Financial signaling and stock return synchronicity may not be at crossroads. This paper optimizes the signaling effect of firms’ financial indicators on stock return synchronicity. The ultimate objective is to align firms’ financial signaling and stock return synchronicity, which implies a benefit of hedging against fluctuations in the stock market index. The data cover quarterly periods from June 1992 to March 2022 for the non-financial firms listed in the DJIA30 and NASDAQ100. This paper examines the observed return synchronicity as the dependent variable. The independent variables are classified into six groups namely, Solvency (or Liquidity) ratios, Assets Efficiency ratios, Expense Control ratios, Debt (or Leverage) ratios, Profitability ratios, and Dividend ratios. The analysis is conducted on two different groups. The first group examines the observed firms’ financials that affect observed stock return synchronicity. The second group examines optimal firms’ financials that help optimize stock return synchronicity. The final results show that (a) current stock return synchronicity is affected positively by cash ratio, and negatively by receivables and historical growth of earnings; (b) optimal stock return synchronicity can be elevated using significant financial indicators namely, Inventory/Current Assets, Net Working Capital/Total Assets, Net worth/Fixed Assets, and Sales Annual Growth; (c) agency conflicts between managers and shareholders can be mitigated by the aforementioned financial indicators, which do not include debt financing being the common source of agency conflicts; and (d) dividends are still insignificant to stock return synchronization.

The progress of financial markets depends on the way world investors foresee the market potential of the country of choice. Countries that are associated with favorable economic incentives are able to motivate investments in their respective stock markets. The objective of this paper is to examine the role of the many economic components which constitute the Market Potential Index in enhancing stock market progress. The methodology goes through testing and estimation. The tests include linearity versus nonlinearity (RESET), normality, and cointegration. The estimation includes cointegration regression and discriminant analysis to distinguish between high and low stock market progress. This study examines unbalanced panel data that covers the years 1996–2022 for 54 countries where a stock market exists. The results show the following: (a) increases in people’s expenditure result in decreases in consumption of investment in financial securities; (b) the investments in infrastructure technology is positively associated with stock market progress; (c) the positive effect of economic freedom indicates that further adaptive trading regulations are beneficial to stock market progress; (d) increases in imports consume large proportions of people’s income, coming at the expense of investment in financial securities; (e) stock markets that are associated with high country risk are characterized by a positive risk–return tradeoff, i.e., a high risk premium; (f) the stock markets listed in the MPI can reach high progress by improving three indicators, namely commercial infrastructure, market receptivity, and country risk. This paper offers a thorough and unique examination of the institutional arrangements and stock market progress. The paper offers a guide to policy makers about how economic institutional arrangements can be promoted in order to reach high stock market progress.

This study distinguishes between observed, uncertain, and stochastic uncertain firm growth. Observed firm growth is measured via historical growth of fixed assets scaled by growth of sales revenue. Uncertain firm growth is the volatility of unobserved (estimated error terms) firm growth. The latter is simulated using nonuniform Monte Carlo to generate stochastic uncertain firm growth. The objective of this study is to examine the relationships among the firm specific, economic, and institutional factors that affect the uncertain and stochastic uncertain growth of a firm. The sample includes the nonfinancial firms listed in the DJIA30 and NASDAQ100, covering quarterly data from 1996Q1 to 2022Q4 for 121 companies. The results reveal that (a) sales growth, profitability, cash flow, and long-term financing help reduce a firm’s uncertain growth, (b) high involvement in exporting exposes firms to higher geopolitical uncertainty, (c) institutional quality (especially political stability and regulatory quality) paradoxically contribute to uncertain firm growth. This study contributes to related studies via offering perspectives to firm managers and policy makers about the factors that help manage the uncertainties of firm growth.

Efficient management of bankruptcy risk requires treating distant-to-default (DD) stochastically as long as historical stock prices move randomly and, thus, do not guarantee that history may repeat itself. Using long-term data that date back to 1952–2023, including the nonfinancial companies listed in the Dow Jones Industrial Average and National Association of Securities Dealers Automated Quotations indexes, this study estimates the historical and stochastic DDs via the geometric Brownian motion (GBM). The results show that (a) the association between the debt-to-equity ratio and the stochastic DD can be used as an indicator of excessive debt financing; (b) debt tax savings have a positive effect on stochastic DD; (c) bankruptcy costs have negative effects on stochastic DD; (d) in terms of the size of the company being proxied by sales revenue and the equity market value of the company, the DD is a reliable measure of bankruptcy costs; (e) in terms of macroeconomic influences, increases in the percentage change in manufacturing output are associated with lower observed and stochastic DD; and (f) in terms of the influences of industry, the stochastic DD is affected by the industry average retail inventory to sales. This paper contributes to related studies in terms of focusing on the indicators that a company’s management can focus on to address the stochastic patterns inherent in the estimation of the DD.