What drives European spinoff value effects? : impact of corporate governance, information asymmetry, and investor irrationality on firm values

The thesis explores the magnitude and determinants of spinoff value effects using robust methodologies and different theoretical perspectives. From a sample of 170 European spinoffs in the period 1987-2005, I find that spinoff announcement returns are significantly positive while the long-run shareholder value performance of postspinoff firms is insignificant when the cross-sectional return dependence problem is controlled. This is consistent with market efficiency overall in relation to spinoffs. However, this overall efficiency may conceal irrational investor behaviour towards certain types of spinoffs. Assuming investor irrationality, I examine whether investor sentiment affects spinoff wealth effects and spinoff decisions. I use four different proxies to measure investor demand for corporate focus and glamour stocks, and observe a positive association between these proxies and spinoff announcement returns. In addition, I find that offspring, born of spinoffs to cater to investor demand for glamour stocks, significantly underperform various benchmarks including the performance of less glamourous offspring. An improvement in operating efficiency of post-spinoff firms may not be realised if post-spinoff firms have weak corporate governance and agency conflicts are not mitigated. I investigate this issue by examining changes of corporate governance mechanisms around spinoffs. I observe that spinoff firms with a controlling family shareholder have higher announcement stock returns but lower post-spinoff performance than others. Moreover, controlling family shareholders generally reduce their stock ownership in post-spinoff firms, indicating that they may undertake spinoffs to reshuffle their wealth portfolios. I also find that board monitoring and takeover threats for post-spinoff firms positively affect the long-run performance of post-spinoff firms. This thesis further inspects the relationship between information asymmetry between the pre-spinoff parent and the stock market, and spinoff value effects. By employing four different information asymmetry proxies, I find no evidence that a spinoff resolves information asymmetry problems. In contrast, I document some evidence that the information asymmetry problem may be exacerbated following spinoffs when the liquidity of post-spinoff firms is decreased. Taken together, my findings suggest that managers and shareholders should assess the desirability of a spinoff more carefully and take investor irrationality into account. This is the first study that focuses on European spinoffs over a long period and tests various theories concerning the sources of value. It also provides the first time empirical evidence on the validity of the catering theory in the context of spinoffs.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

What drives European spinoff value effects? Impact of corporate governance, information asymmetry, and investor irrationality on firm values

The thesis explores the magnitude and determinants of spinoff value effects using robust methodologies and different theoretical perspectives. From a sample of 170 European spinoffs in the period 1987-2005, I find that spinoff announcement returns are significantly positive while the long-run shareholder value performance of postspinoff firms is insignificant when the cross-sectional return dependence problem is controlled. This is consistent with market efficiency overall in relation to spinoffs. However, this overall efficiency may conceal irrational investor behaviour towards certain types of spinoffs. Assuming investor irrationality, I examine whether investor sentiment affects spinoff wealth effects and spinoff decisions. I use four different proxies to measure investor demand for corporate focus and glamour stocks, and observe a positive association between these proxies and spinoff announcement returns. In addition, I find that offspring, born of spinoffs to cater to investor demand for glamour stocks, significantly underperform various benchmarks including the performance of less glamourous offspring. An improvement in operating efficiency of post-spinoff firms may not be realised if post-spinoff firms have weak corporate governance and agency conflicts are not mitigated. I investigate this issue by examining changes of corporate governance mechanisms around spinoffs. I observe that spinoff firms with a controlling family shareholder have higher announcement stock returns but lower post-spinoff performance than others. Moreover, controlling family shareholders generally reduce their stock ownership in post-spinoff firms, indicating that they may undertake spinoffs to reshuffle their wealth portfolios. I also find that board monitoring and takeover threats for post-spinoff firms positively affect the long-run performance of post-spinoff firms. This thesis further inspects the relationship between information asymmetry between the pre-spinoff parent and the stock market, and spinoff value effects. By employing four different information asymmetry proxies, I find no evidence that a spinoff resolves information asymmetry problems. In contrast, I document some evidence that the information asymmetry problem may be exacerbated following spinoffs when the liquidity of post-spinoff firms is decreased. Taken together, my findings suggest that managers and shareholders should assess the desirability of a spinoff more carefully and take investor irrationality into account. This is the first study that focuses on European spinoffs over a long period and tests various theories concerning the sources of value. It also provides the first time empirical evidence on the validity of the catering theory in the context of spinoffs

Radiomics Response Signature for Identification of Metastatic Colorectal Cancer Sensitive to Therapies Targeting EGFR Pathway.

BACKGROUND: The authors sought to forecast survival and enhance treatment decisions for patients with liver metastatic colorectal cancer by using on-treatment radiomics signature to predict tumor sensitiveness to irinotecan, 5-fluorouracil, and leucovorin (FOLFIRI) alone (F) or in combination with cetuximab (FC). METHODS: We retrospectively analyzed 667 metastatic colorectal cancer patients treated with F or FC. Computed tomography quality was classified as high (HQ) or standard (SD). Four datasets were created using the nomenclature (treatment) - (quality). Patients were randomly assigned (2:1) to training or validation sets: FCHQ: 78:38, FCSD: 124:62, FHQ: 78:51, FSD: 158:78. Four tumor-imaging biomarkers measured quantitative radiomics changes between standard of care computed tomography scans at baseline and 8 weeks. Using machine learning, the performance of the signature to classify tumors as treatment sensitive or treatment insensitive was trained and validated using receiver operating characteristic (ROC) curves. Hazard ratio and Cox regression models evaluated association with overall survival (OS). RESULTS: The signature (area under the ROC curve [95% confidence interval (CI)]) used temporal decrease in tumor spatial heterogeneity plus boundary infiltration to successfully predict sensitivity to antiepidermal growth factor receptor therapy (FCHQ: 0.80 [95% CI = 0.69 to 0.94], FCSD: 0.72 [95% CI = 0.59 to 0.83]) but failed with chemotherapy (FHQ: 0.59 [95% CI = 0.44 to 0.72], FSD: 0.55 [95% CI = 0.43 to 0.66]). In cetuximab-containing sets, radiomics signature outperformed existing biomarkers (KRAS-mutational status, and tumor shrinkage by RECIST 1.1) for detection of treatment sensitivity and was strongly associated with OS (two-sided P < .005). CONCLUSIONS: Radiomics response signature can serve as an intermediate surrogate marker of OS. The signature outperformed known biomarkers in providing an early prediction of treatment sensitivity and could be used to guide cetuximab treatment continuation decisions

Machine learning-based identification of contrast-enhancement phase of computed tomography scans.

Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew's correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement

Flow chart of cross-validation dataset.

Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew’s correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.</div

This figure shows the precision-recall curves (PRCs) for each supervised learning model trained with Input C.

(A), (B), (C), (D) and (E) display the PRCs for the logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and gradient-boosted decision tree (GBDT) models, respectively. For each model, the graphs evaluated using a One vs. Rest (OvR) approach are shown on the top and a One vs. One (OvO) approach are shown on the bottom (note that only the OvO PRCs for consecutive phases are shown). See S2 Fig for more details on their interpretation. (PDF)</p

The 95% confidence intervals of accuracy and MCC of the supervised learning models for the main dataset.

The 95% confidence intervals of accuracy and MCC of the supervised learning models for the main dataset.</p

This figure shows the precision-recall curves (PRCs) for each supervised learning model trained with Input B.

(A), (B), (C), (D) and (E) display the PRCs for the logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and gradient-boosted decision tree (GBDT) models, respectively. For each model, the graphs evaluated using a One vs. Rest (OvR) approach are shown on the top and a One vs. One (OvO) approach are shown on the bottom (note that only the OvO PRCs for consecutive phases are shown). See S2 Fig for more details on their interpretation. (PDF)</p