7 research outputs found
PrediktĂv modellek teljesĂtmĂ©nyĂ©nek vizsgálata Covid-19 Ă©s az orosz-ukrán háborĂş idĹ‘szakában
Tanulmányunkban arra a kĂ©rdĂ©sre keressĂĽk a választ, hogy mennyire hatĂ©konyan lehet a mestersĂ©ges intelligencia segĂtsĂ©gĂ©vel elĹ‘rejelezni a rĂ©szvĂ©nypiaci trendeket a világ vezetĹ‘ rĂ©szvĂ©nypiacain a 2010. 01. 01. Ă©s a 2022. 09. 16. közötti idĹ‘szakban. A Covid-19 Ă©s az orosz–ukrán háborĂş erĹ‘teljesen Ă©reztette hatását a tĹ‘kepiacokon is, ezĂ©rt egy rendkĂvĂĽl volatilitásintenzĂv környezetben folyt a vizsgálat. Az elemzĂ©s során három idĹ‘intervallumon kĂ©t kĂĽlönbözĹ‘ komplexitásĂş gĂ©pi tanulási algoritmust (döntĂ©si fa, LSTM) Ă©s egy parametrikus statisztikai modellt (lineáris regressziĂł) alkalmaztunk. A kapott eredmĂ©nyek kiĂ©rtĂ©kelĂ©sĂ©t az átlagos abszolĂşt százalĂ©kos hiba alapján (MAPE) Ă©rtĂ©keltĂĽk. Tanulmányunkban igazoltuk, hogy a prediktĂv modellek a kiemelt volatilitásĂş idĹ‘szakban jobban tudnak teljesĂteni, mint a lineáris regressziĂł. Emellett fontos eredmĂ©nyĂĽnk, hogy az orosz–ukrán háborĂşt követĹ‘ idĹ‘szakban jobban teljesĂtettek az elĹ‘rejelzĹ‘ modellek, mint a Covid-19 kitörĂ©se után. Az árfolyam-elĹ‘rejelzĂ©s a fundamentális Ă©s technikai elemzĂ©sek során is fontos szerepet kaphat, beĂ©pĂthetĹ‘ az algoritmikus kereskedĂ©s döntĂ©si szempontjai közĂ©, azonban akár önmagában is alkalmas lehet a kereskedĂ©s automatizálására
Az ESG-Ă©rtĂ©kelĂ©s Ă©s a rĂ©szvĂ©nypiaci teljesĂtmĂ©ny kapcsolata
A vállalatok fenntarthatĂł működĂ©se Ă©s a pĂ©nzĂĽgyi teljesĂtmĂ©ny összefĂĽggĂ©seinek vizsgálata napjainkban kiemelkedĹ‘ kutatási terĂĽletnek számĂt. FelmerĂĽl ugyanakkor a kĂ©rdĂ©s, hogy az ESG-Ă©rtĂ©kelĂ©s hogyan befolyásolja a cĂ©gek gazdasági hatĂ©konyságát. A szerzĹ‘k kutatásukban a rĂ©szvĂ©nypiacokra fĂłkuszálva vizsgálták, hogy milyen kapcsolat van a top 100 ESG-besorolással rendelkezĹ‘ USA szĂ©khelyű vállalat Ă©s azok rĂ©szvĂ©nypiaci teljesĂtmĂ©nye között a 2022-es Ă©s a 2023-as idĹ‘szakban. Arra a kĂ©rdĂ©sre kerestĂ©k a választ, hogy a hozam, a kockázati mutatĂłk Ă©s a szektorbeli hovatartozás befolyásolják-e az ESG-pontszámok alakulását. A kapott eredmĂ©nyek alapján arra a következtetĂ©sre jutottak, hogy a rĂ©szvĂ©nyek valĂłs hozamainak alakulása nincs hatással az ESG-pontszámra, valamint az ESG-Ă©rtĂ©kelĂ©sek sem hatnak a rĂ©szvĂ©nypiaci teljesĂtmĂ©nyre. EredmĂ©nyeik rávilágĂtottak arra, hogy a hozamok szĂłrása Ă©s az ESG-Ă©rtĂ©kelĂ©s között negatĂv kapcsolat figyelhetĹ‘ meg, mely arra utal, hogy a stabilabb Ă©s kevĂ©sbĂ© kockázatos vállalatok az ESG szempontjábĂłl magasabban rangsoroltak, mint a volatilisebb társaik. MegállapĂtották továbbá azt is, hogy csak a 2022-es adatsor esetĂ©ben van szignifikáns kapcsolat az adott szektorban elfoglalt hely Ă©s az ESG-pontszám között
Analysis of the performance of predictive models during Covid-19 and the Russian-Ukrainian war
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
The Uptake of Green Finance Tools in Agriculture : Results of a Q-methodology
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. Journal of Economic Literature (JEL) codes: D25, O13, Q1
Az ESG-Ă©rtĂ©kelĂ©s Ă©s a rĂ©szvĂ©nypiaci teljesĂtmĂ©ny kapcsolata = The Relationship Between ESG Ratings and Stock Market Performance
