Quarterly Review of Economics and Finance最新文献

The effectiveness of inflation targeting is linked to the stationarity properties of inflation. Without making apriori assumptions about the order of integration, we examine whether there is a change in the inflation persistence in one hundred and twenty-seven countries (developed and developing) using monthly data over the 1970–2021 period. For the inflation targeters, we find that the endogenously identified break dates are not consistent with the formal adoption of IT. Logit analysis reveals that inflation targeters do not experience an increased probability of a change in inflation persistence. The quality of institutions emerges as more significant for taming inflation.

This paper modifies the quantity theory of money to forecast inflation, relating the latter to scale variables such as monetary aggregate M2 and government bonds that measure the money demand for asset transactions. The out-of-sample forecast results show that at least since the early 1990s, the money/asset model that uses the money supply/government debt ratio as a predictor has been significantly improved upon univariate and multivariate models, such as Phillips curve and term spread models, for forecasting U.S. inflation over one- to three-year horizons. In using real-time vintage data, I find that, since 2000Q1, the forecasts derived from the money/asset model have slightly improved upon those from the Greenbook in forecasting quarter-over-quarter CPI inflation at short horizons, from two- to four-quarter. These results imply that the Federal Reserve can use the money supply/government debt ratio to forecast and control the inflation rates, coordinating monetary policy with fiscal policy. Moreover, the money supply/government debt ratio can partly explain the U.S. inflation dynamics from the early 1960s until COVID-19.

Using firm-level data from Turkiye, we investigate the effects of earthquakes on firms’ balance sheets. We find that earthquakes increase firms’ liabilities but have a smaller effect on firms’ assets, both in magnitude and significance. Using surveys sent to the finance and/or accounting managers of the largest 100 firms in Turkiye we identify common themes in their perceptions. Our findings reveal a consensus among respondents attributing the increased liabilities to exchange rate depreciation and lower business activity following a disaster. Conversely, higher availability of external credit is associated with a decrease in liabilities. Our analysis also indicates that finance managers with higher educational attainment may be underestimating the effects of earthquakes.

The European Monetary Union (EMU) sovereign debt crisis has been thoroughly investigated in the literature. However, our analysis attempts to shed light on the link between the U.S. and the PIIGS (Portugal, Ireland, Italy, Greece, and Spain) bond markets during the Great Recession. We employ a daily 12-year period dataset and utilize an EGARCH-X approach. Our results reveal significant contagion effects from the U.S. bond market towards the yields of PIIGS bonds. However, our findings suggests that the distribution imposed on the standardized residuals is crucial for identifying the magnitude of the contagion.

This study examines the relationship between environmental, social, and corporate disclosures and its affects on financial position of the firm. The study is based on India’s listed companies which disclosed the ESG variable over the last decade. We examine the five measures of financial performance and firm ESG disclosures. In addition to financial performance measures as response variables, this study uses of Piotroski F scores as a proxy for financial position is unique and novel approach in sustainable finance research. The results show the social disclosures have positive significant affect on firm financial position. Firm value, valuation of the stock and cost of capital. The study will help regulators to strengthen the ESG disclosures and for investors it gives insight about the relationship between ESG disclosure and financial performance of a firms.

This study utilizes weekly datasets on loan growth in Colombia to develop a daily indicator of credit expansion using a two-step machine learning approach. Initially, employing Random Forests (RF), missing data in the raw credit indicator is filled using high frequency indicators like spreads, interest rates, and stock market returns. Subsequently, Quantile Random Forest identifies periods of excessive credit creation, particularly focusing on growth quantiles above 95 %, indicative of potential financial instability. Unlike previous studies, this research combines machine learning with mixed frequency analysis to create a versatile early warning instrument for identifying instances of excessive credit growth in emerging market economies. This methodology, with its ability to handle nonlinear relationships and accommodate diverse scenarios, offers significant value to central bankers and macroprudential authorities in safeguarding financial stability.

Using data on Chinese A-share listed firms from 2010 to 2020, this study employs a partial observable bivariate probit model and introduces fraud triangle theory to explain the mechanisms of overconfidence, short selling, and corporate fraud. Our findings show that overconfidence offers rationalization to investors and corporations, reduces fraud detection, and increases corporate incentives to commit fraud. Short selling promotes information transparency, increases fraud detection, and reduces the opportunities to commit fraud. Moreover, it moderates the relationship between overconfidence and corporate fraud. In addition, overconfidence and short selling affect different types of fraud (operational, executive, and information disclosure fraud). Furthermore, our results show heterogeneity among the ownership types. This study provides a theoretical basis for corporate fraud governance in China’s stock market.

This study analyzes how monthly stock returns in the United States react to conventional and unconventional shadow rates from February 1994 to April 2023. The study uses a nonstationary heterogeneous panel data technique appropriate for analyzing large cross-sections and long periods. The analysis is separated into turbulent and tranquil periods. The findings suggest that, although the shadow rate is expected to align with the long-term rate, its ability to boost economic activity in the stock markets is only applicable in the short term. Despite the Federal Funds Rate (FFR) being unable to be lowered below zero bounds, the study shows results that support the effectiveness of the FFR in stimulating stock returns in the long run, particularly during crisis periods. The study also reveals that both conventional and unconventional shadow rates share a common feature, which is that they demonstrate how the stock markets can be downward-sticky in the long run with a rising shadow rate in virtually all 50 states in the U.S. The findings provide sturdy insights into the usefulness of unconventional monetary policy measures for stock market performance during crises and normal periods.

A joint decision-making model for debt financing and operations is constructed in this study. The proposed model is designed so that enterprises can actively adjust the transparency of private information while considering the risks of corporate bankruptcy, bank premium behavior, and possible damage to the product market. We systematically analyzed the mechanisms of bank interest rate pricing, corporate information transparency, and corresponding stocking strategies. We examined the impact of the companies’ inventories and transparency of different types of information on bank interest rates, which revealed financing and management-related decision-making issues based on the bank’s interest rate response function. Various levels of information transparency are appropriate for companies in the context of debt financing. We discuss the impact of changes in exogenous parameters in the product and financial markets on the basis of global equilibrium, as well as corporate characteristics affecting optimal decision-making.

We examine liquidity policies in an environment in which banks can cover their liquidity needs by hoarding liquidity or selling long-term assets to expert investors. Investors can acquire costly information regarding asset quality and deprive banks with bad assets from accessing the asset market. To prevent expert scrutiny, banks must accept fire sale prices for their assets. These depressed prices induce banks to hoard inefficiently low (high) amounts of liquidity when the likelihood of a liquidity shock is relatively low (high). We show that policy interventions aimed at maintaining opacity in the asset market encourage (discourage) liquidity hoarding when there is underhoarding (overhoarding) of liquidity. This suggests that ex-post interventions can serve as substitutes for ex-ante liquidity regulations.