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• MONTE CARLO SIMULATION FOR STRESS TESTING ENDOGENOUS PROFITABILITY FACTORS DURING POLYCRISIS: A CASE STUDY FROM THE POULTRY SUBSECTOR

  Máté Varga

  Patrícia Becsky-Nagy

  Views:

  64

  Can historical company data help estimate future performance during economic uncertainty? This study investigates whether past business cycles can be used to estimate profitability in the context of a polycrisis – a period marked by overlapping disruptions such as avian influenza, COVID-19 trade restrictions, extreme weather events, and rising feed and energy prices. These shocks have severely impacted agro-related industries, such as poultry processing. Focusing on three Central European poultry processing companies, we use Monte Carlo simulations for stress testing their profitability for the 2023 period, aiming to support financial planning by analysing firm-specific, endogenous, management-controllable factors. Return on Sales (ROS) and Return on Equity (ROE) are used to evaluate profitability, incorporating variables such as euro exchange rates in the case of export-driven firms. Our results indicate that Company “A,” characterized by stable operations, had the lowest probability of negative ROE, while Companies “B” and “C” demonstrated greater volatility. We found that the model provides a good estimate of the factors affecting the companies’ profitability that are directly or indirectly reflected in their accounting data. Indicating that the test could be a valuable tool for supporting managerial decision-making in financial planning, though further refinements are needed to enhance accuracy.

• Examination of pig farm technology by computer simulation

  25-29

  Szilvia Szőke

  Lajos Nagy

  Sándor Kovács

  Péter Balogh

  Views:

  291

  Agricultural production is among the riskiest production activities. Similarly to other branches of agriculture in animal breeding the finished product is the result of complex procedures. The biological technological procedure, the creation of the product is affected by an outstanding number of environmental factors which also cause uncertainties. In the North Great Plain Region of Hungary, sows, gilts and slaughter pigs are produced on a corporate farm. The reliable operation data of this company provide a stable basis for and estimating future costs and revenue and their distributions. Monte Carlo methods are one of the generally accepted tools for modeling risks. The significant independent variables, their ranges and probability distributions, and the correlation between them were inputs to the model. The values of the variables were produced using a random number generator. The computer simulation was performed using @Ris (PalisadeCorporation) software. The study concentrates on the factors affecting the number of off spring (piglets). Model inputs were the mating, mortality and farrowing rates; the costs and the income values based on these rates have been analysed as the output data of the model.

• Analysis of the influential factors on gross value added in the Hungarian sheep sector

  107-112

  Béla Cehla

  Sándor Kovács

  András Nábrádi

  Views:

  418

  The competitiveness of the Hungarian sheep sector has been in steady decline for some time now. Crucial has been the problem that the value added in the sector is not generated in Hungary, as most of the produced lambs in Hungary leave the country with an average weight of 21 kilograms, with slaughtering happening abroad.A model has been constructed for our investigations, which introduces the connections between the product cycle phases for mutton in Hungary. This model allows us to calculate the volume of gross value added generated within specific product cycle phases. We used Monte Carlo simulation for our examination, for which the Crystall ball software package was utilized, namely the OptQuest module, for optimization. First, we conducted an optimization of an experiment number of 500,000 for “Gross value added” in the case of the slaughterhouse. During the optimization, Easter, Christmas and August lamb ratio and ewe number, as well as progeny, were set as decision variables and examined as values of gross value added, the decision variables of which contribute to obtaining the best results. The gained decision variables were set in the model and a Monte Carlo simulation was run with an experiment number of 500,000, where only the values of the conditions were changed along the pre-set dispersion; the values of the decision variables were fixed. The most significant aim of our investigation was to identify the volume of gross value added generated during processing in various phases of the product chain and the change of which inputs affected this volume the most. The findings proved that, in the case of capital uniformity, the output of processing was most influenced by sheep progeny on the bottom level of the mutton product chain. This factor is followed by that of weight gain in the source material producing and fattening sub-modules, as well as the gross wage in starter lamb feed and meadow hay in the source material producing sub-modules. Contour plots helped to describe the connections between these factors. By using contour plots, the volume of gross value added might be forecast for various combinations of factors.