# Role of International Trade Competitive Advantage and Corporate Governance Quality in Predicting Equity Returns: Static and Conditional Model Proposals for an Emerging Market

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Conceptual Framework

_{i}) and shows the sensitivity of asset returns to the market as a measure of systematic risk level. The simplest (ex-ante) form of the CAPM is given in the equation below, where E(R

_{i}) refers to expected rate of return on the asset i while E(R

_{m}) and R

_{f}stand for expected return on the market portfolio and the risk-free rate of return, respectively.

_{mt}and R

_{it}are referred to as actual rates of return on the market portfolio and the asset i at time t. correspondingly, while ε denotes error term. The resulting form (Equation (3) is the ex-post form of the CAPM.

- r
_{i}: return on the asset i in excess of the risk-free rate of return; - β
_{ij}: measure for sensitivity of the asset i returns to the jth systematic risk factor returns on the factor portfolio. with unit sensitivity to the jth risk factor and zero sensitivities to other factors); - r
_{Fj}: excess return of the factor portfolio over the risk-free rate.

## 3. Literature Review

## 4. Empirical Research: Data, Models and Techniques

#### 4.1. Sample Selection and Research Variables

_{f}) have been calculated for each portfolio and used as the dependent variable series (BIST 30, BIST Financials, BIST Industrials, and BIST Services). To approximate the risk-free rate, the 2-year TL treasury bond yields have been used as a benchmark. Then, they are regressed on two independent variables; MPREM calculated as the rate of return on the market portfolio (BIST All) minus the risk-free rate, and CGIOVERM defined as return on the Corporate Governance Index Portfolio (BIST Governance), consisting of all the listed firms with an overall governance quality score of 7 and over, in excess of the return on the market portfolio. For the purpose of capturing the effect of changes in Turkey’s foreign trade competitive advantage on the portfolio’s CAPM beta coefficient, two dummy variables, REERDUM1 and REERDUM2, are featured in the models. REERDUM1 is 1 in case of degraded competitive advantage, and 0 otherwise. Conversely. REERDUM2 takes on the value 1 in the event of improved competitiveness, but 0 otherwise. Degraded competitive advantage is concluded if there is any increase in the real effective exchange rate index value while improvement is assessed as no change, or any decrease in the index value (OECD 2020). By using these dummy variables, we plan to create dynamic model versions by allowing model constant and/or the beta coefficient to vary with respect to the time segments by degradation and improvement in the trade competitiveness (MPREM*REERDUM1 and MPREM*REERDUM2). This attempt is intended to help us question the empirical results of the previous researches (Topaloğlu and Karakozak 2018; Akçoraoğlu and Yurdakul 2002; Karatepe et al. 2002; Baillie and Cho 2016; Malliaropulos 1998; Wong 2017; Wong et al. 2018) which suggest remarkable variations of slope coefficients in the traditional CAPM with changes in economic circumstances, providing some evidence that such macroeconomic variables as the foreign exchange rate, foreign trade volume, interest rate, inflation, gold prices and money supply may have direct effects on stock returns and systematic risk levels.

#### 4.2. Model Versions and Econometric Techniques Employed

#### 4.2.1. Static and Dynamic Models

_{p}refers to the portfolio’s excess return over the risk-free rate while β and θ stand for the coefficients of the market portfolio’s excess return and excess return on the BIST Governance index portfolio over return on the market portfolio, respectively. Finally. C is the model constant that is assumed to be the measure for the portfolio’s return in excess of its expected return estimated by the model.

_{1}represents the model constants for the degradation and improvement periods, correspondingly.

_{1}denotes the market risk premium coefficient during degradation, but β

_{2}is referred to as the same coefficient in case of improved competitive advantage.

