Since its conception, the cryptocurrency market has been frequently described as an immature market, characterized by significant swings in volatility and occasionally described as lacking rhyme or ...reason. There has been great speculation as to what role it plays in a diversified portfolio. For instance, is cryptocurrency exposure an inflationary hedge or a speculative investment that follows broad market sentiment with amplified beta? We have recently explored similar questions with a clear focus on the equity market. There, our research revealed several noteworthy dynamics such as an increase in the market's collective strength and uniformity during crises, greater diversification benefits across equity sectors (rather than within them), and the existence of a "best value" portfolio of equities. In essence, we can now contrast any potential signatures of maturity we identify in the cryptocurrency market and contrast these with the substantially larger, older and better-established equity market. This paper aims to investigate whether the cryptocurrency market has recently exhibited similar mathematical properties as the equity market. Instead of relying on traditional portfolio theory, which is grounded in the financial dynamics of equity securities, we adjust our experimental focus to capture the presumed behavioral purchasing patterns of retail cryptocurrency investors. Our focus is on collective dynamics and portfolio diversification in the cryptocurrency market, and examining whether previously established results in the equity market hold in the cryptocurrency market and to what extent. The results reveal nuanced signatures of maturity related to the equity market, including the fact that correlations collectively spike around exchange collapses, and identify an ideal portfolio size and spread across different groups of cryptocurrencies.
This paper uses new and recently introduced methodologies to study the similarity in the dynamics and behaviours of cryptocurrencies and equities surrounding the COVID-19 pandemic. We study two ...collections; 45 cryptocurrencies and 72 equities, both independently and in conjunction. First, we examine the evolution of cryptocurrency and equity market dynamics, with a particular focus on their change during the COVID-19 pandemic. We demonstrate markedly more similar dynamics during times of crisis. Next, we apply recently introduced methods to contrast trajectories, erratic behaviours, and extreme values among the two multivariate time series. Finally, we introduce a new framework for determining the persistence of market anomalies over time. Surprisingly, we find that although cryptocurrencies exhibit stronger collective dynamics and correlation in all market conditions, equities behave more similarly in their trajectories and extremes, and show greater persistence in anomalies over time.
Abstract
Aims
We investigated the relationship between clinically assessed left ventricular ejection fraction (LVEF) and survival in a large, heterogeneous clinical cohort.
Methods and results
...Physician-reported LVEF on 403 977 echocardiograms from 203 135 patients were linked to all-cause mortality using electronic health records (1998–2018) from US regional healthcare system. Cox proportional hazards regression was used for analyses while adjusting for many patient characteristics including age, sex, and relevant comorbidities. A dataset including 45 531 echocardiograms and 35 976 patients from New Zealand was used to provide independent validation of analyses. During follow-up of the US cohort, 46 258 (23%) patients who had undergone 108 578 (27%) echocardiograms died. Overall, adjusted hazard ratios (HR) for mortality showed a u-shaped relationship for LVEF with a nadir of risk at an LVEF of 60–65%, a HR of 1.71 95% confidence interval (CI) 1.64–1.77 when ≥70% and a HR of 1.73 (95% CI 1.66–1.80) at LVEF of 35–40%. Similar relationships with a nadir at 60–65% were observed in the validation dataset as well as for each age group and both sexes. The results were similar after further adjustments for conditions associated with an elevated LVEF, including mitral regurgitation, increased wall thickness, and anaemia and when restricted to patients reported to have heart failure at the time of the echocardiogram.
Conclusion
Deviation of LVEF from 60% to 65% is associated with poorer survival regardless of age, sex, or other relevant comorbidities such as heart failure. These results may herald the recognition of a new phenotype characterized by supra-normal LVEF.
This paper uses new and recently established methodologies to study the evolutionary dynamics of the cryptocurrency market, and compares the findings with that of the equity market. We begin by ...applying random matrix theory and principal components analysis (PCA) to correlation matrices of both collections, highlighting clear differences in the eigenspectra exhibited. We then explore the heterogeneity of both asset classes, studying the time-varying dynamics of underlying sector behaviours, and determine the collective similarity within each collection. We then turn to a study of structural break dynamics and evolutionary power spectra, where we quantify the collective affinity in structural breaks and evolutionary behaviours of underlying sector time series. Finally, we implement two algorithms simulating ‘portfolio choice’ dynamics to compare the effectiveness of stock selection and sector allocation in cryptocurrency portfolios. There, we highlight the importance of both endeavours and comment on noteworthy implications for cryptocurrency portfolio management.
•This paper uses time-varying random matrix theory to contrast cryptocurrency and equity dynamics.•Cryptocurrency and equity sector dynamics are compared.•Structural break dynamics are studied with a new framework.•A method to study collective similarity in evolutionary power spectra is introduced.•Two new algorithms test the effectiveness of cryptocurrency security selection and sector allocation.
Objectives
To compare the MRI texture profile of acetabular subchondral bone in normal, asymptomatic cam positive, and symptomatic cam-FAI hips and determine the accuracy of a machine learning model ...for discriminating between the three hip classes.
Methods
A case–control, retrospective study was performed including 68 subjects (19 normal, 26 asymptomatic cam, 23 symptomatic cam-FAI). Acetabular subchondral bone of unilateral hip was contoured on 1.5 T MR images. Nine first-order 3D histogram and 16 s-order texture features were evaluated using specialized texture analysis software. Between-group differences were assessed using Kruskal–Wallis and Mann–Whitney
U
tests, and differences in proportions compared using chi-square and Fisher’s exact tests. Gradient-boosted ensemble methods of decision trees were created and trained to discriminate between the three groups of hips, with percent accuracy calculated.
