#
Accuracy of European Stock Target Prices^{ †}

^{1}

^{2}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

## 2. Literature Overview

## 3. Data and Methodology

#### 3.1. Data

**Definition**

**1.**

#### 3.2. Research Design

- (A)
- FP vs. TP: we evaluate the accuracy of TP forecasts made by analysts.
- (B)
- FP vs. CP: to compare the accuracy of a forecast as naive as CP to analysts’ TP forecast.
- (C)
- TP vs. CP: to evaluate to what extent TP can be determined by CP.

#### 3.2.1. Overall Panel Regressions

#### 3.2.2. Panel Robustness

- The pre-crisis period, until the end of August 2008;
- The crisis period, from September 2008 until the end of 2012;
- The pots crisis period, from 2013 onwards.

#### 3.2.3. Individual Regressions

## 4. Results

#### 4.1. Overall Panel Regressions

- In our overall sample and on average, target prices overestimate future prices (positive and statistically significant negative $\alpha =-1.424$);
- While capitalised prices tend to under estimate them (positive and statistically significant positive $\alpha =+1.789$).

- Overall, there is no evidence that target prices can forecast future prices—the second column of results in Table 3. In fact, the regression not only shows and ${R}^{2}$ of 0.000, but also the coefficient associated with the independent variable is also not statistically different from zero (as attested by its t-statistics);
- Although is true we also find no forecasting power in the simple capitalisation rule forecasts (from Equation (1))—the fourth column of results in Table 3—as we observe an ${R}^{2}$ of $0.001$, in this case the coefficient associated with the dependent variable is at least statistically different from zero;
- The ability capitalised prices have to explain analysts’ forecasts is very limited—sixth column of results in Table 3. In fact, we only get an ${R}^{2}=0.008$. Nonetheless, in relative terms this regression is the “best”, as attested by the all model selection statistics.

#### 4.2. Panel Robustness

- The reason why, overall, our forecast variables (both TP and CP ) have no predicting power over future prices cannot be explained by firm-specific components.

- Firm-specific variables may explain optimism/pessimism in target prices forecasts, as we obtained a wide range of ${\mu}_{i}$ values.

- Analysts became particularly pessimistic during the crisis-period (positive and significant $\alpha =5.4675$ crisis period level intercept) and optimistic in the post-crisis period (negative and significant $\alpha =-1.02577$ for the equivalent post-crisis intercept);
- Absence of accuracy, of both target prices and capitalised prices, became even more severe during the crisis period (lowest adjusted ${R}^{2}$).

#### 4.3. Individual Regressions

- For each of the eight companies presented, the accuracy is not as bad as in the overall sample; the ${R}^{2}$ levels of the “FP vs TP” regressions range from 0.0012 (Inditex) to 0.1157 (Safran), suggesting that the accuracy of target prices is less than 12%, and varies considerably from firm to firm.
- Similarly, ${R}^{2}$ levels of the “FP vs CP” regressions range from 0.0021 (Essilor) to 0.1214 (Volkswagen), suggesting similar levels of accuracy of the two forecasts with target prices working better for some firms and capitalised prices for others.
- It is interesting that the highest ${R}^{2}$ levels are found for the “TP vs CP” regressions, where the ${R}^{2}$ levels range from 0.0904 (Fresenius) to as high as 0.3685 (Adidas), suggesting that at least between 10% and 35% of target prices can be explained by simple capitalisation rules.

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Appendix A

FP vs. TP | FP vs. CP | TP vs. CP | ||||
---|---|---|---|---|---|---|

(1) | (2) | (3) | (4) | (5) | (6) | |

Dependent Variable | $FP$ | $\Delta FP$ | $FP$ | $\Delta FP$ | $TP$ | $\Delta TP$ |

Mean dependent var | 38.516 | 0.070 | 38.516 | 0.070 | 48.456 | 0.057 |

S.D. Dependent var | 39.241 | 1.839 | 39.241 | 1.839 | 42.886 | 1.758 |

Intercept | ||||||

Coefficient | −0. 811 | 0.069406 | 4.186 | 0.068 | 14.029 | 0.051 |

Std. Error | 0.179 | 0.010 | 0.108 | 0.010 | 0.090 | 0.009 |

t-Statistic | −4.532 | 7.212 | 38.883 | 7.033 | 153.288 | 5.542 |

Prob. | 0.000 | 0. 0000 | 0.000 | 0.000 | 0.000 | 0.000 |

Independent Variable | $TP$ | $\Delta TP$ | $CP$ | $\Delta CP$ | $CP$ | $\Delta CP$ |

Coefficient | 0.812 | 0.004 | 0.857 | 0.026 | 0.859 | 0.080 |

Std. Error | 0.003 | 0.005 | 0.002 | 0.005 | 0.002 | 0.005 |

t-Statistic | 246.739 | 0.757 | 382.297 | 5.401 | 450.965 | 1.706 |

Prob. | 0.000 | 0.449 | 0.000 | 0.000 | 0.000 | 0.000 |

Regression Statistics | ||||||

R-squared | 0.843 | 0.003 | 0.916 | 0.004 | 0.949 | 0.010 |

Adjusted R-squared | 0.843 | 0.001 | 0.916 | 0.002 | 0.949 | 0.009 |

S.E. Of regression | 15.544 | 1.838 | 11.350 | 1.837 | 9.649 | 1.750 |

Sum square resid | 8,819,023 | 123,084 | 4,701,829 | 122,988 | 3,398,162 | 111,608 |