#### 4.2.2. Robust and Bayesian Regression Techniques: A Brief Introduction

## 5. Methodology and Findings

#### 5.1. Research Methodology

- Model Design 1: Model without any informative prior distribution assumption, but relying on the flat and Jeffrey’s distributions;
- Model Design 2: Model with the standard normal distribution assumption;
- Model Design 3: Model with the multivariate normal distribution assumption proposed by Zellner and Revankar (1969);
- Model Design 4: Model with the multivariate normal distribution assumption with blocked variance and model constant.

^{2}, Amemiya’s prediction criterion (PC), and Akaike information criterion (AIC) (Muzır and Çağlar 2009).

#### 5.2. Empirical Findings

#### 5.2.1. Robust Linear Regression Findings

#### 5.2.2. Bayesian Linear Regression Findings

#### 5.2.3. Comparison between Robust and Bayesian Models

#### 5.3. An Illustration of How to Calculate and Interpret the Governance Quality Scores

## 6. Results and Discussion

## 7. Concluding Remarks and Recommendations

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**(

**a**) BIST 30 convergence graphs for MPREM and CGIOVERM. (

**b**) BIST 30 convergence graphs for model constant and variance. Source: STATA output (author’s compilation).

**Figure 2.**(

**a**) BIST Financials convergence graphs for MPREM and CGIOVERM. (

**b**) BIST Financials convergence graphs for model constant and variance. Source: STATA output (author’s compilation).

**Figure 3.**(

**a**) BIST Industrials convergence graphs for MPREM and CGIOVERM. (

**b**) BIST Industrials convergence graphs for model constant and variance. Source: STATA output (author’s compilation).

**Figure 4.**(

**a**) BIST Services convergence graphs for MPREM and CGIOVERM. (

**b**) BIST Services convergence graphs for model constant and variance. Source: STATA output (author’s compilation).

Variable: | BIST 30 | BIST Financials | BIST Services | BIST Industrials | MPREM | CGIOVERM |
---|---|---|---|---|---|---|

Average | −0.003397 | −0.005064 | −0.001486 | 0.001585 | −0.002409 | −0.008644 |

Median | −0.002391 | −0.001210 | 0.002382 | 0.006190 | −0.000367 | −0.008855 |

Maximum | 0.134039 | 0.151315 | 0.123426 | 0.117553 | 0.115626 | 0.021447 |

Minimum | −0.148899 | −0.193182 | −0.148394 | −0.182615 | −0.152541 | −0.045002 |

Standard Deviation | 0.066399 | 0.074329 | 0.057695 | 0.057279 | 0.062008 | 0.013012 |

Skewness | −0.050167 | −0.055367 | −0.441077 | −0.397933 | −0.171635 | 0.166896 |

Kurtosis | 2.294102 | 2.285280 | 2.599989 | 2.993221 | 2.250969 | 2.712600 |

Sum | −0.417840 | −0.622894 | −0.182746 | 0.194913 | −0.296333 | −1.063158 |

Sum of Squares | 0.537875 | 0.674027 | 0.406109 | 0.400263 | 0.469083 | 0.020655 |

Observations | 123 | 123 | 123 | 123 | 123 | 123 |

Univariate Normal Distribution Tests | ||||||

Jarque–Bera Statistic | 2.605 | 2.680 | 4.808 | 3.246 | 3.479 | 0.994 |

Significance (p-value) | 0.2718 | 0.2617 | 0.0903 | 0.1972 | 0.1755 | 0.6082 |

Multivariate Normal Distribution Tests (Dependent Variable feat. MPREM and CGIOVERM) | ||||||

Doornik–Hansen Statistic | 5.798 | 12.319 | 5.214 | 6.237 | ||

Significance (p-value) | 0.4462 | 0.0552 | 0.5167 | 0.3972 |

Variable | Including Constant (at Level) | Including Constant and Time Trend (at Level) | ||
---|---|---|---|---|

