Major Indices Research Articles

How does inflation propagate among CPI components? Evidence from the euro area

This study employs a time-varying parameter vector autoregressive (TVP-VAR) model to investigate infla- tion propagation and spillovers among twelve major Consumer Price Index (CPI) components across eleven euro area countries from 1999 to 2023. We uncover empirical regularities in the transmission channels of inflation, noting Transport and Clothing and Footwear as primary shock transmitters, whereas Communi- cation, Alcohol and Tobacco consistently absorb shocks. These insights, consistent across various contexts and countries, contribute to understanding inflation persistence and households’ inflation experiences in the Euro area.

Engineering Approach to Explore Language Reflecting Well-Being

Although well-being is helpful in measuring the state of society from various perspectives, past research has been limited to (1) questionnaire surveys, which make it difficult to target a large number of people, and (2) the major indices focus on individual factors and do not incorporate group factors. To tackle these issues, we collected daily reports from the company employees that included text, their individual subjective well-being, and team subjective well-being. By using the collected data, we constructed a well-being estimation model based on the Large Language Model and examined an indicator called ``sharedness index'', as a state of the team that influences an individual well-being, measured using both score- and text-based methods.

Hematological Profile of Silver Pompano (Trachinotus blochii) Broodstock with Hormonal Induction and Phytobiotic Supplementation in Feed

This study evaluates the hematological profile of potential broodstock silver pompano (Trachinotus blochii) through hormonal induction and phyto-biotic supplementation via feed. An experimental trial was conducted for 30 days to assess the hematological profile of potential broodstock Trachinotus blochii. This study employed a completely randomized design (CRD) in two research experiments. The first experiment utilized hormonal induction with estradiol-17β hormone on female silver pompano broodstock, consisting of 3 treatments: E1 (0 mg/kg), E2 (20 mg/kg), and E3 (60 mg/kg), whereas the second experiment employed phyto-biotic induction using Tribulus terrestris extract (ETT) and gonadotropin hormone, consisting of 5 treatments: T1: ETT 0 mg/kg feed, T2: ETT 50 mg/kg feed, T3: ETT 250 mg/kg feed, T4: ETT 50 mg/kg feed + gonadotropin 1000 IU/kg body weight, and T5: ETT 250 mg/kg feed + gonadotropin 500 IU/kg body weight, both experiments used 8 individuals as replicate per treatment. The results of the study indicated that all major hematological indices such as total erythrocytes, total leukocytes, and hematocrit levels were higher in male silver pompano broodstock supplemented with Tribulus terrestris extract compared to female silver pompano broodstock induced hormonally with estradiol-17β hormone.

Review of the Impacts of Previous General Elections on Stock Market Performance: Insights for Investors

This paper aims to examine how the announcement of election results affects the NSE (Nifty) and BSE (Sensex) indices. The analysis focuses on measuring the impact of daily average returns and volatility during different time periods before and after the election result announcement: thirty days, fifteen days, and seven days. The paper is based on secondary data as opinions and views of different authors who have conducted such kind of studies are referred for the purpose of writing this research paper and consolidated views are mugged up in the article form. The findings indicate that the impact of election results is predominantly observed in the short term, gradually diminishing over the medium and long term. The study concludes that there is no significant relationship between the announcement of election results and the performance of the stock market. It suggests that the stock market initially reacts unusually to this information, causing short-term anomalies that subsequently correct themselves in the following days. This study aims to analyse the effect of election result announcements on the performance of India's major stock indexes, Sensex (BSE) and Nifty (NSE). By examining previous research and focusing specifically on the election result's impact on these indexes, this study aims to provide insights for investors to navigate the market with agility and caution during election periods, maximizing their investment outcomes..

Efficacy of Laurus nobilis L. for Tight Junction Protein Imbalance in Leaky Gut Syndrome.

Laurus nobilis L. (LNL) belongs to the evergreen Lauraceae family. It is native to the Mediterranean and widely distributed in the southern United States, Europe, and the Middle East. LNL is rich in active ingredients of the sesquiterpene lactone series and has been reported to have antioxidant, anti-inflammatory, and anticancer effects. And parthenolide, known as a sesquiterpene lactone-based compound, inhibits the activation of lipopolysaccharide-binding protein (LBP), which is a major trigger for leaky gut syndrome. However, the effectiveness of LNL in improving the state of increased intestinal permeability has not yet been reported. Therefore, we demonstrated the efficacy of LNL, which is known to be rich in parthenolide, in improving intestinal permeability induced by IL-13. We investigated the improvement in permeability and analyzed major tight junction proteins (TJs), permeability-related mechanisms, weight and disease activity indices, and corresponding cytokine mechanisms. LNL maintained TJs homeostasis and clinical improvement by reducing increased claudin-2 through the inhibition of IL-13/STAT6 activation in TJ-damaged conditions. These results are expected to be effective in preventing leaky gut syndrome through the TJ balance and to further improve intestinal-related diseases, such as inflammatory bowel disease.

Post-COVID-19 technology adoption and noise trading: elucidation of investors' sentiments across cultures

PurposeDespite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.Design/methodology/approachDrawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.FindingsThe empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.Originality/valueThis study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

Least squares parameter estimation for uncertain fractional differential equations and application to stock model

In recent years, uncertain fractional differential equations was proposed for the description of complex uncertain dynamic systems with historical characteristics. For wider applications of uncertain fractional differential equations, researches on parameter estimation for uncertain fractional differential equations are of great importance. In this paper, based on the thought of least squares estimation and uncertain hypothesis test, an algorithm of parameter estimation for uncertain fractional differential equations is discussed. Finally, we consider the application of uncertain fractional differential equations based model to predict the forecasting stock price of three major indexes of U.S. stocks and make a comparison between uncertain fractional differential equations, uncertain differential equations and stochastic differential equations.

Multiracial Reading the Mind in the Eyes Test (MRMET): An inclusive version of an influential measure

Can an inclusive test of face cognition meet or exceed the psychometric properties of a prominent less inclusive test? Here, we norm and validate an updated version of the influential Reading the Mind in the Eyes Test (RMET), a clinically significant neuropsychiatric paradigm that has long been used to assess theory of mind and social cognition. Unlike the RMET, our Multiracial Reading the Mind in the Eyes Test (MRMET) incorporates racially inclusive stimuli, nongendered answer choices, ground-truth referenced answers, and more accessible vocabulary. We show, via a series of large datasets, that the MRMET meets or exceeds RMET across major psychometric indices. Moreover, the reliable signal captured by the two tests is statistically indistinguishable, evidence for full interchangeability. We thus present the MRMET as a high-quality, inclusive, normed and validated alternative to the RMET, and as a case in point that inclusivity in psychometric tests of face cognition is an achievable aim. The MRMET test and our normative and validation data sets are openly available under a CC-BY-SA 4.0 license at osf.io/ahq6n.

News-driven stock market index prediction based on trellis network and sentiment attention mechanism

Applying deep learning, especially time series neural network, to stock market prediction, has become one of the important applications in the quantitative finance field. However, due to the multi-correlation and volatility of the stock market, how to timely and accurately predict it has become a challenging issue. In order to cope with this challenge, a news-driven stock market index prediction model based on TrellisNet and a sentiment attention mechanism (SA-TrellisNet) is proposed. A sentiment analysis model based on CNN and LSTM is presented to obtain the sentiment index of massive news crawled from authoritative financial websites. Furthermore, a sentiment attention mechanism is designed for data fusion of stock data and news sentiment index as the input of the simple and efficient TrellisNet network for model training and prediction. The performance of our model is systematically evaluated using seven major international stock market indices including S&P500, NYSE, DJI, NASDAQ, FTSE 100, Nikkei 225 and SSE, and comparative experiments demonstrate that SA-TrellisNet is competitive to the other state-of-the-art methods in predicting stock market indices.

Quantile frequency connectedness between crude oil volatility, geopolitical risk and major agriculture and livestock markets

ABSTRACT This article investigates the quantile frequency risk connectedness between the global geopolitical risk (GPR) index, the CBOE Crude Oil Volatility Index (OVX), and the major agricultural and livestock indexes using a quantile vector autoregression (QVAR)-based frequency connectedness approach. The empirical results suggest that spillover effects are higher in extreme market conditions than in normal market conditions. The GPR has a more significant impact on other markets in the long-term domain of extreme market conditions. Moreover, grain markets are the primary transmitters of spillovers, while OVX predominantly assumes the role of a net receiver of risk spillover effects in most instances. Meanwhile, although the spillover structure between frequencies is homogeneous at the same quantile level, the connection between major agricultural and livestock index markets and two uncertainty indexes is time-varying, and spillovers are heterogeneous at different quantile levels. Our research provides investors and policymakers with new understandings of the development of differentiated crude oil volatility and geopolitical risk aversion strategies under different market conditions, given the heterogeneity of volatility spillover connectedness under different market conditions and frequency domains.

News intensity and asset returns: the case of currency volatility

ABSTRACT There are both theoretical reasons and empirical evidence for financial markets rewarding investors who put effort into acquiring relevant information. This article shows how a systematic approach of encoding text, ‘semantic fingerprinting’ can be applied to a set of news headlines from The Wall Street Journal to measure the ‘news intensity’ − the volume of relevant news − pertaining to three major currency indices: dollar, pound and euro. In a dataset that spans two decades, we find a persistently positive link between the ‘news intensity’ and the volatility of currency returns, that becomes significantly stronger in times of recession: ‘bad news’ tends to translate into higher volatility.

A Deep Neural Network-Based Intelligent Forecasting Approach for Multi-Dimensional Economic Indexes in Smart Cities

Intelligent forecasting of economic indexes has been an important demand for sustainable management of smart cities. Existing methods for this purpose were mostly established upon the basis of economic mechanism. Econometric models are the most general technical means in this area. However, in era of digital economy, increasing amount of big data has brought great change to traditional production. It is becoming more difficult for conventional technological forecasting methods to deal with multi-dimensional economic indexes. To deal with such challenge, this paper introduces the artificial intelligence algorithms to implement automatic information processing, and proposes a deep neural network-based intelligent forecasting method for multi-dimensional economic indexes in smart cities. Specifically, a deep neural network with three-layer structure is developed as the backbone methodology. For empirical analysis, the real-world data from “Chengdu–Chongqing Economic Circle” in China from 2012 to 2022 are selected as the main simulation scenario. Four major indexes are selected as the main research object: gross product (GDP), per capita GDP, GDP growth rate and the proportion of tertiary industry in GDP. The experimental results show that the proposal can well deal with such forecasting problem from a data-driven perspective, with a proper forecasting effect on historical data.

EXPLORING CEEMDAN DECOMPOSITION FOR IMPROVED FINANCIAL MARKET FORECASTING: A CASE STUDY ON DOW JONES INDEX

This study presents an innovative financial time series analysis approach by integrating Complete Ensemble Empirical Mode Decomposition (CEEMDAN) with the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model. The primary contribution of the research lies in significantly enhancing the predictive accuracy and understanding of the dynamics governing major stock indices. CEEMDAN adaptively decomposes complex financial time series into intrinsic mode functions (IMFs), a technique that has yet to be extensively utilized in this domain. IMFs are combined with ARIMAX's predictive proficiency, which accounts for the influence of historical trends and external factors. Our study showcases an R² of 0,93, aligning with some of the high-performing models in the literature. However, the unique strength of our model lies in its lag-free predicting of the DJIA, effectively mirroring its volatility and major movements with high fidelity, making it highly practical for financial applications.

Data Don’t Lie: A Comparative Study of Nepal’s Development under Absolute Monarchy and Post-Monarchy Democratic Era

A widespread perception among the Nepalese people is that Nepal was in a better position in terms of development during the party-less Panchayat political system under the absolute monarchy from 1960 to 1990 than in the post-Panchayat democratic period after 1990. This article presents a comparative study of major development indexes during the Panchayat and post-Panchayat periods and aims to clarify the origins and reality of this perception. Data were obtained and analyzed using secondary global sources such as UNICEF, UNFPA, UNESCO, Education Statistics, World Bank, etc. Further comparison is made with corresponding Indian indexes for a better assessment of development after the emergence of globalization. The comparison shows that, contrary to the common perception, Nepal is in a much better development position in most of the indexes in the post-Panchayat democratic period.

Modeling assessment of air pollution control measures and COVID-19 pandemic on air quality improvements over Greater Bay Area of China

A remarkable progress has been made toward the air quality improvements over the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China from 2017 to 2020. In this study, for the first time, the emission reductions of regional control measures together with the COVID-19 pandemic were considered simultaneously into the development of the GBA's emission inventories for the years of 2017 and 2020. Based on these collective emission inventories, the impacts of control measures, meteorological variations together with temporary COVID-19 lockdowns on the five major air quality index pollutants (SO2, NO2, PM2.5, PM10, and O3, excluding CO) were evaluated using the WRF-CMAQ and SMAT-CE model attainment assessment tool over the GBA region. Our results revealed that control measures in the Pearl River Delta (PRD) region affected significantly the GBA, resulting in pollutant reductions ranging from 48 % to 64 %. In contrast, control measures in Hong Kong and Macao contributed to pollutant reductions up to 10 %. In PRD emission sectors, stationary combustion, on-road, industrial processes and dust sectors stand out as the primary contributors to overall air quality improvements. Moreover, the COVID-19 pandemic during period I (Jan 23-Feb 23) led to a reduction of NO2 concentration by 7.4 %, resulting in a negative contribution (disbenefit) for O3 with an increase by 2.4 %. Our findings highlight the significance of PRD control measures for the air quality improvements over the GBA, emphasizing the necessity of implementing more refined and feasible manageable joint prevention and control policies.

Assessment of Potential Human Health Risk Associated With Heavy Metals in Soil – Vegetables System Irrigated by Rift Valley Lake Ziway, Ethiopia

Metal contamination of vegetables is a major concern, especially in areas where irrigation has been used for a long time. The Central Ethiopian Rift Valley Region is highly affected due to the heavy use of agrochemicals throughout the year. Although some studies have been conducted on the concentration and level of heavy metal pollution, little attention has been paid to their impact on human health. This study determined the concentrations of nine metals (As, Cd, Cr, Co, Cu, Hg, Ni, Pb, and Zn) in a soils and vegetables (tomatoes and onions) irrigated by Lake Ziway, Ethiopia. An inductively coupled plasma optical emission spectrometer (ICP-OES) was used to determine the heavy metal levels. Moreover, the study evaluated the health risks associated with consuming contaminated vegetables by employing major indices, including non-carcinogenic and carcinogenic risks. The results showed that the mean concentrations of Pb, As, Hg, and Cr in all tomato and onion samples and the average Zn, Pb, Cd, and Hg levels in all soil samples exceeded the FAO/WHO threshold. Non-carcinogenic health risk estimates showed that As, Pb, Hg, and Co for children and Pb and Co for adults had THQ values greater than 1, indicating the possibility of systemic health risks. Carcinogenic health risk showed that Ni posed a risk of developing cancer for both adults and children, with values ranging from 10−3 to 10−1. In addition, children are likely to have a higher cancer risk due to exposure to As and Cr. Therefore, it is crucial to raise awareness among stakeholders and implement effective monitoring policies to safeguard public health both within and beyond the study area.

Multiscale association between large‐scale climate variability modes and drought in India via wavelet analysis

AbstractExtreme events, such as droughts, are influenced by climate variability modes such as El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Atlantic Multi‐decadal oscillation (AMO) and Equatorial Indian Ocean Oscillation (EQUINOO). Due to the significant interdependency among the climatic indices at various frequencies, a time‐frequency framework is necessary to better understand the teleconnection. The present study evaluates the association between climate variability modes and drought index in India using different variants of wavelet analysis such as wavelet coherence analysis (WCA), partial wavelet coherence analysis (PWCA), multiple wavelet coherence analysis (MWCA) and wavelet reconstruction methods. In this study, four major climatic indices (ENSO, PDO, AMO, EQUINOO) and the drought index, standard precipitation index (SPI), at four different monthly time periods (1, 3, 6 and 12) are considered. The results from the WCA analysis highlight significant teleconnection spectra for ENSO, PDO and EQUINOO at 2–8, 8–16 and 16–32 year time‐frequency bands. Further, PWCA results reveal significant interdependency in the teleconnection of ENSO and PDO at all the scales, while ENSO and EQUINOO are found to be independent modes of interannual drought variability in India. Finally, MWCA results show that the combination of EQUINOO and ENSO better explains the interannual variability of droughts in Northwest and Central northeast India. The results of this study will help improve drought prediction and early warning systems in India, by selection of appropriate climatic oscillations for different regions in India.

Trade Agreements and Financial Market Integration in Latin America and the US

The primary objective of this study is to examine the extent of financial integration between Latin American and US financial markets, particularly in light of recent efforts to foster integration through trade agreements. Spanning from 1 January 1990 to 31 December 2019, the sample focuses on major market indices and key sectors. Financial integration is quantified using a DCC multivariate GARCH model, incorporating a smooth transition model, structural breaks, and regression-based approaches. Results indicate increased comovement with the US for main market indices in Argentina, Chile, Colombia, Mexico, and Peru, while Brazil shows a decrease. Similar trends are observed in sectoral analyses. This study also reveals heightened correlation post-trade agreements. Structural break analysis highlights significant shifts in dynamic correlations for countries with US free trade agreements. These findings support the argument of increased financial integration, bearing significance for portfolio diversification and international policy formulation.

Gold Smuggling in India and Its Effect on the Bullion Industry

This study strives to examine when and where most of the gold smuggling takes place in India. It further analyses the causal relationship between smuggled gold and other macroeconomic variables. Finally, it analyses how the smuggled gold affects the Indian bullion industry. The data related to gold smuggling has been sourced from the website of the Directorate Revenue Intelligence and analysed using graphs and the Granger causality test. The variables used in the study are the quantity of smuggled gold, exchange rates, the major stock indices in the world, the number of auspicious days in a month, domestic and international gold prices, India’s jewellery export, the GDP, customs duty, and the domestic gold supply. The results revealed that most of the gold smuggling takes place on Fridays and mostly occurs in the months of October, November, and December. The states of West Bengal, Delhi, Maharashtra, and Tamil Nadu account for most of the gold smuggling in India. A positive correlation is observed between the smuggled gold, India’s gold demand, the number of auspicious days in the month, India’s jewellery export, India’s GDP, India’s domestic gold supply, and stock indices such as SENSEX, FTSE100, DFMGI. Gold smuggling in India is caused by India’s gold demand, the level of jewellery export, the GDP, domestic and international gold prices, and India’s customs duty.

Left atrial strain in patients with β-thalassemia major: a cross-sectional CMR study.

The aim of this cross-sectional study was to investigate the association of left atrial (LA) strain parameters with demographics, clinical data, cardiovascular magnetic resonance (CMR) findings, and cardiac complications (heart failure and arrhythmias) in a cohort of patients with β-thalassemia major (β-TM). We considered 264 β-TM patients (133 females, 36.79 ± 11.95 years) consecutively enrolled in the Extension-Myocardial Iron Overload in Thalassemia (E-MIOT) project. Moreover, we included 35 sex- and age-matched healthy controls (14 females, mean age 37.36 ± 17.52 years). Reservoir, conduit, and booster LA functions were analysed by CMR feature tracking using dedicated software. Compared to the healthy control group, β-TM patients demonstrated lower LA reservoir strain and booster strains, as well as LA reservoir and booster strain rates. However, no differences were found in LA conduit deformation parameters. In β-TM patients, ageing, sex, and left ventricle (LV) volume indexes were independent determinants of LA strain parameters. The number of segments with late gadolinium enhancement (LGE) significantly correlated with all LA strain parameters, with the exception of the LA conduit rate. Patients with cardiac complications exhibited significantly impaired strain parameters compared to patients without cardiac complications. In patients with β-TM, LA strain parameters were impaired compared to control subjects, and they exhibited a significant correlation with the number of LV segments with LGE. Furthermore, patients with cardiac complications had impaired left atrial strain parameters. Clinical relevance statement In patients with β-thalassemia major, left atrial strain parameters were impaired compared to control subjects and emerged as a sensitive marker of cardiac complications, stronger than cardiac iron levels. • Compared to healthy subjects, β-thalassemia major patients demonstrated significantly lower left atrial reservoir strain and booster strains, as well as left atrial reservoir and booster strain rates. • In β-thalassemia major, ageing, sex, and left ventricular volume indexes were independent determinants of left atrial strain parameters, while left atrial strain parameters were not correlated with myocardial iron overload. • An independent association between reduced left atrial strain parameters and a history of cardiac complications was found in β-thalassemia major patients.