Lia Cornelia Culda

(1) Background: This research evaluates the efficacy of an expanded suite of Benford’s Law analyses—integrating highly sensitive Kolmogorov-Smirnov (KS) and Anderson-Darling (AD) tests—across diverse cryptocurrencies and temporal scales. By examining both legitimate digital assets and a documented Ponzi scheme, the study identifies robust statistical methodologies for detecting manipulated trading-volume data in de-centralized markets; (2) Methods: The analytical framework assesses the conformity of first- and second-digit distributions with theoretical Benford expectations. The methodology employs a multi-test battery including Pearson Chi-square, Mean Absolute Deviation (MAD), Anderson-Darling, Kolmogorov-Smirnov, Kuiper and Cramérvon Mises tests to ensure sensitivity to both central and tail-end distributional anomalies; (3) Results: Empirical findings demonstrate that Dogecoin, Litecoin, and Luckycoin exhibit significant conformity with Benford’s distribution across the KS and AD metrics. Conversely, Bitconnect demonstrates systemic nonconformity across all applied statis-tical tests; (4) Conclusions: While Benford’s Law is an effective initial diagnostic, it is not a standalone solution for cryptocurrency fraud detection. Among the methods tested, the Anderson-Darling (A-D) and Kolmogorov-Smirnov (K-S) tests exhibited the highest sensitivity, providing a robust framework for identifying sophisticated data manipulation.

The rapid growth of the global economy is increasingly driven by innovation, with artificial intelligence being at the core of business processes transformation. As AI continues to reshape industries, the entrepreneurial economy must adapt to these changes to stay competitive. Often, media narratives influence perceptions and guide decision-making. Our study provides comparative sentiment analysis of how AI integration into entrepreneurship is portrayed by regional media in the UK, USA, and Europe. Analyzing 905 news articles collected via GNews API between August 5, 2024 and April 1, 2025, we applied the VADER tool to assess sentiment in headlines and article descriptions on a daily and weekly basis. The results reveal regional differences and distinct sentiment patterns between headlines and content, highlighting a complex media narrative around AI adoption. By associating media coverage to entrepreneurial perception, our research contributes to the literature on technology-driven economic changes and offers a foundation for future studies exploring the intersection of media news, innovation, and business strategy.

This study investigates the complex interplay between public sentiment, as captured through news titles and descriptions, and the stock prices of three major tech companies: Microsoft (MSFT), Tesla (TSLA), and Apple (AAPL). Leveraging advanced analytical methods including Pearson correlation, wavelet coherence, and regression analysis, this research probes the degree to which stock-price fluctuations can be attributed to the polarity of media sentiment. The methodology combines statistical techniques to assess sentiment’s predictive power for stock opening and closing prices, while wavelet coherence analysis unveils the temporal dynamics of these relationships. The results demonstrate a significant correlation between sentiment polarity and stock prices, with description polarity affecting Microsoft’s opening prices, title polarity influencing Tesla’s opening prices, and a positive impact of title polarity on Apple’s closing prices. However, Tesla’s stock showed no significant coherence, indicating a potential divergence in how sentiment affects stock behavior across companies. The study highlights the importance of sentiment analysis in forecasting stock-market trends, revealing not only direct correlations but also lagged influences on stock prices. Despite its focus on large-cap tech firms, this research provides a foundational understanding of sentiment’s financial implications, suggesting further investigation into smaller firms and other market sectors.

This study investigates the complex interplay between public sentiment, as captured through news titles and descriptions, and the stock prices of three major tech companies: Microsoft (MSFT), Tesla (TSLA), and Apple (AAPL). Leveraging advanced analytical methods including Pearson correlation, wavelet coherence, and regression analysis, this research probes the degree to which stock price fluctuations can be attributed to the polarity of media sentiment. The methodology combines statistical techniques to assess sentiment's predictive power on stock opening and closing prices, while wavelet coherence analysis unveils the temporal dynamics of these relationships. Results demonstrate a significant correlation between sentiment polarity and stock prices, with description polarity affecting Microsoft's opening prices, title polarity influencing Tesla's opening prices, and a positive impact of title polarity on Apple's closing prices. However, Tesla's stock showed no significant coherence, indicating a potential divergence in how sentiment affects stock behavior across companies. The study highlights the importance of sentiment analysis in forecasting stock market trends, revealing not only direct correlations but also lagged influences on stock prices. Despite its focus on large-cap tech firms, this research provides a foundational understanding of sentiment's financial implications, suggesting further investigation into smaller firms and other market sectors.

This study explores the potential of data science software solutions like Customer Relationship Management Software (CRM) for increasing the revenue generation of businesses. We focused on those businesses in the accommodation and food service sector across the European Union (EU). The investigation is contextualized within the rising trend of data-driven decision-making, examining the potential correlation between data science applications and business revenues. By employing a comprehensive evaluation of Eurostat datasets from 2014 to 2021, we used both univariate and multivariate analyses, assessing the percentage of companies that have e-commerce sales across the EU countries, focusing on the usage of big data analytics from any source and the use of CRM tools for marketing purposes or other activities. Big data utilization showed a clear, positive relationship with enhanced e-commerce sales. However, CRM tools exhibited a dualistic impact: while their use in marketing showed no significant effect on sales, their application in non-marketing functions had negative effects on sales. These findings underscore the potential role of CRM and data science solutions in enhancing business performance in the EU’s accommodation and food service industry.

This study explores the potential of data science software solutions like Customer Relationship Management Software (CRM) for increasing the revenue generation of businesses. We focused on those businesses in Accommodation and Food Service sector across the European Union (EU). The investigation is contextualized within the rising trend of data-driven decision making, examining the potential correlation between data science application and business revenue. Employing a comprehensive evaluation of Eurostat datasets from 2014 to 2021, we used both univariate and multivariate analyses, we assessed e-commerce sales data across countries, focusing on the usage of big data and CRM tools. Big data utilization showed a clear, positive relationship with enhanced e-commerce sales. However, CRM tools exhibited a dualistic impact: while their use in marketing showed no significant effect on sales, their application in non-marketing functions had a negative correlation. These findings underscore the potential role of CRM and data science solutions in enhancing business performance in the EU's Accommodation and Food Service industry.