Market Variables Research Articles

146 Development of a Mathematical Model for Cost Calculation and Sustainability Analysis of Beef Cattle Grazing Systems

Abstract A mathematical model to estimate: a) the cost of beef cattle production in grazing systems; and b) the environmental sustainability of production systems using the Emergy Synthesis Model was evaluated. All items were considered and allocated, according to the precepts of the Economic Theory, under Fixed Cost (FC), Variable Cost (VC), and Total Cost (TC). The model estimated costs in the case-study for the entire property and the different production lots within the same property. Costs were allocated per animal and per kg of beef cattle produced. The total cost per animal of cattle produced was US$ 416.86. It was observed that the VC represented 66% of the TC. The cattle purchase price, including under VC, was the factor that represented the most significant portion of the production cost, in which the current operation reached 59% of the TC. Therefore, the currently developed tool may be used to facilitate understanding and potentially help to manage risk and profit. To examine from the environmental point of view, the synthesis of emergy was utilized. Emergy is the memory of the available energy used directly and indirectly to produce a service or product. It was observed that the property considered in the current case-study showed a renewability of 47.6%, indicating that less than one-half of all the energy used came from renewable resources. The emergetic yield ratio (EYR), which measures how much natural energy was incorporated into produced goods or services, was 1.91. The survey showed that the emergetic exchange ratio (EER), which indicates how balanced the exchange ratio between the energy in products and the energy gross revenue, was 12.45, showing that the system delivers much more energy than it receives in a monetary form. It seems like the current system analysis was highly dependent on cost-effective resources. The fuel used for pasture management services was the non-renewable resource that mostly affected the design. For the emergy synthesis, such input represented 51.2% of the total energy used, but from the economic point of view, such resource represented only 1% of the total production cost. The system could be less dependent on market variability if good pasture management practices were adopted, such as using a reasonable pasture stocking rate. By using the emergy indexes, it was possible to analyze the cattle system from an environmental perspective, allowing to assist technology/technique adoptions capable of increasing the sustainability indicators, which has been recently valued by worldwide beef consumers.

Generalized Methods on Access Bank Share Price Changes and Variations in Nigeria Stock Market

Purpose: Stock market performance and operation have been widely recognized as a viable investment field in financial markets. This paper studied the share price movement of Access Bank, PLC.Methodology:The share price data was subjected to a 3-step transition matrix for independent share prices, which enabled us to proffer precise conditions for obtaining future share price changes and placed criteria that enabled us to come up with a 2x2 matrix solution of Principal Component Analysis (PCA) on the share price of Access bank.Findings: The solution matrix of the stochastic analysis showed that Access Bank PLC has the best probability of price increasing shortly by 12%, the best probability of reducing in the future by 21%, and the best probability of no change shortly by 20%.Implication:More so, an increase in the trading days also increases the value of future share price changes, and other statistical variables were also found. Finally, the influences of the relevant parameters of stock market variables were effectively discussed in this paper.

The Influence of Direct Marketing, Digital Marketing and Insta-gram Celebrity Support on Purchase Decisions in Samarinda City, Indonesia

This study examines the marketing strategy of the Momoo Milk Fresh micro business in Samarinda City. This research was conducted with the background that the local micro business has been running for more than six years with 15 branches in Samarinda City. This study aims to determine the effect of direct marketing, digital marketing, Instagram celebrity support that has been carried out by the micro business on their product purchasing decisions. This research is included in qualitative research. The population of this study were all buyers with the criteria of having bought before, and samples were taken from 15 branches of Momoo Milk Fresh in Samarinda City with a total of 152 respondents. The method used is a questionnaire, then documentation is done. The results of this study indicate that the direct marketing and digital marketing variables get a t-table value (> 1.960), while the Instagram celebrity support variable has a t-table value (> 1.960). From the results obtained, it can be concluded that direct marketing and digital marketing have a positive and significant relationship with purchasing decisions, while Instagram celebrity support does not have a positive and significant influence.

Factors Affecting the Initial Return of Initial Public Offerings (IPOs) and IPO Underpricing in the Colombo Stock Exchange

The initial public offering is an important milestone for a company; however, this can be also turned into a reason which causes great wealth loss as well. The purpose of this study is to identify the factors affecting IPO initial return and examine the level of underpricing that prevails in the Colombo Stock Exchange (CSE). The study considers 68 IPOs from 2006 to 2018. The selected factors for the study are ASPI Return, Sector Price/Earnings ratio, Age, Earnings Per Share, Debt Ratio, Net Asset Value, Return on Asset, Price/Earnings Ratio, Debt/Equity Ratio, Offer Price, and Over Subscription Rate. Multiple regression results reveal that ASPI return and over-subscription rate are positively influenced by market-adjusted initial return (MAIR) and offer price is negatively affected. Underpricing analysis revealed that the MAIR and the prevailing level of underpricing is raw initial return equal to 14% and market-adjusted return is 18%. The beverage, food, and tobacco sector, Banking, Finance, and Insurance sector, and diversified holding sector are identified as highly underpriced sectors of CSE. It is recommended that Investors must consider capital market variables and IPO variables for IPO investment decisions regardless of the financial indicators of the company.

Analyzing the Effect of Market Segmentation and Price on Purchase Interest of Batik Ciwaringin Products

Background: Provide an adequate background covering the literature review and the gap of the research with other relevant former research works.
 Aim: This study aims to analyze the influence of market segmentation, price and interest in buying Batik Ciwaringin products, Cirebon Regency.
 Method: Data collection method using questionnaires to Ciwaringin batik buyers. The key informants in this study are consumers who buy Ciwaringin batik products. This research uses quantitative analysis techniques.
 Findings: The significance test t obtained market segmentation variable showed the t-count value = 3.772 > t-table = 1.996 with a significance value of 0.000<0.05. Thus, hypothesis 1 is accepted. The significance test t obtained the price variable showed the t-count value = 3.742 > t-table= 1.984 with a significance value of 0.000 < 0.05. Thus, hypothesis 2 is accepted. The result for free variables (market and price segmentation) was 92,203 with a significant rate of 0.000. While the Ftabel value at ? = 0.05 is 3.13, the variables of market and price segmentation simultaneously have a positive and significant effect on consumers' buying interest in Batik Ciwaringin. Thus, hypothesis 3 is accepted.

PENGARUH DESIGN PRODUCT, PERSONAL BRANDING, DISTRIBUTION, DAN DIGITAL MARKETING TERHADAP KEPUTUSAN PEMBELIAN ALBUM K-POP (studi kasus pembeli pada Weverse Shop)


 In order to ascertain the impact of product design, personal branding, distribution, and digital marketing on purchasing choices for kpop albums on the Weverse Shop app, study was carried out. An electronic survey that was given to both women and men who had purchased K-pop records through the Weverse Shop program served as the basis for this study's quantitative approach, which included a purposive sampling method of 112 respondents. Multiple linear regression analysis, partial analyzing (t testing), and testing the coefficient of determination are all employed in data analysis (R2). The analysis's results, which were obtained using SPSS version 25, also reveal that while the distribution and digital marketing variables have a significant impact on buying behavior, the product design and personal brand variables have a significant effect.

A Mixed Frequency BVAR for the Euro Area Labour Market*

AbstractWe introduce a Bayesian mixed frequency VAR model for the aggregate euro area labour market that features a structural identification via sign restrictions. The purpose of this paper is twofold: we aim at (i) providing reliable and timely forecasts of key labour market variables and (ii) enhancing the economic interpretation of the main movements in the labour market. We find satisfactory results in terms of nowcasting and forecasting, especially for employment growth. Furthermore, we look into the shocks that drove the labour market and macroeconomic dynamics from 2002 to 2022, with an insight also on the COVID‐19 recession. While demand shocks were the main drivers during the Global Financial Crisis, technology and wage bargaining factors, reflecting the degree of lockdown‐related restrictions and job retention schemes, have been important drivers of key labour market variables during the pandemic.

Estimating Global Friction Periods for Economic Evaluation: A Case Study of Selected OECD Member Countries.

The friction cost approach (FCA) offers an alternative to the dominant human capital approach to value productivity losses. Application of the FCA in practice is limited largely due to data availability. Recent attempts have tried to standardise the estimation of friction periods across Europe, but to date, this has not been attempted elsewhere. Our aim was to estimate friction periods for 17 Organisation for Economic Co-operation and Development (OECD) member countries between 2010 and 2021 based on routinely published data. We derived friction period estimates for Australia, Austria, Canada, Czechia, Finland, Germany, Hungary, Japan, Korea, Luxembourg, Norway, Poland, Portugal, Sweden, Switzerland, the United Kingdom and the United States. Vacancy stock and flow data was sourced from the OECD's short-term labour situation database from 2010 to 2021, and included the impact of Covid-19 on the labour market. The estimated friction periods were applied to cost cancer-related premature mortality for the United States as an illustrative case. The average friction period in the five non-European countries (Australia, Canada, Korea, Japan and the United States) was 61.0 days (SD 9.4) (range between 44.8 days in Korea and 82.2 days in Canada) and the average friction period in the 12 European countries was 60.6 days (SD 14.8) (range between 34.1 days in Switzerland and 137.3 days in Czechia). In both cases, the outbreak of Covid-19 increased the length of the friction period. Our illustrative case revealed that productivity costs in the US were over a third lower using the study-specific friction period (56 days) compared with the conventionally assumed 90-day friction period applied in the literature as a default measure. Our results expand the potential application of the FCA outside of Europe and will support greater utilisation of the FCA and wider inclusion of productivity costs in societal-based economic evaluations based on the use of widely available and updated key labour market variables in our selected countries.

The Effect of Service Quality, Company Image, And Experiental Marketing On Consumer Loyalty of Ace Hardware BG Junction Surabaya

The purpose of this study is to determine the effect of Service Quality, Company Image and Experiential Marketing on Consumer Loyalty of ACE Hardware BG Junction Surabaya simultaneously and partially. The analysis technique uses Multiple Regression Test (multiple regression) with the SPSS program. The results showed that the variables of service quality, company image and experiential marketing had a joint or simultaneous effect on customer satisfaction at ACE Hardware at BG Junction Surabaya. For Service quality has a positive and significant influence on customer satisfaction. Company image does not have a positive and significant effect on customer satisfaction. Experiential marketing has a positive and significant influence on customer satisfaction.

PENGARUH DIGITAL MARKETING TERHADAP MINAT BELI KOSUMEN PADA MARKETPLACE SHOPEE

This research is used to test the magnitude of the influence of digital marketing which is based on search engine marketing indicators, websites, e-mail marketing, and social media marketing on consumer buying interest in the Shopee marketplace. The scope of this research is the influence of digital marketing variables on consumer buying interest variables. A quantitative approach was used in this study with a total of 120 respondents based on a purposive sampling technique. In this study, primary data was obtained using a questionnaire in which the questionnaire used a measurement scale, namely the Likert scale. The technique used to process the results of the respondents is simple regression analysis. Based on the results of data analysis, it was found that the two variables, namely digital marketing variables and also consumer buying interest, have a positive and significant influence. In this study, consumer buying interest was influenced by 26.8%. This study provides an overview of the theoretical application that can be used as reference material for similar research. Managerial implications in the form of suggestions for marketplace shopee in order to improve the quality of the website and social media as a means of marketing this marketplace so that there will be more and more buying interest from customers.
 Keywords: Consumer buying interest, Digital Marketing, Consumer Behavior

KEBERHASILAN E-SATISFACTION DAN E-REPURCHASE INTENTION BUKALAPAK: PERAN DIGITAL MARKETING, E-SERVICE QUALITY DAN E-TRUST

This study aims to analyze the effect of Promotion, Service Quality, and E-Trust, on the E-Repurchase Intention and E-Satisfaction of Bukalapak Consumers. Data analysis in this study used the Structural Equation Modeling (SEM) analysis method using AMOS 22. The use of Structural Equation Modeling analysis examines in detail the influence between variables. This study uses a survey method, measuring variables by distributing google forms to 150 respondents. The results showed that digital marketing, e-service quality, and e-trust have a direct and significant effect on e-repurchase intention and e-satisfaction. In this study, the role of variables is very significant. The intervening variable, namely e-repurchase intention, successfully mediates. Digital marketing variables, e-service quality, and e-trust have a direct and significant effect on e-repurchase intention through e-satisfaction. The managerial implication of this research is the importance of Buka Lapak online stores in optimizing marketing. digital marketing, e-service quality, and e-trust that make consumers feel satisfied and more familiar with e-commerce. The higher the satisfaction that customers get, the more buying interest will appear. Buyers who feel satisfied will increase repurchase interest. Consumer trust can lead to a sense of satisfaction and interest in buying back through e-commerce.

The Interplay of Digital Marketing Dimensions and Customer’s Patronage of Fast Food Industries in Akwa Ibom State, Nigeria

This study was carried out in order to examine the effect of digital marketing dimensions on customer patronage of fast food industries in Akwa Ibom State. The researchers adopted a survey research technique for this study. The population of the study was sixty-four (64) customers of registered fast food industries operating in Akwa Ibom State. A sample size of 361 customers were drawn from the population using quota sampling techniques from across the four major cities in Akwa Ibom State that visited the eateries during the period of data collection for the study. The digital marketing dimensions (independent variable) considered for this study were Social Media Marketing (SMM), Email Marketing (EM) and Search Engine Optimization (SEO). The dependent variable was Customer Patronage. Using a 5 point Likert scale, the hypotheses stated were tested using simple linear regression model at a 5% level of significance. From the data analysis, the major findings revealed that there is a significant relationship between each of the three variables of digital marketing (social media, email marketing, search engine marketing) and customer patronage of fast food in Akwa Ibom state. The result of the first hypothesis shows that social media marketing influences customer patronage of fast food in Akwa Ibom State with a regression coefficient of 0.687. The result of the second hypothesis also reveals a regression coefficient of 0.694 which denotes that there is a strong positive relationship between email marketing and customer patronage in Akwa Ibom State. The third hypothesis reveals a regression coefficient of 0.688 which denotes that there is a strong positive relationship between search engine optimization and customer patronage of fast food in Akwa Ibom State. From the finding, it was concluded that digital marketing dimensions can increase customer patronage at any point in time. Based on the findings, it was recommended that marketing campaign should be emphasized on social media platforms since the age group in those platforms constitutes the people with interest in patronizing fast food product. Also, management should place adverts on popular search engines so as to go global. By doing this, vendors will be able to reach more customers, have steady link between the products and brands with their customers, enhance their sales volume and guide them on how to improve their products with reference to customers’ feedback, mitigate increased competition, and create avenue to access new markets.

Drivers of Realized Volatility for Emerging Countries with a Focus on South Africa: Fundamentals versus Sentiment

We use a quantile machine learning (random forests) approach to analyse the predictive ability of newspapers-based macroeconomic attention indexes (MAIs) on eight major fundamentals of the United States on the realized volatility of a major commodity-exporting emerging stock market, namely South Africa. We compare the performance of the MAIs with the performance of a news sentiment index (NSI) of the US. We find that both fundamentals and sentiment improve predictive performance, but the relative impact of the former is stronger. We document how the impact of fundamentals and sentiment on predictive performance varies across the quantiles of the conditional distribution of realized volatility, and across different prediction horizons. Specifically, fundamentals matter more at the extreme quantiles at short horizons, and at the median in the long-run. In addition, we report several robustness checks (involving sample period and alternative definitions of realized volatility), and indicate that the obtained results for South Africa also tend to carry over to other emerging countries such as, Brazil, China, India, and Russia. Our results have important implications for investors with volatility being an input for portfolio allocation decisions. In addition, with stock market variability also capturing financial uncertainty, its accurate prediction based on US fundamentals and sentiment also has a role in policy design to prevent possible collapse.

