Data Mining in Economics, Finance, and Marketing

Jessen, Hans C.; Παλιούρας, Γεώργιος

doi:10.1007/3-540-44673-7_18

Cited by 3 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Statistical analysis has been available to businesses for years, but somehow DM has captured the interest of businesses in a way that classical statistical analysis never did. The main reason for this widespread popularity is the real financial benefit to businesses (Jessen and Paliouras, ; Lee and Siau, ; Bose, ). DM in FM is an emerging field with potential benefits for both academics and practitioners.…”

Section: Related Literaturementioning

confidence: 99%

Corporate Dividend Policy Determinants: Intelligent Versus a Traditional Approach

Longinidis

Symeonidis

2013

Intell. Sys. Acc. Fin. Mgmt.

View full text Add to dashboard Cite

SUMMARY Dividend is the return that an investor receives when purchasing a company's shares. The decision to pay these dividends to shareholders concerns several other groups of people, such as financial managers, consulting firms, individual and institutional investors, government and monitoring authorities, and creditors, just to name a few. The prediction and modelling of this decision has received a significant amount of attention in the corporate finance literature. However, the methods used to study the aforementioned question are limited to the logistic regression method without any implementation of the advanced and expert methods of data mining. These methods have proven their superiority in other business‐related fields, such as marketing, production, accounting and auditing. In finance, bankruptcy prediction has the vast majority among data‐mining implementations, but to the best of the authors’ knowledge such an implementation does not exist in dividend payment prediction. This paper satisfies this gap in the literature and provides answers that help to understand the so‐called ‘dividend puzzle’. Specifically, this paper provides evidence supporting the hypothesis that data‐mining methods perform better in accuracy measures against the traditional methods used. The prediction of dividend policy determinants provides valuable benefits to all related parties, as they can manage, invest, consult and monitor the dividend policy in a more effective way. Copyright © 2013 John Wiley & Sons, Ltd.

show abstract

Section: Related Literaturementioning

confidence: 99%

Corporate Dividend Policy Determinants: Intelligent Versus a Traditional Approach

Longinidis

Symeonidis

2013

Intell. Sys. Acc. Fin. Mgmt.

View full text Add to dashboard Cite

show abstract

“…The data must be transformed into information and knowledge that can be turned into tools to help organizations improve their decision-making [3]. In this regard, machine learning (ML) allows for generating useful results for companies with less effort and time [4], [5], and it is increasingly being used in marketing research. For example, ML is employed in market segmentation, tourism, customer lifetime value, loyalty and client segment, direct market, marketing campaigns, and other applications [6].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Marketing: A Systematic Literature Review

2022

View full text Add to dashboard Cite

Even though machine learning (ML) applications are not novel, they have gained popularity partly due to the advance in computing processing. This study explores the adoption of ML methods in marketing applications through a bibliographic review of the period 2008-2022. In this period, the adoption of ML in marketing has grown significantly. This growth has been quite heterogeneous, varying from the use of classical methods such as artificial neural networks to hybrid methods that combine different techniques to improve results. Generally, maturity in the use of ML in marketing and increasing specialization in the type of problems that are solved were observed. Strikingly, the types of ML methods used to solve marketing problems vary wildly, including deep learning, supervised learning, reinforcement learning, unsupervised learning, and hybrid methods. Finally, we found that the main marketing problems solved with machine learning were related to consumer behavior, recommender systems, forecasting, marketing segmentation, and text analysis-content analysis.

show abstract

Buy When? Survival Machine Learning Model Comparison for Purchase Timing.

Vallarino

2023

SSRN Journal

View full text Add to dashboard Cite

Data Mining in Economics, Finance, and Marketing

Cited by 3 publications

References 2 publications

Corporate Dividend Policy Determinants: Intelligent Versus a Traditional Approach

Corporate Dividend Policy Determinants: Intelligent Versus a Traditional Approach

Machine Learning and Marketing: A Systematic Literature Review

Buy When? Survival Machine Learning Model Comparison for Purchase Timing.

Contact Info

Product

Resources

About