Consumption-wealth ratio and expected stock returns: evidence from panel data

This paper investigates the role of consumption-wealth ratio on predicting future stock returns through a panel approach. We follow the theoretical framework proposed by Lettau and Ludvigson (2001), in which a model derived from a nonlinear consumer’s budget constraint is used to settle the link between consumption-wealth ratio and stock returns. Using G7’s quarterly aggregate and financial data ranging from the first quarter of 1981 to the first quarter of 2014, we set an unbalanced panel that we use for both estimating the parameters of the cointegrating residual from the shared trend among consumption, asset wealth and labor income, cay, and performing in and out-of-sample forecasting regressions. Due to the panel structure, we propose different methodologies of estimating cay and making forecasts from the one applied by Lettau and Ludvigson (2001). The results indicate that cay is in fact a strong and robust predictor of future stock return at intermediate and long horizons, but presents a poor performance on predicting one or two-quarter-ahead stock returns.

Consumption-Wealth Ratio and Expected Stock Returns: Evidence from Panel Data on G7 Countries

Using the theoretical framework of Lettau and Ludvigson (2001), we perform an empirical investigation on how widespread is the predictability of cay {a modi ed consumption-wealth ratio { once we consider a set of important countries from a global perspective. We chose to work with the set of G7 countries, which represent more than 64% of net global wealth and 46% of global GDP at market exchange rates. We evaluate the forecasting performance of cay using a panel-data approach, since applying cointegration and other time-series techniques is now standard practice in the panel-data literature. Hence, we generalize Lettau and Ludvigson's tests for a panel of important countries. We employ macroeconomic and nancial quarterly data for the group of G7 countries, forming an unbalanced panel. For most countries, data is available from the early 1990s until 2014Q1, but for the U.S. economy it is available from 1981Q1 through 2014Q1. Results of an exhaustive empirical investigation are overwhelmingly in favor of the predictive power of cay in forecasting future stock returns and excess returns.

Bank relationships: effect on the availability and marginal cost of credit for firms in Argentina

The paper provides evidence on what affects at the margin the cost and availability of bank credit for firms in Argentina. We study in particular how banks use different pieces of private and public information to screen firms and overcome informational asymmetries in the credit market. Some private information is transferable, like balance sheet data. Private information generated in relationships is not. To capture the closeness of bank relationships, we resort to the concentration of bank credit and the number of credit lines in a bank. We also consider public information available in the Central de Deudores. The cost of credit is measured using overdrafts, the most expensive line of credit, at the bank that charges the highest rate for overdrafts. We find that the cost of credit is smaller for a firm with a close relationship to the marginal bank. Firms with large assets, a high sales/assets ratio, and a low debt/assets ratio pay a lower interest rate at the margin. A good credit history (no debt arrears and no bounced checks) and collateral also reduce the marginal interest rate. The availability of credit is measured by unused credit lines as a proportion of total liabilities with the main bank. The availability of credit depends positively on a close relationship with the main bank. Large assets, a high return over assets, a high sales/assets ratio, a low debt/assets ratio, a good credit history, and collateral lead to higher credit availability. Our measure of unused credit lines is less ambiguous than traditional measures like leverage, which may indicate financial distress rather than availability of credit.

Detecting patterns of the spinoff decision of companies and accessing the determination of the abnormal returns

This paper examines value created through spinoffs over a period from 2002-2010. The net debt to average share price ratio and the debt to asset ratio of a company impacts the decision for this restructuring process statistically significant. The announcement of a spinoff yields abnormal returns (AR) for the stockholders of the parent. The relative size of the spin and the financial leverage correlated with the AR positively, whereas the net debt per share and the return on asset negatively. Therefore, no direct wealth transfer from the debt holders of a company to the equity holders can be derived from these results.

Predictable dividends and returns

The conventional wisdom is that the aggregate stock price is predictable by the lagged pricedividend ratio, and that aggregate dividends follow approximately a random-walk. Contrary to this belief, this paper finds that variation in the aggregate dividends and price-dividend ratio is related to changes in expected dividend growth. The inclusion of labor income in a cointegrated vector autoregression with prices and dividends allows the identification of predictable variation in dividends. Most of the variation in the price-dividend ratio is due to changes in expected returns, but this paper shows that part of variation is related to transitory dividend growth shocks. Moreover, most of the variation in dividend growth can be attributed to these temporary changes in dividends. I also show that the price-dividend ratio (or dividend yield) can be constructed as the sum of two distinct, but correlated, variables that separately predict dividend growth and returns. One of these components, which could be called the expected return state variable, predicts returns better than the price-dividend ratio does.

Optimal two-sided tests for instrumental variables regression with heteroskedastic and autocorrelated errors

This paper considers two-sided tests for the parameter of an endogenous variable in an instrumental variable (IV) model with heteroskedastic and autocorrelated errors. We develop the nite-sample theory of weighted-average power (WAP) tests with normal errors and a known long-run variance. We introduce two weights which are invariant to orthogonal transformations of the instruments; e.g., changing the order in which the instruments appear. While tests using the MM1 weight can be severely biased, optimal tests based on the MM2 weight are naturally two-sided when errors are homoskedastic. We propose two boundary conditions that yield two-sided tests whether errors are homoskedastic or not. The locally unbiased (LU) condition is related to the power around the null hypothesis and is a weaker requirement than unbiasedness. The strongly unbiased (SU) condition is more restrictive than LU, but the associated WAP tests are easier to implement. Several tests are SU in nite samples or asymptotically, including tests robust to weak IV (such as the Anderson-Rubin, score, conditional quasi-likelihood ratio, and I. Andrews' (2015) PI-CLC tests) and two-sided tests which are optimal when the sample size is large and instruments are strong. We refer to the WAP-SU tests based on our weights as MM1-SU and MM2-SU tests. Dropping the restrictive assumptions of normality and known variance, the theory is shown to remain valid at the cost of asymptotic approximations. The MM2-SU test is optimal under the strong IV asymptotics, and outperforms other existing tests under the weak IV asymptotics.