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on Financial Markets |
| By: | David Hirshleifer; Liang Ma |
| Abstract: | We test and compare the effects of introduction of two new financial information technologies, EDGAR and XBRL, on well-known asset pricing anomalies often attributed to mispricing. EDGAR facilitates easier access to public accounting information about public firms; XBRL reduces the cost of processing such information. Using stacked difference-in-differences regressions, we find that both EDGAR and XBRL reduce mispricing for accounting-based anomalies but not for non-accounting-based anomalies. The economic magnitudes of the effects on accounting-based anomalies are similar for EDGAR and XBRL. These results suggest that both easier access to and less costly processing of public information enhance market efficiency. |
| JEL: | G12 G14 G4 G40 M40 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32767 |
| By: | Menda, Emir |
| Abstract: | This study investigates the relationship between cryptocurrency adoption and stock market capitalization across countries, while controlling for GDP per capita. Using cross-sectional data from 154 countries, we employ ordinary least squares regression to analyze this relationship. The findings reveal a statistically significant positive association between cryptocurrency adoption and stock market capitalization. Specifically, that a one-unit increase in the cryptocurrency adoption score is associated with a 182.614 percentage point increase in stock market capitalization as a percentage of GDP. In addition, GDP per capita shows a significant positive relationship with stock market capitalization, confirming the connection between economic development and financial market depth. These results suggest that cryptocurrency adoption complements traditional financial markets rather than substituting, offering important insights for policymakers, investors, and researchers in understanding the evolving financial landscape. This study contributes to the growing literature on cryptocurrency markets by providing a broader, cross-country perspective on how digital currency growth affects traditional financial markets. Our findings have implications for financial market development, economic policy, and investment strategies in an increasingly digitalized global economy. |
| Keywords: | Cryptocurrency adoption; Stock market capitalization; Cross-country analysis; Financial markets; Economic development |
| JEL: | G00 |
| Date: | 2024–08–22 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:121792 |
| By: | Moitra, Agnij |
| Abstract: | Boglehead investing, founded on the principles of John C. Bogle is one of the classic time tested long term, low cost, and passive investment strategy. This paper uses various machine learning methods, and fundamental stock data in order to predict whether or not a stock would incur negative returns next year, and suggests a loss averted bogle-head strategy to invest in all stocks which are expected to not give negative returns over the next year. Results reveal that XGBoost, out of the 44 models trained, has the highest classification metrics for this task. Furthermore, this paper shall use various machine learning methods for exploratory data analysis, and SHAP values reveal that Net Income Margin, ROA, Gross Profit Margin and EBIT are some of the most important factors for this. Also, based on the SHAP values it is interesting to note that the current year has negligible contribution to the final prediction. Investors can use this as a heuristic guide for loss averted long term (1-year) stock portfolios. |
| Date: | 2024–07–27 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:y3mr6 |