_{1}

^{*}

This study investigates the association between firm characteristics and foreign analyst coverage. Using a sample of 366 firms listed on the Taiwan Stock Exchange that were followed by foreign analysts from January 2006 to June 2012, I employ the finite Tobit regression mixture model to analyze which firm characteristics influence foreign analyst coverage. The results find that mixture model may express differential influences on analyst coverage based on the heterogeneity of firm characteristics in different subgroups. Price-to-book ratio, firm size, and institutional ownership are the important characteristic variables that influence foreign analyst coverage. Among them, price-to-book ratio and foreign analyst coverage may present an inverted U-shaped or a U-shaped correlation in different subgroups. Also, there is an endogenous relationship between foreign analyst coverage and foreign institutional ownership, and thus, the characteristics of target firms followed by foreign analysts resulting from changes in foreign institutional ownership cannot be ignored. Finally, I also find that large-sized firms with high institutional ownership and medium stock turnover rate have the highest probability of being followed by foreign analysts.

Since financial analysts provide information to capital market, they play an important role as information intermediators (Jegadeesh and Kim [

The analyst reports have high information value (Call et al. [

Relevant studies argue that analysts prefer firms with optimistic forecasts. For example, Tehranian et al. [

Some studies assert that an inadequate internal control system decreases the credibility and quality of a firm’s financial information. It is usually more costly to collect information from the firm; hence, analysts are less likely to be attracted to firms with low-quality information. For example, McNichols and O’Brien [

Given the above discussion, price-to-book ratio can be used to observe if a firm’s stock price is overvalued or undervalued relative to net worth. Popular stocks, such as those of large-sized firms or firms with high stock turnover rate, attract more attentions from investors. Furthermore, firms with better corporate governance which enables the analysts to make more accurate predictions for the firms. As a consequence, it can effectively reduce the reputation risk of sell- side security houses and analysts. Previous studies argue that firms’ financial characteristics are some of the important factors of analyst coverage, e.g. firm size (Firth et al. [

As of May 2017, the total amount of foreign investor ownership accounted for approximately 40% of the total market value of all listed and OTC companies. Therefore, the investment behavior of foreign investors is keenly observed by domestic investors. The reports by foreign analysts are the primary reference for foreign institutional investors to make investment decisions on the Taiwan Stock Market. Consequently, the influences of foreign analyst coverage on investors and the stock market cannot be neglected. Since most prior studies concentrate on the information role of analysts, without much discussion on the preferences of the analysts, this study focuses on the highly influential foreign analyst reports in the Taiwan Stock Market to discuss which characteristics of target firms attract the attention of foreign securities houses and analysts. Understanding what characteristics foreign analysts are interested in as they choose their target stocks will be an extremely valuable reference for investors.

This study contributes to the academic research and practical application as follows. 1) This study analyzes whether the firms followed by analysts have certain characteristics from the perspectives of foreign analysts. This issue is rarely discussed in previous studies and can offer a new way of thinking about the role of sell-side analysts in the capital market in terms of private information and investor recognition. 2) Finding out which important firm characteristics analysts are concern about will be of valuable reference to investors. This study helps to find the firm characteristics targeted by foreign analysts, and reduce investor loss due to investment behavior biases.

The remainder of this paper is organized as follows. Section 2 introduces a sample construction, describes variable definitions and the econometric method, and outlines the empirical model design. Section 3 reports the interpretations of the results. Section 4 illustrates the robustness tests and discussion. Finally, Section 5 presents the conclusions of this study and suggestions for further studies.

The empirical analyses are carried out on a panel dataset of 366 listed companies in the Taiwan Stock Exchange from January 2006 to June 2012. The sample excludes firms from the financial industry and those which were delisted after 2012. The data of foreign analyst reports come from the Eikon database of Thomson Reuters. I downloaded the original PDF files of foreign analyst reports provided by forty-five foreign security houses, and built empirical data manually. The firms’ financial statements and corporate governance data are obtained from Taiwan Economic Journal Company (TEJ).

