Optimal Mortgage Refinancing: A Closed‐Form Solution

We derive the first closed‐form optimal refinancing rule: refinance when the current mortgage interest rate falls below the original rate by at least In this formula W(.) is (the principal branch of) the Lambert W‐function, where ρ is the real discount rate, λ is the expected real rate of exogenous mortgage repayment, σ is the standard deviation of the mortgage rate, is the ratio of the tax‐adjusted refinancing cost and the remaining mortgage value, and τ is the marginal tax rate. This expression is derived by solving a tractable class of refinancing problems. Our quantitative results closely match those reported by researchers using numerical methods.     

Superkurtosis

Very little is known on how traditional risk metrics behave under intraday trading. We fill this void by examining the finiteness of the returns' moments and assessing the impact of their infinity in a risk management framework. We show that when intraday trading is considered, assuming finite higher order moments, potential losses are materially larger than what the theory predicts, and they increase exponentially as the trading frequency increases—a phenomenon we call $superkurtosis$. Hence, the use of the current risk management techniques under intraday trading imposes threats to the stability of financial markets, as capital ratios are severely underestimated.     

Reassessing False Discoveries in Mutual Fund Performance: Skill,Luck, or Lack of Power?

Barras, Scaillet, and Wermers propose the false discovery rate (FDR) to separate skill (alpha) from luck in fund performance. Using simulations with parameters informed by the data, we find that this methodology is conservative and underestimates the proportion of nonzero‐alpha funds. For example, 65% of funds with economically large alphas of $\pm 2\%$ are misclassified as zero alpha. This bias arises from the low signal‐to‐noise ratio in fund returns and the resulting low statistical power. Our results question FDR's applicability in performance evaluation and other domains with low power, and can materially change the conclusion that most funds have zero alpha.     

Sentiment or habits: Why not both?

Habits and sentiment are important psychological behaviors in asset pricing. In this article I nest consumer sentiment as a risk factor into the Campbell–Cochrane (CC) habit model and examine its impact on asset prices. The model provides an economic mechanism for the pricing of sentiment risk through its impact on habit sensitivity and equilibrium habit levels but finds its market price of risk much lower than fundamentals. The additional sentiment factor does not improve the CC model, with both models returning a matched moments error of $12\%$ from 1980Q1 to 2021Q4. The sentiment factor, however, subsumes risk aversion with a lower resulting risk coefficient than the CC model without sentiment. Furthermore, the model shows that during the COVID period, the risk premium was driven more by consumption growth than sentiment.     

Predictive Regressions: A Present‐Value Approach

We propose a latent variables approach within a present‐value model to estimate the expected returns and expected dividend growth rates of the aggregate stock market. This approach aggregates information contained in the history of price‐dividend ratios and dividend growth rates to predict future returns and dividend growth rates. We find that returns and dividend growth rates are predictable with $R^2$ values ranging from 8.2% to 8.9% for returns and 13.9% to 31.6% for dividend growth rates. Both expected returns and expected dividend growth rates have a persistent component, but expected returns are more persistent than expected dividend growth rates.     

A Stability Approach to Mean‐Variance Optimization

I jointly treat two critical issues in the application of mean‐variance portfolios, that is, estimation risk and portfolio instability. I find that theory‐based portfolio strategies, which are known to outperform naive diversification () in the absence of transaction costs, heavily underperform it under transaction costs. This is because they are highly unstable over time. I propose a generic method to stabilize any given portfolio strategy while maintaining or improving its efficiency. My empirical analysis confirms that the new method leads to stable and efficient portfolios that offer equal or lower turnover than and larger Sharpe ratio, even under high transaction costs.     

Default and Recovery Implicit in the Term Structure of Sovereign CDS Spreads

This paper explores the nature of default arrival and recovery implicit in the term structures of sovereign CDS spreads. We argue that term structures of spreads reveal not only the arrival rates of credit events ${(\lambda }^{?})$, but also the loss rates given credit events. Applying our framework to Mexico, Turkey, and Korea, we show that a single‐factor model with ${\lambda }^{?}$ following a lognormal process captures most of the variation in the term structures of spreads. The risk premiums associated with unpredictable variation in ${\lambda }^{?}$ are found to be economically significant and co‐vary importantly with several economic measures of global event risk, financial market volatility, and macroeconomic policy.     

Internal Capital Markets in Business Groups: Evidence from the Asian Financial Crisis

This paper examines capital reallocation among firms in Korean business groups ( $chaebol$) in the aftermath of the 1997 Asian financial crisis, and the consequences of this capital reallocation for the investment and performance of $chaebol$ firms. We show that $chaebol$ transferred cash from low‐growth to high‐growth member firms, using cross‐firm equity investments. This capital reallocation allowed chaebol firms with greater investment opportunities to invest more than control firms after the crisis. These firms also showed higher profitability and lower declines in valuation than control firms following the crisis. Our results suggest that chaebol internal capital markets helped them mitigate the negative effects of the Asian crisis on investment and performance.     

Predicting base station return on investment in the telecommunications industry: Machine-learning approaches

Investment in the right location ensures sustainable competition. In the telecommunication sector, the number of base stations (BSs) is one of the most significant investment parameters. When a potential BS is subject to be selected, practitioners will first consider investing in a BS where the return on investment (ROI) is highest. Therefore, the quantifiable objectives are distinctly defined, as it makes sense to choose maximizing features that raise per unit investment. This study provides a solution to evaluate the best BS installation alternative with machine-learning approaches as well as to estimate ROI value by changing the properties that affect the ROI value. For this purpose, the estimation performance of logistic regression, random forest, and XGBoost methods are compared and further strengthened by random forest hyperparameter optimization to provide the best performance. The model, with a success rate of 98.7% according to the $F$-score, showed that it was a robust algorithm. The three most essential features for the ROI value are determined to be voice traffic, data traffic, and frequency cost. These parameters enable a review of the prediction results of telecommunications managers and planning specialists responsible for BS investment.     

Why Are U.S. Stocks More Volatile?

U.S. stocks are more volatile than stocks of similar foreign firms. A firm's stock return volatility can be higher for reasons that contribute positively (good volatility) or negatively (bad volatility) to shareholder wealth and economic growth. We find that the volatility of U.S. firms is higher mostly because of good volatility. Specifically, stock volatility is higher in the United States because it increases with investor protection, stock market development, new patents, and firm‐level investment in R&D. Each of these factors is related to better growth opportunities for firms and better ability to take advantage of these opportunities.     

Forecasting National Recessions Using State‐Level Data

We investigate whether there is information useful for identifying U.S. business cycle phases contained in subnational measures of economic activity. Using a probit model to forecast the National Bureau of Economic Research expansion and recession classification, we assess the incremental information content of state‐level employment growth over a commonly used set of national‐level predictors. As state‐level data adds a large number of predictors to the model, we employ a Bayesian model averaging procedure to construct forecasts. Based on a variety of forecast evaluation metrics, we find that including state‐level employment growth substantially improves nowcasts and very short‐horizon forecasts of the business cycle phase. The gains in forecast accuracy are concentrated during months of national recession.     

Regulation and Bonding: The Sarbanes-Oxley Act and the Flow of International Listings

In this paper, we examine the economic impact of the Sarbanes‐Oxley Act (SOX) by analyzing foreign listing behavior onto U.S. and U.K. stock exchanges before and after the enactment of SOX in 2002. Using a sample of all listing events onto U.S. and U.K. exchanges from 1995–2006, we develop an exchange choice model that captures firm‐level, industry‐level, exchange‐level, and country‐level listing incentives, and test whether these listing preferences changed following the enactment of SOX. After controlling for firm characteristics and other economic determinants of these firms' exchange choice, we find that the listing preferences of large foreign firms choosing between U.S. exchanges and the London Stock Exchange's (LSE) Main Market did not change following the enactment of SOX. In contrast, we find that the likelihood of a U.S. listing among small foreign firms choosing between the NASDAQ and LSE's Alternative Investment Market decreased following the enactment of SOX. The negative effect among small firms is consistent with these marginal companies being less able to absorb the incremental costs associated with SOX compliance. The screening of smaller firms with weaker governance attributes from U.S. exchanges is consistent with the heightened governance costs imposed by SOX increasing the bonding‐related benefits of a U.S. listing.     

Taking the Long Way Home: U.S. Tax Evasion and Offshore Investments in U.S. Equity and Debt Markets

We empirically investigate one form of illegal investor‐level tax evasion and its effect on foreign portfolio investment. In particular, we examine a form of round‐tripping tax evasion in which U.S. individuals hide funds in entities located in offshore tax havens and then invest those funds in U.S. securities markets. Employing Becker's ( 1968 ) economic theory of crime, we identify the tax evasion component by examining how foreign portfolio investment varies with changes in the incentives to evade and the risks of detection. To our knowledge, this is the first empirical evidence of investor‐level tax evasion affecting cross‐border equity and debt investment.