ADF Statistic | Prob (p-Value) | ADF Statistic | Prob (p-Value) | |

BIST 30 | −12.05041 | 0.000 | −12.02601 | 0.000 |

BIST Financials | −12.10318 | 0.000 | −12.07932 | 0.000 |

BIST Services | −12.83095 | 0.000 | −12.78004 | 0.000 |

BIST Industrials | −11.10752 | 0.000 | −11.10160 | 0.000 |

MPREM | −11.94444 | 0.000 | −11.79790 | 0.000 |

CGIOVERM | −10.21594 | 0.000 | −10.94135 | 0.000 |

Dependent Variable | Model Version | Independent Variable | VIF Statistics | ||
---|---|---|---|---|---|

Variance | Uncentered VIF | Centered VIF | |||

BIST30 | Static | MPREM | 0.000162 | 1.001781 | 1.000259 |

CGIOVERM | 0.003677 | 1.445283 | 1.000259 | ||

Dynamic with Varying Slope | MPREM*REERDUM1 | 0.000364 | 1.071158 | 1.005423 | |

MPREM*REERDUM2 | 0.000296 | 1.087822 | 1.006221 | ||

CGIOVERM | 0.003438 | 1.446268 | 1.000941 | ||

BIST Financials | Static | MPREM | 0.000609 | 1.001781 | 1.000259 |

CGIOVERM | 0.013832 | 1.445283 | 1.000259 | ||

Dynamic with Varying Slope | MPREM*REERDUM1 | 0.001459 | 1.071158 | 1.005423 | |

MPREM*REERDUM2 | 0.001185 | 1.087822 | 1.006221 | ||

CGIOVERM | 0.013780 | 1.446268 | 1.000941 | ||

BIST Industrials | Static | MPREM | 0.000715 | 1.001781 | 1.000259 |

CGIOVERM | 0.016231 | 1.445283 | 1.000259 | ||

Dynamic with Varying Slope | MPREM*REERDUM1 | 0.001730 | 1.071158 | 1.005423 | |

MPREM*REERDUM2 | 0.001406 | 1.087822 | 1.006221 | ||

CGIOVERM | 0.016343 | 1.446268 | 1.000941 | ||

BIST Services | Static | MPREM | 0.001532 | 1.001781 | 1.000259 |

CGIOVERM | 0.034792 | 1.445283 | 1.000259 | ||

Dynamic with Varying Slope | MPREM*REERDUM1 | 0.003654 | 1.071158 | 1.005423 | |

MPREM*REERDUM2 | 0.002969 | 1.087822 | 1.006221 | ||

CGIOVERM | 0.034522 | 1.446268 | 1.000941 |

Dependent Variable | Performance Measure | Robust Model Version | |||
---|---|---|---|---|---|

Static Model | Conditional with Varying Constant | Conditional with Varying Slopes | Conditional with Varying Constant & Slopes | ||

BIST 30 | Rn-Square Statistic | 8668.585 (0.0000) | 8557.604 (0.0000) | 8888.416 (0.0000) | 8810.190 (0.0000) |

Rw-Square | 0.9897 | 0.9896 | 0.9897 | 0.9897 | |

Adjust Rw-Square | 0.9896 | 0.9896 | 0.9897 | 0.9897 | |

BIST Financials | Rn-Square Statistic | 2253.665 (0.0000) | 2232.792 (0.0000) | 2304.384 (0.0000) | 2279.748 (0.0000) |

Rw-Square | 0.9592 | 0.9591 | 0.9609 | 0.9609 | |

Adjust Rw-Square | 0.9592 | 0.9591 | 0.9609 | 0.9609 | |

BIST Industrials | Rn-Square Statistic | 1020.097 (0.0000) | 1024.952 (0.0000) | 1005.239 (0.0000) | 1012.674 (0.0000) |

Rw-Square | 0.9135 | 0.9143 | 0.9138 | 0.9142 | |

Adjust Rw-Square | 0.9135 | 0.9142 | 0.9138 | 0.9142 | |

BIST Services | Rn-Square Statistic | 404.819 (0.0000) | 403.537 (0.0000) | 410.466 (0.0000) | 409.691 (0.0000) |

