Global Finance Journal最新文献

We introduce a method that combines Euclidean distancing and OLS techniques to project synthetic capitalization rate indices (‘SCXs’) for metropolitan statistical areas in the US. SCXs are projected independently of market prices, asset specific characteristics and geographic location (ex-ante). In contrast to market cap rates, driven by geographic proximity and market comparables, our new method is driven by economic proximity. We find SCXs provide better forward guidance than market cap rates for commercial real estate (‘CRE’) defaults and CRE values before and during the Covid pandemic. Our method establishes CRE benchmark cap rate indices across property types that explicitly connect CRE valuation at the MSA level to macroeconomic indicators through economic proximity.

Given the potential unforeseen impacts of environmental regulation changes on the innovative activities of micro agents, it is crucial to examine the effects of economic policy uncertainty (EPU) on business development. Several studies have shown that uncertainty acts as a hindrance and can hinder business innovation. This study utilizes data from 807 listed companies spanning from 2007 to 2019 and employs a double fixed-effects model to investigate the influence of EPU on enterprises' green technology innovation activities. The results reveal that EPU decreased firms' green technology innovation. Moreover, EPU inhibits corporate green technology innovation by increasing corporate financing constraints. Thus, financing constraints mediate between EPU and enterprises' level of green technology innovation, with increased market competition reducing the inhibiting effect of EPU on innovation. Furthermore, EPU decreases green technology innovation more among nonstate-owned and low-tech businesses than state-owned and high-tech businesses. This paper reveals the intrinsic mechanism of EPU on firms' innovation, clarifies the technological innovation process, and provides insights into governmental environmental governance from the perspective of EPU.

This study examines the effects of trade openness on income inequality and the moderating role of institutional quality in this dynamic within the Association of Southeast Asian Nations. We find the inverted U-shaped relationship between trade openness and income inequality. At a low level of trade openness, an increase in trade openness leads to higher income inequality. Beyond a certain threshold, however, trade openness starts to decrease income inequality. Institutional quality plays a moderating role, proving essential in reducing inequality. As policies and regulations improve and mature, they support private sector growth, contributing to a decrease in inequality. We highlight the importance of institutional quality as a moderator, not only to promote economic growth but also to ensure an equitable distribution of its benefits, particularly those derived from trade openness, across society.

In today's digital era, technology serves as a fundamental pillar of the digital economy, greatly transforming business models and re-evaluating the enterprises' worth. This theoretical study incorporates digital technology factors into a task-based model to discuss the impact of digital technology advancement and entry price on corporate total factor productivity (TFP) using 2008–2021 data from Chinese-listed companies. The findings show that advances in digital technology considerably help in improving corporate TFP. According to digital technology's pricing strategy, a decrease in the entry price is essential to advancing digital technology and improving TFP. An improvement in corporate management and operational capabilities contributes to incorporating digital technologies at a lower price, thus enhancing corporate TFP. In addition, digital technology advancement generates a heterogeneous TFP effect, which is highlighted in regions with well-protected intellectual property rights, enterprises with high-tech certifications, and nonstate enterprises. These findings demonstrate the empowering effect of emerging technologies on high-quality corporate development and provide strategic insights for companies to achieve digital transformation.

This study uniquely explores the link between nonfungible tokens (NFTs) and the stock markets, providing vital insights for investors to optimize portfolios during global uncertainties such as the health crisis and geopolitical conflicts. We employ the quantile vector autoregression (QVAR) model on daily data from March 14, 2018 to December 23, 2022. Subsequently, the statistics for portfolio analysis are computed using the DCC-GARCH model. Our results highlight that total connectedness at both extremes is significantly higher than at the mean and median quantiles suggesting strong impact of extreme events. The findings reveal that the equity markets of the BRICS countries receive shocks from the system, and NFTs act as transmitters of these shocks. Finally, the pre-COVID-19 pandemic optimal weights remained lower than the COVID-19 pandemic weights, proposing that to reduce risk investors should increase investment in BRICS markets. Similarly, the higher hedge ratio during the turmoil period implies a higher hedging cost. Our findings imply that investors should consider adjusting their investment strategies during periods of heightened global uncertainty to minimize risk and maximize returns.

