Financial Strategies Driving Market Performance During Recession in Nigerian Manufacturing Firms
Bibliographic record
Abstract
This study examines the interplay between leverage, dividend policy, and market performance in Nigeria’s manufacturing sector during the economic downturn of 2016–2020. Drawing on signaling and trade-off theories, we investigate how firms balanced leverage and dividend payouts to sustain performance amidst macroeconomic shocks, including currency depreciation, inflation, and weakened consumer demand. Using panel data from 26 Nigerian Stock Exchange-listed firms, the study applies pooled ordinary least squares (POLS) and fixed-effect models (FEM) to analyze the direct and interactive effects of leverage and dividend policy on market performance, controlling for profitability, firm size, and taxation. The findings reveal that leverage generally exerts a negative effect on firm value, particularly long-term debt, which increases financial distress risks. However, the interaction between leverage and dividend payouts positively moderates this relationship, suggesting that firms use dividends strategically to signal stability and mitigate leverage-related risks. Profitability emerges as a key determinant of firm value, while short-term debt provides operational flexibility, and taxation imposes significant financial strain. Larger firms demonstrate greater resilience, benefiting from scale economies and diversified funding sources. This research highlights the importance of an integrative financial strategy during periods of economic uncertainty, emphasizing the complementary roles of leverage and dividend policy in enhancing firm value. The findings offer critical insights for policymakers and corporate managers in emerging markets, advocating for tax reforms and prudent financial management to improve business resilience. By addressing gaps in the literature, this study contributes to the understanding of financial decision-making in developing economies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".