In this study, intensity of foreign analyst reports serves as a proxy for foreign analyst coverage (Wang et al. [

Industry | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012Q2 | Total foreign analyst reports |
---|---|---|---|---|---|---|---|---|

Electronic industry | 2125^{†}^{ } (77.16%) | 3208 (76.31%) | 3405 (72.12%) | 3390 (74.54%) | 3652 (68.98%) | 4305 (68.67%) | 1242 (75.87%) | 21237 (72.39%) |

Biotechnology industry | 25 (0.91%) | 20 (0.48%) | 23 (0.49%) | 62 (1.36%) | 81 (1.53%) | 105 (1.68%) | 16 (0.98%) | 332 (1.13%) |

Others | 604 (21.93%) | 976 (23.21%) | 1293 (27.39%) | 1096 (24.10%) | 1561 (29.49%) | 1859 (29.65%) | 379 (23.15%) | 7768 (26.48%) |

Total foreign analyst reports | 2754 | 4204 | 4721 | 4548 | 5294 | 6269 | 1637 | 29337 |

Note: ^{†}denotes the sum of foreign analyst reports for industry i in a given year. The percentage of the number of foreign analyst reports for a given industry to the total number of foreign analyst reports in a given year is displayed in parentheses.

rate, hence the natural logarithm of total assets is used as a proxy variable for firm size. Return on assets is used as a proxy for profitability, measured by net income before extraordinary items divided by the average total assets. Leverage ratio is included to control for firms’ financial distress, measured by total debt divided by total assets. Stock turnover rate is used to account for stock liquidity or investors’ attention, defined as the total number of shares traded divided by total number of outstanding shares. Since institutional investors are the primary buyers of analyst reports, security house may gain underwriting revenues or commission fees from the provided reports (Firth et al. [

Most statistical methods assume that a sample of observations comes from the

Variable | Symbol | Measurement |
---|---|---|

Analyst reports | Report | The number of foreign analyst reports for a firm in a given year. |

Analyst coverage | Coverage | Natural logarithm of the number of foreign analyst reports for a firm in a given year. |

Price-to-book ratio | PB | Stock price per share divided by net assets per share at end of fiscal year. |

Firm size | Size | Natural logarithm of total assets at the end of fiscal year. |

Return on assets | ROA | Net income before extraordinary items divided by the average total assets at the end of fiscal year. |

Debt ratio | Lev | Total debt divided by total assets at the end of fiscal year. |

Stock turnover rate | Liq | Total number of a stock traded divided by the total number of outstanding shares for the stock at the end of fiscal year. |

Institutional ownership | Inst | Shareholdings of mutual funds, pension funds, and other institutional investors divided by the total number of outstanding shares at the end of fiscal year. |

Note: this table defines main variables of interest.

same distribution and make the restrictive hypothesis that the data distribution has a single mode, so they cannot properly detect heterogeneity in the sample. Previous studies have yet to jointly consider the issues of heterogeneity and clustering in data. The presence of heterogeneity, if not dealt with appropriately, could result in biased estimates as well (Caiazza et al. [

A finite mixture model is a convex combination of two or more probability density functions and uses a mixture of parametric distributions to model data, estimating both the parameters for the separate distributions and the probabilities of component membership for each observation. By combining the properties of the individual probability density functions, finite mixture models provide a flexible framework for analyzing a variety of data. Therefore, a finite mixture model provides a parametric alternative that describes the unknown distribution in terms of mixtures of known distributions and provides a mechanism that can account for unobserved heterogeneity in the data.

In a finite mixture model, y is the vector of observed response variables assumed to come from k distinct groups f 1 , f 2 , ⋯ , f k in proportions π 1 , π 2 , ⋯ , π k , x is the vector of independent variables, and β j is the vector of the coefficients for y j . The expression of the density of likelihood of a response value y in a general k-component mixture model is

f ( y ) = ∑ j = 1 k π j f j ( y | x ′ β j ) (1)

where π j is the probability for the jth group, 0 ≤ π j ≤ 1 and ∑ π j = 1 , and f j ( . ) is the conditional probability density function for the observed response in the jth group model. The finite mixture models can be efficiently estimated via maximum likelihood and multinomial Logistic distribution is used to model the probabilities for the latent groups. The probability for the jth latent group is given by

π j = exp ( γ j ) ∑ j = 1 k exp ( γ j ) (2)

where γ j is the linear prediction for the jth latent group. The likelihood is computed as the sum of the probability-weighted conditional likelihood from each latent group.

pooled foreign analyst reports for empirical data of this study and the solid line is a theoretical normal distribution. The figure displays that the histogram does not follow a well-known distribution. The asymmetry occurs because the distribution could be a mixture of more than one subgroup. Models fit by using traditional techniques (single mode) can perform poorly, because they cannot necessarily tell which group a firm belongs to. A finite mixture model contains any number of subgroups and may be appropriate for these data. Therefore, a finite mixture model can be used to account for these unknown predictors and estimate the means and variances of the two or three underlying subgroups to make inferences about each subgroup and classify individual observations into different subgroups.

Because the foreign analyst reports are more than or equal to 0, a Tobit regression finite mixture model is used for a sample of 366 firms listed on the Taiwan Stock Market. The model is as follows:

C o v e r a g e i j t = α j + β j 1 P B i j t − 1 + β j 2 P B i j t − 1 2 + β j 3 S i z e i j t − 1 + β j 4 R O A i j t − 1 + β j 5 L e v i j t − 1 + β j 6 L i q i j t − 1 + β j 7 I n s t i j t − 1 + ε i j t (3)

where Coverage_{ijt} is foreign analyst coverage measured by the natural logarithm of the number of analyst reports for the firm i of the jth subgroup in the year t. In order to avoid bias from endogeneity among variables, independent variables of Equation (3) are measured by a one-year lag. PB_{ijt-}_{1} is the price-to-book ratio measure by stock price per share divided by net assets per share for the firm i of the jth subgroup at the end of year t − 1. P B i j t − 1 2 is the square of PB_{ijt-}_{1} to depict the nonlinear correlation between price-to-book ratio and foreign analyst reports (Wang et al. [_{ijt−}_{1} is the firm size defined as the natural logarithm of total assets for the firm i of the jth subgroup at the end of the year t − 1. ROA_{ijt-}_{1} is return on assets and equals net income before extraordinary items divided by the average total assets for the firm i of the jth subgroup at the end of year t − 1. Lev_{ijt−}_{1} is the leverage ratio and equals total liabilities divided by total assets for the firm i of the jth subgroup at the end of year t − 1. Liq_{ijt−}_{1} is the stock turnover rate calculated by the total number of shares traded divided by the total number of outstanding shares for the firm i of the jth subgroup at the end of year t − 1. Inst_{ijt−}_{1} is shareholdings of mutual funds, pension funds, and other institutional investors divided by the total number of outstanding shares for the firm i of the jth subgroup at the end of year t − 1. j is the jth subgroup, j = 1 , ⋯ , k . ε i j t is an error term.

I use the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for model selection, and pick the model with the smallest AIC and BIC. The AIC (BIC) for the single mode model, two-subgroup mixture model, and three-subgroup mixture model are 4806.669 (4855.231), 4609.428 (4711.946), 4604.903 (4761.379), respectively. The AIC clearly favors the three-subgroup mixture model, whereas the BIC marginally favors the two-subgroup mixture model. This study will proceed with the three-subgroup mixture model.

Variable | Obs. | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|

Report | 2.562 | 11.45 | 26.05 | 0 | 307 |

PB | 2.928 | 2.01 | 1.56 | 0.08 | 17.06 |

TA (ten billion) | 2.928 | 4.51 | 10.88 | 0.03 | 191.64 |

ROA (%) | 2.928 | 7.22 | 7.98 | −57.49 | 51.09 |

Lev (%) | 2.928 | 43.46 | 16.38 | 3.18 | 97.82 |

Liq | 2.928 | 212.44 | 199.06 | 0.08 | 1581.28 |

Inst (%) | 2.928 | 45.15 | 22.29 | 0 | 97.96 |

Panel B. Correlation matrix

Note: Report is the number of foreign analyst reports. Coverage is the foreign analyst coverage. PB is the price-to-book ratio. TA is the total assets. Size is the firm size. Lev is the debt ratio. ROA is the return on assets. Liq is the stock turnover rate. Inst is the institutional ownership. The detailed definitions of variables are shown in

correlation are different among the three subgroups. In addition, the joint coefficients testing (χ^{2}) for mixture model shows that price-to-book ratio, firm size, ROA, and institutional ownership have significant differences among the three subgroups. Moreover, in general, foreign analyst coverage is significantly related with firm size and institutional ownership for the three subgroups. For subgroups 1 and 2, foreign analyst coverage and price-to-book ratio have an inverted U-shaped correlation (the coefficients of PB and PB^{2} for subgroup 1 are 0.6166 and −0.0234, for subgroup 2 are 0.3969 and −0.0204, respectively). Conversely, subgroup 3 has a U-shaped correlation, but it is insignificant. Additionally, firms’ profitability, leverage and stock liquidity also affect foreign analyst coverage for subgroups 1 and 2. However, the determinants are firm size (the coefficient is 0.4976) and institutional ownership (the coefficient is 0.0262) for subgroup 3. In general, determinants of foreign analyst coverage are alike between subgroups 1 and 2. The variances of Coverage (σ^{2}_{Coverage}) in sequence are 1.9281, 0.6383, and 0.1048. This illustrates that subgroup 1 has a higher variation in foreign analyst coverage.

After fitting the model, the marginal probability (π) and mean (μ) for the jth latent subgroup can be predicted for each of observation. Panel B reports the

Variable | Coverage_{t}_{ } | PB_{t}_{−1 } | Size_{t}_{−1 } | ROA_{t}_{−1 } | Lev_{t}_{−1 } | Liq_{t}_{−1 } | Inst_{t}_{−1 } | VIF |
---|---|---|---|---|---|---|---|---|

Coverage_{t}_{ } | 1 | |||||||

PB_{t−}_{1 } | 0.2039 | 1 | 0.590 | |||||

Size_{t−}_{1} | 0.5493 | −0.1453 | 1 | 0.436 | ||||

ROA_{t−}_{1} | 0.1492 | 0.5922 | −0.1921 | 1 | 0.494 | |||

Lev_{t−}_{1} | 0.0261 | −0.1513 | 0.3516 | −0.3754 | 1 | 0.552 | ||

Liq_{t−}_{1} | −0.0935 | 0.1986 | −0.2246 | 0.1036 | −0.0996 | 1 | 0.812 | |

Inst_{t−}_{1} | 0.4602 | 0.1648 | 0.505 | 0.1625 | 0.0786 | −0.2782 | 1 | 0.614 |

Panel B. Predictive probabilities and means for three subgroups

Note: The dependent variable is the Coverage. PB is the price-to-book ratio. Size is the firm size. Lev is the debt ratio. ROA is the return on assets. Liq is the stock turnover rate. Inst is the institutional ownership. t − 1 represents a one-year lag. The detailed definitions of variables are shown in ^{2} is the joint test of estimated coefficients for three subgroups. σ^{2}_{Coverage} is the variance of Coverage. π and μ is the marginal probability and mean for the jth latent subgroup. ^{†}denotes the delta-method standard error. Asterisks ***, **, * indicate significant levels of 1%, 5%, and 10%, respectively.

predictive probabilities and means for three subgroups and shows that about 21.83% of observations are in subgroup 1, about 68.42% are in subgroup 2, and about 9.75% are in subgroup 3. The subgroups 2 and 3 correspond to more foreign analyst coverage, with average analyst coverage of 2.11 and 2.87, respectively. As a whole, the foreign analysts are in favor of subgroup 3, and determinants affecting foreign analyst following are firm size and institutional ownership.

In conclusion, the determinants of foreign analyst coverage have substantial differences among the three subgroups. The results of the mixture model show that coefficients of independent variables for the three subgroups are significantly different from one another, with the exceptions of financial leverage and stock turnover rate. Using a single-mode model can explain the average effect for whole sample, but a mixture model can interpret the heterogeneous effects in different subgroups.

I further depict the scatter matrixes for foreign analyst reports, price-to-book ratio, total assets, stock turnover rate and institutional ownership.

For further analysis, the price-to-book ratio, firm size (measured by total assets), stock turnover rate and institutional ownership of whole sample are arranged in descending order in a given year and classified into three distinct categories based on a bottom 30%, middle 40% and top 30% criteria (labeled as small or low, medium, and large or high categories). Next, each firm belongs to one of the three categories, varying for any given year.

and high institutional ownership attracts significantly more analyst following.

In short, aforementioned results indicate that price-to-book ratio, firm size, stock turnover rate, and institutional ownership are the important characteristics affecting foreign analyst coverage. Further, I detect the different influence effects on foreign analyst coverage from these characteristics. First, the number of foreign analyst reports of whole sample is arranged in descending order in a given year. Thus, the samples are divided into two classes (i.e. high foreign analyst coverage and low foreign analyst coverage) based on the 70th percentile, varying for any given year. And also, the price-to-book ratio, firm size (measured by total assets), stock turnover rate and institutional ownership of whole sample are arranged in descending order in a given year, and they are also classified into three distinct categories based on a bottom 30%, middle 40% and top 30%

criteria (labeled as small or low, medium, and large or high categories). Each firm belongs to one of the three categories, varying for any given year.

