Stock Repurchase and the Role of Signaling： A Comparative Analysis between U.S. and China

This paper compares different methods used for stock repurchase and examines the role of signaling in the U.S. and China＇s capital markets. We find that the ways to buyback stocks are very different in the two countries. Most U.S. stocks are repurchased through open market and the signals sent to the market through open market repurchase are getting weaker. Even though stock repurchases are at their early stage in the Chinese stock market and the dominated way to buyback is through a negotiated repurchase agreement for non-floating shares, the power of signaling seems much stronger. Examining stock prices pre- and post-repurchase, we find that stock repurchase records an average abnormal return of 3.42% on the announcement date and 3.24% on the date of actual renurchase.     

The impact of inventory dynamics on long-term stock returns – An empirical investigation of U.S. manufacturing companies

This paper investigates the relationship between the inventory dynamics and long-term stock returns of a large panel of U.S. manufacturing firms over the time period from 1991 to 2010. We propose two measures of inventory dynamics: one metric to assess the fluctuations of quarterly inventories within the year and a second metric to quantify relative year-over-year inventory growth. Our results indicate that within-year inventory volatility (IV) and abnormal year-over-year inventory growth (ABI) are associated with abnormal stock returns. Both metrics cannot be entirely explained by common risk factors. We find that firms with high IV and low ABI have the best long-term stock returns, and that stock performance decreases monotonically with higher ABI values. Our results are robust to various control variables including size, book-to-market value, industry and prior performance. We therefore conclude that changes in inventory levels provide valuable insights into the risks and opportunities faced by a company.     

The ‘COVID’ crash of the 2020 U.S. Stock market

We employed the log-periodic power law singularity (LPPLS) methodology to systematically investigate the 2020 stock market crash in the U.S. equities sectors with different levels of total market capitalizations through four major U.S. stock market indexes, including the Wilshire 5000 Total Market index, the S&P 500 index, the S&P MidCap 400 index, and the Russell 2000 index, representing the stocks overall, the large capitalization stocks, the middle capitalization stocks and the small capitalization stocks, respectively. During the 2020 U.S. stock market crash, all four indexes lost more than a third of their values within five weeks, while both the middle capitalization stocks and the small capitalization stocks have suffered much greater losses than the large capitalization stocks and stocks overall. Our results indicate that the price trajectories of these four stock market indexes prior to the 2020 stock market crash have clearly featured the obvious LPPLS bubble pattern and were indeed in a positive bubble regime. Contrary to the popular belief that the 2020 US stock market crash was mainly due to the COVID-19 pandemic, we have shown that COVID merely served as sparks and the 2020 U.S. stock market crash had stemmed from the increasingly systemic instability of the stock market itself. We also performed the complementary post-mortem analysis of the 2020 U.S. stock market crash. Our analyses indicate that the probability density distributions of the critical time for these four indexes are positively skewed; the 2020 U.S. stock market crash originated from a bubble that had begun to form as early as September 2018; and the bubble profiles for stocks with different levels of total market capitalizations have distinct temporal patterns. This study not only sheds new light on the makings of the 2020 U.S. stock market crash but also creates a novel pipeline for future real-time crash detection and mechanism dissection of any financial market and/or economic index.     

Expense preference behavior and management “outsourcing”: a comparison of adopters and non-adopters of contract management in U.S. hospitals

Specialized managerial expertise, coupled with the threat of non-renewal should improve efficiency in firms that opt for contract management arrangements. To examine this we apply a generalized version of tests for expense preference behavior to U.S. hospitals in the 1990s. Extending prior literature, we create a quasi-experimental design for a comparison of adopters and non-adopters of contracts using propensity score methods. We generate the distribution of ‘expense preference’ parameters for all contract adopters in both the pre- and post-adoption states, and for a matched control group of non-adopters over the same period. Our results show that contract adoption leads to reduced expense preference behavior, but that this result depends critically on the input being examined.

Regression evidence of safety-net support in Canada and the U.S., 1893–1992

Using annual deviations between the stock market and accounting valuations of major banks, this paper constructs synthetic century-long time series for the intangible safety-net capital generated by reporting and supervisory policies in Canada and the U.S. The credibility of the modeling exercise that produces these synthetic time series is supported by evidence that, in each country, all sustained surges in the value of estimated safety-net capital correlate in appropriate ways with regulatory events and crisis pressures. We invite others to test the qualitative usefulness of our framework by applying the method to data from other countries.     

