International Journal of Finance & Economics最新文献

Information flows are a theoretical explanation for stock market volatility, but controversy remains regarding how to measure them. Based on cross-sectional and temporal properties of information flows, we decompose total trading volume into four types: cross-country shocks and country-specific shocks due to arrivals of private information, and trading volume shocks and stock volatility shocks due to public information. We then use a Structural Vector Autoregressive model to reconstruct historical trading volume resulted from the four types of information shocks. The evidence shows that the historical trading volumes due to private information flow can explain volatility clustering of stock markets. By analysing sources of information flow, we find private information flow reflects systemic risk in the global financial system. The result conforms to Mixture of Distribution Hypothesis and finds that quality of information content is what differentiates privately informed trading from public information trading. It further suggests the main drivers of stock market volatility are uncertainties about fundamental values of assets and about other investors' behaviours.

In this study, we explore how the personal traits of CEOs and corporate governance mechanisms moderate the link between say-on-pay (SOP) votes and various aspects of firm efficiency. Our sample consists of 1931 firms listed in four Anglo-Saxon economies (i.e., USA, UK, Canada and Australia) during a period of notable regulatory changes. Our findings reveal a significant and positive impact of SOP votes on firm efficiency. This suggests that company executives recognise that lower efficiency leads to lower pay or even job loss. Interestingly, our analysis indicates that younger managers can contribute more to creating value and improving business performance compared with their older counterparts. However, the relationship between gender and firm efficiency remains inconclusive. Furthermore, our study highlights the limited involvement of the board of directors in driving firm efficiency. This could be attributed to inadequate monitoring, cooperation and communication among board members, particularly in the case of audit committees, which seem to have less skilled members. Alternatively, this lack of board engagement may be due to the influence of powerful managers within the company. This paper also offers practical implications to policymakers and practitioners and suggests avenues for future research that can build upon our evidence.

This study examines the determinants of a change in currency expectations for the Turkish Lira (TL) versus the US dollar with different maturities (1 month, 3 months and 1 year). The risk premium is estimated using the interest rate differential and a latent component called the missing risk premium. The empirical model is extended to break down the risk component by introducing other explanatory variables, such as currency swap agreements, credit default swap (CDS), foreign reserves and the volatility index (VIX). A state-space model is employed to explain the behaviour of an unobserved variable over the period between January 2005 and March 2023 with daily and weekly data frequencies. Our findings suggest that the uncovered interest parity (UIP) condition does not hold consistently in Turkey during this period. Deviations from UIP can be attributed to a time-varying risk premium as outlined in Fama's framework. Additionally, our analysis also shows that interest rates and swaps play a significant role in explaining the variations in the TL's risk premium. Moreover, we found a substantial increase in both the level and volatility of the missing risk premium for longer maturities after 2018. Incorporating observable variables substantially reduces both the magnitude and the long-lasting impact of the missing risk premium shocks on expectations. Overall, this study sheds light on the intricate relationship between monetary policy changes, exchange rates and risk premia in the context of an emerging market.

This study explores how temperature anomalies, a novel form of systematic risk, affect financial markets, expanding the traditional understanding of market-wide risks. While climate change is becoming an important consideration, the extent to which temperature anomalies disrupt economic activities and influence stock returns is urgently needed to assess. Using data from 479 Thai companies (2010–2023), we apply linear and nonlinear autoregressive distributed lag (ARDL) models to examine the impact of temperature anomalies and investor sentiment on stock returns. Our findings reveal that (1) temperature anomalies significantly affect short-term stock returns, especially when prioritising sustainability and environmental, social, and governance (ESG) factors; (2) public awareness, measured by Google Search Volume Index (GSVI), has a complex, nonlinear impact on the stock market; (3) temperature anomalies act like traditional risk measures, influencing stock returns similarly to market volatility. The study highlights the growing importance of climate change in financial decision-making and offers insights into investor reactions to climate risks and economic sentiment. It emphasises the need to consider short-term market reactions to climate-related news and suggests that temperature anomalies could be viewed as a systematic risk in financial markets.

We show that expansionary monetary policy is positively (negatively) associated with household portfolio allocation to high-risk (low-risk) assets, in line with ‘reaching for yield’ behaviour. Our main findings are based on an analysis of US household-level data using alternative measures of monetary policy shifts over the period 1999–2007. Using the two-part Fractional Response Model, we show that changes in the Federal Funds Rate (FFR) have a stronger impact on the decision to hold high-risk assets relative to the impact on the decision to hold low-risk assets. In addition, our findings indicate that the impact of FFR changes is stronger for active investors. Finally, our findings are robust over an extended time period (1999–2019) that includes the global financial crisis using a monetary policy measure that accounts for the post-crisis ZLB period.

This paper examines the effects of fiscal rules (FRs) and independent fiscal institutions (IFIs) on sovereign risk. To address potential endogeneity issues, we employ the System Generalised Method of Moments (GMM) estimator in an analysis comprising 24 European Union member states throughout the 2007–2019 period. Our results indicate that FRs are effective in mitigating sovereign default risk, as measured by credit default swap (CDS) spreads on sovereign bonds. In addition, we document that Member States with better numerical compliance rates with European Union fiscal rules have lower sovereign CDS spreads and thus lower risk. By overseeing and fostering compliance with numerical fiscal targets and enhancing the transparency of the budgetary process, IFIs exert a beneficial impact on the probability of sovereign default, particularly those subject to institutional reforms. Furthermore, more developed financial markets supported by both FRs and IFIs contribute to a reduction in sovereign CDS premiums. Our findings have critical policy implications against the backdrop of European economic and fiscal governance reform.

Predicting corporate financial distress is critical for bank lending and corporate bond investment decisions. Incorrect identification of default status can mislead lenders and investors, leading to substantial losses. This paper proposes an ensemble model that minimises the overall cost of misjudgment by considering the imbalanced ratio weighted loss of the unbalanced ratio of Type I and Type II errors in the objective function. Unlike existing static financial distress prediction models, the proposed model integrates panel data by using time-shifting to account for credit risk dynamics. To validate the prediction model, data were collected for Chinese listed companies, considering geographic area, ownership structure and firm size. We demonstrate that by weighting predictions from different classification models, the overall misjudgment cost can be minimised. This study identifies earnings per share and the product price index as the most relevant indicators affecting the financial performance of Chinese-listed companies. Overall, the results indicate that the proposed model has a predictive capacity of up to 5 years, with 98.7% for 1-year forecasting horizons and 96.8% for 5-year-ahead forecasting horizons. Furthermore, the proposed model outperforms existing distress prediction models in overall prediction performance by correctly identifying defaulting companies while avoiding misjudging good companies.