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This paper examines the impact of selected variables on the value creation of tourism enterprises in Western Austria (Salzburg, Tyrol, and Vorarlberg) with the objective of developing an insolvency prediction model for Alpine tourism businesses. This study specifically highlights practical applications for business owners, policymakers, and financial institutions, providing strategies to mitigate bankruptcy risks in the tourism sector. The objective of this study is to address existing gaps in the literature regarding the early detection of insolvency in the tourism sector, particularly in German-speaking regions. To this end, a hybrid analytical method is employed, combining quantitative statistical approaches with sector-specific contextual analysis. A dataset comprising tourism firms was divided into two groups, namely solvent and insolvent, with a view to identifying significant explanatory variables influencing insolvency risks. The primary findings indicate that enterprise size, company age, and debt levels (as indicated by the equity ratio) are significant risk factors for insolvency. Furthermore, the results suggest that small and medium-sized enterprises (SMEs) are particularly vulnerable to financial crises. Finally, the findings demonstrate that macroeconomic structures and sectoral effects significantly impact insolvency probabilities. These findings emphasise the necessity for the development of early warning systems that are tailored to the specific requirements of SMEs in Alpine tourism.This research is particularly pertinent in light of the rising insolvency rates in the tourism industry and the lack of comprehensive official statistics, which impede effective crisis management. By addressing these challenges, the study contributes to the development of targeted risk management and intervention strategies to enhance the resilience of tourism enterprises in the region.

This study aimed to perform a screening for economic interrelationships among market participants from the stock market, global stock indices, and commodities from fossil energy, agricultural, and the metals sector. Particular focus was put on the comovements of the light crude oil benchmarks West Texas Intermediate (WTI) and Brent crude oil. In finance research and the crude oil markets, identifying novel groupings and interactions is a fundamental requirement due to the extended impact of crude oil price fluctuations on economic growth and inflation. Thus, it is of high interest for investors to identify market players and interactions that appear sensitive to crude oil price volatility triggers. The price development of 14 stocks, 25 leading global indices, and 13 commodity prices, including WTI and Brent, were analyzed via data mining applying the hierarchical correlation cluster mapping technique. All price data comprised the period from January 2012 – December 2018 and were based on daily returns. The technique identifies and visualizes existing hierarchical clusters and correlation patterns emphasizing comovements that indicate positively correlated processes. The method successfully identified clustering patterns and a series of relevant and partly unexpected novel comovements in all investigated economic sectors. Although additional research is required to reveal the causative factors, the study offers an insight into in-depth market interrelationships.

Abnormal volatility has a damaging effect on the macroeconomy and is seen as a measure of risk in asset and commodity markets. This investigation had the aim to analyze the supposed transatlantic volatility inducing effect of the most prominent scheduled macroeconomic news announcements from the United States (US) on Brent Blend crude oil price intraday volatility over a period of seven years from 2012 to 2018. The objective was to generate a ranking list of scheduled US macroeconomic news that forecast high intraday volatility episodes at precise points in time. A total of 38 US news was analyzed using a data mining workflow. Data modeling was conducted using a simple ordinary least squares regression model and performed with programming language Python. A one hour window of rolling standard deviation based on one minute high-frequency closing prices were applied. As a result, 20 scheduled US macroeconomic news was successfully identified to significantly impact Brent crude oil price volatility. The model strongly supports the forecast of high price fluctuations and provides an opportunity for market players to adjust their risk management strategies right in time.

This study had the purpose to investigate the impact of 38 scheduled major United States (US) macroeconomic news on WTI crude oil intraday volatility for the period 2012-2018. It was the aim to provide a news ranking that indicates upcoming high volatility episodes at a specific point in time. The West Texas Intermediate (WTI) light crude oil represents a benchmark since it has a signal effect on market players. High crude oil price volatility is a measure of risk and known to increase inflation, to affect producers, consumers, and investors and to destabilize economic growth. In this research approach one-minute high-frequency bid close prices provided the basis for a 1h window rolling standard deviation. Data modeling was performed using simple and multiple robust ordinary least squares (OLS) regression performed with programming language Python. The model successfully identified 21 significant news announcements in both, the simple and multiple regression models, however, simple OLS-regression appears to be more sensitive. It also provided a ranking of US news impacting WTI volatility risk. The results support the prediction of approaching high price volatility and thus, display an opportunity for market participants and decision-makers to minimize risk.