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Abstract

Private information puts naïve traders at a significant trading disadvantage and at the same it provide crucial signals managers for investment adjustment. These two forces have opposing effects on the cost of equity, and the overall effect is determined by which force dominates. For clinching this effect, this study finds out how investment adjustment plays the moderating role between private information and COE. The study employs data of non-financial firms listed on PSX from 2008 to 2019. Further, the study employs a two-step system GMM dynamic panel estimator to analyze the data. The findings of the study show that companies with a low investment adjustment flexibility known as “value firms” do not gain as much from information incorporated in market prices compared to firms having high flexibility in adjusting investment “growth firms.” This study adds to the literature by revealing unique insight on the effect of investment adjustment in reducing the influence of private information on COE and corporate investment as well.

JEL classification: D82, G14, G410.

Keywords

cost of equity corporate investment private information GMM panel estimator

Introduction

The provision of information is one of the essential function of financial markets. Traders are not uniformly well-versed; private information is propagated into stock prices through trading. As a result of the information asymmetry, uninformed traders seek a risk premium as recompense for incurring the information risk (Easley & O’Hara, 2004). While, on the other side, a body of research has examined how the private information incorporated in stock prices may be used by managers to adjust investments.

The amount of private information ingrained in the stock prices affects investment-Q sensitivity (Chen et al., 2007). Stock price informativeness, according to Zhi-Zhu and Xiao-Feng (2008), “can reduce the susceptibility of overinvestment to free cash flows and underinvestment to financing constraints.”Loureiro and Taboada (2015) investigated that the impact of information chaos on insiders’ capacity to learn from outsiders. For instance, with the implementation of the International Financial Reporting Standards, new outside investors from international markets have emerged, with whom insiders are less familiar. These potential overseas investors incorporate their personal information into stock pricing (DeFond et al., 2011; Florou & Pope, 2012). Edmans et al.(2017) suggested that, “not only the total amount of information in prices counts for genuine efficiency, but also the source of information in prices—whether the information is previously known to the decision makers or not.”

Ouyang and Szewczyk (2019) identified that “managers learn from financial markets in recognizing strategic merger investment opportunities by shifting assets from badly managed businesses to well managed firms.” Furthermore, the acquisition size is increased by the target’s stock prices, which is firm-specific information demonstrating that well-informed acquirer management is more likely to undertake big merger investments. High stock price levels, according to Chan et al. (2017), obstruct informed stock trading and impair price informativeness.

Therefore, when the managers adjust this information in investments, it affects the firm’s fundamentals, renders informed traders’ information stale, and therefore reduces the information risk carried by ignorant traders (Huang & Kang, 2018). Accordingly, investment adjustments make the stocks more appealing to uninformed traders while also lowering the cost of equity (COE). However, the study raises the question of whether firms have equal opportunities to make investment adjustments by employing this information? Since private information has two competing forces impacting the COE, the net effect is defined by which force dominates, which in turn determines the amount to which investment may be adjusted. As a result, this study investigates how information and investment adjustment together affect COE, arguing that the dynamics between private information and the COE may be altered via information-driven investment adjustment.

Companies with little investment flexibility, known as “value firms,” can’t get as much advantage from information incorporated in stock prices as companies with strong investment flexibility, known as “growth firms.” As a result, this study contends that value stocks have a greater risk premium for information than growth firms. Further, this study explores the influence of information risk on investment Q sensitivity by taking into account the firms’ varied degrees of flexibility in modifying their investments. In the dynamic framework, it is postulated that the influence of information risk on Investment-Q sensitivity is more prominent for growth firms than for value firms.

However, in the context of Pakistan, investigating the function of investment adjustment in information risk management is important. The Pakistan Stock Exchange (PSX) has little to no attraction to unrepresented small investors. More insider trading has been one of the primary reasons for deterring actual investors, often known as retail investors. As a result, the country’s stock market is controlled by speculators who participate in the short-term buying and selling of shares. The absence of long-term investment activity has an adverse influence on the state’s rate of capital formation. In comparison to industrialized and mature markets, the quality of financial reporting in emerging markets such as Pakistan is weak. The market system does not preclude earnings restatements, and lax restrictions would cause issues for investors (Shah et al., 2020). In this situation, there is a dare need to evaluate the role of investment adjustment in improving the image of private information in the eyes of retail investors which can boost the investment activity.

This study adds to the literature on the empirical effect of private information on COE. Faraz and Hassan (2016) investigated whether the information efficiency premium is regarded as a systematic risk and is priced by the market. According to Ahmad et al. (2021), firms with substantial information asymmetry have a negative influence on investment choices and exacerbate the negative effect of leverage on company investment. “Private information, according to Saleem and Usman (2021), enhances the likelihood of a stock price drop and, as a result, COE.” None of the studies have addressed the feedback effect from the financial market to the real economy. Therefore, this study looks at how investment adjustment could affect the relationship between private information and COE. The information risk is priced differently in value firms and growth firms as a result of the investment adjustment. In value firms, private information produces the information premium, but in growth firms, the information effect is discounted.

The remainder of this article is organized as follows. Section “Theoretical Framework and Hypothesis Development” presents theoretical framework and research hypotheses. Section “Theoretical Framework and Hypothesis Development” describes the sample data and the methods of analysis. Section “Results and Discussion” presents our empirical results, and Section “Conclusion” offers concluding remarks.

Theoretical Framework and Hypothesis Development

This study builds a theoretical model that combines investment adjustment and information risk as motivation for our empirical work. “Private information raises the COE in the truancy of investment adjustment, Easley and O’Hara (2004).” This is attributed to information risk; uninformed investors expect larger yields for stocks with information asymmetry because they have too few “good” assets and too many “bad” assets in their portfolio. In addition to Easley and O’Hara (2004), this study believes that managers can adjust investments based on stock price movements. Precisely, a dip in the stock price implies that informed traders may be selling the stock in response to negative news. When the stock price falls below a certain threshold, it spurs the mangers to adjust the firm’s investment whose goal is to maximize the firm’s worth. When the managers adjust this information in investments, it reduce the information asymmetry and increase the investment and firm value in the following ways.

