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Predictability and the cross-section of expected returns: a challenge for asset pricing models
(2020)
Many modern macro finance models imply that excess returns on arbitrary assets are predictable via the price-dividend ratio and the variance risk premium of the aggregate stock market. We propose a simple empirical test for the ability of such a model to explain the cross-section of expected returns by sorting stocks based on the sensitivity of expected returns to these quantities. Models with only one uncertainty-related state variable, like the habit model or the long-run risks model, cannot pass this test. However, even extensions with more state variables mostly fail. We derive criteria models have to satisfy to produce expected return patterns in line with the data and discuss various examples.
The possibility to investigate the impact of news on stock prices has observed a strong evolution thanks to the recent use of natural language processing (NLP) in finance and economics. In this paper, we investigate COVID-19 news, elaborated with the ”Natural Language Toolkit” that uses machine learning models to extract the news’ sentiment. We consider the period from January till June 2020 and analyze 203,886 online articles that deal with the pandemic and that were published on three platforms: MarketWatch.com, Reuters.com and NYtimes.com. Our findings show that there is a significant and positive relationship between sentiment score and market returns. This result indicates that an increase (decrease) in the sentiment score implies a rise in positive (negative) news and corresponds to positive (negative) market returns. We also find that the variance of the sentiments and the volume of the news sources for Reuters and MarketWatch, respectively, are negatively associated to market returns indicating that an increase of the uncertainty of the sentiment and an increase in the arrival of news have an adverse impact on the stock market.
This article discusses the counterpart of interactive machine learning, i.e., human learning while being in the loop in a human-machine collaboration. For such cases we propose the use of a Contradiction Matrix to assess the overlap and the contradictions of human and machine predictions. We show in a small-scaled user study with experts in the area of pneumology (1) that machine-learning based systems can classify X-rays with respect to diseases with a meaningful accuracy, (2) humans partly use contradictions to reconsider their initial diagnosis, and (3) that this leads to a higher overlap between human and machine diagnoses at the end of the collaboration situation. We argue that disclosure of information on diagnosis uncertainty can be beneficial to make the human expert reconsider her or his initial assessment which may ultimately result in a deliberate agreement. In the light of the observations from our project, it becomes apparent that collaborative learning in such a human-in-the-loop scenario could lead to mutual benefits for both human learning and interactive machine learning. Bearing the differences in reasoning and learning processes of humans and intelligent systems in mind, we argue that interdisciplinary research teams have the best chances at tackling this undertaking and generating valuable insights.
Using experimental data from a comprehensive field study, we explore the causal effects of algorithmic discrimination on economic efficiency and social welfare. We harness economic, game-theoretic, and state-of-the-art machine learning concepts allowing us to overcome the central challenge of missing counterfactuals, which generally impedes assessing economic downstream consequences of algorithmic discrimination. This way, we are able to precisely quantify downstream efficiency and welfare ramifications, which provides us a unique opportunity to assess whether the introduction of an AI system is actually desirable. Our results highlight that AI systems’ capabilities in enhancing welfare critically depends on the degree of inherent algorithmic biases. While an unbiased system in our setting outperforms humans and creates substantial welfare gains, the positive impact steadily decreases and ultimately reverses the more biased an AI system becomes. We show that this relation is particularly concerning in selective-labels environments, i.e., settings where outcomes are only observed if decision-makers take a particular action so that the data is selectively labeled, because commonly used technical performance metrics like the precision measure are prone to be deceptive. Finally, our results depict that continued learning, by creating feedback loops, can remedy algorithmic discrimination and associated negative effects over time.
In this paper we adopt the Hamiltonian Monte Carlo (HMC) estimator for DSGE models by implementing it into a state-of-the-art, freely available high-performance software package. We estimate a small scale textbook New-Keynesian model and the Smets-Wouters model on US data. Our results and sampling diagnostics confirm the parameter estimates available in existing literature. In addition we combine the HMC framework with the Sequential Monte Carlo (SMC) algorithm which permits the estimation of DSGE models with ill-behaved posterior densities.
Background: Posttraumatic stress disorder (PTSD) is one of the psychopathological consequences of sexual and/or physical abuse. The economic burden is assumed to be high, whereas health-related quality of life and education is negatively affected. This study aims to determine health care costs, health-related quality of life, and educational interruption in adolescents and young adults with PTSD after sexual and/or physical abuse in Germany.
