A klímaváltozás terheivel sújtott korunkban a green finance típusú finanszírozástól komplex hatást várunk, hogy az egyes tevékenységek jövedelmezőségének javításával kezelje a gazdasági és környezeti kockázatokat. Az agráriumban jól azonosítható zöld fejlesztési pontokat találunk, melyekhez ilyen pénzügyi forrás szükséges. Kutatásunkban azt vizsgáljuk, hogy az úgynevezett „green finance” eszközök mennyire lehetnek hatékonyak az agrárium egy kiemelt részterületének, a sertésszektornak a fenntarthatóságát célzó fejlesztések finanszírozásában. A termékpálya szereplőivel lefolytatott Q-módszeres felmérés eredményeként azt tapasztaltuk, hogy a zöld finanszírozás számukra ismeretlen terület. Bizonytalanok és pesszimisták azzal kapcsolatban, hogy a zöld finanszírozási eszközök képesek az ágazat fejlődését szolgálni, és milyen mértékben, de abban mindannyian egyetértenek, hogy az ágazatban a fenntarthatóságot szolgáló beruházások megkövetelhetik az állami szerepvállalást. Tehát egy speciálisan ágazati green finance program sikerre viteléhez gazdaságpolitikai eszközök alkalmazása is szükséges lehet.
The paper seeks to answer the question of how price forecasting can contribute to which techniques gives the most accurate results in the futures commodity market. A total of two families of models (decision trees, artificial intelligence) were used to produce estimates for 2018 and 2022 for 21- and 125-day periods. The main findings of the study are that in a calm economic environment, the estimation accuracy is higher (1.5% vs. 4%), and that the AI-based estimation methods provide the most accurate estimates for both time horizons. These models provide the most accurate forecasts over short and medium time periods. Incorporating these forecasts into the ERM can significantly help to hedge purchase prices. Artificial intelligence-based models are becoming increasingly widely available, and can achieve significantly better accuracy than other approximations.
The paper presents the tools for improving efficiency, from its historical beginnings to modern business intelligence systems, furthermore includes good practices and case studies that illustrate both the potential and the benefits of lean methodology. We review the main characteristics of the lean methodology and the most basic components that can be applied by an organizational culture to improve processes. As the case studies show, these methodologies can contribute not only to optimising the operations of manufacturing firms, but also to improving different corporate cultures. The importance of the data analysis tools available through modern technology is also a key focus of the study, and these are increasingly being used in lean methodologies. These software tools are essential to help in collecting and analysing data generated by the application of efficiency improvement methods, as well as in plan-actual comparisons. The study emphasises the importance of continuous improvement of business processes, focusing on the synergies between modern technologies and proven efficiency improvement methodologies. The implementation of these methods is crucial for the operation and increasing the profitability of SMEs at the dawn of the 4th industrial revolution.
In our paper, we investigate how effectively artificial intelligence can be used to predict stock market trends in the world’s leading equity markets over the period 01/01/2010 to 09/16/2022. Covid-19 and the Russian-Ukrainian war have had a strong impact on the capital markets and therefore the study was conducted in a highly volatile environment. The analysis was performed on three time intervals, using two machine learning algorithms of different complexity (decision tree, LSTM) and a parametric statistical model (linear regression). The evaluation of the results obtained was based on mean absolute percentage error (MAPE). In our study, we show that predictive models can perform better than linear regression in the period of high volatility. Another important finding is that the predictive models performed better in the post-Russian-Ukrainian war period than after the outbreak of Covid-19. Stock market price forecasting can play an important role in fundamental and technical analysis, can be incorporated into the decision criteria of algorithmic trading, or can be used on its own to automate trading.
In this period of climate change, green finance is expected to have complex consequences to address economic and environmental risks by improving the profitability of individual activities. There are clearly identifiable areas of green development in agriculture that require such funding. Our research investigates the effectiveness of green finance tools in financing the sustainable development of the pig sector, a key agricultural sub-sector. The results of a Q-methodology study carried out with actors in the product chain showed that green finance is an unknown area for them. They are uncertain and pessimistic about whether and to what extent green finance tools can contribute to the development of the sector, but all share the view that sustainable investment in the sector may require public intervention. The use of economic policy instruments may therefore be necessary to make a sector-specific green finance programme a success.
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