Macroeconomics and the Spanish stock market, impact-response analysis

The aim of this paper is to identify potential causal relationships between macroeconomic variables and the stock market in Spain. Numerous articles recognize the influence of macroeconomic variables on the stock market and value this knowledge as essential for good investment management. However, there are very few empirical studies that justify the influence of disaggregated macroeconomic variables on the stock market in Spain and vice versa. This article uses the general index of the Madrid Stock Exchange as a proxy variable of the stock market and numerous macroeconomic variables, analyzing monthly data from January 2001 to December 2020 from various published data sources. A descriptive analysis is carried out and a vector autoregressive model (VAR) is applied. Finally, the causality is analyzed identifying the transmission of effects between them. The results confirm the impact of lagged interest rate, monetary aggregate M1 and unemployment rate on the stock market but also identify new features, such as the influence of the stock market on the interest rate, industrial production index, manufacturing activity index and economic sentiment index. This research is useful for Public Administration to detect possible risks in the economy, and it enables investors to better manage their investments.

Definition of Regulatory Targets for Electricity Non-Technical Losses: Proposition of an Automatic Model-Selection Technique for Panel Data Regressions

Non-technical losses (NTLs) are one of the main problems that electricity distribution utilities face in developing regions such as Latin America, the Caribbean, sub-Saharan Africa, and South Asia. Particularly in Brazil, based on the socioeconomic and market variables concerning all the distribution utilities, the National Electric Energy Agency (ANEEL) has formulated several specifications of econometric models for panel data with random effects, all aimed at determining an index that reflects the difficulty of combating NTLs according to the intrinsic characteristics of each distribution area. Nevertheless, given the exhaustive search for combinations of explanatory variables and the complexity inherent to defining regulatory NTL targets, this process still requires the evaluation of many models through hypothesis and goodness-of-fit tests. In this regard, this article proposes an automatic model-selection technique for panel data regressions to better assist the Agency in establishing NTL regulatory targets for the distribution of utilities in this country. The proposed technique was applied to panel data containing annual observations from 62 Brazilian electricity distribution utilities from 2007 to 2017, thus generating 1,097,789 models associated with the regression types in the panel data. The main results are three selected models that showed more adherence to the actual capacity of Brazilian distribution utilities to reduce their NTLs.

Investigation into the International Financial Reporting Standards 9 model flaws exposed during the pandemic

Orientation: Market events during the coronavirus disease 2019 (COVID-19) pandemic exposed flaws in the econometric models used to derive International Financial Reporting Standards (IFRS) 9 impairments. Models were unable to capture the level of government intervention or predict the economic recovery process because of the unprecedented nature of the pandemic.Research purpose: This study examines the causes of the challenges experienced with the IFRS 9 models during the pandemic and approaches to minimise this risk in the future.Motivation for the study: Structural correlation breaks forced banks to replace the IFRS 9 models with expert overlays or rapidly rebuild the models to reduce impairment volatility and manage the impact on earnings. Expert judgement may lead to biased outcomes.Research approach/design and method: Behavioural finance theory suggests that emotion and cognitive biases often lead to irrational investment decisions with disastrous consequences to the market. The link between market sentiment and economic outcomes is tested with natural language processing. Archimedean copulas are used to compare the dependence structures of market variables between different stress periods.Main findings: Market sentiment is closely related to the trends observed in major macroeconomic indicators. The nature of the dependence structures differs between stress periods.Practical/managerial implications: Sentiment may be a valuable exogenous variable to incorporate into economic forecast models. Learnings from one stress period are not necessarily applicable to another.Contribution/value-add: Government intervention and market sentiment had a significant impact on the economic outcomes and correlation breaks observed during the pandemic. Developing bespoke models for the different phases of the economic cycle may not necessarily lead to improved outcomes.

Nowcasting the state of the Italian economy: The role of financial markets

AbstractThis paper compares several methods for constructing weekly nowcasts of recession probabilities in Italy, with a focus on the most recent period of the Covid‐19 pandemic. The common thread of these methods is that they use, in different ways, the information content provided by financial market data. In particular, a battery of probit models are estimated after extracting information from a large dataset of more than 130 financial market variables observed at a weekly frequency. The accuracy of these models is explored in a pseudo out‐of‐sample nowcasting exercise. The results demonstrate that nowcasts derived from probit models estimated on a large set of financial variables are, on average, more accurate than those delivered by standard probit models estimated on a single financial covariate, such as the slope of the yield curve. The proposed approach performs well even compared with probit models estimated on single time series of real economic activity variables, such as industrial production, business tendency survey data or composite PMI indicators. Overall, the financial indicators used in this paper can be easily updated as soon as new data become available on a weekly basis, thus providing reliable early estimates of the Italian business cycle.

Pricing Demand-Side Flexibility With Noisy Consumers: Mean-Variance Trade-Offs

We formulate and analyze a demand-side management framework in which an aggregator sets energy prices to flexible consumers who have a noisy demand. The aggregator strategizes between purchasing energy in the day-ahead market, and settling any possible mismatches due to demand variability in the real-time market. Because consumers are sensitive to volatility in their electricity bill, the aggregator sets an upper bound on the variance of the payments made by consumers. We derive the optimal strategies for the consumers and the aggregator in this problem, and show that the aggregator limits dynamic pricing for consumers with too large demand variance, so as to avoid large uncertainty in their payments. On the other hand, consumers with low enough demand variance are charged the same price that would emerge in a noise-free formulation. We also identify two instances of a mean-variance trade-off: one in the consumer payment, which can be made less uncertain at the expense of a higher mean value, and another one in the aggregator’s utility, which becomes less uncertain and lower on average as the consumer payment variance constraints become binding. We corroborate our analysis with a numerical case study.

Examining market and weather events as signals of an increased probability of Shiga toxin-producing Escherichia coli outbreaks linked to romaine lettuce

Romaine lettuce has become a recurring source of foodborne illness outbreaks over the past decade. Most lettuce consumed in the United States originates from two primary sources: California and Arizona. Federal, state, and local authorities that investigate outbreaks linked to the consumption of romaine lettuce encounter many challenges during traceback and on-farm investigations that hinder their ability to determine the root cause of contamination. In this study, we investigate market signals (including price and shipment volumes) and weather parameters and their correlation with past outbreaks of Shiga Toxin-producing Escherichia coli illnesses linked to the consumption of romaine lettuce. We construct a daily panel of economic and weather data for eight outbreaks linked to romaine growing regions in California and Arizona from 2011 to 2019 and estimate the relative importance of these indicators on the probability of identifying each outbreak. Results suggest that prior increases in price and shipments are positively associated with outbreaks. In addition, some weather variables, most consistently daily maximum temperature, are positively associated with the probability of identifying an outbreak. Monitoring data for market and weather signals, coupled with a deeper understanding of regional supply shifts could help investigators quickly identify conditions that may signal an increased probability of outbreaks linked to romaine lettuce. Early detection can lead to improved education and outreach as well as targeted inspectional and sampling efforts. • Romaine lettuce has become a recurring source of foodborne illness outbreaks. • Increased prices and shipments are positively associated with outbreaks. • Market and weather signals may help identify conditions that could lead to outbreaks. • Lagged values seem more consistent predictors for market variables. • Defined cutoff values seem more consistent predictors for weather variables.

The Influence of Viral Marketing and Product Quality on Purchase Decision on Contemporary Foods, Sang Pisang

This study aims to find out the effect of Viral Marketing on Purchase Decisions for the current food Sang Pisang in Cempaka mas. The second is to determine the effect of product quality on purchasing decisions for Sang Pisang contemporary food in Cempaka Mas. The third is to find out the effect of Viral Marketing and Product Quality on Purchase Decisions for the current Sang Pisang food in Cempaka Mas. This research uses a quantitative approach. The data collection used by researchers is through a questionnaire. The population in this research are people who know Sang Pisang products. Determination of the sample used is a non-probability sampling technique with purposive sampling method. The sample used was 162 respondents with the criteria of respondents coming from consumers who had bought Sang Pisang products. Based on the research that has been done, the researcher can conclude that the Viral Marketing Variable separately (partially) influences purchasing decisions. Product quality variables separately (partially) influence purchasing decisions. Viral marketing variables (X1) and product quality (X2) simultaneously influence purchasing decisions (Y)