An Empirical Comparative Analysis of NAV Forecasting Using Machine Learning Techniques

Hota, Sarbeswara; Jena, Sanjay Kumar; Gupta, Bhupendra; Mishra, Debahuti

doi:10.1007/978-981-15-6202-0_58

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Moreover, machine learning models have been used to predict the behavior of various aspects of financial markets given some input features [35]. These models have also been applied in developing the financial time series forecasting task and found to outperform other statistical models [36].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction

Kadkhoda,

Amiri

2024

IEEE Access

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Financial distress prediction is a crucial aspect of financial planning, particularly in the face of emerging uncertainties. This study introduces a novel methodology for predicting financial distress by amalgamating network analysis and machine learning techniques. The approach involves establishing two networks among companies based on their similarity and correlation in key financial indicators. The first network reflects similarity across five features, while the second captures correlation in the most critical feature. Subsequently, seven network-centric features are extracted and integrated into the dataset as new variables. Additionally, community detection algorithms are applied to cluster companies, with the resulting labels added as categorical variables. This process yields a modified dataset comprising both initial and network-based variables. Five classification algorithms are employed to forecast financial distress across three scenarios. Initially, models are trained using only the initial features. In subsequent scenarios, network-centric features from both similarity and correlation networks are incorporated, enhancing the predictive accuracy of machine learning models. Notably, features from the similarity network play a pivotal role in this improvement. The proposed model not only showcases superior predictive capabilities but also offers a holistic understanding of the dynamic interactions among financial entities. The results underscore the efficacy of network-based strategies in refining financial distress prediction models, providing valuable insights for decision-makers.

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Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction

Kadkhoda,

Amiri

2024

IEEE Access

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“…As a result of the review of the scientific literature, regarding the implementation of techniques based on Machine Learning, for the approach of predictive solutions supported in time series, it was identified that the “Multi-Layered Perceptron” technique of the category Artificial Neural Networks has produced very good results in different fields of action, such as: investment models based on mutual funds [ 34 , 35 ], epidemiological models [ 35 , 36 ], estimation of the water recharge rate underground [ 37 , 38 ], analysis of the pedals interactions of race car drivers [ 39 ], efficient energy systems based on the prediction of natural gas consumption [ 40 , 41 ], and money flow prediction [ 42 ], among other studies.…”

Section: Contributionsmentioning

confidence: 99%

SISME, Estuarine Monitoring System Based on IOT and Machine Learning for the Detection of Salt Wedge in Aquifers: Case Study of the Magdalena River Estuary

Ariza-Colpas

Ayala-Mantilla²,

Shaheen

et al. 2021

Sensors

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This article contains methods, results, and analysis agreed for the development of an application based on the internet of things and making use of machine learning techniques that serves as a support for the identification of the saline wedge in the Magdalena River estuary, Colombia. As a result of this investigation, the process of identifying the most suitable telecommunications architecture to be installed in the estuary is shown, as well as the characteristics of the software developed called SISME (Estuary Monitoring System), and the results obtained after the implementation of prediction techniques based on time series. This implementation supports the maritime security of the port of Barranquilla since it can support decision-making related to the estuary. This research is the result of the project “Implementation of a Wireless System of Temperature, Conductivity and Pressure Sensors to support the identification of the saline wedge and its impact on the maritime safety of the Magdalena River estuary”.

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Prediction of Turkish mutual funds’ net asset value using the fund portfolio distribution

YILMAZ>¹,

Orbak²

2023

Neural Comput & Applic

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An Empirical Comparative Analysis of NAV Forecasting Using Machine Learning Techniques

Cited by 4 publications

References 12 publications

A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction

A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction

SISME, Estuarine Monitoring System Based on IOT and Machine Learning for the Detection of Salt Wedge in Aquifers: Case Study of the Magdalena River Estuary

Prediction of Turkish mutual funds’ net asset value using the fund portfolio distribution

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