Environmental governance has always been a focus of academic discourse. Innovatively, this study explores the environmental gains of the digital economy through the lens of market integration. Here, we provide several novel findings using staggered difference-in-differences and dynamic spatial Durbin models based on urban panel data from China from 2011 to 2019. First, the digital economy significantly improves air pollution, resulting in environmental gains for neighboring cities. Second, a heterogeneity analysis shows that the digital economy's environmental gains are more pronounced in regions with high endowment structures and urbanization rates. Finally, our mechanism analysis indicates that as commodity market segmentation and capital factor market distortion increase, the digital economy becomes more capable of improving air quality. The alleviation of capital factor market distortion and commodity market segmentation emerges as the mechanism through which the digital economy exerts its environmental gains.

We evaluate the impact of a large set of daily sentiment measures for predicting Ethereum (ETH) returns using Machine Learning (ML) methods. We examine ETH predictability and evaluate 5 W's: What, Which, When, Why, and hoW. What ML methods work best? Which variables robustly predict ETH returns? When and why does predictability occur? And how can we improve predictability? We extract information from fifty sentiment measures from Refinitiv's MarketPsych Analytics using ML methods including Lasso, Elastic Net, Principal Components, Partial Least Squares, Neural Net and Random Forest. We then apply an ensemble procedure that exponentially weights forecasts from these traditional ML methods based on recent MSFE criteria. By discounting past model performance, our ensemble procedure accommodates time variation in model selection and generates investment gains and significant out-of-sample pre- dictability. Our study offers practical implications for investing in ETH, including considering an array of sentiment measures, diversifying your model forecasts using an ensemble approach, and the importance of transaction costs in trading simulations.

Using regulatory data on transactions and positions, we provide a comprehensive overview of the activity in the foreign exchange (FX) derivatives markets, including futures, swaps, and options, covering exchange-traded and over-the-counter (OTC) products. The heretofore publicly unavailable statistics trace the behavior of dealers, hedge funds, asset managers, pension funds, insurance companies, and sovereign and supranational institutions before, during, and in the aftermath of the market stress of March 2020. We show that when the COVID market shock sharply increased the demand for the US dollar (USD), certain client sectors (e.g., hedge funds and sovereigns), along with dealers, provided USD liquidity by significantly increasing their long-USD swap positions. We find that client sectors are heterogeneous with respect to their liquidity needs and that their aggregate positions are small compared to dealer inventories. In addition to the inter-sector heterogeneity, we highlight the heterogeneity of firms within a client sector by focusing on hedge funds' USD/Euro swap positions—the most active client sector and currency pair in our data. Conversely, the FX dealers follow similar strategies, are competitive, and engage in multilateral netting arrangements to significantly reduce their risk exposure. Finally, using a sample of hedge funds that simultaneously participated in swaps and futures markets, we present evidence on trading volumes and frequencies that suggests that the OTC market is the preferred space for FX risk transfer, whereas the exchange-traded derivatives market serves the price discovery and immediacy functions for smaller trades.

This paper investigates stock market reactions to judicial events in the United States Supreme Court (SCOTUS) involving at least one public firm. Using a comprehensive dataset of >500 SCOTUS cases from 1948 to 2018, we find that the stock market reacts significantly to both the grant of certiorari and the announcement of the final decision. In particular, the stock market reaction to the petitioner and respondent being granted certiorari is significantly negative, portending general higher uncertainty ahead. Furthermore, the stock market reaction to the final decisions for winning (losing) firms is positive (negative). In addition, we find that case characteristics, such as parties involved and the type of legal issue, explain some of the cross-sectional variations in the stock returns across cases. Our tests also show that there is no prior information leakage and no stock price drift following the events.