Volatility Modeling and Dependence Structure of ESG and Conventional Investments

The question of whether environmental, social, and governance investments outperform or underperform other conventional financial investments has been debated in the literature. In this study, we compare the volatility of rates of return of selected ESG indices and conventional ones and investigate dependence between them. Analysis of tail dependence is important to evaluate the diversification benefits between conventional investments and ESG investments, which is necessary in constructing optimal portfolios. It allows investors to diversify the risk of the portfolio and positively impact the environment by investing in environmentally friendly companies. Examples of institutions that are paying attention to ESG issues are banks, which are increasingly including products that support sustainability goals in their offers. This analysis could be also important for policymakers. The European Banking Authority (EBA) has admitted that ESG factors can contribute to risk. Therefore, it is important to model and quantify it. The conditional volatility models from the GARCH family and tail-dependence coefficients from the copula-based approach are applied. The analysis period covered 2007 until 2019. The period of the COVID-19 pandemic has not been analyzed due to the relatively short time series regarding data requirements from models’ perspective. Results of the research confirm the higher dependence of extreme values in the crisis period (e.g., tail-dependence values in 2009–2014 range from 0.4820/0.4933 to 0.7039/0.6083, and from 0.5002/0.5369 to 0.7296/0.6623), and low dependence of extreme values in stabilization periods (e.g., tail-dependence values in 2017–2019 range from 0.1650 until 0.6283/0.4832, and from 0.1357 until 0.6586/0.5002). Diversification benefits vary in time, and there is a need to separately analyze crisis and stabilization periods.

Multiscale Entropy Analysis of Short Signals: The Robustness of Fuzzy Entropy-Based Variants Compared to Full-Length Long Signals

Multiscale entropy (MSE) analysis is a fundamental approach to access the complexity of a time series by estimating its information creation over a range of temporal scales. However, MSE may not be accurate or valid for short time series. This is why previous studies applied different kinds of algorithm derivations to short-term time series. However, no study has systematically analyzed and compared their reliabilities. This study compares the MSE algorithm variations adapted to short time series on both human and rat heart rate variability (HRV) time series using long-term MSE as reference. The most used variations of MSE are studied: composite MSE (CMSE), refined composite MSE (RCMSE), modified MSE (MMSE), and their fuzzy versions. We also analyze the errors in MSE estimations for a range of incorporated fuzzy exponents. The results show that fuzzy MSE versions—as a function of time series length—present minimal errors compared to the non-fuzzy algorithms. The traditional multiscale entropy algorithm with fuzzy counting (MFE) has similar accuracy to alternative algorithms with better computing performance. For the best accuracy, the findings suggest different fuzzy exponents according to the time series length.

Tropical forest dynamics correspond to fair games in economic theory of financial markets

Ecological and economic systems both comprise of autonomous adaptive agents. It is thus possible that similar mechanisms determine the organization of both these complex systems. Indeed several economic theories have already been successfully applied in an ecological context. Here we show that 'efficient market theory' in economics, where future earnings are distributed between competitors by a 'fair game', corresponds to fitness-equalizing mechanisms of coexistence in ecology. In contrast to stabilizing mechanisms, which promote coexistence by giving each species an equilibrium abundance that is resilient to perturbations, equalizing mechanisms promote coexistence without such resilience by minimizing the net fitness differences between species. However, identifying stabilizing and equalizing mechanisms from the short time-series data that are typically available in ecology is challenging. We used techniques from economics that are applied to collections of short time-series from a system. We found that observed species abundance dynamics in a neotropical forest are generally in agreement with efficient market theory implying a dominant role of equalizing mechanisms, which finding quantifies and supports what was generally believed about that specific forest system. Our study highlights that complex systems from ecology and economics share common features suggesting the possibility of further synergy between ecology and economics in future.

A Bayesian test for seasonality in medical data

In medical research, the results from seasonality analyses provide valuable information that eventually can help to clarify the etiology of poorly understood diseases. We present a Bayesian procedure for the analysis of seasonal variation in medical data. The method is a Bayesian version of a frequentist test that performs very well. Statistical seasonality analyses of medical data often involve a short time series, 12 observations with small amplitude and small sample size. Among the specialized procedures already developed for such analyses, only one is Bayesian; the method we present in this paper appears to be the second such Bayesian procedure. Easy to understand and apply, the method is versatile because it can be used to analyze different types of seasonal variation. We illustrate the procedure’s application with two examples of real data.

