scholarly journals A Study of Multi-Scale Relationship Between Investor Sentiment and Stock Index Fluctuation Based on the Analysis of BEMD Spillover Index

2021 ◽  
Vol 9 (4) ◽  
pp. 399-420
Author(s):  
Weiguo Chen ◽  
Shufen Zhou ◽  
Yin Zhang ◽  
Yi Sun
Keyword(s):  
Stock Price ◽  
Composite Index ◽  
Investor Sentiment ◽  
Stock Index ◽  
Financial Industry ◽  
Medium Term ◽  
Short Term ◽  
Frequency Components ◽  
Original Scale

Abstract According to behavioral finance theory, investor sentiment generally exists in investors’ trading activities and influences financial market. In order to investigate the interaction between investor sentiment and stock market as well as financial industry, this study decomposed investor sentiment, stock price index and SWS index of financial industry into IMF components at different scales by using BEMD algorithm. Moreover, the fluctuation characteristics of time series at different time scales were extracted, and the IMF components were reconstructed into short-term high-frequency components, medium-term important event low-frequency components and long-term trend components. The short-term interaction between investor sentiment and Shanghai Composite Index, Shenzhen Component Index and financial industries represented by SWS index was investigated based on the spillover index. The time difference correlation coefficient was employed to determine the medium-term and long-term correlation among variables. Results demonstrate that investor sentiment has a strong correlation with Shanghai Composite Index, Shenzhen Component Index and different financial industries represented by SWS index at the original scale, and the change of investor sentiment is mainly influenced by external market information. The interaction between most markets at the short-term scale is weaker than that at the original scale. Investor sentiment is more significantly correlated with SWS Bond, SWS Diversified Finance and Shanghai Composite Index at the long-term scale than that at the medium-term scale.

scholarly journals ANALISIS KERENTANAN PERIKANAN TANGKAP AKIBAT PERUBAHAN IKLIM PADA SKALA PROVINSI (Province Scaled Fisheries Vulnerability on Climate Change)

2018 ◽  
Vol 8 (2) ◽  
pp. 223
Author(s):  
Allasay Kitsash Addifisyuka Cintra ◽  
Isdradjad Setyobudiandi ◽  
Achmad Fahrudin
Keyword(s):  
Climate Change ◽  
Adaptive Capacity ◽  
Composite Index ◽  
High Sensitivity ◽  
Vulnerability Index ◽  
Medium Term ◽  
Short Term ◽  
High Exposure ◽  
Purposive Sampling