Log Likelihood | −152,118 | −73,975 | −140,624 | −73,960 | −134,690 | −72,188 |

F-statistic | 3928.6 | 2.014 | 8007.9 | 2.588 | 13,710 | 7.608 |

Prob (F-statistic) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |

Model Statistics | ||||||

AIC | 8.327 | 4.056 | 7.698 | 4.055 | 7.373 | 3.958 |

BIC | 8.339 | 4.068 | 7.710 | 4.067 | 7.385 | 3.970 |

HQC | 8.330 | 4.060 | 7.701 | 4.059 | 7.377 | 3.962 |

Residuals Autocorr. | ||||||

Durbi–Watson stat | 0.022 | 2.061 | 0.047 | 2.061 | 0.058 | 2.081 |

FP vs. TP | FP vs. CP | TP vs. CP | ||||
---|---|---|---|---|---|---|

(1) | (2) | (3) | (4) | (5) | (6) | |

Dependent Variable | $FP$ | $\Delta FP$ | $FP$ | $\Delta FP$ | $TP$ | $\Delta TP$ |

Mean dependent var | 28.077 | 0.035 | 28.077 | 0.035 | 41.649 | 0.135 |

S.D. Dependent var | 23.214 | 1.196 | 23.214 | 1.196 | 35.118 | 1.750 |

Intercept | ||||||

Coefficient | 3.157 | 0.037 | 2.220 | 0.027 | 0.992 | 0.124 |

Std. Error | 0.164 | 0.013 | 0.143 | 0.013 | 0.152 | 0.019 |

t-Statistic | 19.289 | 2.873 | 15.503 | 2.135 | 6.527 | 6.590 |

Prob. | 0.000 | 0.004 | 0.000 | 0.033 | 0.000 | 0.000 |

Independent Variable | $TP$ | $\Delta TP$ | $CP$ | $\Delta CP$ | $CP$ | $\Delta CP$ |

Coefficient | 0.598 | 0.017 | 0.931 | 0.089 | 1.464 | 0.132 |

Std. Error | 0.003 | 0.007 | 0.004 | 0.013 | 0.004 | 0.020 |

t-Statistic | 199.172 | −2.285 | 235.154 | 6.678 | 348.352 | 6.775 |

Prob. | 0.000 | 0.022 | 0.000 | 0.000 | 0.000 | 0.000 |

Regression Statistics | ||||||

R-squared | 0.819 | 0.001 | 0.863 | 0.005 | 0.933 | 0.005 |

Adjusted R-squared | 0.819 | 0.000 | 0.863 | 0.005 | 0.933 | 0.005 |

S.E. of regression | 9.868 | 1.195 | 8.580 | 1.193 | 9.107 | 1.746 |

Sum square resid | 851,841 | 12,426 | 643,983 | 12,370 | 725,530 | 26,514 |

Log Likelihood | −32,446 | −13,895 | −31,222 | −13,876 | −31,744 | −17,192 |

F-statistic | 39,670 | 5.220 | 55,297 | 4.460 | 121,349 | 45.898 |

Prob (F-statistic) | 0.000 | 0.022 | 0.000 | 0.000 | 0.000 | 0.000 |

Model Statistics | ||||||

AAIC | 7.417 | 3.195 | 7.137 | 3.190 | 7.256 | 3.953 |

SIC | 7.418 | 3.196 | 7.139 | 3.192 | 7.258 | 3.954 |

HQC | 7.417 | 3.195 | 7.138 | 3.191 | 7.257 | 3.953 |

Residuals Autocorr. | ||||||

Durbi–Watson stat | 0.027 | 2.072 | 0.028 | 2.067 | 0.056 | 1.944 |

FP vs. TP | FP vs. CP | TP vs. CP | ||||
---|---|---|---|---|---|---|

(1) | (2) | (3) | (4) | (5) | (6) | |

Dependent Variable | $FP$ | $\Delta FP$ | $FP$ | $\Delta FP$ | $TP$ | $\Delta TP$ |

Mean dependent var | 26.324 | 0.038 | 26.324 | 0.038 | 41.647 | 0.073 |

S.D. Dependent var | 22.156 | 1.582 | 22.156 | 1.582 | 33.856 | 2.539 |

Intercept | ||||||

Coefficient | 5.468 | 0.038 | 2.480 | 0.038 | 3.087 | 0.072 |

Std. Error | 0.213 | 0.015 | 0.158 | 0.015 | 0.187 | 0.024 |

t-Statistic | 25.705 | 2.557 | 15.672 | 2 557854 | 16.495 | −3.008 |

Prob. | 0.000 | 0.011 | 0.000 | 0.011 | 0.000 | 0.003 |

Independent Variable | $TP$ | $\Delta TP$ | $CP$ | $\Delta CP$ | $CP$ | $\Delta CP$ |

Coefficient | 0.501 | 0.000 | 0.816 | 0.001 | 1.320 | 0.026 |

Std. Error | 0.004 | 0.006 | 0.004 | 0.008 | 0.005 | 0.013 |

t-Statistic | 126.367 | 0.057 | 194.373 | 0.117 | 265.826 | 1.947 |

Prob. | 0.000 | 0.954 | 0.000 | 0.907 | 0.000 | 0.052 |

Regression Statistics | ||||||

R-squared | 0.586 | 0.000 | 0.770 | 0.000 | 0.862 | 0.000 |

Adjusted R-squared | 0.586 | 0.000 | 0.770 | 0.000 | 0.862 | 0.000 |

S.E. of regression | 14.262 | 1.582 | 10.631 | 1.582 | 12.570 | 2.539 |

Sum square resid | 2,298,141 | 28,138 | 1,276,771 | 28,138 | 1,785,249 | 72,490 |

Log Likelihood | −46,064 | −21,120 | −42,743 | −21,120 | −44,637 | −26,443 |

F-statistic | 15,969 | 0.003 | 37,781 | 0.014 | 70,664 | 3.791 |

Prob (F-statistic) | 0.000 | 0.954 | 0.000 | 0.907 | 0.000 | 0.052 |

Model Statistics | ||||||

AIC | 8.153 | 3.755 | 7.566 | 3.755 | 7.901 | 4.701 |

SIC | 8.155 | 3.756 | 7.567 | 3.756 | 7.902 | 4.703 |

HQC | 8.154 | 3.755 | 7.566 | 3.755 | 7.901 | 4.702 |

Residuals Autocorr. | ||||||

Durbi–Watson stat | 0.0203 | 2.2066 | 0.0417 | 2.2067 | 0.0761 | 2.2790 |

FP vs. TP | FP vs. CP | TP vs. CP | ||||
---|---|---|---|---|---|---|

(1) | (2) | (3) | (4) | (5) | (6) | |

Dependent Variable | $FP$ | $\Delta FP$ | $FP$ | $\Delta FP$ | $TP$ | $\Delta TP$ |

Mean dependent var | 52.401 | 0.107 | 52.401 | 0.107 | 56.730 | 0.106 |

S.D. Dependent var | 49.365 | 2.242 | 49.365 | 2.242 | 50.105 | 0.895 |

Intercept | ||||||

Coefficient | −1.026 | 0.098 | 2.701 | 0.103 | 5.021 | 0.093 |

Std. Error | 0.170 | 0.018 | 0.150 | 0.017 | 0.090 | 0.007 |

t-Statistic | −6.016 | 5.562 | 18.063 | 5.862 | 55.952 | 13.744 |

Prob. | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |

Independent Variable | $TP$ | $\Delta TP$ | $CP$ | $\Delta CP$ | $CP$ | $\Delta CP$ |

Coefficient | 0.942 | 0.086 | 0.919 | 0.033 | 0.957 | 0.097 |

Std. Error | 0.002 | 0.020 | 0.002 | 0.007 | 0.001 | 0.003 |

t-Statisti | 418.079 | 4.406 | 459.943 | 4.518 | 797.498 | 34.875 |

Prob. | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |

Regression Statistics | ||||||

R-squared | 0.914 | 0.001 | 0.928 | 0.001 | 0.975 | 0.069 |

Adjusted R-squared | 0.914 | 0.001 | 0.928 | 0.001 | 0.975 | 0.069 |

S.E. of regression | 14.498 | 2.241 | 13.278 | 2.241 | 7.967 | 0.864 |

Sum square resid | 3,467,636 | 82,572 | 2,908,700 | 82,567 | 1,047,283 | 12,266 |

Log Likelihood | −67,532 | −36,611 | −66,082 | −36,611 | −57,655 | −20,927 |

F-statistic | 174,790 | 1.941 | 211,548 | 2.041 | 636,003 | 1216 |

Prob (F-statistic) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |

Model Statistics | ||||||

AIC | 8.186 | 4.451 | 8.010 | 4.451 | 6.989 | 2.545 |

SIC | 8.187 | 4.452 | 8.011 | 4.452 | 6.990 | 2.546 |

HQC | 8.186 | 4.452 | 8.011 | 4.452 | 6.989 | 2.545 |

Residuals Autocorr. | ||||||

Durbi–Watson stat | 0.027 | 2.009 | 0.054 | 2.005 | 0.079 | 1.306 |

**Figure A1.**Adidas. Individual time series regressions using three price series on Adidas: future prices (FP), target prices (TP), and capitalised prices (CP) forecasts. On the left-hand-side are images of level regressions and on the right-hand-side are the regressions in differences.

**Figure A2.**Anheuser. Individual time series regressions using three price series on Anheuser: future prices (FP), target prices (TP), and capitalised prices (CP) forecasts. On the left-hand-side images of level regressions and on the right-hand-side of regressions in differences.

**Figure A3.**ASML. Individual time series regressions using three price series on ASML: future prices (FP), target prices (TP), and capitalised prices (CP) forecasts. On the left-hand-side are images of level regressions and on the right-hand-side are regressions in differences.

**Figure A4.**Essilor. Individual time series regressions using three price series on Essilor: future prices (FP), target prices (TP), and capitalised prices (CP) forecasts. On the left-hand-side are images of level regressions and on the right-hand-side are regressions in differences.

**Figure A5.**Fresenius. Individual time series regressions using three price series on Fresenius: future prices (FP), target prices (TP), and capitalised prices (CP) forecasts. On the left-hand-side are images of level regressions and on the right-hand-side are regressions in differences.

**Figure A6.**Inditex. Individual time series regressions using three price series on Inditex: future prices (FP), target prices (TP), and capitalised prices (CP) forecasts. On the left-hand-side are images of level regressions and on the right-hand-side are regressions in differences.

**Figure A7.**Safran. Individual time series regressions using three price series on Safran: future prices (FP), target prices (TP), and capitalised prices (CP) forecasts. On the left-hand-side are images of level regressions and on the right-hand-side are regressions in differences.

**Figure A8.**Volkswagen. Individual time series regressions using three price series on Volkswagen: future prices (FP), target prices (TP), and capitalised prices (CP) forecasts. On the left-hand-side are images of level regressions and on the right-hand-side are regressions in differences.

