Managerial Finance最新文献

Purpose

This study investigates the mispricing dynamics of NIFTY 50 Index futures, drawing upon daily data spanning from January 2008 to July 2023.

Design/methodology/approach

The study employs both a single regime analysis and a tri-regime model to understand the fluctuations in NIFTY 50 Index futures mispricing.

Findings

The study reveals a complex interplay between various market factors and mispricing, including forward-looking volatility (measured by the NIFVIX index), changes in open interest, underlying index return, futures volume, index volume and time to maturity. Additionally, the relationships are regime-dependent, specifically identifying the regime-dependent nature of the relationship between forward-looking volatility and mispricing, the impact of futures volume on mispricing, the effect of open interest on mispricing, the varying influence of index volume and the influence of time to maturity across the three distinct regimes.

Practical implications

These findings offer valuable insights for policymakers and investors by providing a detailed understanding of futures market efficiency and potential arbitrage opportunities. The study emphasizes the importance of understanding market dynamics, transaction costs and timing, offering guidance to enhance market efficiency and capitalize on trading opportunities in the evolving Indian derivatives market.

Originality/value

The Vector Autoregression (VAR) and Threshold Vector Autoregression Regression (TVAR) models are deployed to disentangle the interrelationships between NIFTY 50 Index futures mispricing and related endogenous determinants.

Research highlights

• (1)

  This study investigates the Nifty 50 Index futures mispricing across three distinct market regimes.

• (2)

  We highlight how factors like volatility, futures volume, and open interest vary in their impact.

• (3)

  The study employs vector auto-regressive and threshold vector auto-regressive models to explore the complex relationships influencing mispricing.

• (4)

  We provide valuable insights for investors and policymakers on improving market efficiency and identifying potential arbitrage opportunities.

Purpose

This paper analyzes the ex-ante determinants of asset securitization in Moroccan banks, providing a detailed exploration of factors influencing securitization in the Moroccan banking sector.

Design/methodology/approach

The study focuses on funding, performance, risk transfer and regulatory capital arbitrage hypotheses. By employing a probit model, we examined all Moroccan banks that securitized their assets from 2002 to 2022. Additional analyses were conducted with alternative variables and by splitting the sample into two periods, 2002–2013 and 2014–2022, to assess the impact of the regulation law 119-12 implemented in 2013 on the Moroccan securitization market.

Findings

The results indicate that the search for alternative funding sources and bank size emerge as significant factors driving securitization in Morocco. Additionally, there is limited evidence that loan portfolio quality is a decisive factor to securitize. Meanwhile, there is no evidence that securitization is driven by performance and regulatory capital arbitrage. Robustness tests further support these findings, while also suggesting that banks may engage in securitization to enhance their performance and, to a lesser extent, reduce regulatory capital.

Originality/value

This paper contributes to the empirical literature by identifying the determinants that drive Moroccan banks to securitize, addressing a research gap in the relatively understudied Moroccan securitization market. The findings provide valuable insights for bankers, investors and policymakers, highlighting the potential benefits of securitization and suggesting policy changes to foster a robust securitization market while ensuring financial stability.

Purpose

Empirical examinations of initial public offering (IPO) initial returns often rely heavily on linear regression models. However, these models can prove inefficient owing to their susceptibility to outliers, a common occurrence in IPO data. This study introduces a machine learning method, known as random forest, to address issues that linear regression may struggle to resolve.

Design/methodology/approach

The study’s sample comprises 352 fixed-priced IPOs from the year 2004 until 2021. A unique aspect of this research is its application of the random forest method. The accuracy of random forest in comparison to other methods is evaluated. The findings indicate that the random forest model significantly outperforms other methods in all of the evaluated aspects.

Findings

The variable importance measure indicates that investors’ demand, divergence of opinion among investors and offer price are the most crucial predictors of IPO initial returns. These determinants hold particular significance due to the widespread use of the fixed-price method in Malaysia, as this method amplifies the information asymmetry in the IPO market.

Originality/value

To the best of the authors’ knowledge, this study is among the pioneering works in Malaysian literature to apply the random forest method to address the constraints of conventional linear regression models. This is achieved by considering a more extensive array of factors and acknowledging the influence of outliers. Additionally, this study adds value to Malaysian literature by ranking and identifying the ex-ante information that best signals the issuing firm’s quality. This contribution facilitates prospective investors’ decision-making processes and provides issuing firms with effective means to communicate their value and quality to the IPO market.

Purpose

This study aimed to explore the determining factors for green bond markets in ASEAN plus three countries. In contrast to previous publications that primarily examined the incentives for green bonds and institutional differences among economies, the analysis focused on the role of competition among sub-financial sectors in fostering the growth of green bond markets.

Design/methodology/approach

This study adopted Driscoll and Kraay fixed effect panel methods to account for country-level heterogeneity and enhance efficiency, using quarterly data from 2016 to 2022.

Findings

The findings showed that healthy competition among sub-financial sectors was crucial for the growth of green bond markets. Growth in specific sub-financial sectors such as brown corporate bond and stock markets as well as banks contributed positively to these markets. Variables related to market microstructure also had no significant impact on green bonds but macroeconomic factors did.

Practical implications

The findings suggested that governments should promote healthy competition among sub-financial sectors and implement diverse policies to ensure the sustainable growth of green bond markets.

Originality/value

This study further pioneered the importance of competition among sub-financial sectors for the development of green bond markets.

