Applications of Machine Learning and Determinants of Dividend Decision : Evidence from Indian Firms (Record no. 54735)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02201nam a22001937a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230623141038.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230620b |||||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Vodwal, Sandeep |
| 9 (RLIN) | 38498 |
| 245 ## - TITLE STATEMENT | |
| Title | Applications of Machine Learning and Determinants of Dividend Decision : Evidence from Indian Firms |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 8-24 p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Purpose : The theories of dividend decision have disentangled the firms’ critical drivers of the dividend announcement, and their performances are empirically evaluated by employing ordinary least squares (OLS). However, after more than half a century of research, the debate over the determinants of dividend policy in firms is inconclusive. Therefore, the current study attempted to contribute to the literature by exploring new insights into the dividend decisions of Indian firms by employing machine learning.<br/><br/>Methodology : This study is based on secondary data, and empirical analysis has used a novel dataset of 919 listed Indian nonfinancial firms from 1999–2019. The study utilized the least absolute shrinkage and selection operator and logistic regression methodologies.<br/><br/>Findings : The findings revealed that the idiosyncratic variables are critically significant for dividend announcements by Indian firms. The results demonstrated that large, profitable, liquid, and firms with high market share were more likely to announce dividends in India than small, loss-making, illiquid, and low-market share firms. The direct relationship between Tobin’s Q and the likelihood of paying dividends is a new insight into the dividend decision for Indian firms.<br/><br/>Practical Implications : The results will guide the dividend seeker investors to hold the shares of a high market share firm to receive the expected dividend.<br/><br/>Originality/Value : This current study extended the literature by studying the dividend decisions of Indian firms by employing the machine learning methodology. |
| 653 ## - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | overfitting |
| Uncontrolled term | machine learning |
| Uncontrolled term | dividend decision |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Negi, Vipin |
| 9 (RLIN) | 38499 |
| 773 0# - HOST ITEM ENTRY | |
| Host Biblionumber | 29384 |
| Host Itemnumber | 82710 |
| Main entry heading | GILANI,S. |
| Other item identifier | 55513574 |
| Title | INDIAN JOURNAL OF FINANCE |
| International Standard Serial Number | 0973-8711 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Journal Article |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Price effective from |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Main Library | Main Library | 20/06/2023 | Vol 17, Issue 5/55513574JA1 | 55513574/ JA1 | 20/06/2023 | 20/06/2023 |