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The impact of network connectivity on factor exposures, asset pricing and portfolio diversification
(2017)
This paper extends the classic factor-based asset pricing model by including network linkages in linear factor models. We assume that the network linkages are exogenously provided. This extension of the model allows a better understanding of the causes of systematic risk and shows that (i) network exposures act as an inflating factor for systematic exposure to common factors and (ii) the power of diversification is reduced by the presence of network connections. Moreover, we show that in the presence of network links a misspecified traditional linear factor model presents residuals that are correlated and heteroskedastic. We support our claims with an extensive simulation experiment.
Causality is a widely-used concept in theoretical and empirical economics. The recent financial economics literature has used Granger causality to detect the presence of contemporaneous links between financial institutions and, in turn, to obtain a network structure. Subsequent studies combined the estimated networks with traditional pricing or risk measurement models to improve their fit to empirical data. In this paper, we provide two contributions: we show how to use a linear factor model as a device for estimating a combination of several networks that monitor the links across variables from different viewpoints; and we demonstrate that Granger causality should be combined with quantile-based causality when the focus is on risk propagation. The empirical evidence supports the latter claim.
Software evolves. Developers and programmers manifest the needs that arise due to evolving software by making changes to the source code. While developers make such changes, reusing old code and rewriting existing code are inevitable. There are many challenges that a developer faces when manually reusing old code or rewriting existing code. Software tools and program transformation systems aid such reuse or rewriting of program source code. But there are significantly occuring development tasks that are hard to accomplish manually, where the current state-of-the-art tools are still not able to adequately automate these tasks. In this thesis, we discuss some of these unexplored challenges that a developer faces while reusing and rewriting program source code, the significance of such challenges, the existing automation support for these challenges and how we can improve upon them.
Modern software development relies on code reuse, which software developers
typically realize through hand-written abstractions, such as functions,
methods, or classes. However, such abstractions can be challenging to
develop and maintain. An alternative form of reuse is \emph{copy-paste-modify}, in which developers explicitly duplicate source code to adapt the duplicate for a new purpose. Copy-pasted code results in code clones, i.e., groups of code fragments that are similar to each other. Past research strongly suggests that copy-paste-modify is a popular technique among software developers. In this paper, we perform a small user study that shows that copy-paste-modify can be substantially faster to use than manual abstraction.
One might propose that software developers should forego hand-written abstractions in favour of copying and pasting. However, empirical evidence also shows that copy-paste-modify complicates software maintenance and increases the frequency of bugs. Furthermore, the developers in an informal poll we conducted strongly preferred to read code written using abstractions. To address the concern around copy-paste-modify, we propose a tool that merges similar pieces of code and automatically creates suitable abstractions. Our tool allows developers to get the best of both worlds: easy reuse together with custom abstractions. Because different kinds of abstractions may be beneficial in different contexts, our tool provides multiple abstraction mechanisms, which we selected based on a study of popular open-source repositories.
To demonstrate the feasibility of our approach, we have designed and implemented a prototype merging tool for C++ and evaluated our tool on a number of clones exhibiting some variation, i.e near clones, in popular Open Source packages. We observed that maintainers find our algorithmically created abstractions to be largely preferable to existing duplicated code. Rewriting existing code can be considered as a form of program transformation, where a program in one form is transformed into a program in another form. One significant form of program transformation is data representation migration that involves changing the type of a particular data structure, and then updating all of the operations that has a control or data dependence on that data structure according to the new type. Changing the data representation can provide benefits such as improving efficiency and improving the quality of the computed results. Performing such a transformation is challenging, because it requires applying data-type specific changes to code fragments that may be widely scattered throughout the source code connected by dataflow dependencies. Refactoring systems are typically sensitive to dataflow dependencies, but are not programmable with respect to the features of particular data types. Existing program transformation languages provide the needed flexibility, but do not concisely support reasoning about dataflow dependencies.
To address the needs of data representation migration, we propose a new approach to program transformation that relies on a notion of semantic dependency: every transformation step propagates the transformation process onward to code that somehow depends on the transformed code. Our approach provides a declarative transformation specification language, for expressing type-specific transformation rules. We further provide scoped rules, a mechanism for guiding rule application, and tags, a device for simple program analysis within our framework, to enable more powerful program transformations.
We have implemented a prototype transformation system based on these ideas for C and C++ code and evaluate it against three example specifications, including vectorization, transformation of integers to big integers, and transformation of array-of-structs data types to struct-of-arrays format. Our evaluation shows that our approach can improve program performance and the precision of the computed results, and that it scales to programs of at least 3700 lines.
This dissertation provides a comprehensive account of the grammar of relative clause extraposition in English. Based on a systematic review and evaluation of the empirical generalizations and theoretical approaches provided in the literature on generative grammar, it is shown that none of the previous theories is able to account for all the relevant facts. Among the most problematic data are the Principle C and scope effects of relative clause extraposition, cases with obligatory relative clauses, and relative clauses with elliptical NPs as antecedents.
I propose a new analysis of relative clause extraposition within the constraint-based, monostratal grammatical framework of Head-driven Phrase Structure Grammar (HPSG), enhanced with the semantic theory of Lexical Resource Semantics (LRS). Crucially, it is a general analysis of relative clause attachment, since both canonical and extraposed relative clauses are licensed by the same syntactic and semantic constraints. The basic assumption is that a relative clause can be adjoined to any phrase that contains a suitable antecedent of the relative pronoun. The semantic information that licenses the relative clause is introduced by the determiner of the antecedent NP. The techniques of underspecified semantics and the standard semantic representation language used by LRS make it possible to formulate constraints which yield the correct intersective interpretation of the relative clause (arbitrarily distant from its antecedent NP) and at the same time link the scope of the antecedent NP to the adjunction site of the relative clause.
In combination with the revised HPSG binding theory developed in this dissertation, the proposed analysis is able to capture the major properties of relative clause attachment within a unified and internally consistent monostratal constraint-based grammatical framework.
People who delay claiming Social Security receive higher lifelong benefits upon retirement. We survey individuals on their willingness to delay claiming later, if they could receive a lump sum in lieu of a higher annuity payment. Using a moment-matching approach, we calibrate a lifecycle model tracking observed claiming patterns under current rules and predict optimal claiming outcomes under the lump sum approach. Our model correctly predicts that early claimers under current rules would delay claiming most when offered actuarially fair lump sums, and for lump sums worth 87% as much, claiming ages would still be higher than at present.
The international diffusion of technology plays a key role in stimulating global growth and explaining co-movements of international equity returns. Existing empirical evidence suggests that countries are heterogeneous in their attitude toward innovation: Some countries rely more on technology adoption while other countries rely more on internal technology production. European countries that rely more on adoption are also typically characterized by lower fiscal policy exibility and higher labor market rigidity. We develop a two-country model – where both countries rely on R&D and adoption – to study the short-run and long-run effects of aggregate technology and adoption probability shocks on economic growth in the presence of the aforementioned asymmetries. Our framework suggests that an increase in the ability to adopt technology from abroad stimulates economic growth in the country that benefits from higher adoption rates but the beneficial effects also spread to the foreign country. Moreover, it helps explaining the differences in macro quantities and equity returns observed in the international data.
Asymmetric social norms
(2017)
Studies of cooperation in infinitely repeated matching games focus on homogeneous economies, where full cooperation is efficient and any defection is collectively sanctioned. Here we study heterogeneous economies where occasional defections are part of efficient play, and show how to support those outcomes through contagious punishments.
This paper sets the background for the Special Issue of the Journal of Empirical Finance on the European Sovereign Debt Crisis. It identifies the channel through which risks in the financial industry leaked into the public sector. It discusses the role of the bank rescues in igniting the sovereign debt crisis and reviews approaches to detect early warning signals to anticipate the buildup of crises. It concludes with a discussion of potential implications of sovereign distress for financial markets.
Low probability events are overweighted in the pricing of out-of the-money index puts and single stock calls. We find that this behavioral bias is strongly time-varying, linked to equity market sentiment, and higher moments of the risk-neutral density. An implied volatility (IV) sentiment measure that is jointly derived from index and single stock options explains investors' overweight of tail events the best. Our findings also suggest that IV-sentiment predicts equity markets reversals better than overweight of small probabilities itself. When employed in a trading strategy, IV-sentiment delivers economically significant results, which are more consistent than the ones produced by the market sentiment factor. The joint use of information from the single stock and index option markets seems to explain the forecasting power of IV-sentiment. Out-of-sample tests on reversal prediction show that our IV-sentiment measure adds value over and above traditional factors in the equity risk premium literature, especially as an equity-buying signal. This reversals prediction seems to improve time-series and cross-sectional momentum strategies.
BACKGROUND: The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements.
RESULTS: The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF).
CONCLUSION: Here we describe a new, efficient method for the analysis of sparse and heterogeneous time course data with high detection sensitivity and transparency. It is implemented as R package TTCA (transcript time course analysis) and can be installed from the Comprehensive R Archive Network, CRAN. The source code is provided with the Additional file 1.
Donald Trump’s foreign policy agenda has been characterized as unpredictable, unprecedented and – after a telling neologism of the president himself – unpresidented. In this blog post I will argue that the constituent parts of Trump’s foreign policy are all but new. What is new is their combination. Moreover, while Trump’s Jeffersonianism-Jacksonianism stands in stark contrast to the Wilsonianism-Hamiltonianism that Hillary Clinton embraced during her election campaign, it is only a partial departure from Barack Obama’s Jeffersonianism-Hamiltonianism.
Das Yin und Yang des Terrors
(2017)
Man stelle sich folgendes Szenario vor: Björn Höcke und Abū Bakr al-Baġdādī diskutieren bei Sandra Maischberger darüber, welche Konsequenzen Deutschland aus dem islamistischen Terrorismus ziehen sollte – und sie sind sich dabei einig.
Die Fiktion mag hanebüchen klingen. Nicht nur, weil der Möchtegern-Kalif des „Islamischen Staats“ (IS) unter keinen Umständen als Gast einer deutschen Talkshow in Erscheinung treten könnte – oder auch nur wollte. Sondern auch, weil der Möchtegern-Goebbels aus Thüringen sich stets als ritterlicher Gegner der Islamisten präsentiert, deren Denken und Handeln völlig unvereinbar sei mit dem Willen des hiesigen Volkes, den die Rechtspopulisten zu repräsentieren meinen. Gleicht man allerdings die (migrations-)politischen Willensbekundungen, die AFD und Co. als Antwort auf den islamistischen Terror bieten, ab mit den politischen Reaktionen, die sich der IS von Anschlägen in Europa erhofft, fällt es schwer, hier einen Widerspruch zu sehen. Vielmehr lässt ein solcher Abgleich die deutschen – und auch europäischen – Rechtspopulisten, für die Höcke hier stellvertretend steht, fast schon als heimliche Partner des IS erscheinen, deren Absichten sich bestens mit den Vorstellungen der IS-Strategen ergänzen.
Damit ist ein dialektisches Problem angesprochen, das dem Umgang mit dem Terrorismus stets inhärent ist. Immerhin gehört es zu den Binsenweisheiten der Konfliktforschung, dass terroristische Akteure Reaktionen provozieren wollen, die ihnen neue Handlungsmöglichkeiten eröffnen. Und dies wiederum verweist darauf, dass Politiker und Sicherheitsbehörden, aber auch die Medien dabei (ungewollt) zu Erfüllungsgehilfen werden können, wenn deren Reflexe dem terroristischen Kalkül entsprechen. Die Dynamik des Terrors speist sich eben nicht nur aus den Aktionen der Terroristen, sondern auch aus dem Verhalten ihrer Antagonisten und der Öffentlichkeit. Um jene Dynamik zu verstehen, reicht daher der Blick auf terroristische Strategien keineswegs aus. Vielmehr ist deren Effekt von den Reaktionen relationaler Akteure abhängig. Wer also dem Terrorismus das Wasser abgraben will, der muss – so paradox das klingt – auch Vorsicht gegenüber denen walten lassen, die Anstoß an ihm nehmen.
Diese Prämisse eröffnet eine kritische Perspektive auf Formen des Anti-Terrorismus. Denn demnach können Versuche, dem Terror die Stirn zu bieten, unbeabsichtigte (Neben-)Folgen mit sich bringen, welche die Dynamik des Terrors verstärken. Derartige backfire mechanisms sind es, die in diesem Beitrag problematisiert werden. Jedoch widmet er sich nicht der staatlichen Antiterrorpolitik, sondern erörtert, inwiefern das Zusammenspiel von Medien und Rechtspopulismus zur aktuellen Dynamik des islamistischen Terrorismus beiträgt. Diese Frage stellt sich vor dem Hintergrund, dass die jüngsten Anschläge in Europa eine virtuelle Form des Terrorismus darstellen, wo häufig Einzelpersonen im Namen des IS agieren, aber kaum bis gar nicht im Kontakt mit der Organisation standen. Es scheint daher die Vermutung nicht abwegig, dass co-konstitutive Faktoren – wie die Berichterstattung über den Terror und anti-muslimische Diskurse – eine nicht unbedeutende Rolle bei deren Radikalisierung und Aktivierung spielen.
We investigate the effect of overreaction in the fine art market. Using a unique sample of auction prices of modern prints, we define an overvalued (undervalued) print as a print that was bought for a price above (below) its high (low) auction pricing estimate. Based on the overreaction hypothesis, we predict that overvalued (undervalued) prints generate a negative (positive) excess return at a subsequent sale. Our empirical findings confirm our expectations. We report that prints that were bought for a price 10 percent above (below) its high (low) pricing estimate generate a positive (negative) excess return of 12 percent (17 percent) after controlling for the general price movement on the prints market. The price correction for overvalued (undervalued) prints is more pronounced during recessions (expansions).
In situ single particle analysis of ice particle residuals (IPRs) and out-of-cloud aerosol particles was conducted by means of laser ablation mass spectrometry during the intensive INUIT-JFJ/CLACE campaign at the high alpine research station Jungfraujoch (3580 m a.s.l.) in January–February 2013. During the 4-week campaign more than 70 000 out-of-cloud aerosol particles and 595 IPRs were analyzed covering a particle size diameter range from 100 nm to 3 µm. The IPRs were sampled during 273 h while the station was covered by mixed-phase clouds at ambient temperatures between −27 and −6 °C. The identification of particle types is based on laboratory studies of different types of biological, mineral and anthropogenic aerosol particles. The outcome of these laboratory studies was characteristic marker peaks for each investigated particle type. These marker peaks were applied to the field data. In the sampled IPRs we identified a larger number fraction of primary aerosol particles, like soil dust (13 ± 5 %) and minerals (11 ± 5 %), in comparison to out-of-cloud aerosol particles (2.4 ± 0.4 and 0.4 ± 0.1 %, respectively). Additionally, anthropogenic aerosol particles, such as particles from industrial emissions and lead-containing particles, were found to be more abundant in the IPRs than in the out-of-cloud aerosol. In the out-of-cloud aerosol we identified a large fraction of aged particles (31 ± 5 %), including organic material and secondary inorganics, whereas this particle type was much less abundant (2.7 ± 1.3 %) in the IPRs. In a selected subset of the data where a direct comparison between out-of-cloud aerosol particles and IPRs in air masses with similar origin was possible, a pronounced enhancement of biological particles was found in the IPRs.
BACKGROUND: Despite its impact on female health worldwide, no efforts have been made to depict the global architecture of ovarian cancer research and to understand the trends in the related literature. Hence, it was the objective of this study to assess the global scientific performance chronologically, geographically and in regards to economic benchmarks using bibliometric tools and density equalizing map projections.
METHODS: The NewQIS platform was employed to identify all ovarian cancer related articles published in the Web of Science since 1900. The items were analyzed regarding quantitative aspects (e.g. publication date, country of origin) and parameters describing the recognition of the work by the scientific community (e.g. citation rates).
