Increasing Impact Of Stock Market Performance On Government Tax Revenues

The aim of this paper is to investigate the relationship between fiscal policy, economic growth and stock market in the United States. This issue has gained importance in the last decade because the market has changed. A significance break has been detected which impacts the nature of the nexus between certain variables. The correlation between the tax revenues and the stock market has increased noticeably, encouraging the revision of the current approach to fiscal policy. This study examines relationship between three variables, namely real GDP, federal government current tax receipts and the stock market represented by the Wilshire 5000 Total Market Index. Quarterly data from 1971 to 2015 are used, divided into two subsets in the year 2000, because there is an obvious change in trend and volatility of the variables. The analysis uses ADF and KPSS unit root tests to find the order of the integration of the data. Subsequent analysis applies Johansen cointegration test, vector error correction model, Granger causality tests and variance decomposition analysis. The results demonstrate that the selected variables are cointegrated, and performance of the stock market significantly increases its influence on government tax revenues in the second period. The findings of this paper are significant for policy makers. Understanding how stock market development and economic growth influence tax revenues and vice versa is crucial for the efficient implementation of successful fiscal policy. Investors in the economy of the United States will be also able to benefit from these results which will help them to understand economic conditions and improve their investment decisions.  Keywords: Economic growth, stock market, tax revenue, VECM, variance decompositio

Comparative Analysis of Credit Risk Models in Relation to SME Segment

The importance of credit risk management is well known and was deeply investigated by the banking industry. There is a pressure on financial institutions to still improve their credit risk management systems, so the credit risk of a bank is an unflagging object of discussion. The aim of this article to compare the predicting abilities of several bankruptcy models to the SME segment in the Czech Republic and its subsegments - medium sized, small and micro enterprises. We have focused on small and medium sized enterprises (SMEs) considering their fundamental role played in the Czech economy and the considerable attention placed on SMEs. We have chosen popular bankruptcy models that are often applied, namely the Altman Z-score, Altman model developed especially for SMEs in 2007, the Ohlson O-score, the Zmijewski’s model, the Taffler’s model, and the IN05 model. The basic form of the models was used as proposed by their authors. The results were compared using the contingency table and ROC curve. We have found that the best prediction models are Zmijewski´s and Ohlson´s models which use probit and logit methodologies and according to our analysis, their prediction ability is better than that of models based on discriminant analysis. Surprisingly, model IN05 designed for Czech companies provides average results only. One of the worst performing models is Altman 2007, which was created specifically for SMEs, but according to our analysis it only provides subordinates results

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Increasing Impact of Stock Market Performance on Government Tax Revenues

The aim of this paper is to investigate the relationship between fiscal policy, economic growth and stock market in the United States. This issue has gained importance in the last decade because the market has changed. A significance break has been detected which impacts the nature of the nexus between certain variables. The correlation between the tax revenues and the stock market has increased noticeably, encouraging the revision of the current approach to fiscal policy. This study examines relationship between three variables, namely real GDP, federal government current tax receipts and the stock market represented by the Wilshire 5000 Total Market Index. Quarterly data from 1971 to 2015 are used, divided into two subsets in the year 2000, because there is an obvious change in trend and volatility of the variables. The analysis uses ADF and KPSS unit root tests to find the order of the integration of the data. Subsequent analysis applies Johansen cointegration test, vector error correction model, Granger causality tests and variance decomposition analysis. The results demonstrate that the selected variables are cointegrated, and performance of the stock market significantly increases its influence on government tax revenues in the second period. The findings of this paper are significant for policy makers. Understanding how stock market development and economic growth influence tax revenues and vice versa is crucial for the efficient implementation of successful fiscal policy. Investors in the economy of the United States will be also able to benefit from these results which will help them to understand economic conditions and improve their investment decisions.  Keywords: Economic growth, stock market, tax revenue, VECM, variance decompositio

Russia's Ruble during the onset of the Russian invasion of Ukraine in early 2022: The role of implied volatility and attention

The onset of the Russo-Ukrainian crisis has led to the rapid depreciation of the Russian ruble. In this study, we model intraday price fluctuations of the USD/RUB and the EUR/RUB exchange rates from the $1^{st}$ of December 2021 to the $7^{th}$ of March 2022. Our approach is novel in that instead of using daily (low-frequency) measures of attention and investor's expectations, we use intraday (high-frequency) data: google searches and implied volatility to proxy investor's attention and expectations. We show that both approaches are useful in predicting intraday price fluctuations of the two exchange rates, although implied volatility encompasses intraday attention.Comment: To be published in Finance Research Letter

Cytokinin N-glucosides: Occurrence, Metabolism and Biological Activities in Plants

Cytokinins (CKs) are a class of phytohormones affecting many aspects of plant growth and development. In the complex process of CK homeostasis in plants, N-glucosylation represents one of the essential metabolic pathways. Its products, CK N7- and N9-glucosides, have been largely overlooked in the past as irreversible and inactive CK products lacking any relevant physiological impact. In this work, we report a widespread distribution of CK N-glucosides across the plant kingdom proceeding from evolutionary older to younger plants with different proportions between N7- and N9-glucosides in the total CK pool. We show dramatic changes in their profiles as well as in expression levels of the UGT76C1 and UGT76C2 genes during Arabidopsis ontogenesis. We also demonstrate specific physiological effects of CK N-glucosides in CK bioassays including their antisenescent activities, inhibitory effects on root development, and activation of the CK signaling pathway visualized by the CK-responsive YFP reporter line, TCSv2::3XVENUS. Last but not least, we present the considerable impact of CK N7- and N9-glucosides on the expression of CK-related genes in maize and their stimulatory effects on CK oxidase/dehydrogenase activity in oats. Our findings revise the apparent irreversibility and inactivity of CK N7- and N9-glucosides and indicate their involvement in CK evolution while suggesting their unique function(s) in plants

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants