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Do UN missions reduce forced displacement? Facing insecure environments, civilians are left with three choices: staying; moving to a safer community; or moving outside their country. Their aspiration and ability to move depend on individual characteristics and macro-level factors, such as the social, economic and political context in which these people live. Research shows that UN missions can impact and reset the macro-level context altered by war, especially in the security and economic domain. However, we lack empirical evidence on whether this impact helps UN peacekeeping tackle forced displacement and returns. This article offers the first global analysis of whether and how UN missions can shape aggregate population movements during civil wars. We combine data on outflows and returns of refugees and internally displaced people (IDPs) with data on distinct UN missions' features that we expect to affect population movements, namely the size of their contingents and their mandated tasks. Using matched samples, we find that the unfolding of the outflows and inflows processes are affected by different features of UN missions. Sizeable deployments decrease IDPs flows and encourage their return; refugee outflows, on the other hand, may increase in presence of UN missions. Furthermore, missions with displacement-related mandates are associated with decreasing IDP flows overall, but only encourage refugees' returns. [ABSTRACT FROM AUTHOR]

Background: With the advancement of green revolution over 3-4 decades, the production and productivity of crop is decreasing with reduction in soil productivity in terms of nutritional disorders, micronutrient deficiencies, poor soil physical condition and livelihood supporting systems. In view of this, natural and organic agriculture systems emerged as an alternative to the chemical oriented agriculture systems. Organic farming and organically produced food products are gaining popularity very rapidly in India and world. To trounce the reliance on chemical fertilizers for crop production and to reduce cost of production experiments were conducted to know the effect of different management practices on blackgram. Methods: The field experiments were conducted with randomized complete block design with 4 replications. Effects of these different crop management practices were evaluated on growth, yield parameters, yield and nutrient status of blackgram (TAU-1) was carried out during Kharif 2019, 2020 and 2021. Result: Results showed that package of practice recorded significantly higher growth, yield attributes and yields in blackgram followed by organic farming and farmers practice over three years. Based on current investigation it can be concluded that practicing organic farming and natural farming over the years there will be sustainability in food production compared to initial years. [ABSTRACT FROM AUTHOR]

The study determined the effect of size, equity, labour, loans, and deposits on the performance of the financial technology (FinTechs; return on assets — ROA and return on equity — ROE). Data on FinTech performance was collected from the Refinitive Eikon platform. As prior studies have found, variables such as size, equity, labour, loans, and deposits can impact the ROA and ROE (Abbasi et al., 2021; Akbar, 2021). Drawing upon prior work examining similar relationships at the firm level one, the research analyzes a sample of 148 FinTech from 10 Organisation for Economic Co-operation and Development (OECD) countries from 2000–2022. Consistent with Abbasi et al.’s (2021) findings regarding FinTech adoption and efficiency, the impact of variables, including size, equity, loans, and deposits on returns, are evaluated. According to the study, it was clear that a rise in the company’s size led to a decline in its financial performance. The findings revealed that assets positively affected the performance of FinTechs across the different financial periods. Labour had a negative effect on the ROA across the OECD countries’ FinTechs, while loans positively affected the performance of the different FinTechs. From the study, there is a need for more collaborative research across academics, policymakers, and industry experts to better the outcomes. [ABSTRACT FROM AUTHOR]

During the revelation of the Harvey Weinstein scandal and the reemergence of the #MeToo movement, firms with a nonsexist corporate culture, proxied by having women among the five highest‐paid executives, earn excess returns of 1.3% relative to firms without female top executives. These returns are driven by changes in investor preferences toward firms with a nonsexist culture. Institutional ownership increases in firms with a nonsexist culture after the Weinstein/#MeToo events, particularly for investors with larger holdings and investors with a lower ESG focus ex ante. Firms without female top executives improve gender diversity after these events, particularly in more sexist states and in industries with few women executives. Our evidence attests to the value of having a nonsexist corporate culture and indicates that changes in societal norms toward women are permeating into capital markets and corporations. [ABSTRACT FROM AUTHOR]

This research study aims to comprehensively examine and compare the growth of Electronic filing of returns associated with Income Tax in the 28 states of India. It examines the correlation between the states of India and the adoption of e-filing for income tax, aiming to understand variations in e-filing growth among large, medium, and small states.Using data from the department of Income Tax, this study analyzed E-filing trends spanning from 2011-12 to 2022-23. The states were categorized according to their population sizes. Data processing was performed in Microsoft Excel, and the statistical analysis involved descriptive analysis and various tests conducted in Jamovi software.The findings of this study reveal a consistent upward trend in E-filing of tax returns across large, medium, and small states in India. Maharashtra distinguishes itself as the leading state in terms of performance among the large states, Punjab leads in the medium state category, and Goa excels among small states regarding adoption and use of E-filing of retuns.This research contributes value by offering a comprehensive analysis of E-filing trends within different state categories in India, providing a deeper understanding of the variations and significant differences among states in their adoption of electronic income tax filing methods.

Traditional momentum strategies on the stock market are implemented in accordance with the efficient market hypothesis and involve making investments in accordance with market trends. However, this hypothesis has been repeatedly criticized by supporters of behavioral finance, who allow the irrational nature of investment decisions, which led to the emergence of contrarian investment strategies, based on the overreaction hypothesis and the reversal effect (over a longer horizon, loser stocks outperform winners), according to which investments are made in opposite direction to the market, involves buying the losing stocks and selling the winners. According to contrarian investment strategies, when buying shares, investors prefer a lower level of P/E ratios (Price – earnings ratio), which indicates the valuation multiple placed to the company’s market price vis-à-vis its net profits. The article examines the hypothesis that P/E ratio and contrarian investment are related, based on data for 2010–2022 for 50 shares of India, whose stock market is developing rapidly, examines the differences between the top ten P/E stocks and the bottom ten P/E stocks using inflation adjusted return on investment (ROI) to determine, whether or not, an investment in such stocks would generate sufficient returns to outpace inflation over a period of ten years. The article tests 3 hypotheses that investing in stocks with low P/E ratios in the Nifty 50 index (a live benchmark that illustrates how each industry contributes to India’s GDP) will generate higher returns than investing in stocks with high P/E ratios, over a long-term horizon (H1), inflation (Н2), the Nifty 50 index (Н3). The sources of data were finalized to be BSE Official Website, NSE Official Website, Yahoo Finance, Screener, Annual Reports, etc. The article calculates the profits for top 10 P/E stocks (conventional strategy) and bottom 10 P/E stocks (contrarian investing), and determining the returns from top 10 P/E stocks and the bottom 10 P/E stocks (when calculating for 10 years, the same steps were repeated: synchronously, once a year, both types of shares were bought at Purchase Price and sold a year later at Market Price). The calculations confirmed all 3 proposed hypotheses. Contrarian investing delivered returns of around 3.64 times the initial investment, whereas conventional investing produced returns of about 77.5% when comparing the top 10 P/E stocks to the bottom 10 P/E stocks in the Nifty 50 index. An outstanding return of 84.11%, which is a strong return when adjusted for inflation, was achieved using the contrarian strategy. As a result of the conventional strategy’s loss of 10.45% when adjusted for inflation, the study also shows that contrarian investing can assist an investor in hedging against inflation. This emphasizes how crucial it is to take a contrarian attitude when making stock market investments. At the same time, contrarian investment returns of 3.64 times the initial investment over this period surpass the benchmark Nifty 50 Index returns, which is 3.24 times.

This research aims to analyze the capital market reaction to the event study of Prabowo-Gibran's victory in the Indonesian Presidential Election by comparing the variables of stock price, stock return and Infobank15 stock trading volume activity in the research period 20 days before and 20 days after the announcement on March 20 2024. Including quantitative research because the data taken is secondary data sourced from idx.co.id where the population which is also a sample is 15 leading banks with good performance according to the Indonesian Stock Exchange. The research results reveal that there is no difference in the average stock price, average stock return and average TVA before and after the announcement of Prabowo-Gibran's victory.

This study investigates the interdependent structure between Nigeria and developed stock market using directed acyclic graph with Bayesian estimation techniques for the period of March 11, 2020 to July 31, 2021. The dynamic Granger causal test of both the contemporaneous and autoregressive estimation were done using the Markov Chain Monte Carlo (MCMC) simulation method with Metropolis-Hasting (MH) sampling technique. The graphical structure of the instantaneous and autoregressive results shows returns correlation for Nigeria's stock market and both China and India's stock market while absence of return correlation are found for both US and UK stock market. The result implies that Nigeria's stock market has potential diversification benefit for the US and UK investors during pandemic crises. In addition, US stock market returns is found to depend on China stock market returns while volatility spill over runs from the US stock market to China's stock market. Last, UK stock market tends to depend on India's stock market. The implication of the study is that Nigeria's stock market is integrated with China and India's stock market while China's stock market is linked to the US stock market such that any financial shock from the lead stock markets is transmitted to the lagged stock markets. [ABSTRACT FROM AUTHOR]

Financial markets are increasingly interlinked. Therefore, this study explores the complex relationships between the Tadawul All Share Index (TASI), West Texas Intermediate (WTI) crude oil prices, and Bitcoin (BTC) returns, which are pivotal to informed investment and risk-management decisions. Using copula-based models, this study identified Student's t copula as the most appropriate one for encapsulating the dependencies between TASI and BTC and between TASI and WTI prices, highlighting significant tail dependencies. For the BTC–WTI relationship, the Frank copula was found to have the best fit, indicating nonlinear correlation without tail dependence. The predictive power of the identified copulas were compared to that of Long Short-Term Memory (LSTM) networks. The LSTM models demonstrated markedly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE) across all assets, indicating higher predictive accuracy. The empirical findings of this research provide valuable insights for financial market participants and contribute to the literature on asset relationship modeling. By revealing the most effective copulas for different asset pairs and establishing the robust forecasting capabilities of LSTM networks, this paper sets the stage for future investigations of the predictive modeling of financial time-series data. The study highlights the potential of integrating machine-learning techniques with traditional econometric models to improve investment strategies and risk-management practices. [ABSTRACT FROM AUTHOR]

Geopolitical risks play an important role in international financial markets. Previous research found how geopolitical risks negatively impacted foreign exchange markets due to the Russia-Ukraine conflict as a case study. This research aims to investigate how returns and risks in global stock markets respond to changes in overall geopolitical risk. The geopolitical risk index (GPRD) from Caldara and Matteo is designed to assess the geopolitical risks that exist in 44 countries in the world. This research uses panel data methods to examine the relationship between variables covering 44 different countries over a 10 year period. This research also further investigates how differences in economic conditions, political stability, productivity, and levels of happiness influence stock market responses. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Estudios Políticos is the property of Centro de Estudios Politicos y Constitucionales and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

E-commerce is a boom in the modern business. E-commerce (electronic commerce) is the activity of electronically buying or selling products on online services or over the Internet. E-commerce is one of the most dynamic and important sectors of the economy as well as one of the main factors leading to greater competitiveness. The development of E-commerce is driven by rapidly expanding Internet access, but also by growing mobility and popularity of portable devices, via which customers order goods and services at a convenient time and place more and more frequently. The strength of a business is determined by how well it can manage to provide its products and services at the right price, time, and quality. In today's fierce competitive world, E-commerce businesses are often up against their peers from different geographical regions. It makes perfect sense for a company to expand its operations geographically, which would allow them the advantages of quickly reaching out to more customers and building good word of mouth for the brand. But, it also give rise to the risks like additional costs associated with logistics, longer delivery time than expected, problems with adjusting the inventory levels to match customer needs at each location, etc. That's why it makes sense to think about Return to Origin (RTO) before calculating revenue and profits. So, this paper is on impact of RTO on E-commerce business and it also discussed preliminary findings of research undertaken with the Indian ecommerce company. [ABSTRACT FROM AUTHOR]

We use monthly data covering a century-long sample period (1915–2021) to study whether geopolitical risk helps to forecast subsequent gold volatility. We account not only for geopolitical threats and acts, but also for 39 country-specific sources of geopolitical risk. The response of subsequent volatility is heterogeneous across countries and nonlinear. We find that accounting for geopolitical risk at the country level improves forecast accuracy, especially when we use random forests to estimate our forecasting models. As an extension, we report empirical evidence on the predictive value of the country-level sources of geopolitical risk for two other candidate safe-haven assets, oil and silver, over the sample periods 1900–2021 and 1915–2021, respectively. Our results have important implications for the portfolio and risk-management decisions of investors who seek a safe haven in times of heightened geopolitical tensions. [ABSTRACT FROM AUTHOR]

Purpose: This research analyzes online consumer reviews and ratings to assess e-retail order fulfillment performance. The study aims to (1) identify consumer journey touchpoints in the order fulfillment process and (2) determine their relative importance for the consumer experience. Design/methodology/approach: Text mining and analytics were employed to examine over 100 m online purchase orders, along with associated consumer reviews and ratings from Amazon US. Using natural language processing techniques, the corpus of reviews was structured to pinpoint touchpoints related to order fulfillment. Reviews were then classified according to their stance (either positive or negative) toward these touchpoints. Finally, the classes were correlated with consumer rating, measured by the number of stars, to determine the relative importance of each touchpoint. Findings: The study reveals 12 touchpoints within the order fulfillment process, which are split into three groups: delivery, packaging and returns. These touchpoints significantly influence star ratings: positive experiences elevate them, while negative ones reduce them. The findings provide a quantifiable measure of these effects, articulated in terms of star ratings, which directly reflect the influence of experiences on consumer evaluations. Research limitations/implications: The dataset utilized in this study is from the US market, which limits the generalizability of the findings to other markets. Moreover, the novel methodology used to map and quantify customer journey touchpoints requires further refinement. Practical implications: In e-retail and logistics, comprehending touchpoints in the order fulfillment process is pivotal. This understanding helps improve consumer interactions and enhance satisfaction. Such insights not only drive higher conversion rates but also guide informed managerial decisions, particularly in service development. Originality/value: Drawing upon consumer-generated data, this research identifies a cohesive set of touchpoints within the order fulfillment process and quantitatively evaluates their influence on consumer experience using star ratings as a metric. [ABSTRACT FROM AUTHOR]

Beekeeping is a vital economic activity for smallholder farmers; therefore, it is essential to provide beekeepers with information regarding the profitability and productivity of box hives to encourage them to embrace the technology. However, scant information is available to comprehensively compare the profitability and output of traditional and modern beehives. This study seeks to compare the performance of traditional and modern beehives in Baringo South Sub-County. Simple random sampling was used in conjunction with a cross-sectional survey to collect data from the 197 beekeepers who participated in the study. A gross margin analysis was performed to understand the returns of traditional and modern hives. According to the findings, individuals who used traditional beehives had a higher overall operational cost of Ksh. 1449.44 per hive, whereas those who used modern hives had a total operational cost of Ksh. 1000.73. In addition, the study also demonstrated that those that utilised modern beehives received larger gross margins in Kenyan shillings 7917.74, whilst those who used traditional beehives received Ksh. 5590.59. According to the study’s findings, modern beekeeping generates higher returns than traditional beekeeping. As a result, farmers should be encouraged to employ modern beehives to generate higher returns. [ABSTRACT FROM AUTHOR]

This study aims to investigate the relationship between returns and risk of Islamic stock under stable economic conditions, crises, and pandemics within the scope of Indonesian and Asian Islamic capital markets. How do economic conditions affect the risks and returns of investors in the Indonesian and Asian Islamic capital markets? Verification of the veracity of the Islamic capital market serves as a more resilient option for alternative investments. This study uses Granger causality to determine exogenous and endogenous variables when building the model. The model that is formed is then analyzed using regression with dummy variables of stable economic conditions, crises, and pandemics. The first research findings on differences in crisis, stable and pandemic times in the Asian stock market show that there is no significant difference in effect between stable times and during a crisis, but there are differences in the effect during stable and pandemic times. The second research finding states that the return on Asian market Shariah stocks has no influence on increasing or reducing the value of risk or value at risk. The third finding explains that Islamic stocks in Indonesia have a greater risk value during pandemics and crises than in stable times, but the effect of pandemic and crisis conditions is not as great as Islamic stocks in Asia as a whole. In order to stabilize markets and reduce risks, regulatory bodies and governments frequently employ a variety of actions during times of crisis. When applied to trading volume, risk, and return patterns, these findings can help determine the appropriate policy. [ABSTRACT FROM AUTHOR]

This paper argues that firms in certain life cycle stages may be more subjectively valued by individual investors, leading to an optimistic bias in stock prices that is subsequently corrected upon the release of earnings news. Using a cash flow-based life cycle stage classification, introduction and decline stage companies exhibit three-day cumulative abnormal returns (CARs) around earnings announcements that are at least 112 bps lower than firms in growth, maturity, and shake-out stages. Specifically, introduction and decline stage stocks exhibit less positive reactions to positive earnings surprises and more negative reactions to negative earnings surprises relative to companies in other life cycle stages. Lottery stocks' excess returns around earnings announcements (Liu et al. in Journal of Financial Economics 138: 789–817, 2020) also vary based on firm life cycle stage. Our findings suggest that individual investors' optimistic expectations for introduction and decline stage stocks are met with disappointment when value-relevant earnings news is released. This study demonstrates that firm life cycle stage has real implications for stock price reactions to earnings announcements. [ABSTRACT FROM AUTHOR]

The research problem of this paper examines the impact of Brent oil price shocks on stock returns of nine companies from the US market, operating in three different industrial sectors. The observation period covers 2015 to 2023. The research process involves determining the impact of shock transmission using a quantile regression approach. The results show that most of the evaluated quantile parameters are highly statistically significant, i.e. with more than 99% probability. The estimated quantile parameters have the property of being able to observe the spillover effects of shocks in different states of the economy, such as recession, normal state and expansion. The research results suggest that the spillover of shocks from the Brent oil market is most pronounced in the automotive industry sector, that is, in the companies that are most dependent on oil for energy. The significance of the research is reflected in the lack of existing research that deals with the impact of the most important commodity in the world on the prices of company shares with the application of this methodology, which is also a contribution to science. Finally, the results of this research are very relevant for making investment decisions for economic policy makers, investors and company management.

Rare events (RE) are substantial with significant impact but are difficult to predict, often deviating from regular expectations. These events trigger psychological reactions in the market and susceptible to irrational decisions that challenge logical assumptions. The rapidity of the crisis has led to highly volatile market conditions, fostering instances of asymmetric information. Therefore, this study aimed to explore the impact of attention on market dynamics by examining diverse possibilities over time. The article focused on all publicly listed industries on the Indonesian Stock Exchange (IDX/BEI). Using time series regression data from 1997 to 2020, the article comprised 5,615 observations across nine sectors. The primary model was based on three factors originating from the Fama-French and prospect theory, with attention serving as the main risk element to assess the impact of attention on abnormal returns (AR) during RE. The results disclosed that various events showed diverse effects on attention behavior, varying across all sectors. Additionally, moderation analysis showed a correlation between attention and AR. The results signified that RE mitigates the negative relationship between attention and AR. The adverse impact of attention on AR diminishes during RE. These results contributed to the literature by providing insights into the excessive attention to specific information disrupts market mechanisms, triggers disproportionate emotional responses, and alters investor preferences. Furthermore, this study established that events prompting excessive attention have varying effects on attention behavior across all sectors.

This study examines the association between a firm’s fundamental factors and stock returns as well as how investor sentiment influences the association between these variables. The fundamental factors analyzed include accounting variables (earnings yields, change in profitability, and capital investment) and earnings quality measures. Investor sentiment is measured using the South African consumer confidence index. A sample of 1 386 firm-year observations from the companies listed on the Johannesburg Stock Exchange (JSE) between 1990 and 2022 was used. The results show that fundamental factors are related to stock returns, except for measures of earnings quality. Although earnings yields and capital investments cause variation in stock returns, their effects on stock returns are influenced by other variables associated with stock returns. In addition, investor sentiment affects the relationship between fundamental factors and stock returns, suggesting that sentiment influences the outcome of the capital market and the way investors process fundamental information. Profitability seems to be more related to stock returns than other fundamental variables. This study sheds light on how sentiment interacts with a set of fundamental factors and their sensitivity to stock returns and highlights the important role of investor sentiment and accounting information in the capital market.

This study was conducted to estimate costs and returns from production and examined profitability and problems of small-scale potato production in Plateau state. Research on cost and returns analysis can provide valuable insights into the factors affecting the profitability of potato farming in Nigeria. Policymakers can use this information to design effective policies and interventions to support the agricultural sector and improve the livelihoods of potato farmers. A four-stage random sampling technique was employed to obtain primary data from 120 respondents with a well-structured questionnaire. Data were analyzed using descriptive and inferential statistics such as mean, frequency count, percentages, gross margin, and multiple regression analysis. The study revealed that small-scale potato production in the area was profitable as the estimation of cost and profitability of the farmers in the area under study confirmed that with a NFI of N 117,015.02 per hectare and labor constitute 62.73 % of the total cost. Farmers in the study area face constraints such as poor/inadequate storage facilities, high post-harvest losses, high costs of inputs etc. The study recommends that proper storage measures/facilities should be made available to these small- scale farmers at affordable prices, credit facilities should be given to these small-scale potato producers in the area to increase output, and policy interventions to protect local producers should be put in place. [ABSTRACT FROM AUTHOR]

We study the association of shareholder returns with liberalization in government policy during Britain's railway run-up of 1844–5. The findings sustain two main claims. First, the railway returns during the run-up were associated with the advent of liberalizing policies, especially related to free trade, enhanced transparency and governance of firms, and industry consolidation. Second, analysis of cross-sectional variation reveals higher returns to large railways in the South and Midlands of England, several of which were leading consolidators. This study is the first to report an association between policy liberalization and run-up returns and to identify consolidators as the prime beneficiaries of the liberalization. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traditional momentum strategies on the stock market are implemented in accordance with the efficient market hypothesis and involve making investments in accordance with market trends. However, this hypothesis has been repeatedly criticized by supporters of behavioral finance, who allow the irrational nature of investment decisions, which led to the emergence of contrarian investment strategies, based on the overreaction hypothesis and the reversal effect (over a longer horizon, loser stocks outperform winners), according to which investments are made in opposite direction to the market, involves buying the losing stocks and selling the winners. According to contrarian investment strategies, when buying shares, investors prefer a lower level of P/E ratios (Price -- earnings ratio), which indicates the valuation multiple placed to the company's market price vis-à-vis its net profits. The article examines the hypothesis that P/E ratio and contrarian investment are related, based on data for 2010--2022 for 50 shares of India, whose stock market is developing rapidly, examines the differences between the top ten P/E stocks and the bottom ten P/E stocks using inflation adjusted return on investment (ROI) to determine, whether or not, an investment in such stocks would generate sufficient returns to outpace inflation over a period of ten years. The article tests 3 hypotheses that investing in stocks with low P/E ratios in the Nifty 50 index (a live benchmark that illustrates how each industry contributes to India's GDP) will generate higher returns than investing in stocks with high P/E ratios, over a long-term horizon (H1), inflation (H2), the Nifty 50 index (H3). The sources of data were finalized to be BSE Official Website, NSE Official Website, Yahoo Finance, Screener, Annual Reports, etc. The article calculates the profits for top 10 P/E stocks (conventional strategy) and bottom 10 P/E stocks (contrarian investing), and determining the returns from top 10 P/E stocks and the bottom 10 P/E stocks (when calculating for 10 years, the same steps were repeated: synchronously, once a year, both types of shares were bought at Purchase Price and sold a year later at Market Price). The calculations confirmed all 3 proposed hypotheses. Contrarian investing delivered returns of around 3.64 times the initial investment, whereas conventional investing produced returns of about 77.5% when comparing the top 10 P/E stocks to the bottom 10 P/E stocks in the Nifty 50 index. An outstanding return of 84.11%, which is a strong return when adjusted for inflation, was achieved using the contrarian strategy. As a result of the conventional strategy's loss of 10.45% when adjusted for inflation, the study also shows that contrarian investing can assist an investor in hedging against inflation. This emphasizes how crucial it is to take a contrarian attitude when making stock market investments. At the same time, contrarian investment returns of 3.64 times the initial investment over this period surpass the benchmark Nifty 50 Index returns, which is 3.24 times. [ABSTRACT FROM AUTHOR]

This study explores how differences in market yields of soymeal and soyoil, as derived from market prices compared to those obtained through the physical process of crushing soybeans, impact the returns and volatilities of these commodities. Positive (negative) deviations are associated with negative (positive) changes in return. Additionally, these deviations positively correlate with return volatility, suggesting that variations from the expected yields lead to price shifts and heightened return volatility. In summary, deviations from the crushing estimates significantly shape the modeling of soybean commodity returns and volatilities, with implications for understanding returns, hedging ratios, and portfolio return variance. [ABSTRACT FROM AUTHOR]

Copyright of Serbian Journal of Management is the property of Serbian Journal of Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article "Economics of Protected and Open Field Cucumber Cultivation in Jaipur District of Rajasthan" compares the costs and returns of cucumber cultivation in polyhouses and open fields in Jaipur district, Rajasthan. The study found that while the cost of cultivation per acre was higher in polyhouses, the gross return and net return per acre were also higher compared to open fields. The study concludes that polyhouse cultivation is more profitable, but the high investment and seed costs may be challenging for small farmers. [Extracted from the article]

The study estimated the milk production in the Ayodhya mandal of Uttar Pradesh for selected dairy breeds, Sahiwal, Murrah, and Crossbred (with jersey) during the years 2020-2021. Using a pre-tested structured schedule, primary data was collected on 108 dairy animals of the selected dairy breeds and their respective lactation groups. This study used the snowball sampling technique to select animals from different lactation groups and the budgetary technique to calculate the costs and returns. There was a significant increase in the cost of milk production, which was measured by the cost of milk per liter 1.18 times, 1.15 times, and 1.10 times, respectively, in the Sahiwal, Crossbred cow, and Murrah groups as compared to the first to third lactation groups, for milk production during the 7th lactation and above. For the same lactation group of Sahiwal, Crossbred cow, and Murrah, the net return per liter declined by 36 per cent, 17 per cent, and 21 per cent, respectively, for the same group of Sahiwal, Crossbred cow, and Murrah cows. [ABSTRACT FROM AUTHOR]

The aim of this study is to assess the response of the South African stock market returns to oil price volatility, based on the daily South African stock market index, using the GARCH-Copula modelling technique. The results of the analysis show evidence of an asymmetric impact of fluctuations in oil prices on South African stock market returns, using a copula model specification, particularly the bivariate symmetrized Joe-Clayton (SJC) copula. The results also revealed that the EGARCH process is the best univariate model to capture oil price volatility. Interestingly, this study also revealed that the tourism industry is most dependent on oil price fluctuations, due to its heavy reliance on transportation costs. The economic implications of this study also suggest that sectors affected by oil price fluctuations need specific long-term and short-term monetary policy strategies. It is recommended that in the short term, expansionary monetary policy could assist in mitigating the impact of higher oil prices, while in the long-term, policies aimed at reducing the volatility in oil prices would be of great help in alleviating its harmful effect on stock market returns. [ABSTRACT FROM AUTHOR]

Using an event study methodology, we investigate how unexpected political events affect climate-sensitive sectors. We find that events related to climate change policy have significantly impacted returns. The clean energy sector benefitted from the Paris Agreement, Climategate, and Fukushima since these events increased climate change awareness and favor toward policies related to reducing the impact of climate change. For the utilities, energy-intensive, and transport sectors, these events imply increased transition-related political and market risks, which should be compensated. Events weakening climate change policy are associated with positive abnormal returns for the fossil energy sector. We further find that stock market investors are quick to adapt to new information related to climate change. Policymakers should be aware of such events' impact on the stock market because the investors are likely to price in both climate risk and expectation about sectors' growth. [ABSTRACT FROM AUTHOR]

Volatility is an important index for measuring the risk in the financial market. The research on the volatility of the financial market is the basis of risk prevention and asset pricing. The volatility of the market is predicted by using financial time series analysis and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. This paper uses the 5 min closing price of the Shanghai 50 index from January 2021 to December 2021 as the small sample data, and the daily closing price of the Shanghai 50 index from January 2012 to December 2020 as the large sample data. The large sample data of SSE 50 index are divided into five sample groups with different interval lengths, and GARCH models are established to empirically analyze the volatility of its return. The “realized” volatility method is used to process the small sample data of the Shanghai Stock Exchange 50 index, and the estimated value of volatility is obtained as the substitute for the real value. The predicted value of five groups of data is compared with the real value, and the influence of sample size on the predicted value is analyzed. The results show that GARCH’s estimation coefficient is very significant and has the expected characteristics, which verifies the existence of persistent volatility aggregation, and the daily return fluctuation of the closing price of the Shanghai 50 index is largely affected by the news about volatility and lagging volatility in the previous period. This result shows that the daily return of the Shanghai Stock Exchange 50 index has sustained high volatility, the investment risk of the Shanghai stock market increases, and the regulatory authorities should take appropriate measures to prevent systemic risks. [ABSTRACT FROM AUTHOR]

Recent empirical studies have identified the presence of the Turn of the Month (TOM) effect on various stock markets. This effect is a well-known calendar anomaly in stock markets, characterized by higher returns and increased trading volume around the beginning of each month. The researchers have proposed several explanations for this anomaly, including factors such as increased liquidity, window dressing practices, and news announcements. This study focuses on investigating the TOM effect in the Indian stock market, specifically focusing on size of firms and trading activity by individual investors, as well as domestic and foreign institutional investors. We analysed the closing prices of four indices (Nifty 50, S&P BSE Sensex, Nifty SmallCap 50, and BSE SmallCap) from April 2007 to February 2023. Our analysis involved using parametric t-tests and non-parametric Mann-Whitney U tests to assess the significance of our findings. The results indicate that the TOM effect is indeed significant in the Indian stock market, with higher returns observed during the days surrounding the TOM. Furthermore, this effect is more pronounced in smaller indices compared to larger ones. Additionally, we found that domestic institutional investors engage in significant trading activity towards the end of the trading month, while individual investors do not demonstrate the same pattern. Foreign investors also exhibit notable differences in trading activity between the TOM and the rest of the month. [ABSTRACT FROM AUTHOR]

This article draws on materials collected during ethnographic fieldwork among Bosnian Roma refugees who reconstructed homes in an urban shanty at the periphery of Rome (Italy). In the last two decades, many of these Roma started building or refurbishing houses in villages in the Federation of Bosnia and Herzegovina, close to the Serbian Republic (where their former home village is now situated). The construction, refurbishing, and maintenance of these houses played (and still play) a role in the local economy; they also changed the local landscape and became the mark of a new but intermittent presence in post‐Dayton Bosnia. The houses and the transnational practices connected to them have become tokens of economic success and aspirations that revolve around both the Bosnian context and the Roman one. They also express nostalgic attachments to a lost homeland radically transformed by war, foreign interventions, and the advent of the market economy and eventually turned into an unfamiliar place. This article builds on the literature on transnational migration and material culture and explores the ambivalence and complexity of transnational trajectories that stretch between an urban context in the EU and a rural one in non‐EU and reveals complex scenarios of identity, movements, and unlikely returns.

Financial markets are increasingly interlinked. Therefore, this study explores the complex relationships between the Tadawul All Share Index (TASI), West Texas Intermediate (WTI) crude oil prices, and Bitcoin (BTC) returns, which are pivotal to informed investment and risk-management decisions. Using copula-based models, this study identified Student’s t copula as the most appropriate one for encapsulating the dependencies between TASI and BTC and between TASI and WTI prices, highlighting significant tail dependencies. For the BTC–WTI relationship, the Frank copula was found to have the best fit, indicating nonlinear correlation without tail dependence. The predictive power of the identified copulas were compared to that of Long Short-Term Memory (LSTM) networks. The LSTM models demonstrated markedly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE) across all assets, indicating higher predictive accuracy. The empirical findings of this research provide valuable insights for financial market participants and contribute to the literature on asset relationship modeling. By revealing the most effective copulas for different asset pairs and establishing the robust forecasting capabilities of LSTM networks, this paper sets the stage for future investigations of the predictive modeling of financial time-series data. The study highlights the potential of integrating machine-learning techniques with traditional econometric models to improve investment strategies and risk-management practices.