Shareholder Activism and Its Impact on Profitability, Return, and Valuation of the Firms in India
Bibliographic record
Abstract
The paper’s prime objective is to understand the impact of Shareholder activism on firm performance. This study is conducted in a unique setup where traditional activist investors such as pension funds and hedge funds are not present. However, the activism cases are increasing yearly in an emerging economy like India. We have created a comprehensive shareholder activism index (sha index) using multiple activisms and corporate governance factors. To measure firm performance, we have used valuation (Tobin’s Q and Market capitalization), profitability (operating profit margin and net profit margin), and return ratios (Return on capital and return on equity). Panel data analysis (PDA) is employed for the current study as it overcomes the shortcomings of the time series analysis and cross-sectional studies. The sample comprises 37 listed firms’ data for FY2017 to FY2020. Chosen firms have experienced activism instances at least once during the 2017–2020 period. As per our analysis, shareholder activism has a significant negative impact on valuation measured in market capitalization and profitability estimated by operating profit margin. Activism primarily impacts the other four parameters negatively, but it is insignificant. India is in the nascent stage of activism, partly explaining the insignificance of the effects of shareholder activism on firm performance. Also, activist investors are targeting companies. These attacks are not fructifying desired outcomes as promoters own over 50% stake in the listed companies. The latest data for FY2021 has not been considered for the study as covid-19 impacted the businesses during the financial year. Also, we cannot capture activism instances that are not reported in regulatory filings. Unlike past research in this area, we have used a comprehensive activism index as a proxy of activism and have employed PDA instead of event studies to assess the impact on firm performance. Also, this is the first such empirical study conducted in an emerging economy setup where neither large hedge nor pension funds are present.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".