Rw-Square | 0.8062 | 0.8081 | 0.8090 | 0.8119 | |

Adjust Rw-Square | 0.8062 | 0.8081 | 0.8089 | 0.8119 |

Dependent Variable | Conditional Robust Model with Varying Slopes | |||
---|---|---|---|---|

BIST30 | BIST Financials | BIST Industrials | BIST Services | |

C | −0.002700 (0.0023) | −0.006651 (0.0004) | 0.012185 (0.0000) | −0.001898 (0.5513) |

CGIOVERM | −0.128954 (0.0175) | −0.459450 (0.0001) | 0.957911 (0.0000) | −0.418115 (0.0321) |

MPREM*REERDUM1 | 1.114347 (0.0000) | 1.203477 (0.0000) | 0.842487 (0.0000) | 0.756043 (0.0000) |

MPREM*REERDUM2 | 1.037636 (0.0000) | 1.113536 (0.0000) | 0.849429 (0.0000) | 0.878022 (0.0000) |

Performance Statistics | ||||

Rn-Square Statistic | 8888.416 (0.0000) | 2304.384 (0.0000) | 1005.239 (0.0000) | 410.466 (0.0000) |

MSE | 0.008456 | 0.016924 | 0.018397 | 0.026692 |

R-Square | 0.8119 | 0.8307 | 0.7645 | 0.6943 |

Adjusted R-Square | 0.8072 | 0.8264 | 0.7588 | 0.6866 |

Rw-Square | 0.9897 | 0.9609 | 0.9138 | 0.8090 |

Adjust Rw-Square | 0.9897 | 0.9609 | 0.9138 | 0.8089 |

Jarque-Bera Statistic(Residuals) | 6.762821 (0.0339) | 13.17274 (0.0014) | 0.271160 (0.8732) | 1.077918 (0.5834) |

Model Version | Performance Crtierion | BIST 30 | BIST Financials | BIST Industrials | BIST Services | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Model Design 1 | Model Design 2 | Model Design 3 | Model Design 4 | Model Design 1 | Model Design 2 | Model Design 3 | Model Design 4 | Model Design 1 | Model Design 2 | Model Design 3 | Model Design 4 | Model Design 1 | Model Design 2 | Model Design 3 | Model Design 4 | ||

Static | Acceptance Rate | 0.1985 | 0.2250 | 0.2441 | 0.3950 | 0.2623 | 0.2725 | 0.2420 | 0.3555 | 0.2222 | 0.1423 | 0.1812 | 0.3647 | 0.2437 | 0.2005 | 0.2260 | 0.4134 |

Minimum Efficiency | 0.0013 | 0.0342 | 0.0527 | 0.0783 | 0.0258 | 0.0450 | 0.0540 | 0.0681 | 0.0454 | 0.0471 | 0.0435 | 0.0765 | 0.0443 | 0.0364 | 0.0239 | 0.0714 | |

Average Efficiency | 0.0514 | 0.0589 | 0.0690 | 0.1177 | 0.0621 | 0.0644 | 0.0669 | 0.1211 | 0.0598 | 0.0582 | 0.0498 | 0.1202 | 0.0579 | 0.0640 | 0.0614 | 0.1272 | |

Maximum Efficiency | 0.1128 | 0.0859 | 0.0893 | 0.2061 | 0.0858 | 0.0920 | 0.0797 | 0.2050 | 0.0789 | 0.0769 | 0.0589 | 0.1909 | 0.0699 | 0.0933 | 0.0791 | 0.2181 | |

Dynamic with Varying Constant | Acceptance Rate | 0.1908 | 0.2403 | 0.2510 | 0.3506 | 0.2061 | 0.2278 | 0.2286 | 0.3674 | 0.2021 | 0.1787 | 0.2111 | 0.3936 | 0.2133 | 0.2110 | 0.2274 | 0.3925 |

