648 research outputs found
Environmental, Social and Governance Exchange-Traded Funds: Do They Attract More Cash Flows Than Their Conventional Counterparts?
ํ์๋
ผ๋ฌธ (์์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ํ๋๊ณผ์ ๊ธฐ์ ๊ฒฝ์ยท๊ฒฝ์ ยท์ ์ฑ
์ ๊ณต, 2020. 8. Lee Jeong-Dong.The integration of environmental, social and governance (ESG) factors into the investing
process is underpinned by the growing popularity of sustainable development strategies
and global concerns regarding the environment. However, scholars are not in agreement
about whether ESG investing obliges an investor to sacrifice financial returns, does not
affect the performance of financial entities, or can improve it. Such investing can be done
using different financial instruments. This study deals with one of those instruments โ
exchange-traded funds or ETFs. These funds are one of the most prospective investment
vehicles because of their special properties. Moreover, ESG ETFs are being popularised,
and they demand attention from both practitioners and academia. However, existing
studies concerning ESG ETFs are rather scarce, and they focus on performance measured
by risk-return characteristics.
This paper addresses that gap in the literature and investigates a special financial issue
that has not been discovered in the case of ESG ETFs โ whether such funds can attract
more financial flow than their conventional counterparts โ which may reflect investors
demand for ESG ETFs. In particular, this study examines the relationship between the
inflows to such funds and their compliance with ESG criteria, using two types of
regression models. First, it exploits cross-sectional data on bond and equity ETFs traded
in the U.S., and it investigates the relationship between higher ESG scores and higher
inflows. Second, the study uses historical data on U.S. bond and equity ETFs in 2- and 3-
year periods, and it applies pooled OLS and mixed effects models to panel data samples
of ESG and non-ESG ETFs distinguished by a dummy variable.
The positive relationship between financial flows and ESG score was partially proved: a
statistically significant and positive coefficient of variables representing ESG scores was
observed in the case of the equity ETF model. However, its value was too modest to draw
any meaningful conclusions. In the model for bond ETFs, no such evidence was found.
This might be because of the small size of the sample and other methodological
weaknesses. On the contrary, in all specifications operating different samples of panel
data dummy variables representing ESG labelling had relatively high, positive and
statistically significant coefficients. To be precise, on average, compliance of ETFs with
ESG criteria may promise 2.1-3.5% of additional inflows. This is in line with other ESG
fund literature, e.g. studies dealing with mutual funds.
This result gives a strong market signal to ETF providers, other market participants and
policy makers; it contributes to the dilemma regarding the financial impact of ESG factors,
and it opens up many directions concerning financial properties and other specificities of
ESG ETFs. Several implications for the broader literature on exchange-traded funds also
are identified and discussed. At present, there are still many limitations to this type of
research. However, as more ESG ETFs emerge โ those limitations can be overcome. This
paper provides a strong background for further studies in this area, and it suggests possible
improvements for later studies.ESG ์์์ ํฌ์ ํ๋ก์ธ์ค ํตํฉ์ ์ง์๊ฐ๋ฅํ ๋ฐ์ ์ ๋ต ๋ฐ ํ๊ฒฝ ๋ฌธ์ ์ ๊ดํ
์ ์ธ๊ณ์ ๊ด์ฌ์ ์ฆ๋์ ํจ๊ป ๊ด์ฌ์ ๋ฐ๊ณ ์๋ค. ๊ทธ๋ฌ๋, ESG ํฌ์์ ๊ธ์ต ์์ต์
๊ด๊ณ ๋ฐ ๊ธ์ต ๊ธฐ์
์ ์ฑ๊ณผ ์ํฅ ๋ฑ์ ๋ํด์๋ ํฉ์๋ ๊ฒฐ๋ก ์ด ์กด์ฌํ์ง ์๋๋ค.
ESG ํฌ์๋ ๋ค์ํ ๊ธ์ต ์ํ์ ํตํด ์ํ๋ ์ ์์ผ๋, ๋ณธ ์ฐ๊ตฌ์์๋
์์ฅ์ง์ํ๋(ETF)๋ฅผ ์ค์ ์ ์ผ๋ก ์ดํด๋ณธ๋ค. ์์ฅ์ง์ํ๋๋ ๊ณ ์ ์ ํน์ฑ์ผ๋ก
์ธํด ๊ฐ์ฅ ์ ๋งํ ํฌ์ ์๋จ ์ค ํ๋๋ก ์ฃผ๋ชฉ๋ฐ๊ณ ์๋ค. ESG ETF ๋ ๋์คํ๋๊ณ
์์ผ๋ฉฐ ์ค๋ฌด์ ๋ฐ ํ๊ณ์ ๋์ ๊ด์ฌ์ ์ป๊ณ ์์ผ๋, ๊ด๋ จ ๊ธฐ์กด ์ฐ๊ตฌ๋ ๋๋ถ๋ถ
์ํ-์์ต ํน์ฑ์ ์ํด ์ถ์ ๋ ์ํ์ ์ฑ๊ณผ์ ์ด์ ์ ๋ง์ถ๊ณ ์๋ค.
๋ณธ ์ฐ๊ตฌ๋ ESG ETF ์ ๊ด๋ จํ์ฌ ๊ธฐ์กด์ ๋ฐ๊ฒฌ๋์ง ์์๋ ํน๋ณํ ๊ธ์ต ์ด์๋ฅผ
์ ์ํ์ฌ ๊ธฐ์กด ์ฐ๊ตฌ์ ํ๊ณ๋ฅผ ๋ณด์ํ๋ค. ์ด๋ ESG ETF ์ ๋ํ ํฌ์์๋ค์ ์์๋ฅผ
ํํํ์ฌ ๋ค๋ฅธ ์ ํต์ ์ธ ํฌ์ ์๋จ์ ๋๋นํ์ฌ ๋ ๋ง์ ๊ธ์ต ํ๋ฆ์ ์ ๋ํ๋ ESG
ETF ์ ํน์ฑ์ ๊ธฐ๋ฐํ๋ค. ๊ตฌ์ฒด์ ์ผ๋ก, ๋ณธ ์ฐ๊ตฌ๋ ๋ ๊ฐ์ง ์ ํ์ ํ๊ท๋ถ์์
์ฌ์ฉํ์ฌ ESG ๊ธฐ์ค์ ๋ฐ๋ฅธ ์๊ธ์ ์กฐ๋ฌ๊ณผ ์ ์
๊ฐ์ ๊ด๊ณ๋ฅผ ํ์ธํ๋ค. ๋จผ์ ,
๋ฏธ๊ตญ์์ ๊ฑฐ๋๋๋ ์ฑ๊ถ ๋ฐ ์ฃผ๊ฐ ETF ์ ์๋ฃ๋ฅผ ์กฐ์ฌํ์ฌ ๋์ ESG ์ ์์ ETF
๊ด๋ จ ํ๊ธ ํ๋ฆ ๊ฐ์ ๊ด๊ณ๋ฅผ ์กฐ์ฌํ๋ค. ์ด์ด, ๋ฏธ๊ตญ์ ์ฑ๊ถ ๋ฐ ์ฃผ๊ฐ ETF ์ ๊ดํ
2~3 ๋
์ ๊ณผ๊ฑฐ ๋ฐ์ดํฐ๋ฅผ ํ์ฉํ์ฌ ESG ์ ๋น-ESG ๊ฐ ํจ๊ณผ์ ์ฐจ์ด๋ฅผ
์ต์์์น๋ฒ(OLS) ๋ฐ ํผํฉ ํจ๊ณผ ๋ชจํ์ผ๋ก ์ถ์ ํ๋ค.
๋ถ์ ๊ฒฐ๊ณผ, ๊ธ์ต ํ๋ฆ๊ณผ ESG ์ ์ ์ฌ์ด์๋ ์์ ๊ด๊ณ๊ฐ ๋ถ๋ถ์ ์ผ๋ก ์กด์ฌํ๋
๊ฒ์ผ๋ก ๋ํ๋ฌ์ผ๋ ๊ฐํ ์๊ด๊ด๊ณ๋ฅผ ์ฆ๋ช
ํ๊ธฐ์๋ ๋ถ์กฑํ์๋ค. ๊ฒฐํฉ ETF ๊ฐ ์๋
๋ชจํ์์๋ ์ํ ์์ ๋ถ์กฑ ๋ฐ ๋ฐฉ๋ฒ๋ก ์ ํ๊ณ๋ก ์ธํด ์ ์ํ ๊ด๊ณ๊ฐ ๋ํ๋์ง
์์๋ค. ๋ฐ๋ฉด, ESG ๋ผ๋ฒจ๋ง์ ๋ํ๋ด๋ ๋๋ฏธ ๋ณ์๋ฅผ ์ฌ์ฉํ ๋ถ์์์๋ ํต๊ณ์ ์ผ๋ก
101
์ ์ํ ์์ ๊ด๊ณ๊ฐ ๋ํ๋ฌ๋ค. ๊ตฌ์ฒด์ ์ผ๋ก๋, ESG ๊ธฐ์ค์ ๋ฐ๋ฅธ ETF ์ ํ๊ท ์๊ธ
์กฐ๊ฐ์ ์ถ๊ฐ ์ ์
์ 2.1 ~3.5%๋ฅผ ์ฐจ์งํ๋ ๊ฒ์ผ๋ก ๋ํ๋ฌ๋๋ฐ, ์ด๋ ๋ฎค์ถ์ผ ํ๋์
๊ด๋ จํ ๊ธฐ์กด ์ฐ๊ตฌ์ ๊ฒฐ๋ก ๊ณผ ์ผ์นํ๋ค.
๋ณธ ์ฐ๊ตฌ์ ๊ฒฐ๊ณผ๋ ์ ์ฑ
์
์์ ๋ฟ ์๋๋ผ ETF ์ ์ ๊ณต ์
์ฒด ๋ฐ ๊ธฐํ ์์ฅ ์ฐธ์ฌ์์๊ฒ
์์ฌ์ ์ ์ ๊ณตํ๋ค. ๊ตฌ์ฒด์ ์ผ๋ก ESG ์์์ ์ฌ์ ์ ์ํฅ์ ๋ํ ๋๋ ๋ง๋ฅผ
ํํํ๊ณ , ESG ETF ์ ์ฌ๋ฌด ํน์ฑ ๋ฐ ๊ธฐํ ํน์ฑ๊ณผ ๊ด๋ จํ ๋ฐฉํฅ์ฑ์ ํ์ธํ ์ ์๋ค.
์์ฅ์ง์ํ๋์ ๊ด๋ จํ ๋ค์ํ ๋ฌธํ์์ ์ธ๊ธ๋ ๊ฒฐ๋ก ์ญ์ ๋ณธ ์ฐ๊ตฌ์ ๊ฒฐ๊ณผ๋ฅผ ํตํด
ํด์๋ ์ ์๋ค. ํ์ฌ ESG ETF ์ ๊ด๋ จํ ๋ง์ ์ ํ์ด ์์ง๋ง, ๋ ๋ง์ ์ํ์
๋ฑ์ฅ์ ํตํด ํด๊ฒฐํ ์ ์์ ๊ฒ์ผ๋ก ๊ธฐ๋๋๋ค. ๋ณธ ๋
ผ๋ฌธ์ ๊ด๋ จ ๋ถ์ผ์ ํ์ ์ฐ๊ตฌ์
์์ด์ ์ฐ๊ตฌ ๋ฐฐ๊ฒฝ๊ณผ ํฅํ ์ฐ๊ตฌ๋ฅผ ํตํ ๊ฐ์ ์ ์ ์ ์ํ๋ ์๋ฏธ๊ฐ ์๋ค.1. Introduction 1
2. Theory and Hypothesis 6
2.1. Basics of Investment Funds and Passive Investing 6
2.2. Exchange-Traded Funds as the disruptive invention 10
2.3. Environmental, Social and Governance Exchange-Traded Funds: main properties and description of the market 17
3. Literature review 23
4. Methodology and Approach 35
4.1. Common methods in funds literature 35
4.2. Cross-sectional regression model 37
4.3. Panel data model 39
5. Data 43
5.1. Cross-sectional data 43
5.2. Panel data 47
6. Findings 58
6.1. Models with cross-sectional data 58
6.2. Models with panel data 61
7. Discussion and conclusion 75
Bibliography 87
Appendix A 97
Appendix B 98
Appendix C 99
Abstract (Korean) 100Maste
Existence results for regularized equations of second-grade fluids with wall slip
In this paper, we study the equations governing the steady motion of a class of second-grade fluids in a bounded domain of , , with the nonlinear slip boundary condition. We prove the existence of a weak solution without assuming smallness of the data. Moreover, we give estimates for weak solutions and show that the solution set is sequentially weakly closed
Exploring the Intersection of Multi-Omics and Machine Learning in Cancer Research
Cancer biology and machine learning represent two seemingly disparate yet intrinsically linked fields of study. Cancer biology, with its complexities at the cellular and molecular levels, brings up a myriad of challenges. Of particular concern are the deviations in cell behaviour and rearrangements of genetic material that fuel transformation, growth, and spread of cancerous cells. Contemporary studies of cancer biology often utilise wide arrays of genomic data to pinpoint and exploit these abnormalities with an end-goal of translating them into functional therapies.
