City University London

About: City University London is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 5735 authors who have published 17285 publications receiving 453290 citations.

Behavioral Finance: Quo Vadis?

Abstract: Behavioral finance endeavors to bridge the gap between finance and psychology. Now an established field, behavioral finance studies investor decision processes which in turn shed light on anomalies, i.e., departures from neoclassical finance theory. This paper is the summary of a panel discussion. It begins by reviewing the foundations of finance and it ends with a discussion of the future of behavioral finance and a self-critique. We describe the move from the standard view that financial decision making is rational to a behavioral approach based on judgmental heuristics, biases, mental frames, and new theories of choice under risk. A new class of asset pricing models, which adds behavioral elements to the standard framework, is proposed.

Anticipating Cryptocurrency Prices Using Machine Learning

Abstract: Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that nontrivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market.

Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group

Abstract: Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18–75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted “brain age” and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen’s d = 0.14, 95% CI: 0.08–0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.

Predicting uptake of a routine cervical smear test: A comparison of the health belief model and the theory of planned behaviour

Abstract: This paper reports on a study carried out to identify predictors of uptake of cervical screening among 142 women (59% response rate) in inner London Two social cognition models were used: The Health Belief Model (HBM; Becker, 1974) and the Theory of Planned Behaviour (TPB; Ajzen, 1991) and in addition anticipated affect following non-attendance for screening was assessed The TPB emerged as by far the superior model for predicting screening intentions, explaining 51% of the variance in comparison with only 4% explained by the HBM variables However, neither model was able to predict a significant amount of variance in uptake of screening three months later Possible reasons for the poor prediction of this type of behaviour are discussed