Results
Sixty-eight subjects (median age 32 (28–40), 60 male) were evaluated. Significant differences among all three groups were identified with first-order (4 features, all
p
≤ 0.002) and second-order (11 features, all
p
≤ 0.002) texture analyses. First-order texture analysis could differentiate between control and cam positive hip groups (4 features, all
p
≤ 0.002). Second-order texture analysis could additionally differentiate between asymptomatic cam and symptomatic cam-FAI groups (10 features, all
p
≤ 0.02). Machine learning models demonstrated high classification accuracy of 79% (SD 16) for discriminating among all three groups.
Conclusion
Normal, asymptomatic cam positive, and cam-FAI hips can be discriminated based on their MRI texture profile of subchondral bone using descriptive statistics and machine learning algorithms.
Clinical relevance statement
Texture analysis can be performed on routine MR images of the hip and used to identify early changes in bone architecture, differentiating morphologically abnormal from normal hips, prior to onset of symptoms.
Key Points
• MRI texture analysis is a technique for extracting quantitative data from routine MRI images.
• MRI texture analysis demonstrates that there are different bone profiles between normal hips and those with femoroacetabular impingement.
• Machine learning models can be used in conjunction with MRI texture analysis to accurately differentiate between normal hips and those with femoroacetabular impingement.
This paper proposes a new method for financial portfolio optimization based on reducing simultaneous asset shocks across a collection of assets. This may be understood as an alternative approach to ...risk reduction in a portfolio based on a new mathematical quantity. First, we apply recently introduced semi-metrics between finite sets to determine the distance between time series’ structural breaks. Then, we build on the classical portfolio optimization theory of Markowitz and use this distance between asset structural breaks for our penalty function, rather than portfolio variance. Our experiments are promising: on synthetic data, we show that our proposed method does indeed diversify among time series with highly similar structural breaks and enjoys advantages over existing metrics between sets. On real data, experiments illustrate that our proposed optimization method performs well relative to nine other commonly used options, producing the second-highest returns, the lowest volatility, and second-lowest drawdown. The main implication for this method in portfolio management is reducing simultaneous asset shocks and potentially sharp associated drawdowns during periods of highly similar structural breaks, such as a market crisis. Our method adds to a considerable literature of portfolio optimization techniques in econometrics and could complement these via portfolio averaging.
It is customary in an introduction for guest editors to explain the theme of the Special Issue and introduce the authors. While we intend to do both of these things, we also want to explain why we ...chose to undertake this project and address and acknowledge some omissions. We begin, however, with our acknowledgements and thanks. What difference can a special issue of a law journal make? In and of itself, likely very little. However, universities have a social and moral responsibility to engage with the challenges facing humanity and equip graduates with the skills to address those challenges. This special issue, in some small way, advances that goal. As legal scholars, we are duty bound to consider the implications of the climate emergency on our respective areas of expertise and what this means for the way we deliver legal education. This Special Issue of the UQLJ seeks to engage with that duty and to encourage legal scholars and law schools to confront the climate emergency and embed climate law into the law curriculum. In turn, as our graduates step out into the world, this will contribute to enhanced community education and legal literacy: that is our action item. Law is not and cannot be separated from the society it regulates. Correspondingly, legal education cannot ignore one of the most significant challenges to global human society. Climate change, the law and legal education are intimately and inextricably interconnected. We look forward to continuing the important conversations initiated in this special issue of the UQLJ.
This paper analyzes the impact of COVID-19 on the populations and equity markets of 92 countries. We compare country-by-country equity market dynamics to cumulative COVID-19 case and death counts and ...new case trajectories. First, we examine the multivariate time series of cumulative cases and deaths, particularly regarding their changing structure over time. We reveal similarities between the case and death time series, and key dates that the structure of the time series changed. Next, we classify new case time series, demonstrate five characteristic classes of trajectories, and quantify discrepancy between them with respect to the behavior of waves of the disease. Finally, we show there is no relationship between countries’ equity market performance and their success in managing COVID-19. Each country’s equity index has been unresponsive to the domestic or global state of the pandemic. Instead, these indices have been highly uniform, with most movement in March.
•We study the natural history of COVID-19 in conjunction with equity market dynamics.•The changing structure of the cumulative case and death time series are analyzed.•Five classes of daily case trajectories among 92 countries are identified.•New methods analyze the collective dynamics of equity prices and case trajectories.•We show that there is no relationship between COVID-19 cases and market performance.
This paper introduces new methods to study behaviours among the 52 largest cryptocurrencies between 01-01-2019 and 30-06-2021. First, we explore evolutionary correlation behaviours and apply a ...recently proposed turning point algorithm to identify regimes in market correlation. Next, we inspect the relationship between collective dynamics and the cryptocurrency market size—revealing an inverse relationship between the size of the market and the strength of collective dynamics. We then explore the time-varying consistency of the relationships between cryptocurrencies’ size and their returns and volatility. There, we demonstrate that there is greater consistency between size and volatility than size and returns. Finally, we study the spread of volatility behaviours across the market changing with time by examining the structure of Wasserstein distances between probability density functions of rolling volatility. We demonstrate a new phenomenon of increased uniformity in volatility during market crashes, which we term
volatility dispersion
.
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