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Trending Papers in business

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15
Momentum effects in the cryptocurrency market after one-day abnormal returns
From Paper: (PDF) Momentum effects in the cryptocurrency market after one-day abnormal returns
Published: May 2020
  • The following hypotheses are tested: (H1) the intraday behavior of hourly returns is different on abnormal returns days compared to normal days; (H2) there is a momentum effect on abnormal returns days, and (H3) after one-day abnormal returns.
  • abnormal returns can be detected before the day ends and specific timing parametersfor the abnormal returns can be estimated;• prices tend to move in the direction of abnormal returns till the end of the day, i.e.,H2 cannot be rejected, namely there is a momentum effect on days with abnormalreturns;• the behavior of themarket after one-day abnormalreturns inmost cases also confirmsthe existence of a momentum effect, i.e., H3 cannot be rejected. Usually, it is shortterm, and specific timing parameters can be estimated for the asset of interest;• in two cases (BTCUSD positive abnormal returns and ETHUSD negative abnormalreturns) a contrarian effect is detected.On the basis of these results, the following profitable strategies can be developed:Strategy 1 When it becomes clear that the current day is an abnormal returns day(see the timing of abnormal returns in Tables 2 and 3), a position in the direction ofabnormal returns should be opened. This position should then be closed at the end ofthe day.Strategy 2 At the beginning of the day after the abnormal returns, a position in thedirection of the abnormal returns should be opened. This position should then be closedon the basis of the timing parameters for the momentum effect displayed in Tables 2and 3. If this effect is not present, a contrarian trading strategy should be used: at thebeginning of the day after the abnormal returns a position in the opposite direction tothe abnormal returns should be opened
2
Persistence in the market risk premium: evidence across countries
From Paper: CROSSMARK_BW_txt_100x100.eps
Published: Sep 2020
  • The CAPM is still the most popular model for analysing the relationshipbetween risk and return. This paper provides evidence on the degree ofpersistence of one of its key components, namely the market risk premium,as well as its volatility. The analysis applies fractional integration methods todata for the US, Germany and Japan, and for robustness purposes considersdifferent time horizons (2, 5 and 10 years) and frequencies (monthly andweekly). The empirical findings in most cases imply that the market riskpremium is a highly persistent variable which can be characterized as a randomwalk process, whilst its volatility is less persistent and exhibits stationary long-memory behaviour. There is also evidence that in the case of the US the degreeof persistence has changed as a results of various events such as the 1973–74oil crisis, the early 1980s recession resulting from the Fed’s contractionarymonetary policy, the 1997 Asian financial crisis, and the 2007 global financialcrisis; this is confirmed by both endogenous break tests and the associatedsubsample estimates. Market participants should take this evidence into accountwhen designing their investment strategies.
1
The Impact of Social Media on the Academic Development of School Students
  • Social media platforms vary from Web blogs, to micro-sharing platforms, to lifestreams to social networks and much more.
  • Social Media has an an high impact on the academic development of school students.
Ru LLane
2
Influencia de los estilos de liderazgo en el desempeño de las empresas exportadoras colombianas
Published: Apr 2016
  • Se concluye que para alcanzar niveles de desempeño superior los gerentes colombianos ejercen el liderazgo como un equilibrio entre libertad y control.
Alejandro Escandon
5
The importance of responsible investing & strategies to generate returns using this method.
From Paper: Environmental, Social, and Governance Investing: Investor Demand, the Great Wealth Transfer, and Strategies for ESG Investing
  • The results for the overall population of 948 respondents reveal that 72 percent of the overall population has a medium to high interest in ESG investing.
  • Four ESG investment strategies are described below: 1. Consideration funds—These funds consider ESG criteria and performance along with traditional financial analysis during investment analysis with ESG not being the entire focus 2. Integration funds—These funds broadly integrate ESG investment criteria throughout the investment-analysis process. Integration funds comprise the largest group of ESG funds. These funds exhibit a higher level of commitment to ESG investing than do ESG consideration funds. 3. Impact funds—These funds focus on investments made in companies that generate financial return and demonstrate specific social or environmental impact, such as low carbon or natural resource depletion. 4. Sustainable sector funds—These funds focus on investments in specific “green economy” sectors and address initiatives such as renewable energy, environmental services, water infrastructure, and green real estate.
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If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology
Authors:
J. Wallis, Elizabeth Rolando, C. Borgman
Published: Jul 2013
Research on practices to share and reuse data will inform the design of infrastructure to support data collection, management, and discovery in the long tail of science and technology. These are research domains in which data tend to be local in character, minimally structured, and minimally documented. We report on a ten-year study of the Center for Embedded Network Sensing (CENS), a National Science Foundation Science and Technology Center. We found that CENS researchers are willing to share their data, but few are asked to do so, and in only a few domain areas do their funders or journals require them to deposit data. Few repositories exist to accept data in CENS research areas.. Data sharing tends to occur only through interpersonal exchanges. CENS researchers obtain data from repositories, and occasionally from registries and individuals, to provide context, calibration, or other forms of background for their studies. Neither CENS researchers nor those who request access to CENS data appear to use external data for primary research questions or for replication of studies. CENS researchers are willing to share data if they receive credit and retain first rights to publish their results. Practices of releasing, sharing, and reusing of data in CENS reaffirm the gift culture of scholarship, in which goods are bartered between trusted colleagues rather than treated as commodities.
Retrieved from semantic_scholar
0
An inquiry into money laundering tools in the Bitcoin ecosystem
Authors:
M. Moser, R. Bohme, Dominic Breuker
Published: Sep 2013
  • bitcoin and money..save those
Retrieved from semantic_scholar
0
An Analysis of Bitcoin Laundry Services
Authors:
Thibault de Balthasar, J. Hernandez-Castro
Published: Jan 2017
This work briefly (An extended version can be found at https://kar.kent.ac.uk/id/eprint/63502) examines some of the most relevant Bitcoin Laundry Services, commonly known as tumblers or mixers, and studies their main features to try to answer some fundamental questions including their security, popularity, transaction volume, and generated revenue. Our research aims to inform both legitimate users and Law Enforcement about the characteristics and limitations of these services.
Retrieved from semantic_scholar
0
Strengthening Security for Gene Synthesis: Recommendations for Governance.
Authors:
Amanda Kobokovich, R. West, M. Montague, Tom Inglesby, G. Gronvall
Published: Dec 2019
Since the inception of gene synthesis technologies, there have been concerns about possible misuse. Using gene synthesis, pathogens-particularly small viruses-may be assembled "from scratch" in the laboratory, evading the regulatory regimes many nations have in place to control unauthorized access to dangerous pathogens. Progress has been made to reduce these risks. In 2010, the US Department of Health and Human Services (HHS) published guidance for commercial gene synthesis providers that included sequence screening of the orders and customer screening. The industry-led International Gene Synthesis Consortium (IGSC) was formed in 2009 to share sequence and customer screening methods, and it now includes the major international gene synthesis providers among its members. Since the 2010 HHS Guidance was released, however, there have been changes in gene synthesis technologies and market conditions that have reduced the efficacy of these biosecurity protections, leading to questions about whether the 2010 HHS Guidance should be updated, what changes could make it more effective, and what other international governance efforts could be undertaken to reduce the risks of misuse of gene synthesis products. This article describes these conditions and recommends actions that governments should take to reduce these risks and engage other nations involved in gene synthesis research.
Retrieved from semantic_scholar
0
Black Market Value of Patient Data
Author:
C. Czeschik
Published: Sep 2017
Personal health data is a coveted resource for a variety of interested parties. One of these is agents operating in illegal markets, comparable to the black markets on which stolen credit card data and other unlawfully obtained information are sold. Since the safety of personal health data is not only dependent on the quality of safety measures adopted by health care entities but also on the motivation and resources of potential attackers, the question of the value of personal health data on the black market is a highly critical one and not an easy one to answer. Illegal actors can extract profits from patient data in a variety of ways, the best documented of which are direct sale and extortion of ransom. Prices attained in these transactions can help to estimate the financial value of patient data on the black market in the US – where instances of health care data breaches have been most frequent and well documented – and in Germany.
Retrieved from semantic_scholar
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