A Combined Approach for Extracting Financial Instrument-Specific Investor Sentiment from Weblogs

Investor sentiment about future returns of financial instruments is a highly relevant information source for investment managers and other stakeholders in the financial industry. Investor sentiments are abundant in financial blog texts. Making use of these sentiments constitutes a massive information management challenge when considering the millions of blog articles with everchanging and growing amounts of information that need to be acquired and interpreted. We propose a novel approach for investor sentiment extraction from blogs by combining machine-learning on the document-level and knowledgebased information extraction on the sentence-level. The proposed artifact is a financial instrument-specific investor sentiment extraction method, which we apply to a set of blog articles. The evaluation suggests that the combined approach achieves a higher precision compared to a standalone knowledge-based approach

Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions

In many numerical simulations stochastic gradient descent (SGD) type optimization methods perform very effectively in the training of deep neural networks (DNNs) but till this day it remains an open problem of research to provide a mathematical convergence analysis which rigorously explains the success of SGD type optimization methods in the training of DNNs. In this work we study SGD type optimization methods in the training of fully-connected feedforward DNNs with rectified linear unit (ReLU) activation. We first establish general regularity properties for the risk functions and their generalized gradient functions appearing in the training of such DNNs and, thereafter, we investigate the plain vanilla SGD optimization method in the training of such DNNs under the assumption that the target function under consideration is a constant function. Specifically, we prove under the assumption that the learning rates (the step sizes of the SGD optimization method) are sufficiently small but not $L^1$-summable and under the assumption that the target function is a constant function that the expectation of the riskof the considered SGD process converges in the training of such DNNs to zero as the number of SGD steps increases to infinity.Comment: 71 pages, 5 figures, 2 tables, 4 Python source codes. To appear in Electronic Research Archiv

PREDICTING THE DURATION OF SURGERIES TO IMPROVE PROCESS EFFICIENCY IN HOSPITALS

Predicting the duration of surgeries is an important task because of the many dependencies between surgery processes and the hospital processes within other departments. Thus, accurate predictions allow for better coordinating patient processes throughout the hospital. Prior data-driven research provides evidence for accurate predictions of surgery durations enhancing the efficiency of surgery schedules. However, the current prediction models require large sets of features, which make their adoption more intricate. Moreover, prediction models focus on the surgery department and neglect potential effects on other departments. We use a unique dataset of about 17,000 surgeries to study how particular features and machine learning algorithms affect the prediction accuracy of major surgery steps. The prediction models that we study require few features and are easy to apply. The empirical findings can be useful for the design of surgery scheduling systems

Prevalence of depression and anxiety in patients with cystic fibrosis and parent caregivers: results of The International Depression Epidemiological Study across nine countries

Background Individuals with chronic diseases and parent caregivers are at increased risk for symptoms of depression and anxiety. Prevalence of psychological symptoms was evaluated in adolescents and adults with cystic fibrosis (CF) and parent caregivers across nine countries. Methods Patients with CF, ages 12 years and older, and caregivers of children with CF, birth to18 years of age, completed measures of depression and anxiety across 154 CF centres in Europe and the USA. Psychological symptoms were compared across countries using χ2. Logistic regression examined extent of comorbid symptoms, predictors of depression and anxiety, and concordance between parent and adolescent symptomatology. Results Psychological symptoms were reported by 6088 patients with CF and 4102 parents. Elevated symptoms of depression were found in 10% of adolescents, 19% of adults, 37% of mothers and 31% of fathers. Elevations in anxiety were found in 22% of adolescents, 32% of adults, 48% of mothers and 36% of fathers. Overall, elevations were 2–3 times those of community samples. Participants reporting elevated anxiety were more likely to report depression (ORs: adolescents=14.97, adults=13.64, mothers=15.52, fathers=9.20). Significant differences in reports of depression and anxiety were found by patient age and parent respondent. Concordance between 1122 parent–teen dyads indicated that adolescents whose parents reported depression were more likely to be elevated on depression (OR=2.32). Similarly, adolescents whose parents reported anxiety were more likely to score in the elevated range on the anxiety measure (OR=2.22). Conclusions Symptoms of depression and anxiety were elevated in both patients with CF and parents across several European countries and the USA. Annual screening of psychological symptoms is recommended for both patients and parents

ONLINE MEDIA SENTIMENT: UNDERSTANDING MACHINE LEARNING-BASED CLASSIFIERS

Online media is an important source for sentiments exposed by individuals on goods, services, organizations, and other objects of interest. While firms can benefit from using these sentiments for decisionmaking, the classification of sentiments is difficult because of volume, velocity, and variety. Machine learning is an effective technique for sentiment classification, which neither requires formalized knowledge about the domain nor the language used. Although the literature provides a rich body of classification methods, system designers and researchers still face the problem of reasonably selecting designs. In this paper, we seek to contribute to the understanding of machine learning for sentiment classification. We report an experimental study that tests the effects of three design factors, i.e., text representation, feature weighting, and machine learning algorithm, on accuracy. The findings can be useful for empirically informed classifier design