Predictability of tail risks of Canada and the U.S. Over a Century: The role of spillovers and oil tail Risks☆

We examine the tail risk spillovers between Canada and U.S. stock markets using over a century data, and also account for the roles of tail risks of other advanced economies (France, Germany, Japan, Italy, Switzerland, and the UK) and oil-market tail risk. We use the “best” tail risk measure obtained from different variants of the Conditional Autoregressive Value at Risk (CAViaR) model developed by Engle and Manganelli (2004) in the predictive model and compare its performance with that of an AR(1) benchmark model. We find strong evidence of risk spillovers between the two stock markets. We find contrasting evidence for the predictability of oil-market tail risk, with positive predictability in case of the net oil exporter and negative in case of the net oil importer. Further results using tail risks of other advanced economies (combined) support possible diversification potential for Canadian stocks in the presence of market risks of advanced economies other than the U.S. Our findings have implications for investors and are robust to various out-of-sample forecast horizons, alternative data frequencies, data splits, and 1% and 5% VaRs.     

Do buyer cooperative actions matter under relational stress? Evidence from Japanese and U.S. assemblers in the U.S. automotive industry

Buyers can create relational stress even as they work cooperatively with suppliers. This study investigates the moderating effects of relational stress on the ability of buyer-initiated cooperative actions to influence a supplier's willingness to invest in technology that will be of benefit to the buyer. Data on 2012 buying situations were collected from Tier 1 suppliers to three U.S. domestic automotive assemblers (Chrysler, Ford, and General Motors) and three Japanese transplant automotive assemblers (Honda, Nissan, and Toyota) over three consecutive years (2003–2005). The results indicate that (1) buyer-initiated cooperative actions of communication, assistance, and supplier involvement increase a supplier's willingness to invest in technology, (2) the relationship-enhancing effect of buyer assistance increases under high relational stress, while the effectiveness of buyer communication decreases, and (3) that the effect of supplier involvement is not significantly influenced by relational stress levels. Furthermore, we found that supplier relations with Japanese transplant assemblers are characterized by higher levels of cooperative actions, lower levels of relational stress, and higher levels of supplier willingness to invest in technology when compared to those of U.S. domestic assemblers.     

The impact of government intervention on the stabilization of domestic financial markets and on U.S. banks’ asset composition

The 2007–2009 financial crisis that evolved from various factors including the housing boom, aggressive lending activity, financial innovation, and increased access to money and capital markets prompted unprecedented U.S. government intervention in the financial sector. We examine changes in banks’ balance sheet composition associated with U.S. government intervention during the crisis. We find that the initial round of quantitative easing positively impacts bank liquidity across all bank samples. Our results show a positive impact of repurchase agreement market rates on bank liquidity for small and medium banks. We conclude that banks have become more liquid in the post-crisis period, especially the larger banks (large and money center banks). We show that real estate loan portfolio exposures have reverted to pre-crisis levels for money center banks and remained flat for all other bank samples.     

Do mergers and acquisitions improve firms' financial performance? The case of the U.S. generic drug industry

This paper examines the financial performance of all mergers and acquisitions (M&A) involving publicly traded companies that occurred in the U.S. generic drug industry from 1996 to 2017. The control group was chosen using a nearest neighbor matching procedure. Our empirical strategy controls for unobservable firm-specific fixed effects as well M&A fixed effects. Our findings suggest that profit levels do not change significantly following M&As, but total revenues decline after M&As. Firms undergoing an M&A cut operating expenses but not through reduction in labor expenses or number of employees.     

Are innovation and R&D the only sources of firms’ knowledge that increase productivity? An empirical investigation of French manufacturing firms

The economic literature presents knowledge accumulation as one of the most important characteristics of innovation. The accumulation of knowledge arises from complex and dynamic interactions between a firm??s own internal capacity and external expertise. Research and development (R&D) remains important in the innovation process, but it must be integrated with knowledge from other sources, such as training and knowledge capitalization. This paper investigates the relationship between such knowledge sources and the productivity of French firms. Using asymptotic least squares, a Cobb-Douglas function including R&D, innovation, training expenditure and ISO 9000 certification is estimated for 1,213 French manufacturing firms. Our results show that innovation, training and ISO 9000 certification have a positive and significant impact on firms?? productivity.     