Methods: This analysis used data of 87 participants aged 14–21 years of a randomized controlled trial. Health care utilization, health-related quality of life (EQ-5D-5L), sick leave days, productivity, and delay or failure to achieve educational aims were assessed. Health care costs from a payer perspective were calculated using unit costs for the year 2014.
Results: Mean health care costs for a six-month period were 5,243€ (SE 868€). In particular, costs of inpatient stays in psychiatric hospitals, general hospitals and rehabilitation as well as child welfare institutions were high. In addition, health-related quality of life was lower due to anxiety/depression, resulting in a mean EQ-5D index and EQ-VAS score of 0.70 and 61.0, respectively. Furthermore, participants reported on average 27 sick leave days, a productivity loss of 61%, and a delay in education attainment as well as having been unable to achieve educational aims.
Conclusion: PTSD in adolescents and young adults is associated with a high economic burden. Health-related quality of life was substantially reduced. Furthermore, delay and productivity losses in education were observed.
Clinical Trial Registration: German Clinical Trials Register identifier: DRKS00004787; date of registration: 18th March 2013; https://www.drks.de.
Previous studies in developing Xenopus and zebrafish reported that the phosphate transporter slc20a1a is expressed in pronephric kidneys. The recent identification of SLC20A1 as a monoallelic candidate gene for cloacal exstrophy further suggests its involvement in the urinary tract and urorectal development. However, little is known of the functional role of SLC20A1 in urinary tract development. Here, we investigated this using morpholino oligonucleotide knockdown of the zebrafish ortholog slc20a1a. This caused kidney cysts and malformations of the cloaca. Moreover, in morphants we demonstrated dysfunctional voiding and hindgut opening defects mimicking imperforate anus in human cloacal exstrophy. Furthermore, we performed immunohistochemistry of an unaffected 6-week-old human embryo and detected SLC20A1 in the urinary tract and the abdominal midline, structures implicated in the pathogenesis of cloacal exstrophy. Additionally, we resequenced SLC20A1 in 690 individuals with bladder exstrophy-epispadias complex (BEEC) including 84 individuals with cloacal exstrophy. We identified two additional monoallelic de novo variants. One was identified in a case-parent trio with classic bladder exstrophy, and one additional novel de novo variant was detected in an affected mother who transmitted this variant to her affected son. To study the potential cellular impact of SLC20A1 variants, we expressed them in HEK293 cells. Here, phosphate transport was not compromised, suggesting that it is not a disease mechanism. However, there was a tendency for lower levels of cleaved caspase-3, perhaps implicating apoptosis pathways in the disease. Our results suggest SLC20A1 is involved in urinary tract and urorectal development and implicate SLC20A1 as a disease-gene for BEEC.
We estimate the feeddown contributions from decays of unstable A=4 and A=5 nuclei to the final yields of protons, deuterons, tritons, 3He, and 4He produced in relativistic heavy-ion collisions at sNN>2.4 GeV, using the statistical model. The feeddown contribution effects do not exceed 5% at LHC and top RHIC energies due to the large penalty factors involved, but are substantial at intermediate collision energies. We observe large feeddown contributions for tritons, 3He, and 4He at sNN≲10 GeV, where they may account for as much as 70% of the final yield at the lower end of the collision energies considered. Sizable (>10%) effects for deuteron yields are observed at sNN≲4 GeV. The results suggest that the excited nuclei feeddown cannot be neglected in the ongoing and future analysis of light nuclei production at intermediate collision energies, including HADES and CBM experiments at FAIR, NICA at JINR, RHIC beam energy scan and fixed-target programmes, and NA61/SHINE at CERN. We further show that the freeze-out curve in the T-μB plane itself is affected significantly by the light nuclei at high baryochemical potential.
In this paper, we discuss the damping of density oscillations in dense nuclear matter in the temperature range relevant to neutron star mergers. This damping is due to bulk viscosity arising from the weak interaction “Urca” processes of neutron decay and electron capture. The nuclear matter is modelled in the relativistic density functional approach. The bulk viscosity reaches a resonant maximum close to the neutrino trapping temperature, then drops rapidly as temperature rises into the range where neutrinos are trapped in neutron stars. We investigate the bulk viscous dissipation timescales in a post-merger object and identify regimes where these timescales are as short as the characteristic timescale ∼10 ms, and, therefore, might affect the evolution of the post-merger object. Our analysis indicates that bulk viscous damping would be important at not too high temperatures of the order of a few MeV and densities up to a few times saturation density.