## Notes

1 | “Order of integration” is a summary statistic used to describe a unit root process in time series analysis. Specifically, it tells you the minimum number of differences needed to obtain a stationary series (Engle and Granger 1991). |

2 | Our crisis period includes both the global financial crisis and the European sovereign debt crisis. |

3 | According to Granger and Newbold (2001), we should suspect that a regression is spurious if ${R}^{2}>d$, where d is the Durbin–Watson statistic, which is the case for all level regressions and not the case for the regressions in differences. |

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**Figure 1.**Comparison of target prices (TP) and capitalised prices (CP) with actual future prices (FP). Target prices (TP: orange lines) and capitalised prices (CP: grey lines) forecast for the indicated date t, jointly with actually observed future prices (FP: blue lines) att, for the 8 best performing companies over the 15-year period of our sample: Adidas, Anheuser, ASML, Essilor, Fresenius, Inditex, Safran, Volkswagen.

**Figure 2.**Panel regressions’ illustration. Illustration of the panel regressions of Table 3. On the left-hand-side are images of level regressions and on the right-hand–ide are regressions in differences.

Adidas | BASF | E.ON | L’Oreal | Schneider Electric SE |

Air Liquide | Bayer | ENEL | LVMH | Siemens |

Airbus | BNP Paribas | ENI | Mucich RE | Societe Generale |

Allianz | BMW | Essilor | Nokia | Telefonica |

Anheuser | Danone | Fresenius | Orange | Total |

ASML | Carrefour | Iberdrola | Repsol | Unicredit |

Assicurazioni | Daimler | Inditex | Safran | Unilever |

AXA Deutsche | Bank | ING | Saint-Gobain | Vinci |

Banco Bilbao | Deutsche Post | Intesa Sanpaolo | Sanofi | Vivendi |

Banco Santander | Deutsche Telekom | Philips | SAP | Volkswagen |

Future Prices (FP) | Target Prices (TP) | Capitalised Prices (CP) | ||||
---|---|---|---|---|---|---|

Method | Statistic | Prob | Statistic | Prob | Statistic | Prob |

LLC | 6.755 | 1.000 | 7.966 | 1.000 | 7.074 | 1.000 |

IPS | 6.156 | 1.000 | 8.492 | 1.000 | 6.635 | 1.000 |

ADF– Fisher | 60.653 | 0.999 | 39.983 | 0.999 | 53.817 | 1.000 |

PP–Fisher | 57.242 | 1.000 | 40.002 | 1.000 | 49.630 | 1.000 |

FP vs. TP | FP vs. CP | TP vs. CP | ||||
---|---|---|---|---|---|---|

(1) | (2) | (3) | (4) | (5) | (6) | |

Dependent Variable | $FP$ | $\Delta FP$ | $FP$ | $\Delta FP$ | $TP$ | $\Delta TP$ |

Mean dependent var | 38.516 | 0.070 | 38.516 | 0.070 | 48.456 | 0.057 |

S.D. Dependent var | 39.241 | 1.839 | 39.241 | 1.839 | 42.886 | 1.758 |

Intercept | ||||||

Coefficient | −1.424 | 0.069 | 1.789 | 0.068 | 8.433 | 0.051 |

Std. Error | 0.134 | 0.010 | 0.085 | 0.010 | 0.096 | 0.009 |

t-Statistic | −10.590 | 7.192 | 20.922 | 7.012 | 87.712 | 5.525 |

Prob. | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |

Independent Variable | $TP$ | $\Delta TP$ | $CP$ | $\Delta CP$ | $CP$ | $\Delta CP$ |

Coefficient | 0.824 | 0.007 | 0.916 | 0.029 | 0.999 | 0.081 |

Std. Error | 0.002 | 0.005 | 0.001 | 0.005 | 0.002 | 0.004 |

t-Statistic | 396.586 | 1.215 | 613.740 | 5.851 | 594.747 | 17.470 |

Prob. | 0.000 | 0.224 | 0.000 | 0.000 | 0.000 | 0.000 |

Regression Statistics | ||||||

R-squared | 0.811 | 0.000 | 0.912 | 0.001 | 0.906 | 0.008 |

Adjusted R-squared | 0.811 | 0.000 | 0.912 | 0.001 | 0.906 | 0.008 |

S.E. of regression | 17.040 | 1.839 | 11.670 | 1.838 | 13.124 | 1.750 |

Sum square resid | 106.123 | 123,419.8 | 4,977,847 | 123,309 | 6,295,006 | 111,838 |

Log Likelihood | −155,501 | −740,255 | −141,667 | −74,009 | −145,957 | −72,226 |

F-statistic | 157,281 | 1.476 | 376,676 | 34.241 | 353,724 | 305.203 |

Prob (F-statistic) | 0.000 | 0.224 | 0.000 | 0.000 | 0.000 | 0.000 |

Model Statistics | ||||||

AIC | 8.509 | 4.056 | 7.752 | 4.055 | 7.987 | 3.958 |

SIC | 8.509 | 4.057 | 7.753 | 4.056 | 7.987 | 3.958 |

HQC | 8.509 | 4.056 | 7.752 | 4.056 | 7.987 | 3.958 |

Residuals Autocorr. | ||||||

Durbi–Watson stat | 0.019 | 2.055 | 0.047 | 2.055 | 0.037 | 2.007 |

Panel A: FP vs. TP | |||

Pre-crisis | Crisis | Post-crisis | |

In level (1) | |||

Intercept | 3.15669 *** | 5.467543 *** | −1.025766 *** |

In Differences (2) | |||

Intercept | 0.036918 *** | 0.038144 ** | 0.097836 *** |

Independent Variable | 0.016726 ** | 0.000336 | 0.086016 *** |

Adjusted R-squared | 0.000485 | 0.000089 | 0.001118 |

Hannan-Quinn criter. | 3.19534 | 3.755410 | 4.451777 |

Panel B: FP vs. CP | |||

Pre-crisis | Crisis | Post-crisis | |

In level (3) | |||

Intercept | 2.219831 *** | 2.480338 *** | 2.701088 *** |

In Differences (4) | |||

Intercept | 0.027395 ** | 0.038147 ** | 0.102555 *** |

Independent Variable | 0.089025 *** | 0.000953 | 0.032768 *** |

Adjusted R-squared | 0.004987 | 0.000088 | 0.001179 |

Hannan-Quinn criter. | 3.190830 | 3.755 | 4.451717 |

**Table 5.**Future prices vs. target prices: individual asset results. Individual regressions of future prices (FP) on target prices (TP): (

**a**) in levels $F{P}_{t}=\alpha +\beta T{P}_{t}+{\u03f5}_{t}$ and (

**b**) in differences $\Delta F{P}_{t}=\alpha +\beta \Delta T{P}_{t}+{\u03f5}_{t}$.

(a) In levels | ||||||||

Adidas | Anheuser | ASML | Essilor | Fresenius | Inditex | Safran | Volkswagen | |

Regression Statistics | ||||||||

Multiple R | 0.9132 | 0.9245 | 0.9566 | 0.9489 | 0.9143 | 0.9391 | 0.9584 | 0.5404 |

R Square | 0.8339 | 0.8547 | 0.9151 | 0.9004 | 0.8359 | 0.8818 | 0.9185 | 0.2921 |

Adjusted R Square | 0.8336 | 0.8545 | 0.9150 | 0.9003 | 0.8357 | 0.8817 | 0.9184 | 0.2910 |

Standard Error | 23.0141 | 11.6967 | 14.1228 | 10.2800 | 8.7074 | 3.3725 | 8.6591 | 41.2734 |

Observations | 731 | 731 | 731 | 731 | 731 | 731 | 731 | 679 |

Intercept | ||||||||

Coefficient | −6.5788 | 3.2317 | −1.3111 | 2.7221 | 3.6783 | 1.2282 | −7.0116 | 46.9403 |

Standard Error | 1.5922 | 0.8641 | 0.8400 | 0.8709 | 0.5775 | 0.2310 | 0.5916 | 4.2281 |

t Stat | −4.1318 | 3.7398 | −1.5608 | 3.1255 | 6.3691 | 5.3175 | −11.8514 | 11.1019 |

P-value | 0.0000 | 0.0002 | 0.1190 | 0.0018 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |

Lower 95% | −9.7048 | 1.5352 | −2.9602 | 1.0123 | 2.5445 | 0.7748 | −8.1731 | 38.6385 |

Upper 95% | −3.4529 | 4.9283 | 0.3381 | 4.4319 | 4.8121 | 1.6817 | −5.8501 | 55.2421 |

TP Variable | ||||||||

Coefficient | 1.1167 | 0.8049 | 1.1506 | 0.9225 | 0.8532 | 0.8437 | 1.2215 | 0.4511 |

Standard Error | 0.0185 | 0.0123 | 0.0130 | 0.0114 | 0.0140 | 0.0114 | 0.0135 | 0.0270 |

t Stat | 60.4917 | 65.4910 | 88.6353 | 81.1974 | 60.9478 | 73.7592 | 90.6476 | 16.7120 |

P-value | 0.0000 | 0.0001 | 0.0002 | 0.0003 | 0.0004 | 0.0005 | 0.0006 | 0.0007 |

Lower 95% | 1.0805 | 0.7808 | 1.1251 | 0.9002 | 0.8257 | 0.8212 | 1.1950 | 0.3981 |

Upper 95% | 1.1530 | 0.8290 | 1.1761 | 0.9448 | 0.8807 | 0.8661 | 1.2480 | 0.5041 |

ANOVA | ||||||||

SS | 1,938,122 | 586,797 | 1,566,947 | 696,737 | 281,640 | 61,878 | 616,104 | 475,771 |

MS | 1,938,122 | 586,797 | 1,566,947 | 696,737 | 281,640 | 61,878 | 616,104 | 475,771 |

F | 3659 | 4289 | 7856 | 6593 | 3715 | 5440 | 8217 | 279 |

Significance F | 0.0000 | 0.0001 | 0.0002 | 0.0003 | 0.0004 | 0.0005 | 0.0006 | 0.0007 |

(b) In differences | ||||||||

Adidas | Anheuser | ASML | Essilor | Fresenius | Inditex | Safran | Volkswagen | |

Regression Statistics | ||||||||

Multiple R | 0.0124 | 0.0812 | 0.0297 | 0.0345 | 0.0137 | 0.0012 | 0.1157 | 0.0488 |

R Square | 0.0002 | 0.0066 | 0.0009 | 0.0012 | 0.0002 | 0,0000 | 0.0134 | 0.0024 |

Adjusted R Square | −0.0012 | 0.0052 | −0.0005 | −0.0002 | −0.0012 | −0.0014 | 0.0120 | 0.0002 |

Standard Error | 3.1255 | 0.7287 | 2.6244 | 2.1335 | 1.3584 | 0.5834 | 1.5236 | 6.4974 |

Observations | 730 | 730 | 730 | 730 | 730 | 730 | 730 | 470 |

Intercept | ||||||||

Coefficient | 0.2757 | 0.1192 | 0.2204 | 0.1055 | 0.0616 | 0.0336 | 0.1223 | 0.1998 |