Next, a Logistic regression model is used to investigate the influence of the categories of price-to-book ratio, firm size, stock turnover rate and institutional ownership on foreign analyst coverage, and estimate the marginal effects for categorical variables. The computation of marginal effect for level k of categorical variable F is h ( z , θ ) = f ( z , θ | A = b a s e ) , where θ is a vector of parameters in the model fit, and z is a vector of covariate values. The model specification is as follows:

H i g h c o v e r i t = α 0 + β 1 R O A i t − 1 + β 2 L e v i t − 1 + ∑ k = 1 3 φ k P B c l a s s i t − 1 + ∑ k = 1 3 γ k S i z e c l a s s i t − 1 + ∑ k = 1 3 τ k L i q c l a s s i t − 1 + ∑ k = 1 3 δ k I n s t c l a s s i t − 1 + ε i t (4)

where Highcover is an indicator variable, Highcover equals 1 if the number of foreign analyst reports for a firm is greater than the 70th percentile, and 0 otherwise. PBclass, Sizeclass, Liqclass and Instclass are all categorical variables. The firm size (measured by total assets), stock turnover rate and institutional ownership of whole sample are arranged in descending order in a given year, and they are also classified into three distinct categories based on a bottom 30%, middle 40% and top 30% criteria (small or low is labeled as the category “1”, medium is labeled as the category “2”, and large or high is labeled as the category “3”). Each firm belongs to one of the three categories, varying for any given year. ROA and Lev are continuous variables, and those definitions are the same as in Equation (3). ε i t is an error term.

Mixture Model | |||||
---|---|---|---|---|---|

Variable | Single−mode model | Subgroup 1 | Subgroup 2 | Subgroup 3 | χ^{2 } |

PB_{t−}_{1} | 0.4078*** (0.044875) | 0.6166*** (0.179503) | 0.3969*** (0.087112) | −0.0371 (0.115536) | 12.07*** |

PB^{2}_{t}_{−1} | −0.0201*** (0.002941) | −0.0234*** (0.008998) | −0.0204*** (0.005031) | 0.0033 (0.005098) | 11.65*** |

Size_{t}_{−1} | 0.6817*** (0.024701) | 1.2495*** (0.155162) | 0.6337*** (0.04158) | 0.4976*** (0.092851) | 21.89*** |

ROA_{t}_{−1} | 0.0208*** (0.005819) | 0.0624*** (0.020724) | 0.0173** (0.00847) | 0.0075 (0.016081) | 5.10* |

Lev_{t}_{−1} | −0.0097*** (0.002266) | −0.0195* (0.010801) | −0.0098** (0.004974) | 0.0001 (0.013132) | 1.59 |

Liq_{t}_{−1} | 0.0011*** (0.000167) | 0.0021*** (0.000763) | 0.0007*** (0.0002) | 0.0017 (0.001925) | 2.82 |

Inst_{t}_{−1} | 0.0112*** (0.001679) | 0.0151* (0.008788) | 0.0088*** (0.0019) | 0.0262*** (0.00602) | 8.45** |

Constant | −0.0264*** (0.151389) | −3.4453*** (0.780163) | 0.5726*** (0.189547) | 0.7762** (0.371734) | 28.55*** |

σ^{2}_{Coverage} | 1.3183 (0.056565) | 1.9281 (0.389146) | 0.6383 (0.062649) | 0.1048 (0.13626) | |

log pseudo likelihood | −2394.3347 | ||||

Pseudo R^{2} | 0.1692 | ||||

F | 224.26*** | ||||

AIC | 4806.669 | 4604.903 | |||

BIC | 4855.231 | 4761.379 |

Variable | Mixture Model | ||
---|---|---|---|

Subgroup 1 | Subgroup 2 | Subgroup 3 | |

π | 0.218268 (0.038933)^{†} | 0.684212 (0.038933) | 0.09752 (0.066442) |

μ | −0.3487522 (0.4287458)^{†} | 2.114604*** (0.0893956) | 2.870259*** (0.1371312) |

Variable | Odds ratio | Robust Std. Err. | [95% Conf. Interval] | |
---|---|---|---|---|