A vállalatok fenntarthatĂł működĂ©se Ă©s a pĂ©nzĂĽgyi teljesĂtmĂ©ny összefĂĽggĂ©seinek vizsgálata napjainkban kiemelkedĹ‘ kutatási terĂĽletnek számĂt. FelmerĂĽl ugyanakkor a kĂ©rdĂ©s, hogy az ESG-Ă©rtĂ©kelĂ©s hogyan befolyásolja a cĂ©gek gazdasági hatĂ©konyságát. A szerzĹ‘k kutatásukban a rĂ©szvĂ©nypiacokra fĂłkuszálva vizsgálták, hogy milyen kapcsolat van a top 100 ESG-besorolással rendelkezĹ‘ USA szĂ©khelyű vállalat Ă©s azok rĂ©szvĂ©nypiaci teljesĂtmĂ©nye között a 2022-es Ă©s a 2023-as idĹ‘szakban. Arra a kĂ©rdĂ©sre kerestĂ©k a választ, hogy a hozam, a kockázati mutatĂłk Ă©s a szektorbeli hovatartozás befolyásolják-e az ESG-pontszámok alakulását. A kapott eredmĂ©nyek alapján arra a következtetĂ©sre jutottak, hogy a rĂ©szvĂ©nyek valĂłs hozamainak alakulása nincs hatással az ESG-pontszámra, valamint az ESG-Ă©rtĂ©kelĂ©sek sem hatnak a rĂ©szvĂ©nypiaci teljesĂtmĂ©nyre. EredmĂ©nyeik rávilágĂtottak arra, hogy a hozamok szĂłrása Ă©s az ESG-Ă©rtĂ©kelĂ©s között negatĂv kapcsolat figyelhetĹ‘ meg, mely arra utal, hogy a stabilabb Ă©s kevĂ©sbĂ© kockázatos vállalatok az ESG szempontjábĂłl magasabban rangsoroltak, mint a volatilisebb társaik. MegállapĂtották továbbá azt is, hogy csak a 2022-es adatsor esetĂ©ben van szignifikáns kapcsolat az adott szektorban elfoglalt hely Ă©s az ESG-pontszám között.
The relationship between companies’ sustainable operation and their financial performance is currently a key area of research. However, the question arises: how does environmental, social, and governance (ESG) assessment affect firms’ economic efficiency? The authors focused on equity markets to investigate the relationship between the top 100 ESG rated US companies and their stock market performance in 2022 and 2023. They investigated whether returns, risk indicators and sectoral affiliation affect ESG scores. The results indicated that the stock real returns have no impact on the ESG score, and ESG valuations have no impact on stock market performance. There was, however, a negative relationship between the standard deviation of returns and ESG scores, which suggests that more stable and less risky companies tend to have a higher ESG ranking than their more volatile counterparts. There was also a significant relationship between sector position and ESG score only for the 2022 dataset
Navigating Inflation Challenges : AI-Based Portfolio Management Insights
After 2010, the consumer price index fell to a low level in the EU. In the euro area, it remained low between 2010 and 2020. The European Central Bank has even had to take action against the emergence of deflation. The situation changed significantly in 2021. Inflation jumped to levels not seen for 40 years in the EU. Our study aims to use artificial intelligence to forecast inflation. We also use artificial intelligence to forecast stock index changes. Based on the forecasts, we propose portfolio reallocation decisions to protect against inflation. The forecasting literature does not address the importance of structural breaks in the time series, which, among other things, can affect both the pattern recognition and prediction capabilities of various machine learning models. The novelty of our study is that we used the Zivot–Andrews unit root test to determine the breakpoints and partitioned the time series into training and testing datasets along these points. We then examined which database partition gives the most accurate prediction. This information can be used to re-balance the portfolio. Two different AI-based prediction algorithms were used (GRU and LSTM), and a hybrid model (LSTM–GRU) was also included to investigate the predictability of inflation. Our results suggest that the average error of the inflation forecast is a quarter of that of the stock market index forecast. Inflation developments have a fundamental impact on equity and government bond returns. If we obtain a reliable estimate of the inflation forecast, we have time to rebalance the portfolio until the inflation shock is incorporated into government bond returns. Our results not only support investment decisions at the national economy level but are also useful in the process of rebalancing international portfolios
Evaluating the Effectiveness of Modern Forecasting Models in Predicting Commodity Futures Prices in Volatile Economic Times
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