Financial Performance

Cracking the code: Lessons from 15 years of digital health IPOs for the era of AI ArXiv ID: 2410.02709 “View on arXiv” Authors: Unknown Abstract Introduction: As digital health evolves, identifying factors that drive success is crucial. This study examines how reimbursement billing codes affect the long-term financial performance of digital health companies on U.S. stock markets, addressing the question: What separates the winners from the rest? Methods: We analyzed digital health companies that went public on U.S. stock exchanges between 2010 and 2021, offering products or services aimed at improving personal health or disease management within the U.S. market. A search using Google and existing IPO lists identified eligible companies. They were categorized based on the presence or absence of billing codes at the time of their initial public offering (IPO). Key performance indicators, including Compound Annual Growth Rate (CAGR), relative performance to benchmark indices, and market capitalization change, were compared using Mann-Whitney U and Fisher’s Exact tests. Results: Of the 33 companies analyzed, 15 (45.5%) had billing codes at IPO. The median IPO price was $17.00, with no significant difference between groups. Those with billing codes were 25.5 times more likely to achieve a positive CAGR. Their median market capitalization increased 56.3%, compared to a median decline of 80.1% for those without billing codes. All five top performers, in terms of CAGR, had billing codes at IPO, whereas nine of the ten worst performers lacked them. Companies without billing codes were 16 times more likely to experience a drop in market capitalization by the study’s end. Conclusion: Founders, investors, developers and analysts may have overestimated consumers’ willingness to pay out-of-pocket or underestimated reimbursement complexities. As the sector evolves, especially with AI-driven solutions, stakeholders should prioritize billing codes to ensure sustainable growth, financial stability, and maximized investor returns. ...

New intelligent empowerment for digital transformation ArXiv ID: 2406.18440 “View on arXiv” Authors: Unknown Abstract This study proposes an innovative evaluation method based on large language models (LLMs) specifically designed to measure the digital transformation (DT) process of enterprises. By analyzing the annual reports of 4407 companies listed on the New York Stock Exchange and Nasdaq from 2005 to 2022, a comprehensive set of DT indicators was constructed. The findings revealed that DT significantly improves a company’s financial performance, however, different digital technologies exhibit varying effects on financial performance. Specifically, blockchain technology has a relatively limited positive impact on financial performance. In addition, this study further discovered that DT can promote the growth of financial performance by enhancing operational efficiency and reducing costs. This study provides a novel DT evaluation tool for the academic community, while also expanding the application scope of generative artificial intelligence technology in economic research. ...

Capital Structure Theories and its Practice, A study with reference to select NSE listed public sectors banks, India ArXiv ID: 2307.14049 “View on arXiv” Authors: Unknown Abstract Among the various factors affecting the firms positioning and performance in modern day markets, capital structure of the firm has its own way of expressing itself as a crucial one. With the rapid changes in technology, firms are being pushed onto a paradigm that is burdening the capital management process. Hence the study of capital structure changes gives the investors an insight into firm’s behavior and intrinsic goals. These changes will vary for firms in different sectors. This work considers the banking sector, which has a unique capital structure for the given regulations of its operations in India. The capital structure behavioral changes in a few public sector banks are studied in this paper. A theoretical framework has been developed from the popular capital structure theories and hypotheses are derived from them accordingly. The main idea is to validate different theories with real time performance of the select banks from 2011 to 2022. Using statistical techniques like regression and correlation, tested hypotheses have resulted in establishing the relation between debt component and financial performance variables of the select banks which are helping in understanding the theories in practice. ...