Standard Error | 0.1174 | 0.0682 | 0.0992 | 0.0800 | 0.0511 | 0.0218 | 0.0573 | 0.2999 |

t Stat | 2.3488 | 1.7493 | 2.2227 | 1.3178 | 1.2048 | 1.5381 | 2.1327 | 0.6662 |

P-value | 0.0191 | 0.0807 | 0.0265 | 0.1880 | 0.2287 | 0.1245 | 0.0333 | 0.5056 |

Lower 95% | 0.0452 | −0.0146 | 0.0257 | −0.0517 | −0.0388 | −0.0093 | 0.0097 | −0.3895 |

Upper 95% | 0.5061 | 0.253 | 0.415 | 0.2626 | 0.1619 | 0.0765 | 0.2348 | 0.7890 |

DTP Variable | ||||||||

Coefficient | 0.0255 | −0.2023 | 0.0737 | 0.0931 | −0.0352 | −0.003 | 0.3207 | 0.0622 |

Standard Error | 0.0760 | 0.0920 | 0.0918 | 0.0999 | 0.0955 | 0.0914 | 0.1020 | 0.0589 |

t Stat | 0.3356 | −2.1989 | 0.8029 | 0.9317 | −0.369 | −0.0328 | 3.1430 | 1.0563 |

P-value | 0.7373 | 0.0282 | 0.4223 | 0.3518 | 0.7122 | 0.9738 | 0.0017 | 0.2914 |

Lower 95% | −0.1237 | −0.3828 | −0.1066 | −0.1031 | −0.2226 | −0.1825 | 0.1204 | −0.0535 |

Upper 95% | 0.1746 | −0.0217 | 0.2540 | 0.2894 | 0.1522 | 0.1765 | 0.5211 | 0.1780 |

ANOVA | ||||||||

SS | 1.1000 | 15.9185 | 4.4402 | 3.9518 | 0.2513 | 0.0004 | 22.9319 | 47.1055 |

MS | 1.1000 | 15.9185 | 4.4402 | 3.9518 | 0.2513 | 0.0004 | 22.9319 | 47.1055 |

F | 0.1126 | 4.835 | 0.6447 | 0.8682 | 0.1362 | 0.0011 | 9.8782 | 1.1158 |

Significance F | 0.7373 | 0.0282 | 0.4223 | 0.3518 | 0.7122 | 0.9738 | 0.0017 | 0.2914 |

**Table 6.**Future prices vs. capitalised prices: individual asset results. Individual regressions of future prices (FP) on capitalised prices (CP): (

**a**) in levels $F{P}_{t}=\alpha +\beta C{P}_{t}+{\u03f5}_{t}$ and (

**b**) in differences $\Delta F{P}_{t}=\alpha +\beta \Delta C{P}_{t}+{\u03f5}_{t}$.

(a) In levels | ||||||||

Adidas | Anheuser | ASML | Essilor | Fresenius | Inditex | Safran | Volkswagen | |

Regression Statistics | ||||||||

Multiple R | 0.9404 | 0.9262 | 0.9566 | 0.9487 | 0.9328 | 0.9448 | 0.9697 | 0.7627 |

R Square | 0.8843 | 0.8579 | 0.9150 | 0.900 | 0.8702 | 0.8927 | 0.9402 | 0.5818 |

Adjusted R Square | 0.8841 | 0.8577 | 0.9149 | 0.8999 | 0.8700 | 0.8925 | 0.9402 | 0.5811 |

Standard Error | 19.206 | 11.567 | 14.1260 | 10.3022 | 7.7464 | 3.214 | 7.4152 | 31.724 |

Observations | 731 | 731 | 731 | 731 | 731 | 731 | 731 | 679 |

Intercept | ||||||||

Coefficient | 2.183 | 9.2317 | 3.0541 | 7.5376 | 5.1339 | 2.4446 | −1.0466 | 39.3794 |

Standard Error | 1.2048 | 0.7764 | 0.8022 | 0.8199 | 0.4897 | 0.2062 | 0.4568 | 2.6745 |

t Stat | 1.8119 | 11.8897 | 3.8071 | 9.1934 | 10.4831 | 11.8543 | −2.2909 | 14.7239 |

P-value | 0.0704 | 0.0000 | 0.0002 | 0.0000 | 0.0000 | 0.0000 | 0.0223 | 0.0000 |

Lower 95% | −0.1823 | 7.7074 | 1.4792 | 5.928 | 4.1725 | 2.0397 | −1.9434 | 34.1281 |

Upper 95% | 4.5483 | 10.7560 | 4.6291 | 9.1473 | 6.0954 | 2.8494 | −0.1497 | 44.6308 |

CP Variable | ||||||||

Coefficient | 0.9747 | 0.7703 | 0.9547 | 0.8697 | 0.8087 | 0.7918 | 1.0556 | 0.5835 |

Standard Error | 0.0131 | 0.0116 | 0.0108 | 0.0107 | 0.0116 | 0.0102 | 0.0099 | 0.0190 |

t Stat | 74.6454 | 66.3494 | 88.6132 | 81.0032 | 69.897 | 77.8717 | 107.0977 | 30.6864 |

P-value | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |

Lower 95% | 0.9490 | 0.7475 | 0.9335 | 0.8487 | 0.786 | 0.7718 | 1.0363 | 0.5461 |

Upper 95% | 1.0003 | 0.7931 | 0.9758 | 0.8908 | 0.8315 | 0.8118 | 1.075 | 0.6208 |

ANOVA | ||||||||

SS | 2,055,329 | 588,997 | 1,566,881 | 696,404 | 293,167 | 62,639 | 630,679 | 947,694 |

MS | 2,055,329 | 588,997 | 1,566,881 | 696,404 | 293,167 | 62,639 | 630,679 | 947,694 |

F | 5572 | 4402 | 7852 | 6562 | 4886 | 6064 | 11,470 | 942 |

Significance F | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |

(b) In differences | ||||||||

Adidas | Anheuser | ASML | Essilor | Fresenius | Inditex | Safran | Volkswagen | |

Regression Statistics | ||||||||

Multiple R | 0.0387 | 0.0390 | 0.0223 | 0.0021 | 0.0246 | 0.1010 | 0.0432 | 0.1214 |

R Square | 0.0015 | 0.0015 | 0.0005 | 0.0000 | 0.0006 | 0.0102 | 0.0019 | 0.0147 |

Adjusted R Square | 0.0001 | 0.0002 | −0.0009 | −0.0014 | −0.0008 | 0.0088 | 0.0005 | 0.0126 |

Standard Error | 3.1234 | 1.8191 | 2.6249 | 2.1348 | 1.3581 | 0.5805 | 1.5325 | 6.4570 |

Observations | 730 | 730 | 730 | 730 | 730 | 730 | 730 | 470 |

Intercept | ||||||||

Coefficient | 0.2714 | 0.0976 | 0.2309 | 0.1173 | 0.0605 | 0.0303 | 0.1493 | 0.1773 |

Standard Error | 0.1161 | 0.0674 | 0.0976 | 0.0792 | 0.0504 | 0.0215 | 0.0569 | 0.2981 |

t Stat | 2.3382 | 1.4480 | 2.3659 | 1.4817 | 1.1999 | 1.4061 | 2.6235 | 0.5948 |

P-value | 0.0196 | 0.1480 | 0.0182 | 0.1389 | 0.2306 | 0.1601 | 0.0089 | 0.5523 |

Lower 95% | 0.0435 | −0.0347 | 0.0393 | −0.0381 | −0.0385 | −0.0120 | 0.0376 | −0.4085 |

Upper 95% | 0.4993 | 0.2300 | 0.4224 | 0.2727 | 0.1594 | 0.0725 | 0.2611 | 0.7631 |

DCP Variable | ||||||||

Coefficient | 0.0365 | −0.0356 | 0.0224 | 0.0020 | −0.0249 | 0.0924 | 0.0446 | 0.1026 |

Standard Error | 0.0349 | 0.0337 | 0.0373 | 0.0350 | 0.0376 | 0.0337 | 0.0382 | 0.0388 |

t Stat | 1.0460 | −1.0539 | 0.6015 | 0.0571 | −0.6629 | 2.7390 | 1.1676 | 2.6450 |

P-value | 0.2959 | 0.2923 | 0.5477 | 0.9545 | 0.5076 | 0.0063 | 0.2434 | 0.0084 |

Lower 95% | −0.0320 | −0.1018 | −0.0508 | −0.0668 | −0.0988 | 0.0262 | −0.0304 | 0.0264 |

Upper 95% | 0.1051 | 0.0307 | 0.0957 | 0.0708 | 0.0489 | 0.1586 | 0.1196 | 0.1788 |

ANOVA | ||||||||

SS | 10.6731 | 3.6758 | 2.4931 | 0.0149 | 0.8105 | 2.5277 | 3.2016 | 291.6781 |

MS | 10.6731 | 3.6758 | 2.4931 | 0.0149 | 0.8105 | 2.5277 | 3.2016 | 291.6781 |

F | 1.0941 | 1.1108 | 0.3618 | 0.0033 | 0.4394 | 7.5019 | 1.3632 | 6.9958 |

Significance F | 0.2959 | 0.2923 | 0.5477 | 0.9545 | 0.5076 | 0.0063 | 0.2434 | 0.0084 |

**Table 7.**Target prices vs. capitalised prices: individual asset results. Individual regressions of target prices (FP) on capitalised prices (CP): (

**a**) in levels $T{P}_{t}=\alpha +\beta C{P}_{t}+{\u03f5}_{t}$ and (

**b**) in differences $\Delta T{P}_{t}=\alpha +\beta \Delta C{P}_{t}+{\u03f5}_{t}$.

(a) In levels | ||||||||

Adidas | Anheuser | ASML | Essilor | Fresenius | Inditex | Safran | Volkswagen | |

Regression Statistics | ||||||||

Multiple R | 0.9907 | 0.9947 | 0.9944 | 0.9910 | 0.9927 | 0.9926 | 0.9909 | 0.8291 |

R Square | 0.9816 | 0.9895 | 0.9889 | 0.9820 | 0.9855 | 0.9852 | 0.9819 | 0.6875 |

Adjusted R Square | 0.9815 | 0.9895 | 0.9889 | 0.9820 | 0.9855 | 0.9852 | 0.9819 | 0.6870 |

Standard Error | 6.2686 | 3.6153 | 4.2426 | 4.4920 | 2.7701 | 1.3281 | 3.2030 | 32.8506 |

Observations | 731 | 731 | 731 | 731 | 731 | 731 | 731 | 679 |

Intercept | ||||||||

Coefficient | 10.3126 | 7.8353 | 4.0534 | 5.7802 | 2.5836 | 1.6513 | 5.5437 | 50.0617 |

Standard Error | 0.3932 | 0.2427 | 0.2409 | 0.3575 | 0.1751 | 0.0852 | 0.1973 | 2.7695 |

t Stat | 26.2256 | 32.2865 | 16.8234 | 16.1686 | 14.7528 | 19.3773 | 28.0942 | 18.0760 |

