Testing the q-Theory of Anomalies

The q-theory explanations of asset pricing anomalies are quantitatively important. We perform a new asset pricing test by using GMM to minimize the difference between average stock returns in the data and average investment returns constructed from observable firm characteristics. Under various specifications, the model-implied average returns display similar magnitudes of dispersion across portfolios sorted on investment-to-asset and on size and book-to-market. But the predicted dispersions in average returns among portfolios sorted on earnings surprises are somewhat smaller in magnitude than those observed in the dataq-theory, asset pricing anomalies, structural estimation

Regularities

The neoclassical q-theory is a good start to understand the cross section of returns. Under constant return to scale, stock returns equal levered investment returns that are tied directly with characteristics. This equation generates the relations of average returns with book-to-market, investment, and earnings surprises. We estimate the model by minimizing the differences between average stock returns and average levered investment returns via GMM. Our model captures well the average returns of portfolios sorted on capital investment and on size and book-to-market, including the small-stock value premium. Our model is also partially successful in capturing the post-earnings-announcement drift and its higher magnitude in small firms.

Internal Finance and Firm Investment

We examine the neoclassical investment model using a panel of U.S. manufacturing firms. The standard model with no financing constraints cannot be rejected for firms with high (pre-sample) dividend payouts. However, it is decisively rejected for firms with low (pre-sample) payouts (firms we expect to face financing constraints). Hem, investment is sensitive to both firm cash flow and macroeconomic credit conditions, holding constant investment opportunities. Sample splits based on firm size or maturity do not produce such distinctions. The latter comparison identifies firms where "free-cash-flow" problems might be expected to produce correlations between investment and cash flow.

U.S. Corporate Leverage: Developments in 1987 and 1988

macroeconomics, U.S. Corporate Leverage, Developments, 1987, 1988

Will Central Bank Digital Currency Disintermediate Banks?

We estimate a dynamic banking model to quantify the impact of a central bank digital currency (CBDC) on the banking system. Our counterfactuals show that a one-dollar introduction of CBDC replaces bank deposits by around 80 cents on the margin. Bank lending falls by one-fourth of the drop in deposits because banks partially replace lost deposits with wholesale funding. This substitution raises banks’ interest-rate risk exposure and lowers their resilience to negative equity shocks. If CBDC bears interest or is intermediated through banks, it captures a greater deposit market share, amplifying the impact on lending. The effect on lending is amplified for small banks, for which wholesale funding is more expensive

Empirical policy functions as benchmarks for evaluation of dynamic models

We describe a set of model-dependent statistical benchmarks that can be used to estimate and evaluate dynamic models of firms' investment and financing. The benchmarks characterize the empirical counterparts of the models' policy functions. These empirical policy functions (EPFs) are intuitively related to the corresponding model, their features can be estimated very easily and robustly, and they describe economically important aspects of firms' dynamic behavior. We calculate the benchmarks for a traditional trade-off model using Compustat data and use them to estimate some of its parameters. We present two Monte Carlo exercises, one that shows EPF-based estimation has lower average bias and lower variance than traditional moment-based estimation and another that shows EPF-based tests are better at detecting misspecification

Will Central Bank Digital Currency Disintermediate Banks?

We estimate a dynamic banking model to quantify the impact of a central bank digital currency (CBDC) on the banking system. Our counterfactuals show that a one-dollar introduction of CBDC replaces bank deposits by around 80 cents on the margin. Bank lending falls by one-fourth of the drop in deposits because banks partially replace lost deposits with wholesale funding. This substitution raises banks’ interest-rate risk exposure and lowers their resilience to negative equity shocks. If CBDC bears interest or is intermediated through banks, it captures a greater deposit market share, amplifying the impact on lending. The effect on lending is amplified for small banks, for which wholesale funding is more expensive

Gender Differences in Russian Colour Naming

In the present study we explored Russian colour naming in a web-based psycholinguistic experiment (http://www.colournaming.com). Colour singletons representing the Munsell Color Solid (N=600 in total) were presented on a computer monitor and named using an unconstrained colour-naming method. Respondents were Russian speakers (N=713). For gender-split equal-size samples (NF=333, NM=333) we estimated and compared (i) location of centroids of 12 Russian basic colour terms (BCTs); (ii) the number of words in colour descriptors; (iii) occurrences of BCTs most frequent non-BCTs. We found a close correspondence between females’ and males’ BCT centroids. Among individual BCTs, the highest inter-gender agreement was for seryj ‘grey’ and goluboj ‘light blue’, while the lowest was for sinij ‘dark blue’ and krasnyj ‘red’. Females revealed a significantly richer repertory of distinct colour descriptors, with great variety of monolexemic non-BCTs and “fancy” colour names; in comparison, males offered relatively more BCTs or their compounds. Along with these measures, we gauged denotata of most frequent CTs, reflected by linguistic segmentation of colour space, by employing a synthetic observer trained by gender-specific responses. This psycholinguistic representation revealed females’ more refined linguistic segmentation, compared to males, with higher linguistic density predominantly along the redgreen axis of colour space