RESULTS: 23,378 articles on ovarian cancer were analyzed. The USA had the highest activity of ovarian cancer research with a total of n = 9312 ovarian cancer-specific publications, followed by the UK (n = 1900), China (n = 1813), Germany (n = 1717) and Japan (n = 1673). Ovarian cancer-specific country h-index also showed a leading position of the USA with an h-index (HI) of 207, followed by the UK (HI = 122), Canada (HI = 99), Italy (HI = 97), Germany (HI = 84), and Japan (HI = 81). In the socio-economic analysis, the USA were ranked first with an average of 175.6 ovarian cancer-related publications per GDP per capita in 1000 US-$, followed by Italy with an index level of 46.85, the UK with 45.48, and Japan with 43.3. Overall, the USA and Western European nations, China and Japan constituted the scientific power players publishing the majority of highly cited ovarian cancer-related articles and dominated international collaborative efforts. African, Asian and South American countries played almost no visible role in the scientific community.
CONCLUSIONS: The quantity and scientific recognition of publications related to ovarian cancer are continuously increasing. The research endeavors in the field are concentrated in high-income countries with no involvement of lower-resource nations. Hence, worldwide collaborative efforts with the aim to exchange epidemiologic data, resources and knowledge have to be strengthened in the future to successfully alleviate the global burden related to ovarian cancer.
It is long known that Kasugamycin inhibits translation of canonical transcripts containing a 5’-UTR with a Shine Dalgarno (SD) motif, but not that of leaderless transcripts. To gain a global overview of the influence of Kasugamycin on translation efficiencies, the changes of the translatome of Escherichia coli induced by a 10 minutes Kasugamycin treatment were quantified. The effect of Kasugamycin differed widely, 102 transcripts were at least twofold more sensitive to Kasugamycin than average, and 137 transcripts were at least twofold more resistant, and there was a more than 100-fold difference between the most resistant and the most sensitive transcript. The 5’-ends of 19 transcripts were determined from treated and untreated cultures, but Kasugamycin resistance did neither correlate with the presence or absence of a SD motif, nor with differences in 5’-UTR lengths or GC content. RNA Structure Logos were generated for the 102 Kasugamycin-sensitive and for the 137 resistant transcripts. For both groups a short Shine Dalgarno (SD) motif was retrieved, but no specific motifs associated with resistance or sensitivity could be found. Notably, this was also true for the region -3 to -1 upstream of the start codon and the presence of an extended SD motif, which had been proposed to result in Kasugamycin resistance. Comparison of the translatome results with the database RegulonDB showed that the transcript with the highest resistance was leaderless, but no further leaderless transcripts were among the resistant transcripts. Unexpectedly, it was found that translational coupling might be a novel feature that is associated with Kasugamycin resistance. Taken together, Kasugamycin has a profound effect on translational efficiencies of E. coli transcripts, but the mechanism of action is different than previously described.
Pulmonary hypertension (PH) is characterized by the increase of the mean pulmonary arterial pressure in the lung circulation. Despite the large number of experimental and clinical studies conducted on pulmonary hypertension, there is no comprehensive work that analyzed the global research activity on PH so far. We retrieved the bibliometric data of the publications on pulmonary hypertension for two periods from the Web of science database. Here, we set the first investigation period from 1900 to 2007 (t1) due to the cited half life of articles and the relating difficulties to interpret the citation parameters. The second evaluation period (t2) covers the time interval from 2008 onwards including the year 2015. The data were analyzed and processed to density-equalizing maps using the NewQIS platform. A total number of 18,986 publications were identified in t1 that come from 85 countries. The US published the highest number of publications (n = 7,290), followed by the UK, Germany, Japan and France. In t2 19,676 items could be found worked out by 130 countries. The raking started just the same with the USA as most publishing nation with 7,127 publications on PH, followed by the UK and Germany. Japan fell back on 6th place, whereas China came into view on the 5th position. Analyzing the average citation rate as a parameter for research quality, Mexico reached the highest value in t1 and Ireland in t2. While, the country specific h-index underlined the leading position of the US research in both evaluation periods again. The average number of international collaboration items was expanding from none in 1978 to 530 items in 2015 with the USA as the country with the highest number of collaboration articles. The present study is the first large scale density-equalizing mapping and scientometric analysis of global PH research activity. Our data draw a sketch of the global research architecture in this field, indicating a need for specific research programs in countries with a lower human development index.