Does going public in the U.S. facilitate corporate innovation of foreign firms?

We examine how going public in the U.S. IPO market influences corporate innovation. Using 185 foreign and 2948 U.S. domestic firms going public in the U.S. over the 1980–2006 period, we find that while exhibiting similar innovativeness in the pre-IPO period, non-U.S. firms tend to generate more innovation than U.S. domestic firms after going public. The findings are robust to adopting subsample tests, various measures of changes in innovation around the year of the IPO, and accounting for truncation problems and potential endogeneity concerns. Further tests show that changes in innovation around the year of the IPO tend to be less prominent for non-U.S. firms that domiciled in countries with more developed equity market and higher level of economic freedom. Our study provides insights into the real effect of going public in the U.S. IPO market on innovative activities.     

Do banking shocks matter for the U.S. economy?

The quantitative significance of shocks to the financial intermediary (FI) has not received much attention up to now. We estimate a DSGE model with what we describe as chained credit contracts, using Bayesian technique. In the model, credit-constrained FIs intermediate funds from investors to credit-constrained entrepreneurs through two types of credit contract. We find that the shocks to the FIs' net worth play an important role in the investment dynamics, accounting for 17% of its variations. In particular, in the Great Recession, they are the key determinants of the investment declines, accounting for 36% of the variations.     

Measuring real–financial connectedness in the U.S. economy

We analyze the connectedness between the real and the financial sectors of the U.S. economy. Using the weekly ADS index of the Philadelphia Fed (the widely used business conditions indicator) to represent the real side, we find that during times of financial distress and business cycle turning points, the direction of connectedness runs from the real sector to financial markets. The ADS index is derived from a model containing a measure of term structure along with real variables. Therefore, it might not be the best representative of the real activity used in the connectedness analysis. As an alternative, we derive a real activity index (RAI) from a dynamic factor model of the real sector variables only. The behavior of RAI over time is quite similar to that of the ADS index. When we include RAI to represent the real side, connectedness from the real side to financial markets weakens substantially, while the connectedness from financial markets to the real side becomes more pronounced.     

An error-correction model of the demand for equity mutual funds in the U.S. 1973–1994

The purpose of this paper is to estimate an error-correction model of the U.S. demand for equity mutual funds. Using annual data for the period 1973–1994, this study finds that changes in the demand for equity mutual funds have been significantly influenced by the changes in the rate of return on equity mutual funds and savings deposits, as well as by the growth in income over the long run. The authors would like to thank an anonymous referee of this journal for many useful comments and suggestions.     

When was the U.S. housing downturn predictable? A comparison of univariate forecasting methods

This paper uses three classes of univariate time series techniques (ARIMA type models, switching regression models, and state-space/structural time series models) to forecast, on an ex post basis, the downturn in U.S. housing prices starting around 2006. The performance of the techniques is compared within each class and across classes by out-of-sample forecasts for a number of different forecast points prior to and during the downturn. Most forecasting models are able to predict a downturn in future home prices by mid 2006. Some state-space models can predict an impending downturn as early as June 2005. State-space/structural time series models tend to produce the most accurate forecasts, although they are not necessarily the models with the best in-sample fit.     

How can big data enhance the timeliness of official statistics?: The case of the U.S. consumer price index

The daily consumer price index (CPI) produced by the Billion Prices Project (BPP CPI) offers a glimpse of the direction taken by consumer price inflation in real time. This is in contrast to the official U.S. CPI, which is compiled monthly and released with an average of a three-week delay following the end of the reference month. A recent body of research contended that the movements of online prices are representative of those of offline retail prices, making the BPP CPI a natural candidate for accurately improving the timeliness of the official CPI. We assess the predictive content of the BPP CPI using a variety of MIDAS models that accommodate data sampled at different frequencies. These models generate estimates that remain robust to the variety of time periods considered and, by the standard of the existing literature, contribute to a significant upgrade in the forecast accuracy of official consumer price inflation figures. The paper then sketches the broad implications of BPP CPI for the consumer price statistics maintained by national statistics offices and discusses how the proposed improvement in the timeliness of the official CPI fits in this perspective.