors did not use pre-trained dictionaries, but instead opted for FLAIR, a modern open-source framework for natural language processing.

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IPO Performance Prediction During Covid-19 Pandemic in Indonesia Using Decision Tree Algorithm

Cited by 4 publications

References 12 publications

Identification Method of Influencing Factors of Hospital Catering Service Satisfaction Based on Decision Tree Algorithm

Identification Method of Influencing Factors of Hospital Catering Service Satisfaction Based on Decision Tree Algorithm

Discussion on the Enterprise Financial Risk Management Framework Based on AI Fintech

COVID-19: the impact of the pandemic fear on IPO underpricing

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