PBclass_{t}_{−1} | ||||

2 | 4.129085*** | 0.7382869 | 2.908423 | 5.862056 |

3 | 9.383735*** | 1.971884 | 6.215921 | 14.16596 |

Sizeclass_{t}_{−1} | ||||

2 | 5.475737* | 4.84959 | 0.9651069 | 31.06775 |

3 | 55.76454*** | 49.18422 | 9.899198 | 314.135 |

Liqclass_{t}_{−1} | ||||

2 | 0.6970638 | 0.5508359 | 0.1481268 | 3.280285 |

3 | 1.772752 | 1.167591 | 0.4875504 | 6.445792 |

Instclass_{t}_{−1} | ||||

2 | 2.592656 | 1.836844 | 0.6466749 | 10.3945 |

3 | 2.759402 | 2.271615 | 0.5496452 | 13.85311 |

S i z e c l a s s t − 1 × L i q c a l s s t − 1 × I n s t c l a s s t − 1 | ||||

1 × 2 × 2 | 1.097017 | 1.015292 | 0.1788236 | 6.7298 |

1 × 2 × 3 | 2.444862 | 2.702787 | 0.2800619 | 21.34297 |

1 × 3 × 2 | 0.7712119 | 0.6295108 | 0.1557293 | 3.819241 |

1 × 3 × 3 | 1.131952 | 1.153567 | 0.1535933 | 8.342258 |

2 × 1 × 2 | 0.5786175 | 0.593108 | 0.077602 | 4.314297 |

2 × 1 × 3 | 0.5519772 | 0.5986246 | 0.0658847 | 4.624421 |

2 × 2 × 1 | 1.276969 | 1.403108 | 0.1482169 | 11.00179 |

2 × 2 × 2 | 1.289152 | 1.957474 | 0.065739 | 25.28048 |

2 × 2 × 3 | 2.504195 | 3.952845 | 0.1135156 | 55.24343 |

2 × 3 × 1 | 0.7787215 | 0.7498958 | 0.1179476 | 5.141324 |

2 × 3 × 2 | 1.05153 | 1.523961 | 0.0614034 | 18.00739 |

2 × 3 × 3 | 1.650883 | 2.51852 | 0.0830152 | 32.83032 |

3 × 1 × 2 | 0.8067293 | 0.7971702 | 0.1163087 | 5.595557 |

3 × 1 × 3 | 1.67187 | 1.769251 | 0.2100976 | 13.30405 |

3 × 2 × 1 | 1.470303 | 1.615996 | 0.1705531 | 12.67518 |

3 × 2 × 2 | 0.7714073 | 1.168408 | 0.0396292 | 15.01593 |

3 × 2 × 3 | 3.374853 | 5.328192 | 0.1528918 | 74.49473 |

3 × 3 × 1 | 0.4056751 | 0.4531118 | 0.0454406 | 3.621706 |

3 × 3 × 2 | 0.3975546 | 0.5841303 | 0.0223213 | 7.080668 |

3 × 3 × 3 | 0.7159015 | 1.133447 | 0.0321509 | 15.94092 |

ROA_{t}_{−1} | 1.045951*** | 0.0108662 | 1.024869 | 1.067466 |

Lev_{t}_{−1} | 0.9776074*** | 0.0040476 | 0.9697064 | 0.9855728 |

Constant | 0.0085502*** | 0.005327 | 0.0025215 | 0.028993 |

Log pseudolikelihood | −962.42376 | |||

Pseudo R^{2} | 0.3589 | |||

Wald χ^{2} | 624.75*** |

Panel B. Probability for category variables

Note: The dependent variable is Highcover, an indicator variable. PBclass, Sizeclass, Liqclass and Instclass are all categorical variables, small or low is labeled as the category “1”, medium is labeled as the category “2”, and large or high is labeled as the category “3”. Lev is the debt ratio. ROA is the return on assets. t − 1 represents a one-year lag. Asterisks ***, **, * indicate significant levels of 1%, 5%, and 10%, respectively.

ratio or large firm size are 9 times and 55 times more likely to be frequently followed by foreign analysts than firms with low price-to-book ratio or small firm size, respectively. Moreover, Panel B reports the probabilities of category variables and reveals that the likelihoods for firms with high price-to-book ratio, large firm size, high stock turnover rate, high institutional ownership to be frequently followed by foreign analysts are 39.76%, 58.80%, 30.85%, and 36.89%, respectively, which are significantly higher than those for the other types of firms. This means that foreign analysts prefer firms with the aforementioned characteristics. From the combinations of the three variables―firm size, stock turnover rate, and institutional ownership, I further find that the higher institutional ownership is, the more likely it is for the firm to be followed by foreign analysts. And further, in small-sized or medium-sized firms that have high stock turnover rate and high institutional ownership, the mean of probabilities to be frequently followed are 13% and 44%, respectively. The probability for firms with large firm size, medium stock turnover rate and high institutional ownership to be frequently followed by foreign analysts is 79.69%, which is the highest among the 18 combinations. In conclusion, the results of Logistic regression suggest that price-to-book ratio, firm size, and institutional ownership are the three key variables of foreign analyst coverage.

In the Taiwan Stock Market, foreign institutional investors, domestic trust institutional investors, and domestic dealer institutional investors are called the three institutional investors.

Category variable | Predicted probabilities | Delta-method Std. Err. | [95% Conf. Interval] | |
---|---|---|---|---|

PBclass_{t}_{−1}_{ } | ||||

1 | 0.1355545*** | 0.0123824 | 0.1112854 | 0.1598236 |

2 | 0.2840843*** | 0.0112606 | 0.2620139 | 0.3061547 |

3 | 0.3976174*** | 0.0165076 | 0.3652631 | 0.4299716 |

Sizeclass_{t}_{−1} | ||||

1 | 0.0711809*** | 0.0094132 | 0.0527314 | 0.0896305 |

2 | 0.2328578*** | 0.0119475 | 0.2094412 | 0.2562744 |

3 | 0.5879989*** | 0.02451 | 0.5399602 | 0.6360375 |

Liqclass_{t}_{−1 } | ||||

1 | 0.2396476*** | 0.0133295 | 0.2135223 | 0.265773 |

2 | 0.2668302*** | 0.010664 | 0.2459291 | 0.2877312 |

3 | 0.3084775*** | 0.0168676 | 0.2754177 | 0.3415373 |

Instclass_{t}_{−1 } | ||||

1 | 0.1696897*** | 0.0188182 | 0.1328067 | 0.2065727 |

2 | 0.2660432*** | 0.0115586 | 0.2433886 | 0.2886977 |

3 | 0.368912*** | 0.0184931 | 0.3326661 | 0.4051578 |

S i z e c l a s s t − 1 × L i q c a l s s t − 1 × I n s t c l a s s t − 1 | ||||

1 × 1 × 1 | 0.0304525* | 0.0168657 | −0.0026037 | 0.0635088 |

1 × 1 × 2 | 0.070881*** | 0.0227097 | 0.0263709 | 0.1153911 |

1 × 1 × 3 | 0.0746917** | 0.0353158 | 0.0054741 | 0.1439093 |

1 × 2 × 1 | 0.0217427** | 0.0105768 | 0.0010126 | 0.0424729 |

1 × 2 × 2 | 0.0563202*** | 0.0149242 | 0.0270693 | 0.085571 |

1 × 2 × 3 | 0.114758** | 0.0473957 | 0.0218641 | 0.2076519 |

1 × 3 × 1 | 0.0510669*** | 0.0124903 | 0.0265864 | 0.0755474 |

1 × 3 × 2 | 0.0917777*** | 0.0220358 | 0.0485883 | 0.1349671 |

1 × 3 × 3 | 0.129955** | 0.0523405 | 0.0273694 | 0.2325405 |

2 × 1 × 1 | 0.1288703** | 0.0629395 | 0.0055112 | 0.2522294 |

2 × 1 × 2 | 0.1727538*** | 0.0422293 | 0.0899859 | 0.2555217 |

2 × 1 × 3 | 0.1745787*** | 0.0333494 | 0.1092152 | 0.2399423 |

2 × 2 × 1 | 0.1179662*** | 0.0368469 | 0.0457475 | 0.1901848 |

2 × 2 × 2 | 0.2308493*** | 0.0286485 | 0.1746992 | 0.2869994 |

2 × 2 × 3 | 0.347857*** | 0.0404086 | 0.2686577 | 0.4270564 |

2 × 3 × 1 | 0.1630014*** | 0.0303205 | 0.1035744 | 0.2224284 |

2 × 3 × 2 | 0.3484473*** | 0.0297915 | 0.2900571 | 0.4068376 |

2 × 3 × 3 | 0.4419518*** | 0.0539001 | 0.3363096 | 0.547594 |

3 × 1 × 1 | 0.4854079*** | 0.1201868 | 0.2498461 | 0.7209697 |

Note: Foreignholding is the foreign institutional ownership, Trustholding is the domestic investment trust institutional ownership, Dealerholding is the domestic dealer institutional ownership.

the total number of outstanding shares at the end of year; the percentage of domestic dealer institutional ownership (Dealerholding) equals ownership held by domestic dealer institutional investors divided by the total number of outstanding shares at the end of year. In