P-value | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |

Lower 95% | 9.5406 | 7.3589 | 3.5804 | 5.0784 | 2.2398 | 1.484 | 5.1563 | 44.6238 |

Upper 95% | 11.0845 | 8.3118 | 4.5264 | 6.4821 | 2.9274 | 1.8186 | 5.9311 | 55.4995 |

CP Variable | ||||||||

Coefficient | 0.8397 | 0.9501 | 0.8251 | 0.9345 | 0.9223 | 0.9259 | 0.8464 | 0.7598 |

Standard Error | 0.0043 | 0.0036 | 0.0032 | 0.0047 | 0.0041 | 0.0042 | 0.0043 | 0.0197 |

t Stat | 197.029 | 261.8558 | 255.0016 | 199.6117 | 222.9159 | 220.3481 | 198.7974 | 38.5915 |

P-value | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |

Lower 95% | 0.8313 | 0.9430 | 0.8188 | 0.9253 | 0.9142 | 0.9176 | 0.8380 | 0.7212 |

Upper 95% | 0.8480 | 0.9573 | 0.8315 | 0.9437 | 0.9305 | 0.9341 | 0.8548 | 0.7985 |

ANOVA | ||||||||

SS | 1,525,453 | 896,221 | 1,170,423 | 804,003 | 381,300 | 85,646 | 405,440 | 1,607,205 |

MS | 1,525,453 | 896,221 | 1,170,423 | 804,003 | 381,300 | 85,646 | 405,440 | 1,607,205 |

F | 38,820 | 68,568 | 65,026 | 39,845 | 49,691 | 48,553 | 39,520 | 1489 |

Significance F | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |

(b) In differences | ||||||||

Adidas | Anheuser | ASML | Essilor | Fresenius | Inditex | Safran | Volkswagen | |

Regression Statistics | ||||||||

Multiple R | 0.3695 | 0.2876 | 0.2176 | 0.1779 | 0.0904 | 0.1263 | 0.2184 | 0.2012 |

R Square | 0.1366 | 0.0827 | 0.0473 | 0.0316 | 0.0082 | 0.0159 | 0.0477 | 0.0405 |

Adjusted R Square | 0.1354 | 0.0815 | 0.0460 | 0.0303 | 0.0068 | 0.0146 | 0.0464 | 0.0384 |

Standard Error | 1.4169 | 0.7002 | 1.0338 | 0.7785 | 0.5253 | 0.2346 | 0.5400 | 4.9930 |

Observations | 730 | 730 | 730 | 730 | 730 | 730 | 730 | 470 |

Intercept | ||||||||

Coefficient | 0.2103 | 0.1147 | 0.1950 | 0.1214 | 0.0936 | 0.0346 | 0.0914 | 0.1243 |

Standard Error | 0.0527 | 0.0260 | 0.0384 | 0.0289 | 0.0195 | 0.0087 | 0.0201 | 0.2305 |

t Stat | 3.9943 | 4.4181 | 5.0737 | 4.2036 | 4.8017 | 3.9763 | 4.5579 | 0.5392 |

P-value | 0.0001 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0001 | 0,0000 | 0.5900 |

Lower 95% | 0.1069 | 0.0637 | 0.1195 | 0.0647 | 0.0553 | 0.0175 | 0.0520 | −0.3287 |

Upper 95% | 0.3137 | 0.1656 | 0.2704 | 0.1780 | 0.1318 | 0.0517 | 0.1308 | 0.5773 |

DCP Variable | ||||||||

Coefficient | 0.1700 | 0.1052 | 0.0884 | 0.0623 | 0.0356 | 0.0468 | 0.0813 | 0.1332 |

Standard Error | 0.0158 | 0.0130 | 0.0147 | 0.0128 | 0.0146 | 0.0136 | 0.0135 | 0.0300 |

t Stat | 10.7300 | 8.1030 | 6.0144 | 4.8774 | 2.4487 | 3.4341 | 6.0379 | 4.4432 |

P-value | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0146 | 0.0006 | 0.0000 | 0.0000 |

Lower 95% | 0.1389 | 0.0797 | 0.0595 | 0.0372 | 0.0071 | 0.0201 | 0.0548 | 0.0743 |

Upper 95% | 0.2011 | 0.1307 | 0.1172 | 0.0874 | 0.0642 | 0.0736 | 0.1077 | 0.1921 |

ANOVA | ||||||||

SS | 231.1325 | 32.1946 | 38.6599 | 14.4182 | 1.6545 | 0.6491 | 10.6316 | 492.1667 |

MS | 231.1325 | 32.1946 | 38.6599 | 14.4182 | 1.6545 | 0.6491 | 10.6316 | 492.1667 |

F | 115.1337 | 65.6589 | 36.1726 | 23.7889 | 5.9962 | 11.7931 | 36.456 | 19.7418 |

Significance F | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0146 | 0.0000 | 0.0000 | 0.0000 |

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## Share and Cite

**MDPI and ACS Style**

Almeida, J.; Gaspar, R.M.
Accuracy of European Stock Target Prices. *J. Risk Financial Manag.* **2021**, *14*, 443.
https://doi.org/10.3390/jrfm14090443

**AMA Style**

Almeida J, Gaspar RM.
Accuracy of European Stock Target Prices. *Journal of Risk and Financial Management*. 2021; 14(9):443.
https://doi.org/10.3390/jrfm14090443

**Chicago/Turabian Style**

Almeida, Joana, and Raquel M. Gaspar.
2021. "Accuracy of European Stock Target Prices" *Journal of Risk and Financial Management* 14, no. 9: 443.
https://doi.org/10.3390/jrfm14090443