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Corporate governance reforms are increasingly promoted as a method of materially improving social and environmental (ESG) outcomes. This chapter clears up the conceptual confusion about what counts as an action taken primarily for ESG purposes, then considers the incentives, resources, and market constraints that compel corporate actors to avoid unnecessary expenses or lower-value investments. The empirical evidence suggesting corporations are unlikely to voluntarily pursue ESG includes: (1) the revealed preferences of managers, particularly those that emphasize their ESG commitments; (2) the impact of ESG-friendly governance practices on corporate outcomes; and (3) the actual outcomes generated by giving ESG-friendly constituencies (such as socially responsible investors or employees) more power in corporate governance arrangements.
Serotonin-reuptake inhibitors (SRIs) are first-line pharmacotherapy for the treatment of body dysmorphic disorder (BDD), a common and severe disorder. However, prior research has not focused on or identified definitive predictors of SRI treatment outcomes. Leveraging precision medicine techniques such as machine learning can facilitate the prediction of treatment outcomes.
Methods
The study used 10-fold cross-validation support vector machine (SVM) learning models to predict three treatment outcomes (i.e. response, partial remission, and full remission) for 97 patients with BDD receiving up to 14-weeks of open-label treatment with the SRI escitalopram. SVM models used baseline clinical and demographic variables as predictors. Feature importance analyses complemented traditional SVM modeling to identify which variables most successfully predicted treatment response.
Results
SVM models indicated acceptable classification performance for predicting treatment response with an area under the curve (AUC) of 0.77 (sensitivity = 0.77 and specificity = 0.63), partial remission with an AUC of 0.75 (sensitivity = 0.67 and specificity = 0.73), and full remission with an AUC of 0.79 (sensitivity = 0.70 and specificity = 0.79). Feature importance analyses supported constructs such as better quality of life and less severe depression, general psychopathology symptoms, and hopelessness as more predictive of better treatment outcome; demographic variables were least predictive.
Conclusions
The current study is the first to demonstrate that machine learning algorithms can successfully predict treatment outcomes for pharmacotherapy for BDD. Consistent with precision medicine initiatives in psychiatry, the current study provides a foundation for personalized pharmacotherapy strategies for patients with BDD.
The industry of Sustainable and Responsible Investments funds started booming after the financial crisis. The loss of faith in conventional finance made people look for 'other' values for their savings. A need for enhanced transparency popped up and suddenly most players in the financial system offered such products. Or did they? It became difficult not to get lost in an alphabet soup of 'sustainability' related acronyms, and it became more difficult not to get confused and make a valid choice. After different players and even government actors started launching such labels, comparability issues cropped up, adding a further layer of complexity for retail investors. French, Luxembourgish, German, Austrian labels came to the fore with no European framework to facilitate the comparison, except for a methodology based on transparency standards developed by the European Forum for Responsible Investment - Eurosif. The lack of generally accepted definitions for these sustainable funds did not help resolve the fragmentation of standards which abounded in the industry. European Institutions stepped in to facilitate sustainable finance and put some solutions on the table.
This chapter highlights the potential for national, international and EU stewardship developments to bring a ‘public’ coloration into investor-led governance. Departing from previous monolithic views that couch shareholder stewardship as a self-regulating, dis-embedded market mechanism solely protecting and enhancing shareholder primacy, the chapter applies a neo-Polanyian analytical framework and identifies shareholder stewardship as a policy counter-movement that operationalises socially responsible investing and environmental, social and governance investing through shareholder engagement. However, for current stewardship policies to engender fundamental behavioural changes in investment practices, some systematic regulatory intervention which will not result from bottom-up forces and market demand for investor-led norms is necessary. Ways to promote a strong sustainability approach to stewardship include the imposition of regulatory duties and mandatory disclosure regimes. The possibilities for regulatory alternatives may remain fluid, I argue, but it is important for the means of shareholder stewardship to meet its ends.