Minimum Efficiency | 0.0013 | 0.0412 | 0.0375 | 0.0450 | 0.0445 | 0.0378 | 0.0337 | 0.0350 | 0.0017 | 0.0318 | 0.0220 | 0.0464 | 0.0223 | 0.0463 | 0.0031 | 0.0411 | |

Average Efficiency | 0.0172 | 0.0525 | 0.0565 | 0.0779 | 0.0538 | 0.0568 | 0.0495 | 0.0793 | 0.0266 | 0.0418 | 0.0444 | 0.0904 | 0.0534 | 0.0534 | 0.0289 | 0.0849 | |

Maximum Efficiency | 0.0688 | 0.0728 | 0.0883 | 0.1882 | 0.6507 | 0.0711 | 0.0636 | 0.1774 | 0.0531 | 0.0537 | 0.0765 | 0.2048 | 0.0878 | 0.0647 | 0.0573 | 0.1956 | |

Dynamic with Varying Slope | Acceptance Rate | 0.2195 | 0.2698 | 0.1951 | 0.3771 | 0.1803 | 0.2486 | 0.1470 | 0.3453 | 0.2850 | 0.2259 | 0.2383 | 0.3596 | 0.2323 | 0.2271 | 0.3043 | 0.3670 |

Minimum Efficiency | 0.0025 | 0.0066 | 0.0317 | 0.0464 | 0.0018 | 0.0254 | 0.0025 | 0.0472 | 0.0039 | 0.0429 | 0.0337 | 0.0604 | 0.0039 | 0.0106 | 0.0231 | 0.0500 | |

Average Efficiency | 0.0258 | 0.0186 | 0.0478 | 0.0968 | 0.0169 | 0.0440 | 0.0228 | 0.0830 | 0.0393 | 0.0509 | 0.0430 | 0.1013 | 0.0263 | 0.0494 | 0.0420 | 0.0821 | |

Maximum Efficiency | 0.0753 | 0.0288 | 0.0656 | 0.2140 | 0.0597 | 0.0579 | 0.0532 | 0.1574 | 0.0711 | 0.0568 | 0.0732 | 0.1888 | 0.0511 | 0.0787 | 0.1055 | 0.1562 | |

Dynamic with Varying Constant and Varying Slope | Acceptance Rate | 0.2825 | 0.2741 | 0.2514 | 0.3911 | 0.2001 | 0.2182 | 0.2948 | 0.3962 | 0.2244 | 0.1781 | 0.1395 | 0.3825 | 0.2740 | 0.2541 | 0.3019 | 0.3755 |

Minimum Efficiency | 0.0104 | 0.0015 | 0.0071 | 0.0188 | 0.0139 | 0.0166 | 0.0243 | 0.0319 | 0.0195 | 0.0027 | 0.0213 | 0.0334 | 0.0142 | 0.0026 | 0.0131 | 0.0255 | |

Average Efficiency | 0.0336 | 0.0245 | 0.0228 | 0.0680 | 0.0306 | 0.0324 | 0.0343 | 0.0714 | 0.0280 | 0.0068 | 0.0361 | 0.0668 | 0.0209 | 0.0154 | 0.0302 | 0.0615 | |

Maximum Efficiency | 0.0513 | 0.1201 | 0.0325 | 0.1866 | 0.0471 | 0.0380 | 0.0453 | 0.1723 | 0.0400 | 0.0119 | 0.0546 | 0.1851 | 0.0359 | 0.0402 | 0.0491 | 0.1757 |

