Heterogeneous Intermediary Asset Pricing in Iran’s Stock Market: Privately-Owned vs. State-Owned

ABSTRACT

In Iran’s stock market, this paper examines a new dimension of heterogeneity: ownership type, using an intermediary asset pricing model. When only state-owned intermediaries are considered, the price for exposure to capital ratio shocks is negative; thus, the group is not a marginal investor. Considering only privately-owned intermediaries has greater explanatory power than considering both types, and the price for capital risk is positive in both cases. We propose a new criterion to represent the sector with even greater explanatory power: a group of privately-owned intermediaries with positive capital risk prices when tested individually.

KEYWORDS:

• Intermediary asset pricing
• heterogeneity
• capital risk
• state-owned vs. privately-owned intermediaries

JEL:

• G12
• G23
• C33

Acknowledgments

We would like to thank the reviewers for their time and effort. We appreciate their valuable comments and suggestions that helped us improve the quality of this work.

Disclosure Statement

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

Notes

1. Examples are the capital asset pricing model (Lintner 1965; Sharpe 1964), the consumption-based capital asset pricing model (Breeden 1979; Lucas 1978), and the multi-factor asset pricing models (Fama and French 1993, 2015).

2. In the present study we use the data for the period 2011Q2–2021Q3, while in the previous study the last quarter was 2018Q4.

3. According to He, Kelly, and Manela (2017), the consumption of the intermediary sector, $\mathit{C}$, is a constant fraction, $\mathit{\alpha }$, of its total wealth: $C_t=\mathit{\alpha }{W}_t^I$. Given that the economy’s wealth is equal to the sum of the wealth of intermediaries and households: $\mathit{W}=W^I+W^H$, they show that the wealth of intermediaries is a fraction of the total wealth of the economy: $W_t^I={\theta }_t{W}_t$. According to He, Kelly, and Manela (2017), $\mathit{\theta }$ equals the capital ratio of the intermediary sector, $\mathit{\eta }$, in equilibrium.

4. In fact, we use the growth rate of market portfolio excess return as a proxy for total wealth growth rate, $\frac{d{W}_t}{W_t}$. Hence, ${\beta }_W^i$ indicates the market factor’s risk exposure and $\mathit{P}{R}_W$ is its price. For more details, see Section 3.1.

5. See Shanken and Zhou (2007) for more details on EIV problem.

6. There are $\mathit{N}\times (\mathit{K}+2)$ moment conditions and $\mathit{N}\times (1+\mathit{K})+\mathit{K}$ parameters. Here $\mathit{N}$ is the number of asset tests and $\mathit{K}$ is the number of the risk factors.

7. MAPE is calculated as $\frac{1}{N}\mathrm{\Sigma }\left|\frac{{\upsilon }_i}{E_t[R_t^{ei}]}\right|$

8. Specifically, ${\hat{\upsilon }}^{\prime }\mathrm{Cov}(\hat{\upsilon }{)}^{-1}\hat{\upsilon }\sim {\chi }_{N-K}^2$ where $\mathrm{Cov}(\hat{\upsilon })=\frac{1}{T}({\mathrm{\Sigma }}_{\text{isin}}-\mathit{\beta }({\beta }^{\prime }{\mathrm{\Sigma }}_{\text{isin}}^{-1}\mathit{\beta }{)}^{-1}{\beta }^{\prime })(1+\mathit{P}{R}^{\prime }{\mathrm{\Sigma }}_f^{-1}\mathit{PR})$, ${\mathrm{\Sigma }}_{\text{isin}}$ is the variance-covariance matrix of the time series errors, and ${\mathrm{\Sigma }}_f$ is the variance-covariance matrix of risk factors. For more details, see Cochrane (2005).

9. Closed-end investment funds are known as investment companies in Iran.

10. The list of intermediaries can be found in Appendix Table A2.

11. The capital ratios are calculated using data from Mabna’s Rahavard Novin 3 database.

12. The real period is 1390Q1–1400Q2 based on the Iranian calendar. It is relabeled according to the Gregorian calendar. The time-series variables in this study are all consistent with the Iranian calendar, although the quarters are illustrated and labeled in the Gregorian calendar. Note that the first season of the Iranian year usually begins 11 days ahead of the second season of the Gregorian year.

13. The TSE market’s primary index is the TEDPIX index, which is the weighted mean price of all listed stocks in this market.

14. These data come from the TSE market and the Central Bank of Iran (CBI) respectively.

15. Input data including stock returns and the data required to calculate size and book-to-market ratio are obtained from the Rahavard Novin 3 database.

16. The exact date is the end of the second season of the Iranian year, which falls mostly on September 22.

17. The daily average USD to IRR exchange rates in 2011Q2 and 2021Q3 were 11,532 and 258,319 respectively.

18. Those intermediaries are either not yet established or their balance sheets are not published. See Table A2 of the Appendix for more information on the availability of the balance sheet data of our sample.

19. For instance, in 2011Q2, we compute privately-owned and state-owned capital ratios using data from seven and five relevant intermediaries, respectively. These figures increase to twelve and seven in 2014Q2 and stay constant afterward. Apart from these early quarters, in a limited number of quarters that some balance sheets are not published, we interpolate $\mathit{\eta }$ using its average of previous and next quarters.

20. Due to missing data for some intermediaries in the initial quarters of our entire sample (2011Q2–2021Q3), we estimate the model during the shorter sample beginning in 2014Q2. It allows us to compare the results across the intermediaries.

21. Capital risk prices are negative for four out of twelve privately-owned intermediaries and positive for four out of seven state-owned intermediaries. All are statistically significant at the 0.1% level.

22. Because all of the group members’ capital risk price estimates are positive and significant.

23. The significance of the intercept implies that other risk factors are being overlooked in the model.

Additional information

Funding

The data that support the findings of this study are available from the corresponding author, M. H. Dehghani, upon reasonable request. The data were derived from the following resources available in the public domain: Tehran Securities Exchange Technology Management Company at https://tsetmc.com, the Publishers’ Comprehensive Notification System of Iran’s Securities and Exchange Organization at https://codal.ir, and the Economic Time Series Database of the central bank of Iran at https://tsd.cbi.ir.