Purpose

This study investigates the finance-investment nexus in sub-Saharan Africa using data from 41 countries spanning the period from 2000 to 2022. The central question addressed is whether there is a “too little” or “too much” finance problem in the region.

Design/methodology/approach

This study employs a system-generalised method of moments (GMM) approach to analyse the association between finance and private investment. Additionally, a dynamic threshold regression model is used to uncover potential nonlinearities in this relationship.

Findings

Initially, the study identifies a negative correlation between increased finance and private investment. However, further analysis using the dynamic threshold regression model reveals a critical threshold level of finance. Specifically, the threshold is found to be 6.52% of domestic credit to the private sector and 23.18% using the financial development index. Below this threshold, finance negatively impacts private investment, while surpassing this threshold leads to positive growth in private investment. These findings indicate an issue of “too little” finance in the finance and private investment nexus in sub-Saharan Africa. The results are robust across different model specifications.

Research limitations/implications

The implications of this study highlight the importance of identifying critical thresholds for financing to enhance investment expenditures in the region.

Originality/value

This study contributes to the literature by uncovering nonlinearities in the finance-investment nexus in sub-Saharan Africa. The identification of critical thresholds provides valuable insights for policymakers, emphasising the need to strengthen the financial sector in countries operating below these thresholds to promote private investment and economic growth.

Purpose

This research aims to examine the time-varying behavior of the Weekend, Turn-of-the-Month, January, and Halloween effects in eight foreign exchange rates against the U.S. dollar from the Adaptive Market Hypothesis (AMH) perspective. It also explores whether these anomalies can generate excess returns compared to a buy-and-hold strategy.

Design/methodology/approach

Using daily return data from January 2004 to December 2023 in a rolling-window framework, the study employs the Concordance Coefficient test and AR-GARCH models to assess the time-varying behavior of four calendar anomalies. It also assesses the statistical significance of the trading strategies implied by these anomalies using t-tests and applies F-tests for subperiod analysis.

Findings

The results reveal a generalized time-varying presence of calendar anomalies in emerging currencies and, to a lesser extent, developed currencies. However, the trading strategies implied by these anomalies generally did not show statistical significance, except for the Turn-of-the-Month effect, which exhibited statistically significant unprofitability.

Originality/value

The study pioneers an analysis of five calendar anomalies across various currencies from the standpoint of the AMH and proposes case-specific explanations for their occurrence. It also examines the potential for the anomalies’ implied trading strategies to generate excess returns compared to a straightforward buy-and-hold strategy. Additionally, the study introduces the recently developed Concordance Coefficient test as a valuable alternative to other non-parametric methods.

Purpose

This study explores the complex impact of COVID-19 on India's financial sector, moving beyond simplistic public health vs. economy views. We assess market vulnerabilities and analyze how public sentiment, measured through Google Trends, can predict stock market fluctuations. We propose a novel framework using Google Trends for financial sentiment analysis, aiming to improve understanding and preparedness for future crises.

Design/methodology/approach

Hybrid approach leverages Google Trends as sentiment tool, market data, and momentum indicators like Rate of Change, Average Directional Index and Stochastic Oscillator, to deliver accurate, market insights for informed investment decisions during pandemic.

Findings

Our study reveals that the pandemic significantly impacted the Indian financial sector, highlighting its vulnerabilities. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.95% maximum accuracy in forecasting stock market values during such events.

Originality/value

To the best of authors knowledge this model's originality lies in its focus on short-term impact, novel data fusion and methodology, and high accuracy.• Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of COVID-19 on market behavior.• Novel data fusion and framework: A novel framework of sentiment analysis was introduced in the form of Trend Popularity Index. Combining trend popularity index with momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods.• High predictive accuracy: Achieving the prediction accuracy (98.93%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

Purpose

Historically, most CEOs were promoted from within the organization. The internal candidates offer continuity, are well known to the Board, and have deep institutional knowledge. However, if the Board is seeking changes to the firm’s strategy, operations, or culture, they turn to outside candidates with proven track records. Shareholders react positively to the announcement of an external successor but the longer-term evidence on the performance front is mixed. Most empirical studies have tended to focus on a narrow aspect of the hiring choice, whereas we undertake a more holistic examination involving various elements.

Design/methodology/approach

We consider the salience of the CEO hiring choice from several perspectives: educational background, gender, compensation, and firm performance. We use a propensity score matching approach to ensure comparability between internally promoted and externally hired CEOs. The observed covariates for the matching algorithm include firm size, CEO tenure, and industry sector.

Findings

We find that external CEOs are significantly more likely to have received their undergraduate degree from an elite institution, majored in a STEM discipline, and earned an MBA credential. They earn higher total compensation (with a higher proportion of equity-linked performance pay) but do not outperform in terms of the ROA or Tobin’s Q metrics. Gender does not appear to play a distinguishing role in the internal versus external hiring choice. We also do not detect significant differences in firm performance outcomes associated with educational pedigree, a STEM education, or an MBA credential, whether the CEO is internally promoted or hired externally.

Originality/value

Our paper contributes by providing novel evidence on the impact of educational background on external CEO hiring decisions. Our findings carry implications for testing hypotheses stemming from signaling theory and exploring the significant role of social networks associated with elite educational institutions. Also, the finding that externally appointed CEOs do not outperform their internally promoted counterparts may be noteworthy for corporate boards' hiring committees, especially when reviewing candidates who are edge cases.