Model Version | BIST 30 | |||

DIC | LOG(ML) | LOG(BF) | P(M/y) | |

Static Model | −104.6688 | −10.2670 | 0.0000 | 0.6438 |

Dynamic Model with Varying Constant | −102.9783 | −11.5648 | −1.2979 | 0.1758 |

Dynamic Model with Varying Slopes | −102.4693 | −11.5390 | −1.2720 | 0.1804 |

Dynamic Model with Varying Constant and Slopes | −73.0189 | −26.3844 | −16.1175 | 0.0000 |

Model Version | BIST Financials | |||

DIC | LOG(ML) | LOG(BF) | P(M/y) | |

Static Model | −138.2992 | 6.9186 | 0.0000 | 1.0000 |

Dynamic Model with Varying Constant | −101.5432 | −11.8150 | −18.7336 | 0.0000 |

Dynamic Model with Varying Slopes | −101.6660 | −11.7617 | −18.6803 | 0.0000 |

Dynamic Model with Varying Constant and Slopes | −72.8041 | −26.7789 | −33.6975 | 0.0000 |

Model Version | BIST Industrials | |||

DIC | LOG(ML) | LOG(BF) | P(M/y) | |

Static Model | −138.8032 | 7.7105 | 0.0000 | 1.0000 |

Dynamic Model with Varying Constant | −101.1873 | −11.6099 | −18.7151 | 0.0000 |

Dynamic Model with Varying Slopes | −102.5216 | −11.7487 | −18.8539 | 0.0000 |

Dynamic Model with Varying Constant and Slopes | −72.9679 | −26.4737 | −33.5789 | 0.0000 |

Model Version | BIST Services | |||

DIC | LOG(ML) | LOG(BF) | P(M/y) | |

Static Model | −137.2563 | 6.6372 | 0.0000 | 1.0000 |

Dynamic Model with Varying Constant | −100.3344 | −11.9132 | −18.5504 | 0.0000 |

Dynamic Model with Varying Slopes | −100.5585 | −11.9654 | −18.6026 | 0.0000 |

Dynamic Model with Varying Constant and Slopes | −72.4734 | −26.9859 | −33.6231 | 0.0000 |

Variable/Parameter | BIST 30 Bayesian Model Statistics | ||||||||

Mean | Standardized Value | Standard Deviation | Standard Error (MCSE) | Median | 95% Credible Interval Lower Limit | 95% Credible Interval Upper Limit | Effective Sample Size ESS | Efficiency Rate | |

MPREM | 0.9780260 | 0.9144418 | 0.3643173 | 0.012363 | 0.9736101 | 0.2973357 | 1.6986190 | 868.32 | 0.0868 |

CGIOVERM | −0.0777509 | −0.0530367 | 1.7260920 | 0.061694 | −0.1295789 | −3.224778 | 3.326044 | 782.78 | 0.0783 |

C | −0.0006968 | 0.0263154 | 0.000834 | −0.0009780 | −0.0519224 | 0.0517887 | 996.20 | 0.0996 | |

Variance (Sigma2) | 0.0658121 | 0.0083355 | 0.000184 | 0.0649997 | 0.0513910 | 0.0834123 | 2060.98 | 0.2061 | |

Variable/Parameter | BIST Financials Bayesian Model Statistics | ||||||||

Mean | Standardized Value | Standarddeviation | Standard Error (MCSE) | Median | 95% Credible Interval Lower Limit | 95% Credible Interval Upper Limit | Effective Sample Size ESS | Efficiency Rate | |

MPREM | 1.0776480 | 0.7186622 | 0.3051736 | 0.009648 | 1.0840250 | 0.4674241 | 1.6654280 | 1000.56 | 0.1001 |

CGIOVERM | −0.3832704 | −0.4156208 | 1.4536070 | 0.055715 | −0.3539888 | −3.2738730 | 2.5253270 | 680.68 | 0.0681 |

C | −0.0054632 | 0.0229047 | 0.000687 | −0.0059224 | −0.0513845 | 0.0392210 | 1113.18 | 0.1113 | |

Variance (Sigma2) | 0.04998706 | 0.0063380 | 0.000140 | 0.0493739 | 0.0385422 | 0.0633937 | 2050.46 | 0.2050 | |

Variable/Parameter | BIST Industrials Bayesian Model Statistics | ||||||||

Mean | Standardized Value | Standard Deviation | Standard Error (MCSE) | Median | 95% Credible Interval Lower Limit | 95% Credible Interval Upper Limit | Effective Sample Size ESS | Efficiency Rate | |

MPREM | 0.7789489 | 0.8432594 | 0.30121330 | 0.009127 | 0.7611310 | 0.1935407 | 1.3790210 | 1089.27 | 0.1089 |

CGIOVERM | 1.0271850 | 0.2333382 | 1.4447280 | 0.052227 | 1.0849470 | −1.8068540 | 3.9258310 | 765.22 | 0.0765 |

C | 0.0128308 | 0.0230956 | 0.000714 | 0.0128392 | −0.0338013 | 0.0585056 | 1046.26 | 0.1046 | |

Variance (Sigma2) | 0.0494855 | 0.0064047 | 0.000147 | 0.048946 | 0.0383524 | 0.0635376 | 1908.61 | 0.1909 | |

Variable/Parameter | BIST Services Bayesian Model Statistics | ||||||||

Mean | Standardized Value | Standard Deviation | Standard Error (MCSE) | Median | 95% Credible Interval Lower Limit | 95% Credible Interval Upper Limit | Effective Sample Size ESS | Efficiency Rate | |

MPREM | 0.7562075 | 0.8227116 | 0.3120231 | 0.008601 | 0.7581011 | 0.1555893 | 1.3817030 | 984.99 | 0.0985 |

CGIOVERM | −0.3216062 | −0.0977224 | 1.4695690 | 0.054996 | −0.2946360 | −3.0623570 | 2.6019750 | 669.32 | 0.0669 |

C | −0.0022033 | 0.0230130 | 0.000776 | −0.0022647 | −0.0478770 | 0.0429293 | 952.04 | 0.0952 | |

Variance (Sigma2) | 0.0499917 | 0.0064992 | 0.000139 | 0.0496235 | 0.0387848 | 0.0640200 | 2028.89 | 0.2029 |

BIST 30 | 95% Credible Interval | TEST Statistics | |||

Lower | Upper | Probability | Standard Deviation | Standard Error | |

MPREM | 0.2973357 | 1.6986190 | 0.9509 | 0.21609 | 0.0060980 |

CGIOVERM | −3.2247780 | 3.3260440 | 0.9513 | 0.21525 | 0.0063197 |

C | −0.0519224 | 0.0517887 | 0.9491 | 0.21980 | 0.0048018 |

Sigma2 | 0.0513910 | 0.0834123 | 0.9478 | 0.22244 | 0.0041398 |

BIST Financials | 95% Credible Interval | TEST Statistics | |||

Lower | Upper | Probability | Standard Deviation | Standard Error | |

MPREM | 0.4674241 | 1.6654280 | 0.9500 | 0.21796 | 0.0058677 |

CGIOVERM | −3.2738730 | 2.5253270 | 0.9496 | 0.21878 | 0.0054853 |

C | −0.0513845 | 0.0392210 | 0.9500 | 0.21796 | 0.0041725 |

Sigma2 | 0.0385422 | 0.0633937 | 0.9497 | 0.21857 | 0.0044334 |

BIST Industrials | 95% Credible Interval | TEST Statistics | |||

Lower | Upper | Probability | Standard Deviation | Standard Error | |

MPREM | 0.1935407 | 1.3790210 | 0.9499 | 0.21816 | 0.0051419 |

CGIOVERM | −1.8068540 | 3.9258310 | 0.9498 | 0.21837 | 0.0049948 |

C | −0.0338013 | 0.0585056 | 0.9501 | 0.21775 | 0.0046912 |

Sigma2 | 0.0383524 | 0.0635376 | 0.9500 | 0.21796 | 0.0047583 |

BIST Services | 95% Credible Interval | TEST Statistics | |||

Lower | Upper | Probability | Standard Deviation | Standard Error | |

MPREM | 0.1555893 | 1.3817030 | 0.9502 | 0.21754 | 0.0050913 |

CGIOVERM | −3.0623570 | 2.6019750 | 0.9501 | 0.21775 | 0.0053417 |

C | −0.0478770 | 0.0429293 | 0.9503 | 0.21734 | 0.0044676 |

Sigma2 | 0.0387848 | 0.0640200 | 0.9500 | 0.21796 | 0.0444840 |

Dependent Variable | Criterion * Model | RSS | MSE | Theil’s R-square | Amemiya’s PC | AIC | Adjusted R-square/Rw-square |
---|---|---|---|---|---|---|---|

BIST30 | ROBUST WITH VARYING SLOPE | 0.008508 | 0.000071 | 0.000071 | 0.008934 | 0.009080 | 0.9897 |

BAYESIAN STATIC | 0.004681 | 0.000037 | 0.000037 | 0.004836 | 0.004915 | 0.9912 | |

BIST Financials | ROBUST WITH VARYING SLOPE | 0.034083 | 0.000286 | 0.000284 | 0.035787 | 0.036375 | 0.9609 |

BAYESIAN STATIC | 0.037961 | 0.000319 | 0.000314 | 0.039216 | 0.039860 | 0.9427 | |

BIST Industrials | ROBUST WITH VARYING SLOPE | 0.040276 | 0.000338 | 0.000336 | 0.042290 | 0.042985 | 0.9138 |

BAYESIAN STATIC | 0.042350 | 0.000356 | 0.000350 | 0.043750 | 0.044468 | 0.8924 | |

BIST Services | ROBUST WITH VARYING SLOPE | 0.084781 | 0.000712 | 0.000707 | 0.089020 | 0.090482 | 0.8089 |

BAYESIAN STATIC | 0.088476 | 0.000743 | 0.000731 | 0.091401 | 0.092902 | 0.7786 |

Model | Robust Model | Bayesian Model | |||
---|---|---|---|---|---|

Variable: CGIOVERM | Coefficient | p-Value | GQS | Standardized Coefficient | GQS |

BIST 30 | −0.1290 | 0.0175 | 0.4684 | −0.0530 | 0.4867 |

BIST Financial | −0.4595 | 0.0001 | 0.3871 | −0.0651 | 0.4837 |

BIST Industrials | 0.9579 | 0.0000 | 0.7227 | 0.2333 | 0.5581 |

BIST Services | −0.4181 | 0.0321 | 0.4001 | −0.0977 | 0.4756 |

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## Share and Cite

**MDPI and ACS Style**

Muzir, E.; Kizil, C.; Ceylan, B. Role of International Trade Competitive Advantage and Corporate Governance Quality in Predicting Equity Returns: Static and Conditional Model Proposals for an Emerging Market. *J. Risk Financial Manag.* **2021**, *14*, 125.
https://doi.org/10.3390/jrfm14030125

**AMA Style**

Muzir E, Kizil C, Ceylan B. Role of International Trade Competitive Advantage and Corporate Governance Quality in Predicting Equity Returns: Static and Conditional Model Proposals for an Emerging Market. *Journal of Risk and Financial Management*. 2021; 14(3):125.
https://doi.org/10.3390/jrfm14030125

**Chicago/Turabian Style**

Muzir, Erol, Cevdet Kizil, and Burak Ceylan. 2021. "Role of International Trade Competitive Advantage and Corporate Governance Quality in Predicting Equity Returns: Static and Conditional Model Proposals for an Emerging Market" *Journal of Risk and Financial Management* 14, no. 3: 125.
https://doi.org/10.3390/jrfm14030125