Machine learning allows machines to make predictions based on the learnt data without explicit programming. It leverages patterns and inferences from large datasets, making it an invaluable tool in the modern era of large scale genomics. To this end, this doctoral thesis is underpinned by three themes: the application of machine learning, multi-omics, and cancer biology. It focuses on employment of machine learning algorithms to the tasks of cell annotation in single-cell RNA-seq datasets and drug response prediction in pre-clinical cancer models.
In the first study, the author and colleagues developed a pipeline named Ikarus to differentiate between neoplastic and healthy cells within single-cell datasets, a task crucial for understanding the cellular landscape of tumours. Ikarus is designed to construct cancer cell-specific gene signatures from expert-annotated scRNA-seq datasets, score these genes, and distribute the scores to neighbouring cells via network propagation. This method successfully circumvents two common challenges in single-cell annotation: batch effects and unstable clustering. Furthermore, Ikarus utilises a multi-omic approach by incorporating CNVs inferred from scRNA-seq to enhance classification accuracy.
The second study investigated how multi-omic analysis could enhance drug response prediction in pre-clinical cancer models. The research suggests that the typical practice of panel sequencing โ a deep profiling of select, validated genomic features โ is limited in its predictive power. However, incorporating transcriptomic features into the model significantly improves predictive ability across a variety of cancer models and is especially effective for drugs with collateral effects. This implies that the combined use of genomic and transcriptomic data has potential advantages in the pharmacogenomic arena.
This dissertation recapitulates the findings of two aforementioned studies, which were published in Genome Biology and Cancers journals respectively. The two studies illustrate the application of machine learning techniques and multi-omic approaches to address conceptually distinct problems within the realm of cancer biology.Die Krebsbiologie und das maschinelle Lernen sind zwei scheinbar kontrรคre, aber intrinsisch verbundene Forschungsbereiche. Insbesondere die Krebsbiologie ist auf zellul ฬarer und molekularer Ebene hoch komplex und stellt den Forschenden vor eine Vielzahl von Herausforderungen. Zu verstehen wie abweichendes Zellverhalten und die Umstrukturierung genetischer Komponente die Transformation, das Wachstum und die Ausbreitung von Krebszellen antreiben, ist hierbei eine besondere Herausforderung. Gleichzeitig bestrebt die Krebsbiologie diese Abnormalitรคten zu nutzen zu machen, Wissen aus ihnen zu gewinnen und sie so in funktionale Therapien umzusetzen.
Maschinelles Lernen ermรถglicht es Vorhersagen auf der Grundlage von gelernten Daten ohne explizite Programmierung zu treffen. Es erkennt Muster in groรen Datensรคtzen, erschlieรt sich so Erkenntnisse und ist deswegen ein unschรคtzbar wertvolles Werkzeug im modernen Zeitalter der Hochdurchsatz Genomforschung. Aus diesem Grund ist maschinelles Lernen eines der drei Haupthemen dieser Doktorarbeit, neben Multi-Omics und Krebsbiologie. Der Fokus liegt hierbei insbesondere auf dem Einsatz von maschinellen Lernalgorithmen zum Zweck der Zellannotation in Einzelzell RNA-Sequenzdatensรคtzen und der Vorhersage der Arzneimittelwirkung in prรคklinischen Krebsmodellen.
In der ersten, hier prรคsentierten Studie, entwickelten der Autor und seine Kollegen eine Pipeline namens Ikarus. Diese kann zwischen neoplastischen und gesunden Zellen in Einzelzell-Datensรคtzen unterscheiden. Eine Aufgabe, die fรผr das Verst ฬandnis der zellulรคren Landschaft von Tumoren entscheidend ist. Ikarus ist darauf ausgelegt, krebszellenspezifische Gensignaturen aus expertenanotierten scRNA-seq-Datensรคtzen zu konstruieren, diese Gene zu bewerten und die Bewertungen รผber Netzwerkverbreitung auf benachbarte Zellen zu verteilen. Diese Methode umgeht erfolgreich zwei hรคufige Herausforderungen bei der Einzelzellannotation: den Chargeneffekt und die instabile Clusterbildung. Darรผber hinaus verwendet Ikarus, durch das Einbeziehen von scRNA-seq abgeleiteten CNVs, einen Multi-Omic-Ansatz der die Klassifikationsgenauigkeit verbessert.
Die zweite Studie untersuchte, wie Multi-Omic-Analysen die Vorhersage der Arzneimittelwirkung in prรคklinischen Krebsmodellen optimieren kรถnnen. Die Forschung legt nahe, dass die รผbliche Praxis des Panel Sequenzierens - die umfassende Profilierung ausgewรคhlter, validierter genomischer Merkmale - in ihrer Vorhersagekraft begrenzt ist. Durch das Einbeziehen transkriptomischer Merkmale in das Modell konnte jedoch die Vorhersagefรคhigkeit bei verschiedenen Krebsmodellen signifikant verbessert werden, ins besondere fรผr Arzneimittel mit Nebenwirkungen.
Diese Dissertation fasst die Ergebnisse der beiden oben genannten Studien zusammen, die jeweils in Genome Biology und Cancers Journalen verรถffentlicht wurden. Die beiden Studien veranschaulichen die Anwendung von maschinellem Lernen und Multi-Omic-Ansรคtzen zur Lรถsung konzeptionell unterschiedlicher Probleme im Bereich der Krebsbiologie
Fluctuation-Stimulated Variable-Range Hopping
Qualitatively new transport mechanism is suggested for hopping of carriers
according to which the variable-range hopping (VRH) arises from the resonant
tunneling between transport states brought into resonance by Coulomb potentials
produced by surrounding sites with fluctuating occupations. A semiquantitative
description of the hopping transport is given based on the assumption that
fluctuations of energies of hopping sites have spectral density 1/f
Lattice Delone simplices with super-exponential volume
In this short note we give a construction of an infinite series of Delone
simplices whose relative volume grows super-exponentially with their dimension.
This dramatically improves the previous best lower bound, which was linear.Comment: 7 pages; v2: revised version improves our exponential lower bound to
a super-exponential on
Simulation of the phononless hopping in a Coulomb glass
The phononless hopping conductivity of a disordered system with localized
states is studied in a broad range of frequencies by straightforward computer
simulations taking into account Coulomb interactions. At sufficiently low
temperatures, the conductivity is determined by the zero-phonon absorption of
the photon by pairs of states. The laser frequency dependence of the
conductivity is examined and compared with the analytical model of Efros and
Shklovskii and with recent experimental data obtained on Si:P. The range of
parameters is determined, for which the conductivity dependence on photon
energy best reproduces the experimental results.Comment: Presented as a poster at TIDS11, to be published in pss(c
Formation Energies of Antiphase Boundaries in GaAs and GaP: An ab Initio Study
Electronic and structural properties of antiphase boundaries in group III-V semiconductor compounds have been receiving increased attention due to the potential to integration of optically-active III-V heterostructures on silicon or germanium substrates. The formation energies of {110}, {111}, {112}, and {113} antiphase boundaries in GaAs and GaP were studied theoretically using a full-potential linearized augmented plane-wave density-functional approach. Results of the study reveal that the stoichiometric {110} boundaries are the most energetically favorable in both compounds. The specific formation energy ฮณ of the remaining antiphase boundaries increases in the order of ฮณ{113} โ ฮณ{112} < ฮณ{111}, which suggests {113} and {112} as possible planes for faceting and annihilation of antiphase boundaries in GaAs and GaP
Design principles of mobile railway networks
The basic principles of GSM railway network planning are examined. The method of the frequency channels distribution between cells is described. The development of frequency channel reuse scheme is performed. The procedure of an operational planning is proposed. The principles of even radio coverage are considered for cells with mainly free radio propagation and for urban areas
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