Short Time-Series Expression Transcriptome Data Reveal the Gene Expression Patterns of Dairy Cow Mammary Gland as Milk Yield Decreased Process

The existing research on dairy cow mammary gland genes is extensive, but there have been few reports about dynamic changes in dairy cow mammary gland genes as milk yield decrease. For the first time, transcriptome analysis based on short time-series expression miner (STEM) and histological observations were performed using the Holstein dairy cow mammary gland to explore gene expression patterns in this process of decrease (at peak, mid-, and late lactation). Histological observations suggested that the number of mammary acinous cells at peak/mid-lactation was significantly higher than that at mid-/late lactation, and the lipid droplets area secreted by dairy cows was almost unaltered across the three stages of lactation (p > 0.05). Totals of 882 and 1439 genes were differentially expressed at mid- and late lactation, respectively, compared to peak lactation. Function analysis showed that differentially expressed genes (DEGs) were mainly related to apoptosis and energy metabolism (fold change ≥ 2 or fold change ≤ 0.5, p-value ≤ 0.05). Transcriptome analysis based on STEM identified 16 profiles of differential gene expression patterns, including 5 significant profiles (false discovery rate, FDR ≤ 0.05). Function analysis revealed DEGs involved in milk fat synthesis were downregulated in Profile 0 and DEGs in Profile 12 associated with protein synthesis. These findings provide a foundation for future studies on the molecular mechanisms underlying mammary gland development in dairy cows.

LIMITATIONS OF THE PANEL REGRESSION MODEL APPLICATION: THE EXAMPLE OF THE WESTERN BALKAN COUNTRIES

Panel regression model may seem like an appealing solution in conditions of limited time series. This is often used as a shortcut to achieve deeper data set by setting several individual cases on the same time dimension, where cross units visually but not really multiply a time frame. Macroeconometrics of the Western Balkan region assumes short time series issue. Additionally, the structural brakes are numerous. Panel regression may seem like a solution, but there are some limitations that should be considered.

POS0199 TIME-SERIES ANALYSIS IN MODERATE TO SEVERE PLAQUE PSORIASIS UNDER DIFFERENT BIOLOGICS TREATMENTS

Background:Moderate to Severe Plaque Psoriasis is an inflammatory skin disease that is associated with multiple comorbidities and substantially diminishes patients’ quality of life. As one of the most significant therapeutic advancements in the field of dermatology, Biologics such as TNF inhibitors, IL-12/23 inhibitor, IL-17 inhibitors, and IL-23 inhibitors, have higher efficacy compared with oral medications or phototherapy1. However, the previous studies did not focus on the simultaneous comparison of molecular changes in different classes of biologics. The identification of time-series genes (TSGs) could help to uncover the mechanisms underlying transcriptional regulation2.Objectives:In this study, we aimed to compare the differences in expression patterns and functions of time-series genes in Moderate to Severe Plaque Psoriasis under different biologics treatments.Methods:The transcription profile of GSE117239 and GSE51440 were obtained from the Gene Expression Omnibus database (GEO). The GSE117239 included 19 samples treated with Etanercept (TNF inhibitors) and 16 samples treated with Ustekinumab (IL-12/23 inhibitor). The GSE51440 included 4 samples treated with Guselkumab (IL-23 inhibitors). Skin biopsy samples (LS: lesion, NL: non-lesion) were collected at baseline, weeks 1 and 12, respectively. After background adjustment and other pre-procession, differentially expressed genes (DEGs) were extracted from LS skin biopsy and untreated NL skin biopsy at different times after three different biologics treatments, respectively. The Short Time-series Expression Miner (STEM) software was used to cluster and compare average DEGs with coherent changes. Afterward, the different expression patterns of TSGs under the three treatment groups were compared. GO analysis and KEGG pathway enrichment analysis of TSGs were performed by Metascape.Results:Different DEGs varied in LS skin compared with those of NL skin biopsy: 976 genes in Ustekinumab group, 996 genes in Etanercept group, and 601 genes in Guselkumab group detailly (P < 0.05 and [log FC] > 1). Gene landscapes suggested the signatures of LS gradually changed during the treatment process, and gradually converge to NL signatures (Fig.1a, 2a,3a). Time-series genes in the three treatment groups had different expression patterns and functions. In the Ustekinumab group, a total of 448 TSGs in profile 3 showed a stable-stable-decreasing expression trend and significantly associated with mitotic nuclear division and defense response to other organism, whereas in profile 4 represented a stable-stable-increasing expression trend and significantly associated with positive regulation of cellular response to organic 9 compound (Fig.1). With the treatment of Etanercept, 22 TSGs had a stable-increasing-increasing expression tendency and closely associated with fatty acid metabolism and steroid metabolic process (Fig.2). After Guselkumab treatment, 13 TSGs also represented a stable-increasing-increasing expression tendency that mainly characterized by defense response to other organism and epidermis development (Fig.3). Interestingly, both Ustekinumab and Guselkumab treatment dramatically influenced defense response to other organism-related genes, while Etanercept mainly affected genes involved in fatty acid metabolism and steroid metabolic process.Conclusion:Biologics effectively reconstituted the gene signatures of psoriasis in different aspects. TSG features could be one of indicator for precise intervention for psoriasis.References:[1]Armstrong AW, Read C. Pathophysiology, Clinical Presentation, and Treatment of Psoriasis: A Review. Jama 2020;323(19):1945-60. doi: 10.1001/jama.2020.4006 [published Online First: 2020/05/20][2]Ernst J, Bar-Joseph Z. STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics 2006;7:191. doi: 10.1186/1471-2105-7-191 [published Online First: 2006/04/07]Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared