Predicting cash holdings using supervised machine learning algorithms

Özlem, Şirin; Tan, Ömer Faruk

doi:10.1186/s40854-022-00351-8

Cited by 3 publications

(4 citation statements)

References 77 publications

(59 reference statements)

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“…Preventive measures of rowing competition g = x i , the relationship r i between injury causes, the requirements q i of prevention and control of injury causes, and the influencing factors b i of rowing competition. The diagnostic function before the analysis of sports injury causes is K ( d i , q i , b i ), and the diagnostic function after optimization is L ( x i , y i , z i ) [ 5 ]. If the value of S is larger, the optimization effect is better, as shown in formula ( 1 ).…”

Section: Related Conceptsmentioning

confidence: 99%

“…The prevention and control effect mainly analyzed the key indicators, including competition reasons [ 5 ], prevention and control methods, training time, and injury location [ 6 ]. In the control system, the number of control measures is the same in rowing competition [ 7 ], and different optimization degrees represent the best measures.…”

Section: Related Conceptsmentioning

confidence: 99%

“…Randomly select the causes of sports injure, carry out crossanalysis [9], and update the analysis degree. Under the constraint of matching degree, the optimal measure conforming to the weight is obtained through the combination form, and the calculation process is shown in Formula (5).…”

Section: Mathematical Description Of Athletes' Injurymentioning

confidence: 99%

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[Retracted] Research on Injury Causes and Prevention Effect of College Rowing Athletes Based on Multiple Regression and Residual Algorithm

2022

Journal of Environmental and Public Health

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Rowing competition in colleges and universities is an international competition, and it is also a favorite competition for college students. However, in the course of rowing competition, the stability of athletes’ injuries often occurs, which is difficult to solve effectively. Aiming at the problem that the loss of athletes in rowing competition in colleges and universities cannot be accurately prevented, this paper puts forward a multiple regression prevention effect model and makes a comprehensive analysis combined with complex reasons. Through the integration of multiple regression and residual analysis, we can better find out the influencing factors, aiming at finding out the causes of athletes’ injuries and putting forward corresponding countermeasures. First of all, analyze the causes of loss, establish a framework of injury prevention for college rowers, and the overall diagnosis framework is reasonable. Then, according to the “University Rowing Prevention and Control Standards” divided into various prevention measures, through the comprehensive prevention and control measure mechanism to get the cause of injury, finally, the optimal combination of various control measures forms a control system. The results of MATLAB show that the combination of multiple regression and residual analysis can improve the accuracy of athletes’ injury prevention and treatment, make the accuracy more than 90%, shorten the diagnosis time less than 10 minutes, and meet the requirements of athletes’ injury diagnosis under normal rowing competition.

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Section: Related Conceptsmentioning

confidence: 99%

Section: Related Conceptsmentioning

confidence: 99%

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[Retracted] Research on Injury Causes and Prevention Effect of College Rowing Athletes Based on Multiple Regression and Residual Algorithm

2022

Journal of Environmental and Public Health

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“…20,37,38 Their findings suggest that Richardson's method yields better investment efficiency results. Özlem & Tan examine the motives behind firms' decisions to hold cash and cash equivalents, and why they refrain from redistributing or reinvesting their cash 39 . The authors conduct an extensive literature review on the utilization of machine learning algorithms, including MLR, KNN, SVM, DT, extreme gradient boosting algorithm (XGB), and multilayer neural network (MLNN) methods, to predict the cash holding policy of 211 Turkish listed companies in Borsa Istanbul from 2006 to 2015.…”

Section: Previous Studiesmentioning

confidence: 99%

Application of machine learning in classification of overinvestment: Evidence from listed firms in Vietnam stock exchange market

Phan Huy,

Ngo Dinh Linh,

Nguyen Thi Ngoc

et al. 2023

Sci.Tech.Dev.J.-Eco.Law Manag.

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Studies have consistently demonstrated that both overinvestment and underinvestment exert adverse effects on the overall efficacy of business operations, showcasing the significance of understanding and addressing these phenomena in the realm of scholarly research. Therefore, in this study, we aim to develop an accurate machine-learning model to identify overinvestment in firms listed on the HSX and the HNX stock exchanges in Vietnam. We decided to conduct a comparison to identify the optimal model for classifying firms of overinvestment or not, including Logistic Regression, K-Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree, and Random Forest. Using a sample of 658 non-financial listed companies in Vietnam between 2011 and 2021, our result shows that the most importance predictor variable is "FCF" (free cash flow), with an importance value of 0.14. Although both logistic regression and random forest (RD) algorithms demonstrate high accuracy in identifying firms with overinvestment, the Random Forest algorithm exhibits slightly higher precision and recall for class 1 (overinvestment firms) when compared to Logistic Regression. By contrast, the accuracy performance of the four models (NB, KNN, DT, and SVM) is low, ranging from 0.53 to 0.67. At the microeconomic level, this research can help businesses gain insights into their financial performance, identify areas for improvement, and take proactive measures to avoid financial distress and improve profitability by identifying potential cases of overinvestment. Overall, the study provides a valuable contribution to the field of financial analysis using machine learning techniques. We firmly believe that the findings of this research will serve as a significant scholarly reference for future investigations in the field and explore other importance predictors of overinvestment in Vietnam and other emerging markets.

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Is There Any Impact of the World Uncertainty Spillover Index (WUSI) on Firm Investment? Evidence from Turkey

Tan

2023

Bingöl Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi

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Bu makale, Dünya Belirsizlik Yayılma Endeksi-Amerika Birleşik Devletleri'nin (WUSI-USA) 164 Türk imalat firmasının 2005 ve 2019 yılları arasındaki yatırım politikası üzerindeki etkisini incelemektedir. Bulgulara göre, WUSI-USA'nin firmaların yatırım politikası üzerinde olumsuz bir etkisi vardır. Reel opsiyon teorisine göre firmalar belirsiz koşullar altında yatırımlarını ertelemeyi tercih etmektedirler. Alternatif yatırım ölçümlerinin kullanılması, sonuçlarımızın geçerliliğini doğrulamaktadır. Genel olarak, bu çalışma ABD'den gelen belirsizliğin firmaların yatırım kararlarını etkilediğini ortaya koymaktadır. Türk firmaları finansal kararlarına belirsizlik yayılmalarını dahil etmeli ve stratejilerini belirsizliğin yayıldığı zamanlarda firmaya özgü faktörlere göre ayarlamalıdır. Politika yapıcılar ve firma yöneticileri, yatırım stratejilerini oluştururken ABD'deki belirsizlik yayılma etkilerini dikkate almalıdır.

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Predicting cash holdings using supervised machine learning algorithms

Cited by 3 publications

References 77 publications

[Retracted] Research on Injury Causes and Prevention Effect of College Rowing Athletes Based on Multiple Regression and Residual Algorithm

[Retracted] Research on Injury Causes and Prevention Effect of College Rowing Athletes Based on Multiple Regression and Residual Algorithm

Application of machine learning in classification of overinvestment: Evidence from listed firms in Vietnam stock exchange market

Is There Any Impact of the World Uncertainty Spillover Index (WUSI) on Firm Investment? Evidence from Turkey

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