Journal of Contemporary Issues in Business and Government

The international market for fresh fruits and vegetables is truly humongous. And, with countries in Asia experiencing significant addition in the purchasing power of their middle classes, the demand for these, as well as for a host of other agricultural products, is sure to rise exponentially in the future too. From India’s point of view, what makes the prospect even more attractive is the fact, that there aren’t too many countries with the production capacity to meet this ever-expanding demand. Fresh Mangoes, Mangosteens & Guavas represent a particularly promising sub-segment in the larger fresh fruits & vegetables space. In this paper, an attempt has been made to study the production and export performance of India for these products during the period 1991-2020. Data available in the public domain depicts India as a very large producer of most of the tropical fruits, but proportionately not such a large exporter. This dichotomy has baffled many experts and policy makers for quite some time. In this working paper, an attempt has been made not just to trace the trajectory of India as a producer and as an exporter, but also to analyze the factors that have a bearing on the production of these commodities in India, and export of the same from India. As analytical tools to aid the study, statistical measures such as CAGR, RCA, RSCA, time series method (TSM), correlation & regression, standard deviation, mean and coefficient of deviation have been used in this study to derive meaning out of the various disparate data elements. Finally, this study also tries to identify the areas of weaknesses that require urgent remedial steps. And, the study also makes a few key recommendations to improve India’s performance as a producer and exporter of the two commodities.

Sincere approaches to practical forecasts in organisations have been accomplished through operative research (OR) since its inception. Scientists have affected forecasts in other disciplines. Forecasting has an enormous social, economic and environmental impact and has a very important aspect of every business. Several prediction models have been developed to help people decide correctly against future uncertainties. However, there are distinct advantages and limitations for every prediction model. It is important to succeed in selecting correct forecasting methods from other alternatives. This paper aims to analyse predictive techniques to forecast car sales results, Ford Mustang. Companies depend on precise projected data to make the right decisions and to predict the business results over a long and short time. Predictions are usually based on historical results, industry comparisons and developments in the sector. Different model time series foresees were used in this phase, for example the moving average, exponential smoothing, the Holt double exponential smoothing, winter’s three times exponential smoothing and ARIMA. The predictions were made on the basis of the annual (non-seasonal) data and the cumulative annual data (seasonal) in the ARIMA model. For both the threefold exponential smoothing system of winter and the ARIMA model, Minitab was used to produce forecast. In addition, the best prediction method for this given set of data was found to be the double-exponential smoothing process used by Holt when calculating the mean absolute deviation.

Today, talent management is one of the promising areas of work with personnel. PopularTalent Management Systems, which are used both as independent solutions and as subsystems of Enterprise Performance Management. The performance management system used as a basic tool, for example, a balanced scorecard, makes it possible to form key performance indicators for employees based on the strategic goals of the company. Thanks to information systems, HR-managers track the achievement of relevant KPIs, manage the training of employees, conduct their assessment, including professional skills and personal qualities. However, the functionality of the TMS-class information systems on the market does not yet cover all aspects of talent management from a practical point of view.
The article presents the functionality of typical Talent Management Systems. The types and tools of talent assessment as important components of talent management are considered. The necessity and practical importance of predicting the effectiveness of key employees of the enterprise made and justified the choice of the most appropriate methods and models of forecasting. The conclusion is made about the possibility and direction of further development of Talent Management Systems.
The considered aspects of talent management can be used as a basis for the development of functional requirements for the improvement of TMS software.

Contradicting evidence on time-series and financial analysts’ forecasting performance calls for further research in financial markets. Motivation to use time-series models rather than analysts’ forecasts stems from recent research that reports time-series predictions to be superior to analysts’ forecasts in predicting earnings for longer periods and for small firms that are hardly followed by financial analysts. The paper aims to explore performance of time series models in forecasting earnings for six firms considering historical data of 11 years from January, 2010 to December, 2020. Monthly average stock data of last 11 years for five firms namely HCL, TCS, Infosys, Reliance, Tech Mahindra and Wipro was considered from NSE site. Every company had 132 values whose graphical plotting and stationarity check was performed. Data series for each of the five companies was found to be non-stationary. After differencing each of them, the series became stationary and graphical plotting was again done. Then best suited ARIMA Model for each stationary time series was determined upon comparison of goodness of fit statistics. After choosing the best suited ARIMA model, residuals were extracted and were found to be random with no external influence whatsoever. Hence forecasting was done based on chosen model for the monthly average stock price of these top six companies of India in 2020. The paper finds that premier ARIMA family models outperform naive time-series models in terms of mean percentage errors, AIC and average ranks. The findings suggest that investors use the selected ARIMA model to form their expectations

The article is devoted to the analysis of the innovative development of agriculture and its infrastructure services through mathematical materials and the development of prospects.The results of the multifactor econometric model were developed using statistical data and Eviews software. According to the results of the obtained econometric model, the influence of factors on changes in the volume of infrastructure services provided to agriculture was determined. Also, on the basis of the developed trend models, the forecast parameters of the volume of agricultural infrastructure services, gross agricultural output, the number of machine-tractor parks and zoo-veterinary stations until 2023 were determined.

Selection of stocks is a challenging task for investors and finance researchers because of the uncertainty of the return. In portfolio selection, the aim is to obtain a proper proportion of assets for getting maximum profit and least risk. The objective of his paper is to provide an overview of the present research in portfolio optimization with respect in mathematical programing techniques. For this purpose, 82 research papers appearing in the scholarly journal have been observed and investigated, and it has been concluding that fuzzy decision theory and goal programming establish the maximum number of mathematical programming techniques generated to solve the portfolio optimization problem.