Wang et al. [

Panel A reports the results of Tobit regression mixture model and indicates that price-to-book ratio, firm size, and foreign institutional ownership are the important determinants of foreign analyst coverage for the three subgroups. For subgroups 1 and 3, foreign analyst coverage and price-to-book ratio have a significantly inverted U-shaped correlation. In contrast, subgroup 2 has a significant U-shaped correlation. Additionally, both stock turnover rate and domestic trust institutional ownership affect foreign analyst coverage for subgroups 2 and 3. However, stock turnover rate and foreign institutional ownership have a negative correlation with foreign analyst coverage for subgroup 3. The joint coefficients testing (χ^{2}) reveals that estimated coefficients have significant differences for the three subgroups, with the exceptions of profitability (measured by ROA) and domestic dealer institutional ownership. Moreover, subgroups 1 and 3 have higher heterogeneous (variances of Coverage are 0.5776 and 0.5845, respectively) than subgroup 2. Panel B reports the predictive probabilities and means for three subgroups and shows that about 76.49% of observations are in subgroup 3, which also corresponds to more foreign analyst coverage (the mean is 2.20). These

3 × 1 × 2 | 0.6210869*** | 0.0461412 | 0.5306519 | 0.7115219 |
---|---|---|---|---|

3 × 1 × 3 | 0.749726*** | 0.0274375 | 0.6959495 | 0.8035024 |

3 × 2 × 1 | 0.4900233*** | 0.0783641 | 0.3364325 | 0.6436142 |

3 × 2 × 2 | 0.5474978*** | 0.0392161 | 0.4706357 | 0.6243599 |

3 × 2 × 3 | 0.7968666*** | 0.0334876 | 0.7312321 | 0.862501 |

3 × 3 × 1 | 0.4237726*** | 0.1186025 | 0.191316 | 0.6562292 |

3 × 3 × 2 | 0.596981*** | 0.0564475 | 0.486346 | 0.707616 |

3 × 3 × 3 | 0.7077108*** | 0.0834942 | 0.5440653 | 0.8713564 |

Panel B. Predictive probabilities and means for three subgroups

Note: The dependent variable is Coverage. PB is the price-to-book ratio. Size is the firm size. Lev is the debt ratio. ROA is the return on assets. Liq is the stock turnover rate. The detailed definitions of variables are shown in ^{2} is the joint test of estimated coefficients for three subgroups. σ^{2}_{Coverage} is the variance of Coverage. π and μ is the marginal probability and mean for the jth latent subgroup. ^{†}denotes the delta-method standard error. Asterisks ***, **, * indicate significant levels of 1%, 5%, and 10%, respectively.

results indicate that foreign analysts prefer subgroup 3 to the others and the determinants of foreign analyst coverage are price-to-book ratio and firm size. Finally, I also find that the foreign analysts alter their following in the same direction as ownership of foreign and domestic trust institutional investors.

The AIC and BIC for the mixture model in

Since financial statements present backward looking information, many investors think that analyst reports have predictive value. Changes in analyst coverage of a firm might be an early indicator of changes in the firm’s fundamentals and future stock return performance. Therefore, the firm characteristics that interest the analysts will be of valuable reference for investors. In this study, I examine firm characteristics that influence analyst coverage using data of 366 stocks listed firms on the Taiwan Stock Exchange that were followed by foreign analysts from January 2006 to June 2012.

The results find that mixture model may present the differential influences of the heterogeneity in firm characteristics from different subgroups for analyst coverage. Without considering the endogenous relationship between foreign institutional ownership and analyst coverage, price-to-book ratio and firm size are the key characteristic variables of foreign analyst coverage. And also, price-to- book ratio and foreign analyst coverage may present an inverted U-shaped or a U-shaped correlation in different subgroups. After considering the endogenous relationship between foreign analyst coverage and foreign institutional ownership, the price-to-book ratio and firm size are still the key variables of foreign analyst coverage. Furthermore, changes in foreign analyst coverage of a firm are highly correlated with foreign institutional shareholding. Therefore, to observe the firm characteristics targeted by foreign analysts, the influences from changes in foreign institutional ownership cannot be ignored. Finally, I also find that large-sized firms with high institutional ownership and medium share turnover ratio have the highest mean of probability of being followed by foreign analysts

This study focuses on foreign analyst reports, which are highly influential in the Taiwan Stock Market. However, foreign and domestic securities houses have different types of clients in terms of target return, risk preference and investment amount. Thus, in turn, the characteristics of their target firms might also be different. Therefore, to investigate the characteristics that attract domestic analysts or foreign analysts or both is an interesting subject worthy of further investigation in the future.

I thank the anonymous reviewers for their comments of the article and also thank the financial supported from the Ministry of Science and Technology of Taiwan (project no. MOST 105-2410-H-130-005).

Wang, L.-H. (2018) Which Firm Characteristics Affect Foreign Analyst Coverage? Evidence from the Taiwan Stock Market. Journal of Mathematical Finance, 8, 64-85. https://doi.org/10.4236/jmf.2018.81006