Shareholding sizes and stock price informativeness

ABSTRACT

This study examines the relationship between shareholding sizes and stock price informativeness, grounded on the costly acquisition of private firm-specific information. Using data for all non-financial Malaysian public listed companies over the period of 2002–2019, we confirm that the positive relationship between blockholdings and stock price informativeness only holds at the threshold level of 5% and above. Our results provide new evidence that the relationship turns negative for small shareholding sizes, which can be ascribed to the high acquisition costs of private information deterring small shareholders from informed trading. Therefore, regulators and corporate managers should capitalize on modern information technologies to reduce the costs of information acquisition by small investors.

KEYWORDS:

• Shareholding sizes
• block ownership
• small shareholders
• stock price informativeness
• Malaysia

JEL CLASSIFICATION:

• G10
• G14
• G32

Acknowledgements

We would like to thank three anonymous referees of the journal for their constructive comments that have improved the manuscript significantly. This manuscript is the Ph.D. research of the first author, who received financial support from the University of Malaya for her graduate study. The publication is partially funded by the Faculty of Business and Economics, Universiti Malaya Special Publication Fund.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ Refinitiv Datastream provides data on firm size, firm age, financial leverage, profitability, growth opportunities, turnover and the number of financial analysts. From annual reports, we extract percentage ownership for five shareholding sizes, external auditors, board size, board independence, and CEO duality. The list of politically connected firms is extracted from Tee et al. (2017). Local and foreign institutional ownership are subscribed from Bursa Malaysia.

² In the literature of blockholdings – stock price informativeness, the latter is proxied either by the market model R-square or firm-specific return variation. The seminal work of Morck, Yeung, and Yu (2000) computes stock price synchronicity, which is an inverse measure of stock price informativeness, from the R-square of a market model that regresses firm-level stock returns against market returns. A higher value of R-square indicates stronger co-movement between the returns of the firm and market, hence lower stock price informativeness because the price conveys less firm-specific information. Instead, we compute firm-specific return variation, which is precisely 1 – R² of the market model, because it provides direct measure of stock price informativeness. Since it is bounded within the intervals of [0,1], the standard econometric remedy in previous studies is to apply logistic transformation to the dependent variable so that SPI takes values between –∞ and +∞.

³ For other explanatory variables, the correlations are all less than 0.4 except between lnSIZE and ln(1+ANALYST) at 0.5940. This suggests our empirical analysis is not affected by multicollinearity. The full correlation matrix is available upon request.

⁴ We further determine whether the baseline results for the five shareholding sizes are robust across the entire conditional distribution of SPI. Our quantile regression confirms the baseline results remain intact. The graphical plots are not presented due to space constraint, but can be requested from the authors.

⁵ We thank an anonymous referee for highlighting the possibility of structural break affecting our baseline results. To address this valid concern, we re-estimate the baseline model for three sub-periods: (i) 2002–2007 (before crisis); (ii) 2010–2019 (after crisis); (iii) 2002–2019 but excluding the years 2008–2009. This unreported robustness check reveals that the baseline results for the key variable of SHSZ are not affected by the global crisis.

⁶ For instance, McClure, Shi, and Watts (2022) capitalize on the adoption of centralized electronic disclosure systems in 32 countries, and provide evidence of a reduction in disclosure processing costs for small investors.

⁷ The disclosure bands are: less than 1,000 shares, 1,000–5,000 shares, 5,000–10,000 shares, 10,000–50,000 shares, 50,000–100,000 shares, and greater than 100,000 shares.

Additional information

Funding

The work was supported by the Universiti Malaya [Universiti Malaya Special Publication Fund, University of Malaya Student Financial Aid].