Board Governance, Ownership Structure and Foreign Investment in the Saudi Capital Market
Bibliographic record
Abstract
Saudi vision 2030 targets, among other objectives, the attraction of foreign direct investment into the Saudi capital market.This paper examines whether board governance mechanisms and ownership structure play a role in foreign investors' decisions when buying shares in Saudi listed companies.Foreign investment in the Saudi capital market started in 2015 and reached a peak in 2019, with corporate governance regulations having been updated in 2017.We tested the proposed relationships using hand collected data for all Saudi non-financial firms in 2019.While board governance is a critical mechanism in firms, this study found that it does not play a role in attracting foreign investment in the Saudi capital market.Foreign investors also seem to avoid firms with concentrated ownership that either have high government or director ownership; however, accounting and market variables show significant impact on foreign investors' decisions.The outcomes of this study provide empirical evidence that current foreign investors in the Saudi stock market do not place enough merit on board governance and their investment decisions tend to depend on share performance.Thus, our results show that the current governance changes and capital market regulations in Saudi Arabia may not have been sufficient to stimulate the inflow of institutional foreign investment to the country to date, but rather they have attracted individual retail foreign investors.These findings have crucial implications for foreign funds and Saudi market regulators as they highlight issues related to the Qualified Foreign Investor (QFI) program as well as researchers who work toward understanding foreign investors' behaviors in emerging markets.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".