Spurious Regressions in Financial Economics?

Even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions for stock returns related to the classic studies of Yule (1926) and Granger and Newbold (1974). Data mining for predictor variables interacts with spurious regression bias. The two effects reinforce each other, because more highly persistent series are more likely to be found significant in the search for predictor variables. Our simulations suggest that many of the regressions in the literature, based on individual predictor variables, may be spurious

Testing the significance of calendar effects

This paper studies tests of calendar effects in equity returns. It is necessary to control for all possible calendar effects to avoid spurious results. The authors contribute to the calendar effects literature and its significance with a test for calendar-specific anomalies that conditions on the nuisance of possible calendar effects. Thus, their approach to test for calendar effects produces robust data-mining results. Unfortunately, attempts to control for a large number of possible calendar effects have the downside of diminishing the power of the test, making it more difficult to detect actual anomalies. The authors show that our test achieves good power properties because it exploits the correlation structure of (excess) returns specific to the calendar effect being studied. We implement the test with bootstrap methods and apply it to stock indices from Denmark, France, Germany, Hong Kong, Italy, Japan, Norway, Sweden, the United Kingdom, and the United States. Bootstrap p-values reveal that calendar effects are significant for returns in most of these equity markets, but end-of-the-year effects are predominant. It also appears that, beginning in the late 1980s, calendar effects have diminished except in small-cap stock indices.

Asset Pricing Models with Conditional Betas and Alphas: The Effects of Data Snooping and Spurious Regression

This paper studies the estimation of asset pricing model regressions with conditional alphas and betas, focusing on the joint effects of data snooping and spurious regression. We find that the regressions are reasonably well specified for conditional betas, even in settings where simple predictive regressions are severely biased. However, there are biases in estimates of the conditional alphas. When time-varying alphas are suppressed and only time-varying betas are considered, the betas become baised. Previous studies overstate the significance of time-varying alphas.

The Evolution of Giving: Considerations for Regulation of Cryptocurrency Donation Deductions

This Issue Brief looks at the rapidly growing area of cryptocurrency donations to nonprofit organizations. Given the recent IRS guidance issued on taxation of Bitcoin, specifically its decision to treat cryptocurrencies as property, questions now arise as to how charitable contributions of the coins will be valued for tax deductions. Though Bitcoin resembles most other capital gain property, its volatility, general decline in value, anonymity, and potential for abuse require specific guidance on valuation and substantiation so as to handle its unique nature and prevent larger deductions for charitable contributions than those to which taxpayers are entitled

The Evolution of Giving: Considerations for Regulation of Cryptocurrency Donation Deductions

This Issue Brief looks at the rapidly growing area of cryptocurrency donations to nonprofit organizations. Given the recent IRS guidance issued on taxation of Bitcoin, specifically its decision to treat cryptocurrencies as property, questions now arise as to how charitable contributions of the coins will be valued for tax deductions. Though Bitcoin resembles most other capital gain property, its volatility, general decline in value, anonymity, and potential for abuse require specific guidance on valuation and substantiation so as to handle its unique nature and prevent larger deductions for charitable contributions than those to which taxpayers are entitled

Are foreign currency markets interdependent? evidence from data mining technologies

This study uses two data mining methodologies: Classification and Regression Trees (C&RT) and Generalized Rule Induction (GRI) to uncover patterns among daily cash closing prices of eight currency markets. Data from 2000 through 2009 is used, with the last year held out to test the robustness of the rules found in the previous nine years. Results from the two methodologies are contrasted. A number of rules which perform well in both the training and testing years are discussed as empirical evidence of interdependence among foreign currency markets. The mechanical rules identified in this paper can usefully supplement other types of financial modeling of foreign currencies.Foreign Currency Markets