<p align="center"><strong><em>ABSTRACT</em></strong><strong></strong></p><p><em>Fisheries has significant roles for the Indonesian economy. Climate change influences Indonesian fisheries through a range of direct and indirect pathaway. A scientific based approach such as vulnerability is needed to determine the risks of climate change and adaptation strategies. Therefore, this study was conducted to analyze the vulnerability of fisheries to climate change on  province scaled in Indonesia. Vulnerability index (VI) is obtained with composite index of exposure (EI), sensitivity (SI) and adaptive capacity (ACI) of ten provinces representing the eastern and western parts of Indonesia by using purposive sampling method. Source of data for indices variables were using recorded datas from relevant institutions. The results showed that fisheries status of North Sulawesi (VI = 0,78), Central Sulawesi (VI = 0,72) and Gorontalo (VI = 0,61) were very vulnerable despite the composition of constituent vulnerability index was different. This difference determined the specific policies to be taken to each province to reduce vulnerability.</em> <em>Short term policies are taken to reduce the vulnerability of the most vulnerable areas on Sulawesi Utara, Sulawesi Tengah, and Gorontalo. Medium term policy is carried out in high sensitivity areas, namely Kepulauan Riau, Sulawesi Utara, and Kalimantan Timur and in low adaptive capacity areas such as Jambi, Gorontalo and Bangka Belitung. Long term policy is conducted for areas with high exposure such as Sulawesi Tengah, Sulawesi Utara and Kalimantan Timur.</em></p><p><strong><em>Keywords</em></strong><em>: Climate change, fisheries, vulnerability, province</em></p><p><em><br /></em></p><p align="center"><strong>ABSTRAK</strong><strong></strong></p>Perikanan tangkap memiliki peranan penting bagi perekonomian Indonesia. Adanya perubahan iklim akan berdampak merugikan secara langsung maupun tidak langsung pada perikanan tangkap Indonesia. Suatu pendekatan ilmiah diperlukan untuk menentukan risiko perubahan iklim dan strategi adaptasi perikanan tangkap, salah satunya adalah analisis kerentanan (<em>Vulnerability</em>). Oleh karena itu penelitian ini dilakukan untuk menganalisis kerentanan perikanan tangkap akibat perubahan iklim pada skala provinsi di Indonesia. Indeks kerentanan (VI) didapatkan dengan mengkompositkan indeks keterpaparan (EI), kepekaan (SI) dan kapasitas adaptif (ACI) dari sepuluh provinsi yang mewakili bagian timur dan barat Indonesia dengan metode <em>purposive sampling. </em>Sumber variabel penyusun indeks variabel menggunakaan rekaman data dari instansi terkait.  Hasil penelitian menunjukkan bahwa provinsi Sulawesi Utara (VI=0,78), Sulawesi Tengah (VI=0,72) dan Gorontalo (VI=0,61) berstatus sangat rentan walaupun komposisi penyusun indeks kerentanannya tidak sama. Perbedaan ini menentukan bahwa jenis kebijakan yang diambil menjadi spesifik pada tiap provinsi untuk mengurangi kerentanan. <em>Short term policy </em>diambil untuk mengurangi dapak di daerah yang paling rentan yaitu Sulawesi Utara, Sulawesi Tengah, dan Gorontalo. <em>Medium term policy </em>dilakukan pada daerah yang kepekaannya tinggi yaitu Kepulauan Riau, Sulawesi Utara, dan Kalimantan Timur dan kapasitas adaptifnya rendah yaitu Jambi, Gorontalo dan Bangka Belitung. <em>Long term policy </em>dilakukan untuk daerah yang keterpaparannya tinggi yaitu Sulawesi Tengah, Sulawesi Utara dan Kalimantan Timur.<p><strong>Kata kunci</strong>:<em> </em>perubahan iklim, perikanan tangkap, kerentanan, provinsi <strong></strong></p>

Determinants of Anthropometric Failure Among Tribal Children Younger than 5 Years of Age in Palghar, Maharashtra, India

2021 ◽  
Vol 42 (1) ◽  
pp. 55-64
Author(s):  
Angeline Jeyakumar ◽  
Swapnil Godbharle ◽  
Bibek Raj Giri
Keyword(s):  
Odds Ratio ◽  
Composite Index ◽  
Z Score ◽  
Short Term ◽  
Cross Sectional Survey ◽  
Crude Odds Ratio ◽  
Cross Sectional ◽  
Independent Variables ◽  
Child Undernutrition

Background: Measuring undernutrition using composite index of anthropometric failure (CIAF) and identifying its determinants in tribal regions is essential to recognize the true burden of undernutrition in these settings. Objective: To determine anthropometric failure and its determinants among tribal children younger than 5 years in Palghar, Maharashtra, India. Methods: A cross-sectional survey employing CIAF was performed in children <5 years to estimate undernutrition in the tribal district of Palghar in Maharashtra, India. Anthropometric measurements, maternal and child characteristics were recorded from 577 mother–child pairs in 9 villages. Results: As per Z score, prevalence of stunting, wasting, and underweight were 48%, 13%, and 43%, respectively. According to CIAF, 66% of children had at least one manifestation of undernutrition and 40% had more than one manifestation of undernutrition. Odds of anthropometric failure were 1.5 times higher among children of mothers who were illiterate (adjusted odds ratio [AOR] =1.57, 95% CI: 1.0-2.3), children who had birth weight >2.5 kg had lesser odds (AOR: 0.63, 95% CI: 0.4-0.9) of anthropometric failure, and children who had initiated early breastfeeding had 1.5 times higher odds of anthropometric failure (crude odds ratio: 1.5, 95% CI: 1.0-2.1). However, when adjusted for other independent variables, the results were not significant. Conclusion: The alarming proportion of anthropometric failure among tribal children calls for urgent short-term interventions to correct undernutrition and long-term interventions to improve maternal literacy and awareness to prevent and manage child undernutrition.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

Sampling estuarine copepods at different scales and resolutions: a study in Rio de la Plata

2018 ◽  
Vol 99 (5) ◽  
pp. 1059-1064 ◽  
Author(s):  
Sourav Paul ◽  
Danilo Calliari
Keyword(s):  
Sampling Effort ◽  
Rio De La Plata ◽  
Population Densities ◽  
Medium Term ◽  
Short Term ◽  
Río De La Plata ◽  
La Plata ◽  
Resource Limitations ◽  
Paracalanus Parvus

AbstractIn the Rio de la Plata salinity, temperature, chlorophyll a (chl a), and densities (ind. m−3) of the copepods Acartia tonsa and Paracalanus parvus were measured from January to November in 2003 by following a nested weekly and monthly design. Such sampling yielded two separate datasets: (i) Yearly Dataset (YD) which consists of data of one sampling effort per month for 11 consecutive months, and (ii) Seasonal Weekly Datasets (SWD) which consists of data of one sampling effort per week of any four consecutive weeks within each season. YD was assumed as a medium-term low-resolution (MTLR) dataset, and SWD as a short-term high-resolution (STHR) dataset. The hypothesis was, the SWD would always capture (shorter scales generally captures more noise in data) more detail variability of copepod populations (quantified through the regression relationships between temporal changes of salinity, temperature, chl a and copepod densities) than the YD. Analysis of both YD and SWD found that A. tonsa density was neither affected by seasonal cycles, nor temporal variability of salinity, temperature and chl a. Thus, compared to STHR sampling, MTLR sampling did not yield any further information of the variability of population densities of the perennial copepod A. tonsa. Analysis of SWD found that during summer and autumn the population densities of P. parvus had a significant positive relationship to salinity but their density was limited by higher chl a concentration; analysis of YD could not yield such detailed ecological information. That hints the effectiveness of STHR sampling over MTLR sampling in capturing details of the variability of population densities of a seasonal copepod species. Considering the institutional resource limitations (e.g. lack of long-term funding, manpower and infrastructure) and the present hypothesis under consideration, the authors suggest that a STHR sampling may provide useful complementary information to interpret results of longer-term natural changes occurring in estuaries.

scholarly journals Towards the best future for humankind: ZIPAR whitepaper.

2018 ◽  
Author(s):  
Marko Kovic ◽  
Christian Caspar ◽  
Adrian Rauchfleisch
Keyword(s):  
Medium Term ◽  
Short Term ◽  
Profound Impact ◽  
Future Challenges ◽  
The Way

Humankind is facing major challenges in the short-term, medium-term, and long-term future. Those challenges will have a profound impact on humankind’s future progress and wellbeing. In this whitepaper, we outline our understanding of humankind’s future challenges, and we describe the way in which we work towards identifying as well as managing them. In doing so, we pursue the overall goal of ZIPAR: We want to make the best future for humankind (ever so slightly) more probable.

scholarly journals Intralymphatic immunotherapy for allergic rhinoconjunctivitis: a systematic review and meta-analysis

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
M.P. Hoang ◽  
K. Seresirikachorn ◽  
W. Chitsuthipakorn ◽  
K. Snidvongs
Keyword(s):  
Systematic Review ◽  
Meta Analysis ◽  
Specific Ige ◽  
Allergic Rhinoconjunctivitis ◽  
Medium Term ◽  
Short Term ◽  
Intralymphatic Immunotherapy ◽  
Specific Igg4 ◽  
Igg4 Level

BACKGROUND: Intralymphatic immunotherapy (ILIT) is a new route of allergen-specific immunotherapy. Data confirming its effect is restricted to a small number of studies. METHODOLOGY: A systematic review with meta-analysis was conducted. The short-term (less than 24 weeks), medium-term (24-52 weeks), and long-term (more than 52 weeks) effects of ILIT in patients with allergic rhinoconjunctivitis (ARC) were assessed. The outcomes were combined symptom and medication scores (CSMS), symptoms visual analog scale (VAS), disease-specific quality of life (QOL), specific IgG4 level, specific IgE level, and adverse events. RESULTS: Eleven randomized controlled trials and 2 cohorts (483 participants) were included. Compared with placebo, short term benefits of ILIT for seasonal ARC improved CSMS, improved VAS and increased specific IgG4 level but did not change QOL or specific IgE level. Medium-term effect improved VAS. Data on the long-term benefit of ILIT remain unavailable and require longer term follow-up studies. There were no clinical benefits of ILIT for perennial ARC. ILIT was safe and well-tolerated. CONCLUSION: ILIT showed short-term benefits for seasonal ARC. The sustained effects of ILIT were inconclusive. It was well tolerated.

scholarly journals The Effect of Macroeconomic Variables On the Saudi Stock Market

2019 ◽  
Vol 7 (12) ◽  
pp. 126-152
Author(s):  
Amani Mohammed Aldukhail
Keyword(s):  
Stock Market ◽  
Performance Indicators ◽  
Stock Price ◽  
Price Index ◽  
Inflation Rate ◽  
Market Value ◽  
Short Term ◽  
Macroeconomic Variables ◽  
Time Deposits

This study aimed at exploring the effect of macroeconomic variables on the activity of the Saudi stock market for the period 1997-2017. Macroeconomic variables were: GDP, interest rate on time deposits, inflation rate. The variables of the Saudi stock market activity were: stock price index, market value of shares, value of traded shares. To achieve this objective, the researcher used the ARDL model for the self-regression of the lagged distributed time gaps. The most important results of the research are: The effect of macroeconomic variables on the performance indicators in the Saudi stock market is not important in the short term and is statistically significant in the long term according to the proposed models, so investors in this market can rely on macroeconomic variables in Predict the movement of the stock market and predict long-term profits and losses.

scholarly journals ANALISIS HARGA SAHAM INDEX KOMPAS 100 DENGAN PENDEKATAN ERROR CORRECTION MODEL

2021 ◽  
Vol 4 (1) ◽  
pp. 406-414
Author(s):  
Amir Hamzah
Keyword(s):  
Error Correction ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Financial Statements ◽  
Error Correction Model ◽  
Short Term ◽  
Correction Model ◽  
Positive Effect

The purpose of this research is to analyze the short term and long term relationship between ROI, EPS, PER ,inflation, SBI, exchange rate,and GDP on Stock Price. The data in this research is company financial statements which included Compas 100 Index on the Indonesia Stock Exchange. statistical analysis in this research used stasionarity test, The Classical Assumptions Test, Cointegration Test, Error Correction Model Test. This research found that partially ROI, EPS, PER variables a positive effect on stock prices in the short term and long term, KURS and SBI a positive effect on stock prices in the short term, but there is no effect in the long term, inflation and GDP do not affect the stock price both in the short term and long term. Simultaneously affected the stock prices significantly affect on stock price both in the short term and long term.

The Effect of Covid-19, Rupiah Exchange Rate, and Inflation on the Indonesia Sharia Stock Index during the Covid-19 Pandemic

2021 ◽  
Vol 4 (1) ◽  
pp. 313-336
Author(s):  
Achmad Agus Priyono ◽  
Ari Kartiko
Keyword(s):  
Exchange Rate ◽  
Time Series Data ◽  
Negative Impact ◽  
Stock Index ◽  
Series Data ◽  
Stock Market Index ◽  
Short Term ◽  
The Us ◽  
Us Dollar

Purpose of this study is to clarify the effect of the number of daily cases reported to have contracted the Covid-19 virus, the exchange rate of the rupiah against the US dollar and inflation on the movement of the Indonesian Sharia stock index (ISSI) during the Pandemic Covid 19 in the short term and long term. Data analysis methods that used is analysis Error Correction Mechanism (ECM) using Eviews software 10. The data collected is daily time series data starting from March 2, 2020 to May 31, 2021 so that the number of samples collected obtained as many as 283 samples . The results of the study stated that the addition of the daily number of reported cases of contracting the Covid-19 virus has a negative impact on The Indonesian Sharia Stock Market Index (ISSI) during the Covid-19 pandemic, so that encourage the weakening of the Stock Index both in the long and long term short. Likewise, the weakening of the rupiah against the US dollar will caused the fall of the sharia index during the Covid 19 pandemic, both in the long term and long and short term. However, the study found no effect inflation on the Indonesian Sharia Stock Index (ISSI) during the Covid19 pandemic, good long term and short term

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