First, the presence of information asymmetry makes investments more susceptible to stock price fluctuations (Zaigham et al., 2019). This sensitivity surges when the stock prices include more private information (Chen et al., 2007). The learning theory states that managers use stock prices to learn new information and use it to make informed decisions. According to Dow and Gorton (1997), investors are prepared to generate crucial information about the likelihood of future investment opportunities that managers do not possess and to profit from it. Subrahmanyam and Titman (1999) demonstrated that investors can obtain valuable information for manager through their day-to-day activities. Frésard (2012) demonstrates that when stock prices are more informative, cash-saving decisions are also more sensitive to them.

Moreover, Luo (2005) discovered that abnormal returns around merger and acquisition announcements predict whether businesses will actually complete the deals down the road. These empirical findings lend support to the notion that managers gather information from stock prices and use it to guide corporate decisions (Chen et al., 2007). This updating of stock price information in investments renders informed investors’ private information obsolete, thereby reducing the problem of information asymmetry. According to Wang (2010), S. Lai et al. (2014), and Hu et al. (2019), the presence of noise traders trading alongside informed traders significantly boosts trading volume, increases market depth, lowers trading costs and information asymmetry, and subsequently makes it easier for information to be incorporated into prices.

Second, stock prices that are instructive have an impact on corporate decisions by revealing more information about the caliber of manager’s choices. The corporate governance system is improved by informative stock prices, and managers are more motivated to invest significant resources in decisions that will increase the company’s value. This channel is therefore known as the contracting hypothesis (Xu, 2021).

Third, improved information disclosure and higher-quality financial reports are frequently positively correlated with stock price informativeness, which reduces information asymmetry and consequently reduces friction in the financial markets (F. Lai et al., 2021).

Thus, we can conclude based on above mentioned studies, private information makes the prices more informative. When the managers adjust this private information incorporated in stock prices in making investment decisions, uninformed investors can gain benefits from this in two ways. It reduce the information asymmetry in the first place and enhance the production efficiency in the second place. Therefore, the COE is reduced as a result of investment adjustments. If the investment adjustment is substantial enough, its influence on predicted stock returns outweigh the contending effect of information risk.

To put the theoretical prediction to the test, this study looks to the value premium literature for an empirical measure of the firm’s investment flexibility. Zhang (2005) asserted that compared to growth enterprises, value firms are riskier and provide larger returns because value firms have a harder time adjusting their investments, especially when the economy is sluggish.

We utilize book-to-market (BM) equity ratio as a proxy to quantify the amount to which the firm’s investment can be adjusted. If a company’s BM is low, investors believe it has a strong growth potential and are betting on its future cash flows. In the literature, such a company is referred to as a growth company, and it should have a lot of investment flexibility. A value firm, on the other hand, has a high BM; its growth options are constrained, and the stock return is primarily derived from cash flows from fulfilled investments. As a result, investment adjustments in reaction to stock price information do not assist value enterprises as much as growth firms. This disparity suggests that in order to attract uninformed investors, value enterprises must offer greater information risk premiums. Based on this logic, we propose the two hypotheses for empirical testing listed below:

H1: The effect of private information on COE is more pronounced in value firms as compared to growth firms.

H2: Growth firms display greater Investment-Q sensitivity in reaction to private information contained in stock prices than value firms.

Research Methodology

Research Population and Sample

The population of this study is the firms listed on the Pakistan Stock Exchange (PSX). There are 559 firms listed. The non-financial firms are considered for empirical analysis. On the PSX, there are 430 non-financial firms listed. Data is gathered from publicly available annual reports that are downloaded from the firms’ official websites. Share price data is collected from Business Recorder.

Variables Description

Independent Variable

Private Information

“This study uses price non synchronicity as a measure of private information, represented by the variance of firm-specific returns (Roll, 1986). Variations in stock returns can be broken down into various elements like; market-wide, industry-wide, and firm-level variation.” A market-wide and industry-wide variance of stock returns accounts for private information because stock price movement is not associated with identifiable public news. It is reflected in the share price with the trading of risk arbitrageurs who collect and own private information. R Square from the subsequent equation can be used to estimate this:

r i_{i, j} = β_{i, 0} + β_{i, m} r_{m, t} + β_{i, j} β r_{i, j} + ϵ_{ij}

(1)

“Unlike Roll (1986), this research adheres to Durnev et al. (2003), who considers both industry and market returns. The following model is used to calculate industry return”:

r_{i 2, w, t} = \frac{\sum_{k \in i 2} W_{k, w, t} r_{k, w, t} - W_{j, w, t} r_{j, w, t}}{J_{i 2} - 1}

(2)

“Where, J_i2 represents the number of firms in an industry i2 and the value weight of firm k in a particular industry in week w is depicted by W_k,w,t. The variance of ε_j,w,t is further deflated on the variance of dependent variable used in equation (1) to yield the given below model”:

Ψ_{j, t} = \frac{\sum_{w \in t} ε_{j, w, t}^{2}}{\sum_{w \in t} {(r_{j, w, t} - {\bar{r}}_{j, t})}^{2}}

(3)

“Equation (3) is estimated for each firm in each year. A higher value of ψ_j,t depicts that a greater amount of firm-specific information is realized by informed traders in share prices.”

Measurement of Expected Cost of Equity

“The COE can be defined as the required rate of return by equity providers to jeopardize their capital in business.”“To measure the COE, this study follows the Leary & Roberts (2014) by extending the model with book to market, size, and momentum factors given by Fama & French (1997) and Carhart (1997) as follows”

\begin{matrix} R_{ijt} = & α_{ijt} + {β^{M}}_{ijt} * MK T_{t} + {β^{SMB}}_{ijt} * SM B_{t} \\ + {β^{HML}}_{ijt} * HM L_{t} + {β^{MOM}}_{ijt} * MO M_{t} \\ + {β^{IND}}_{ijt} (\bar{R} -_{ijt} - R F_{t)} + η_{ijt} \end{matrix}

(4)

“Where R_ijt refers to the total return on the stock of the firm i in industry j during the month t, MKT_t is the excess return on the market, SMB_t is the size factor, HML_t is the book-to-market factor, MOM_t is the momentum factor, and ( $\bar{R}$ −_ijt − RF_t) is the excess return on the equally weighted industry portfolio, minus firm i’s return. The study includes this last factor in the model to confiscate any common stock returns variation across the industry.”

Using historical monthly returns, the study estimates equation (4) on a rolling annual basis for each company.

A minimum of 24 months of previous year data is required for this purpose. For computing the expected COE, the study first estimates equation (4) using the monthly returns from July 2006 to June 2008. Then uses the proposed coefficients obtained by equation (4) and the monthly factor returns from July 2007 to June 2008, the study determines the expected COE of a particular firm for a specific year by estimating the equation (5) as follows:

\begin{matrix} R_{ijt} = α_{ijt} & + {β^{M}}_{ijt} * MK T_{t} + {β^{SMB}}_{ijt} * SM B_{t} \\ + {β^{HML}}_{ijt} * HM L_{t} + {β^{MOM}}_{ijt} * MO M_{t} \\ + {β^{IND}}_{ijt} (\bar{R} -_{ijt} - R F_{t}) \end{matrix}

(5)

Corporate Investment

Capital expenditures are used to quantify corporate investment (Huang & Kang, 2018).

Moderating Variable

Investment Adjustment

Value firms cannot benefit as much from information incorporated in market pricing as growth firms. Book to market ratio are employed as an indicator of investment adjustment by splitting firms into growth and value categories. The firm with B/M ratio of less than 1 is considered as growth firm, and with higher than 1 is classified as the value firm. B/M is most widely used in determining the firm’s ability to adjust the investment but one issue is that BM is generated from stock prices and therefore, relies on the investor’s perception of the company activities.

Control Variables

“The size of a corporation is calculated using the natural logarithm of its total assets (Kamran & Shah, 2014).” By the following the Hail and Leuz (2006), this paper uses the market to book ratio in order to control for the differences in growth opportunities among firms. CF is calculated as “net income before extraordinary items plus depreciation, amortization, and research and development (R&D) expenses, scaled by the beginning of year book value of assets.” A measure of systemic risk is referred to as “beta.”

Data Estimation Method

To resolve methodological concerns with panel data, the study uses the system GMM. “Arellano and Bond (1991) proposed the difference GMM, which transforms the regressors using the first difference to eliminate unobserved firm-specific fixed effects and does not change over time.” The difference GMM results in skewed and subpar accuracy in linear dynamic panel data models. Because of two factors, it becomes less informative (Blundell & Bond, 2000). First, the variables resemble a random walk (Blundell & Bond, 1998), and second, the unobserved fixed effects grow. The instruments are weakly linked with the regressors in this case.

The article provides a system GMM estimator to handle the problem of weak instruments to overcome it. The number of instruments used to estimate a GMM equation should be smaller than the number of groups. Otherwise, the approximate equation may not have found an acceptable solution (Davidson & MacKinnon, 2004). System GMM enhances estimator efficiency and produces accurate estimates, mainly when time-series data is less than cross-sections. Furthermore, including firm-specific variables may cause an endogeneity problem, resulting in skewed estimates. The endogeneity problem arises because these variables are calculated based on accounting values (Gaud et al., 2005). For instance, information quality and COE are endogenous variables. Several previous studies investigated the cross-sectional relationship by regressing the information risk on the COE. However, there are four ways this approach might result in an endogeneity bias. First, the relationship is driven by omitted variables associated with the response and explanatory variables, such as disclosure cost and business risk, resulting in potential endogeneity. (Nikolaev et al., 2005). This unobserved heterogeneity influences each firm’s overall information environment each year.

Second, much of the experimental accounting literature has focused on panel data, which is a series of repeated measurements on the same set of firms across time. In that case, the interest variable is frequently cross-sectionally and serially correlated. However, there is a growing need to identify the autocorrelation of Yt and Yt − 1 (Eugster, 2020; Gow et al., 2010).

Third, in the case of reverse causality, “where independent and dependent variables impact each other at the same time, reciprocal causal effects emerge (Wooldridge, 2002).” Because the error term in the model includes all unobserved factors impacting the dependent variable and the dependent variable impacts the independent variable in the presence of simultaneity, the error term is also connected with the independent variable, causing endogeneity issues. In this model, the firm’s disclosure choice is influenced by the firm’s previous COE. As the COE rises, the management is forced to offer less accurate and transparent information.

Fourth, the firm’s previous COE is linked to control factors such as size. For instance, if management considers that the current COE is greater than projected. In that circumstance, the company will decline all available projects. In this manner, previous COE will decide the business size (Eugster, 2020).

OLS yields skewed and unreliable estimates in that situation because of the existence autocorrelation caused by unobserved heterogeneity and natural correlation of error term with lagged dependent value. Although random effect can solve the autocorrelation problem, the most significance inconsistency problems still exist. Consistent measurement can be attained by using data differentiation to take the based on a fixed effect approach, the within-group average, eliminating all time of the model’s constant terms, whether observable or not.

Similarly, fixed effect in not vailed when the lagged value of dependent variable is used as a independent variable because of strict exogenous nature of panel models. The potential endogeneity of the dependent variable was controlled in this study by including the lag value of the same variable as the instrument (Bonaimé et al., 2014). To address the issue of endogeneity, the analysis used the two-step GMM Dynamic Panel Estimator method as described by Amidu and Wolfe (2013).

Asset Return and Information

Easley et al. (2002) shows that stocks having more private information measured by (1 − R²) have generated greater returns. This research is furthered by our findings, which show that the impact of private information on COE varies from company to company when considering investment adjustment. Private information raises COE for value companies in particular, but the impact for growth companies can be reversed. This indicates that in order to establish the cross-sectional gap between the returns of growth and value firm, private information must be reflected in stock prices that are prerequisite for investment adjustment. This relationship is ascertained by constructing a dynamic model where the lagged value of response variable is employed as an explanatory variable as follow:

\begin{matrix} EC C_{i, t} = & R_{i, t} - R_{f, t} = Y_{0} + y_{1} ECO C_{i, t - 1} \\ + Y_{2} BET A_{i, t - 1} + Y_{3} INF O_{i, t - 1} \\ + Y_{4} Siz e_{i, t - 1} + Y_{5} In {(BM)}_{i, t - 1} \\ + Y_{6} INF O_{i, t - 1} * DU {M_{G}}_{i, t - 1} \\ + Y_{7} INF O_{i, t - 1} * DU {M_{V}}_{i, t - 1} \\ + Y_{7} DU {M_{G}}_{i, t - 1} + Y_{8} DU {M_{V}}_{i, t - 1} + n_{t} \end{matrix}

(6)

“Here, ECOE is the expected cost of equity, $Y_{0}$ is the intercept, INFO is private information measured by 1 − R², Beta is a factor of market risk in CAPM, Size is the natural logarithm of firm’s total assets, and ln(BM) is the natural logarithm of the ratio of BV of equity to MKT value of equity, as we are interested in determining the effect of private information on COE that vary from growth firm to value firm.” For that, firms are sorted into two quartiles based on their value of B/M ratio and generated two dummy variables: value firm denoted by DUM-V is created by assigning 1 when the firm lies in the top quartile of BM and 0; otherwise, the growth Dummy, DUM-G is equal to 1 when the firm B/M ratio lies in the bottom quartile. Further, this study has created the two interaction terms based on these two Dummies, such as INFO × DUM V and INFO × DUM G.

Investment-Q Sensitivity and Private Information

As it is supposed in the first hypothesis, growth firms have greater flexibility in adjusting the private information reflected in share prices in investment than the value firm. This research builds on Chen et al. (2007) to see whether the impact of private information on investment-Q sensitivity differs across value and growth firms.

\begin{matrix} Ii, t = & 0 + 1 Ii, t - 1 + 1 Qi, t - 1 + β 2 INFOi, t \\ - 1 + 3 INFOi, t - 1 * Qi, t - 1 * DUMGi, t \\ - 1 + 4 INFOi, t - 1 * Qi, t - 1 * DUMVi, t \\ - 1 + 6 * DUMGi, t - 1 + 7 * DUMVi, t - 1 \\ + γ CONTROLS + at + t + i, t \end{matrix}

(7)

Firm i’s investment in year t is the dependent variable, I_i,_t capital investment is employed as a metric of corporate investment. The independent variables α_t and µ_i indicate the year-fixed and firm-fixed effects, respectively. Tobin’s Q is Q_i,t−1. INFOi is an indicator of private information embedded in share price; our research employed 1 − R² as a proxy for this measure based on our asset pricing studies. CONTROLS is an array of control variables that comprises the log of the total assets of the company as a measure of the total assets, the cash flow (CFi, t) of the firm i in year t, Beta as a measure of corporate investment. Chen et al. (2007) concentrated on the private information’s impact on investment-Q sensitivity by generating the interaction term, INFO × Q. This study goes one step further to explore whether the impact of private information on investment-Q sensitivity is influenced by the firm’s ability to adjust corporate investment. For that reason, this study created the triple interaction term by interacting the INFO × Q with two Dummy variables, DUM-G and DUM-V. These triple interaction terms are denoted by INFO × Q × DUM-V and INFO × Q × DUM-G to determine the different effect of investment adjustment between the private information effect on investment-Q sensitivity and this effect is represented by parameters of β3 and β4.

Results and Discussion

Descriptive Statistics

Table 1 demonstrates the summary statistics for the variables used in the current study during the period from 2007 to 2020. The mean value of private information is 0.9012, demonstrating that the market and industry predictability power of company particular return is poor, and that private information explains 90% of variation in firm specific return (Rasheed et al., 2018). The average value of leverage implies that long-term debt accounts for 17% of the firm’s assets on average. Nevertheless, market to book ratio signifies growth opportunities with a mean value of 1.48. This indicates that equity is performing well in the market, and firms are experiencing growth opportunities. Accordingly, the mean value of the book to market is 0.7649. This shows that firms listed on PSX are mostly growth firms. The minimum and maximum values show a significant gap in the COE among the firms registered on PSX.

Table 1.

Descriptive Statistics.

Variables	Mean	SD	Minimum	Maximum
Private info	0.9012	0.12212	0.13921	0.111
Corporate investment (capital expenditure)	0.06451	0.10662	0.0010251	0.190441
CF	0.07751	0.13741	−0.30712	0.57631
Beta	0.41511	0.81155	−1.43012	1.40331
Q	1.4881	2.24654	−9.56771	12.73343
Leverage	0.12081	0.16917	0.000981	0.45874
Investment adjustment (BTM ratio)	0.76491	1.36366	−5.80883	5.99442
Size	17.5267	3.16184	1.12812	25.27333
Cost of equity	0.23641	0.82883	0.04713	0. 50332

Correlation

The link between the variables and the direction of the relationship are shown in Table 2. The study analysis also corroborates the absence of Multicollinearity among the explanatory variables as the variables are moderately correlated.

Table 2.

Correlation Matrix.

	COE	Investment	Private info	BM	MB	Size	Beta	Leverage	CF
COE	1.000
Investment	0.036	1.000
Private info	0.083	−0.04	1.000
Invest adjust	−0.079	0.020	−0.034	1.000
Q	−0.139	0.123	−0.083	−0.224	1.000
Size	−0.032	0.079	0.019	0.044	−0.138	1.000
Beta	0.063	−0.046	0.293	0.045	−0.043	−0.04	1.00
Leverage	0.039	−0.194	0.121	0.004	0.214	−0.125	0.289	1.000
CF	−0.166	0.134	−0.138	0.059	0.311	−0.107	0.028	−0.289	1.000

Private Information, Investment Adjustment, and COE

By taking into account the moderating effect of investment adjustment, Table 3 illustrates the impact of private information on COE. “The lagged value of COE is introduced as an explanatory variable in all the models, and it is statistically significant, showing the dynamic nature of the models (Arellano & Bond, 1991).” This illustrates the COE’s mean reversion tendency, in which previous COE has an effect on its current value. The coefficient value of private information has a considerable and favorable correlation with COE as shown in the second column of Table 3. These findings are corroborated by Easley and O Hara’s (2004) findings. The argument behind this finding is that difference in the composition of private and public information effects the COE because investors seeking higher return for holding the stock with more private and congruently less publicly available information. This greater return is reflected in the fact that private information escalates the risk of incognizant investors holding stocks because cognizant investors are in a better position to adjust their portfolio by integrating new information. While the incognizant investors are unaware from reality, they are, therefore unable to regenerate the optimal weights and turn out to be maintaining a portfolio different from the cognizant investors. As the cognizant and incognizant investors experience the different risk and return, thus the basic separation theorem does not fit in this scenario. Hence, private information contributes toward a new type of systemic risk and as a result, investors need compensation for bearing this risk in equilibrium.

Table 3.

Private Information, Investment Adjustment, and COE.

Variable	Private info	Private info (growth firms)	Private info (value firms)
Constant	−2.05125*** (0.3228)	−2.2577*** (0.4902)	−1.9688*** (0.3336)
COE t − 1	0.1571*** (0.0417)	0.0519** (0.1999)	0.1299** (0.0457)
1 − R²	0.4016*** (0.0540)	0.5118*** (0.1135)	0.1521* (0.9185)
(1 − R²) × DUM_G		−0.1851* (0.1702)
(1 − R²) × DUM_V			0.5600** (0.1636)
DUM_G		−0.4885* (0.2481)
DUM_V			0.8207*** (0.2126)
Book to market	−0.0412** (0.0189)	−0.0527 (0.0434)	−0.0468** (0.0230)
Size	0.1001*** (0.0174)	0.0968*** (0.2319)	0.1147*** (0.2008)
Beta	0.1097*** (0.0281)	0.0915** (0.0301)	0.1338*** (0.0319)
No. of Instruments	149	224	174
No. of Groups	370	370	370
Sargan/Hansen	0.167/0.134	0.231/0.141	0.234/0.145
AR1	0.000	0.000	0.000
AR2	0.860	0.738	0.994

Note. Standard errors are represented in parenthesis and significance level at 1%, 5%, and 10% have shown in terms of “***”, “**”, and “*” respectively

The coefficient value of book to market ratio has positive and significant relationship with COE. Companies with high stock prices appear to have low income, higher financial leverage, greater profit volatility, and are more likely to reduce dividends than low BE/ME. These companies are in addition undervalued, and this mispricing is likely to be more prevalent in companies with a high level of information asymmetry, where fair arbitration is less likely to be successful. Companies with high B/M receive high premium returns because of the higher risk of distress (Griffin et al., 2002). The other justification is the clientele effect. Older investors prefer high capital returns over high dividends. Firms with a high book/market ratio generally attract the stockholders and pay more dividends.

Furth more, the coefficient value of size has positive and negative association with COE. Larger corporations may benefit more from the disclosure of information than smaller firms. Reason for savings due to size; that is, larger companies will incur lower costs when disclosing information, while additional disclosure would make it possible for proprietary costs to rise in smaller companies as well as the risk of disclosure of information; the overall cost of disclosure would increases as compared to larger companies. Cognizant investors have access to private information and are able to make all decisions and can make an attempt to sustain incognizant loss. By increasing the quality of transparency, an investor’s attempt to obtain access to private information is expected to decline as information asymmetry decreases, meaning that investors will demand less expected returns. As capital costs are the least anticipated, it will fall (Embong et al., 2012; Rezaei & Shabani, 2015).

Similarly, Beta as a systematic risk indicator shows the positive association with COE. These result support the capital asset pricing model (CAPM) and predict that when there is more uncertainty in economy and high systematic risk, investors will demand more return. This increase in compensation in the form of premium will increase the COE (Gupta et al., 2018).

Whereas in thirrd column of Table 3, the value of private information is interacted with growth firms dummy DUM_G. The estimated coefficient on (1 − R²) × DUM_G equals to (−0.185 < 10%) illustrate significantly inverse relationship with COE. These findings indicate that growth firms with more private information earn lower returns as compared to growth firms with less private information. Similarly, in fourth column of Table 3, the private information is interacted with value firm dummy DUM_V. The estimated coefficient on (1 − R²) × DUM_V equal to (0.5600 < 5%) demonstrate the positive association with COE at level of 5%. These findings depict that value firms with more firm specific information in share price earn substantially higher returns than the value firms with less private information. Thus, these results support our first hypothesis that private information has a varied impact on the return of value and growth firms. The reason behind this disparity in returns is firms’ ability to adjust firm-specific information in stock prices into investment. As private information disseminates in prices through the trading of informed investors. This information in stock price acts as a key signal on which managers may rely to adjust their investing strategy in order to increase performance. This investment adjustment increases firm value, benefiting informed and uninformed investors. Moreover, investment adjustment changes firm’s fundamental by making the information of informed investor stale and thus alleviating the information risk borne by uninformed investors (Huang & Kang, 2018).

“Table 3 depicts the results relevant to the Two-Step GMM approach for evaluating the moderating role of investment adjustment between relation of private risk and COE.”“Where the cost of equity is the return of the stock above the risk-free rate in the year t; IR is the information risk included private information (PI), PI measured by 1 − R². Investment adjustment is based on ratio of Book value of equity to Market value of equity (BTM) where the firms are divided into two groups based on the median value of BTM such as Growth firms and Value firms.”“The firms that fall in below the median value of BTM ratio are assign to 1 and otherwise 0 to create the growth dummy variable DUM_G.”“The firms that BTM ratio are above the median value of BTM ratio are assign to 1 and otherwise 0 to create the growth dummy variable DUM_V. (1 − R²) × DUM_V and (1 − R²) × DUM_G are the interaction terms. Stock beta (BETA), SIZE (firm size) is measured by taking the natural log of firm’s total assets, Lev is calculated as total long-term debt deflated by total assets.”“The AR1 significance level denotes the presence of a first-order serial correlation, rejecting the null hypothesis that there is no first-order serial correlation among the error terms.”“Furthermore, AR2 demonstrates that in level regression, there is no second-order serial correlation between error terms.”“The value of the Sargan/Hansen test is insignificant, imply the instrument’s reliability and is not overly recognized.”“Ultimately, the GMM is properly defined without any validation issue and this is revealed by the outcomes of Sargan/Hansen, AR(1) and AR(2).”“To determine the existence of nonlinear relationship while developing the dynamic panel model, Ramsey Test is used.”“The lacks of significance of the Ramsey test confirms the linearity of model without any issue of model specification; omitted variable and results are reliable.”“Furthermore, failing to account for cross-sectional correlation while estimating a dynamic panel model can result in severely biased findings and erroneous inference.”“To determine whether the residuals from the fixed effect estimate of the dynamic regression model are partially independent, the null hypothesis stated that there are no cross-sectional association residuals among the residuals.”“Pearson test is used to check the dependence of cross-sectionals, and the test’s insignificance value confirms the cross-sectional independence of residuals in the estimation of fixed effect.”

Private Information, Investment Adjustment and Corporate Investment

Table 4 presents the findings of a two-step system GMM dynamic estimator used to address the influence of private information on corporate investment in order to address the possibility of endogeneity. As a gauge of corporate investment, capital expenditure is a dependent variable while the private information is included as an independent variable. The lagged value of corporate investment is added as an explanatory variable, and it is statistically significant, illustrating the model’s dynamic nature (Arellano & Bond, 1991). “This illustrates the investment’s mean reversion tendency where previous investment affects the current investment.” The findings in column 1 demonstrate that company investment and Tobin q are positively correlated, and that investment q sensitivity depends on private information. This positive association supports the Chen et al. (2007) finding that firms guide their investment decisions based on information gleaned from the stock market.

Table 4.

Private Information, Investment Adjustment and Corporate Investment.

Variable	Investment	Growth firms	Value firms
Constant	−0.5013*** (0.0621)	−0.5648*** (0.0791)	0.2389*** (0.0134)
Investment t − 1	0.1485*** (0.1299)	0.1012*** (0.0224)	0.0055*** (0.0003)
Q	0.1412** (0.0477)	0.6515*** (0.1362)	0.0748*** (0.0076)
1 − R²	−0.0287 (0.0237)	0.0982 (0.1834)	0.0291 (0.0070)
(1 − R²) × Q	0.2162*** (0.0122)
(1 − R²) × Q × DUM_G		0.1791*** (0.0439)
(1 − R²) × Q × DUM_V			−0.2396*** (0.0124)
DUM_G		2.6446*** (0.2789)
DUM_V			−0.4226*** (0.0113)
Leverage	−0.8632*** (0.1970)	−0.4288*** (0.8898)	−0.1719*** (0.0561)
CF	0.0789*** (0.0047)	0.1003*** (0.0014)	0.0987*** (0.0005)
AR1	0.028	0.002	0.032
AR2	0.165	0.208	0.273
Sargan/Hansen	1.000/0.703	0.125/0.409	0.999/0.498
No. of instruments	159	141	103
No. of groups	370	370	370

Note. Standard errors are represented in parenthesis and significance level at 1%, 5%, and 10% have shown in terms of “***”, “**,” and “*” respectively.

These findings also confirm the existence of learning hypothesis which means that managers learn from private information in equity markets while making a corporate investment decision. Since stock prices include both private and public information about the company’s fundamentals and this private information is ingrained in share prices by speculators trading. However, if managers decide on the level of investment at some point in time to boost firm value, they will consider all the information available in stock prices and other information new to managers. Thus, based on this rationale, the direct link between private information embedded by speculators and investment Q sensitivity, implies that manger look at the price while making the investment decisions in order to learn about the information that is new to managers.

However, in second and third column, when the growth firms interact with the interaction of Tobin q and private information as shown by interaction term (1 − R²) × Q × DUM_G, it shows the economically significant and positive association with growth firms. In contract, when this Tobin q interaction with private information interacts with value firm as presented by the interaction term (1 − R²) × Q × DUM_V, it demonstrates that private information ingrained in value firms does not respond to investment Q sensitivity. The results support our second hypothesis and are consistent with the findings of (Huang & Kang, 2018).

The theoretical reasoning behind these findings is that the ability of manger to integrate this information into investment or the flexibility of investment adjustment differs across firms. Growth companies have a lot of leeway when it comes to adjusting the investment on the basis of information incorporated in share prices and the beneficial impact on the COE as a consequence of balances out the negative influence of the information risk, thus creating information discounts. On the other hand, in the case of value firms that do not have flexibility, the investment adjustment cannot significantly favor them, and thus they must charge an information premium to compensate uninformed investors.

Moreover, Leverage has economically significant and negative association with investment. These results support the findings of Vo (2019) and Ali et al. (2019). This lends credence to agency principle of leverage and finds that Firm leverage provides a particular disciplinary perspective by restricting management choices to investments with lesser growth potential. In other words, inefficient investment is increasing on account of the default risk from “underinvestment” or “debt overhang.”

Similarly, cash flow has significant negative association with corporate investment after managing the investment opportunities for the firm. Cash flow is an important investment determinant for firms most likely requires external funds. Since the companies have two sources of finances: internal and external. Thus, firms try to fund long-term investments with long-term debt, whereas current investment with cash flows and short-term debt (Ghafoor, 2018).

Likewise, by dividing the companies into high and low growth enterprises according to their capacity for information adjustment, the sensitivity of the link between private information and investment is further ascertained. Private information of growth firms has a positive coefficient value of (0.1791) at significance level of (p < 1%) and negative value of coefficient (−0.2396, p < 1%) value of value firm also confirmed theoretical explanation that growth firms show larger Investment-Q vulnerability in respond to private information found in share prices. Growth companies respond to private information identified in share prices with increased Investment-Q susceptibility.

Conclusion

The goal of this study is to determine how investment adjustment acts as a moderator between private information and COE. As it is shown in literature that stock prices give information about a company’s achievements and in shaping future investment decisions. This study analyses the cumulative effect of private information and investment adjustment on the COE using GMM panel estimations. The company’s book-to-market equity ratio is used as measure of investment adjustment, this study finds that private information affects growth stocks more than the value stocks. “In response to information on the stock market, growth firms are more sensitive to investments than value firms.”“Among growth firms, the stronger impact of investment adjustment cancels out the inverse effect of information risk on COE; thus, while value stock requires an information risk premium, the growth stocks give a discount on information.”

Our findings have broader implications for corporate policy makers. First, pricing informativeness is connected with increased corporate transparency and lower equity financing costs. This makes it easier for corporations to finance capital expenditures and thereby improves their investment efficiency. Second, the effect of feedback from prices to investment adjustment helps firms achieve investment efficiency. Further, the findings of study have significant implications for policies aiming at promoting market openness and incentivizing the collection of information. Moreover, the study emphasizes the relevance of investment adjustment in lowering the private information detrimental impact on COE.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Sana Saleem

Data Availability Statement

The data available upon request.

References

Ahmad

M. M.

Hunjra

A. I.

Taskin

(2021). Do asymmetric information and leverage affect investment decisions? The Quarterly Review of Economics and Finance 87, 337–345.

Ali

Hameedi

Almagtome

(2019). Does sustainability reporting via accounting information system influence the investment decisions in Iraq. International Journal of Innovation, Creativity and Change, 9(9), 294–312.

Amidu

Wolfe

(2013). The effect of banking market structure on the lending channel: Evidence from emerging markets. Review of Financial Economics, 22(4), 146–157

Arellano

Bond

(1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297.

Blundell

Bond

(1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.

Blundell

Bond

(2000). GMM estimation with persistent panel data: an application to production functions. Econometric Reviews, 19(3), 321–340.

Bonaimé

A. A.

Öztekin

Ö.

Warr

R. S.

(2014). Capital structure, equity mispricing, and stock repurchases. Journal of Corporate Finance, 26, 182–200.

Carhart

M. M.

(1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57–82.

Chan

Lin

J. C.

Lin

T. C.

(2017). What do stock price levels tell us about the firms?. Journal of Corporate Finance, 46, 34–50.

10.

Chen

Goldstein

Jiang

(2007). Price informativeness and investment sensitivity to stock price. The Review of Financial Studies, 20(3), 619–650.

11.

Davidson

MacKinnon

J. G.

(2004). Econometric theory and methods (Vol. 5). Oxford University Press.

12.

DeFond

M. L.

Hung

Carr

Zhang

. (2011). Was the Sarbanes-Oxley act good news for corporate bondholders? Accounting Horizons, 25(3), 465–485.

13.

Dow

Gorton

(1997). Stock market efficiency and economic efficiency: Is there a connection?. The Journal of Finance, 52(3), 1087–1129

14.

Durnev

Morck

Yeung

Zarowin

(2003). Does greater firm-specific return variation mean more or less informed stock pricing? Journal of Accounting Research, 41(5), 797–836.

15.

Easley

Hvidkjaer

O’hara

(2002). Is information risk a determinant of asset returns?. The Journal of Finance, 57(5), 2185–2221.

16.

Easley

O’hara

(2004). Information and the cost of capital. The Journal of Finance, 59(4), 1553–1583.

17.

Edmans

Jayaraman

Schneemeier

(2017). The source of information in prices and investment-price sensitivity. Journal of Financial Economics, 126(1), 74–96.

18.

Embong

Mohd-Saleh

Hassan

M. S.

(2012). Firm size, disclosure and cost of equity capital. Asian Review of Accounting, 20, 119–139.

19.

Eugster

(2020). Endogeneity and the dynamics of voluntary disclosure quality: is there really an effect on the cost of equity capital? Contemporary Accounting Research, 37(4), 2590–2614.

20.

Fama

E. F.

French

K. R.

(1997). Industry costs of equity. Journal of Financial Economics, 43(2), 153–193.

21.

Florou

Pope

P. F.

(2012). Mandatory IFRS adoption and institutional investment decisions. The Accounting Review, 87(6), 1993–2025.

22.

Fraz

Hassan

. (2016). Information efficiency premium can also explain expected stock returns: Evidence from Karachi stock exchange. The Pakistan Development Review, 341–357.

23.

Frésard

(2012). Cash savings and stock price informativeness. Review of Finance, 16(4), 985–1012.

24.

Gaud

Jani

Hoesli

Bender

(2005). The capital structure of Swiss companies: An empirical analysis using dynamic panel data. European Financial Management, 11(1), 51–69.

25.

Ghafoor

Hassan

(2018). Voluntary disclosure, enterprise risk management and stock price synchronicity: Evidence from Shariah compliant and non-Shariah compliant companies. Journal of Islamic Business and Management, 8(1), 102–115.

26.

Gow

I. D.

Ormazabal

Taylor

D. J.

(2010). Correcting for cross-sectional and time-series dependence in accounting research. The Accounting Review, 85(2), 483–512.

27.

Griffin

J. M.

Lemmon

M. L.

(2002). Book-to-market equity, distress risk, and stock returns. The Journal of Finance, 57(5), 2317–2336.

28.

Gupta

Krishnamurti

Tourani-Rad

(2018). Financial development, corporate governance and cost of equity capital. Journal of Contemporary Accounting & Economics, 14(1), 65–82.

29.

Hail

Leuz

(2006). International differences in the cost of equity capital: Do legal institutions and securities regulation matter?. Journal of Accounting Research, 44(3), 485–531.

30.

Zhao

Zhang

(2019). Does low price synchronicity mean more informativeness in stock prices? Empirical evidence on information integration speed in the Chinese stock market. Emerging Markets Finance and Trade, 55(5), 1014–1033.

31.

Huang

Kang

(2018). Information, investment adjustment, and the cost of capital. Journal of Financial and Quantitative Analysis, 53(4), 1715–1754.

32.

Kamran

Shah

(2014). The impact of corporate governance and ownership structure on earnings management practices: Evidence from listed companies in Pakistan. The Lahore Journal of Economics, 19(2), 27–70.

33.

Lai

Zhu

Feng

Yao

(2021). Effects of financial market information on firms’ productivity under operating pressure and financial constraints: Evidence from the Chinese stock market. SAGE Open, 11(4), 21582440211061377.

34.

Lai

Zhang

(2014). Does PIN affect equity prices around the world? Journal of Financial Economics, 114(1), 178–195.

35.

Leary

M. T.

Roberts

M. R.

(2014). Do peer firms affect corporate financial policy? The Journal of Finance, 69(1), 139–178.

36.

Loureiro

Taboada

A. G.

(2015). Do improvements in the information environment enhance insiders’ ability to learn from outsiders? Journal of Accounting Research, 53(4), 863–905.

37.

Luo

(2005). Do insiders learn from outsiders? Evidence from mergers and acquisitions. The Journal of Finance, 60, 1951–1982.

38.

Nikolaev

Van Lent

(2005). The endogeneity bias in the relation between cost-of-debt capital and corporate disclosure policy. European Accounting Review, 14(4), 677–724.

39.

Ouyang

Szewczyk

S. H.

(2019). Stock price firm-specific information on the choice of stock payment in mergers and acquisitions. Accounting & Finance, 59(2), 1299–1340.

40.

Rasheed

Khalid Sohail

Din

S. U.

Ijaz

. (2018). How do investment banks price initial public offerings? An empirical analysis of emerging market. International Journal of Financial Studies, 6(3), 77.

41.

‘Rasheed

M. S.

Bint Saeed

Yousaf

Javed

. (2018). Stock price synchronicity and voluntary disclosure in perspective of Pakistan. European Online Journal of Natural and Social Sciences, 7(2), pp–265.

42.

Rezaei

Shabani

(2015). A study of the effect of firm size and quality of disclosure on the cost of company common stock. International Journal of Innovation and Applied Studies, 10(3), 1038.

43.

Roll

(1986). The hubris hypothesis of corporate takeovers. Journal of Business, 59, 197–216.

44.

Saleem

Usman

(2021). Information risk and cost of equity: The role of stock price crash risk. The Journal of Asian Finance, Economics, and Business, 8(1), 623–635.

45.

Shah

S. F.

Rashid

Malik

W. S.

(2020). Potential substitution between accrual earnings management and real earnings management among pakistani listed firms. Global Business Review. Advance online publication. https://doi.org/10.1177/0972150920957617

46.

Subrahmanyam

Titman

(1999). The going-public decision and the development of financial markets. The Journal of Finance, 54(3), 1045–1082.

47.

X. V.

(2019). Leverage and corporate investment—evidence from Vietnam. Finance Research Letters, 28, 1–5.

48.

Wang

F. A.

(2010). Informed arbitrage with speculative noise trading. Journal of Banking & Finance, 34(2), 304–313.

49.

Wooldridge

J. M.

(2002). Inverse probability weighted M-estimators for sample selection, attrition, and stratification. Portuguese Economic Journal, 1(2), 117–139.

50.

(2021). Stock price informativeness and managerial inefficiency. International Review of Economics & Finance, 74, 348–364.

51.

Yuan

Z. Z.

X. F

. (2008). Stock price informativeness and corporate investment efficiency: Evidence from Chinese manufacturing listed companies between 2001 and 2005. 2008 International Conference on Management Science and Engineering 15th Annual Conference Proceedings, Long Beach, CA, USA, (pp. 1392–1400). IEEE.

52.

Zaigham

G. H. K.

Wang

Ali

H. S.

(2019). Causal relation between stock market performance and firm investment in China: Mediating role of information asymmetry. SAGE Open, 9(4), 2158244019885146.

53.

Zhang

(2005). The value premium. The Journal of Finance, 60(1), 67–103.

54.

Zhi-Zhu

Xiao-Feng

. (2008). Stock price informativeness and corporate investment efficiency: Evidence from Chinese manufacturing listed companies between 2001 and 2005. In 2008 International Conference on Management Science and Engineering 15th Annual Conference Proceedings. IEEE.

Does Private Information Always Hurt Retail Investors? The Impact of Private Information on Cost of Equity: Moderating Role of Investment Adjustment

Abstract

Keywords

Introduction

Theoretical Framework and Hypothesis Development

Research Methodology

Research Population and Sample

Variables Description

Independent Variable

Private Information

Measurement of Expected Cost of Equity

Corporate Investment

Moderating Variable

Investment Adjustment

Control Variables

Data Estimation Method

Asset Return and Information

Investment-Q Sensitivity and Private Information

Results and Discussion

Descriptive Statistics

Correlation

Private Information, Investment Adjustment, and COE

Private Information, Investment Adjustment and Corporate Investment

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

Data Availability Statement

References