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Daniel and Titman (1997) contend that the Fama‐French three‐factor model’s ability to explain cross‐sectional variation in expected returns is a result of characteristics that firms have in common rather than any risk‐based explanation. The primary aim of the current paper is to provide out‐of‐sample tests of the characteristics versus risk factor argument. The main focus of our tests is to examine the intercept terms in Fama‐French regressions, wherein test portfolios are formed by a three‐way sorting procedure on book‐to‐market, size and factor loadings. Our main test focuses on ‘characteristic‐balanced’ portfolio returns of high minus low factor loading portfolios, for different size and book‐to‐market groups. The Fama‐French model predicts that these regression intercepts should be zero while the characteristics model predicts that they should be negative. Generally, despite the short sample period employed, our findings support a risk‐factor interpretation as opposed to a characteristics interpretation. This is particularly so for the HML loading‐based test portfolios. More specifically, we find that: the majority of test portfolios tend to reveal higher returns for higher loadings (while controlling for book‐to‐market and size characteristics); the majority of the Fama‐French regression intercepts are statistically insignificant; for the characteristic‐balanced portfolios, very few of the Fama‐French regression intercepts are significant.

Empirical accounting research frequently makes use of data sets with a time‐series and a cross‐sectional dimension ‐ a panel of data. The literature review indicates that South African researchers infrequently allow for heterogeneity between firms when using panel data and the empirical example shows that regression results that allow for firm heterogeneity are materially different from regression results that assume homogeneity among firms. The econometric analysis of panel data has advanced significantly in recent years and accounting researchers should benefit from those improvements.

The purpose of this paper is to quantify several measures to examine the determinants of profitability for the listed Indian banks. The authors include both public sector (PSUs) and private sector’s banks in the study. The authors have taken all the banks that are registered on the Bombay stock exchange (BSE) in the sample. This paper also intends to identify the association between the net profit margin (PM) and return on assets (ROA) with the several other independent variables of the Indian banking sector including private banks and public banks over the past six years starting from April 1, 2012 to March 31, 2017. Therefore, a sample of 39 listed banking companies and total 195 balanced observations are selected for the analysis purpose.

The authors have used profitability as a dependent variable represented by net PM, ROA and several financial ratios as independent variables. Financial statement and income statement of all listed banks were obtained from BSE and particular company’s website. Panel data regression has been analyzed with both the descriptive research techniques, i.e., fixed effects and random effects. The authors also verified both panel techniques with Hausman’s specification test, which is a widely used procedure for selecting a panel effect. The authors applied PP – Fisher χ², PP – Choi Z-statistics and Hadri to testing whether the data set is free from unit root problem and data set is a stationary series.

Results imply that interest expended interest earned (IEIE) and credit deposit ratio (CRDR) reduced the profitability of private banks in India. IEIE, CRDR and quick ratio (QR) reduced the profitability of public banks in India, while cash deposit ratio (CDR) and Advances to Loan Funds (ALF) increased the effectiveness of public banks. Under the total banks IEIE, CRDR reduced the profitability, on the other side, CDR, ALF and Total Debt to Owners Fund (TDOF) increased the profitability of total banks in India. Under the dependency of ROA, CRDR and TDOF reduced the return of private banks in India, while CDR, ALF and QR enhanced the profitability of private banks.

No variables found significant under public banks while taking ROA as a dependent variable. Under the overall banking data, CRDR reduced the profitability. On the other side, capital adequacy ratio and ALF increased the profitability of total banks in India. The findings of this study will support policy creators, financial executives and investors in constructing investment decisions.

The purpose of this paper is to review and evaluate the methods commonly used in accounting literature to correct for cointegrated data and data that are neither stationary nor cointegrated.

The authors conducted Monte Carlo simulations according to Baltagi et al. (2011), Petersen (2009) and Gow et al. (2010), to analyze how regression results are affected by the possible nonstationarity of the variables of interest.

The results of this study suggest that biases in regression estimates can be reduced and valid inferences can be obtained by using robust standard errors clustered by firm, clustered by firm and time or Fama–MacBeth t-statistics based on the mean and standard errors of the cross section of coefficients from time-series regressions.

The findings of this study are suited to guide future researchers regarding which estimation methods are the most reliable given the possible nonstationarity of the variables of interest.

This paper examines the determinants of happiness index ratings in European countries over 8 time points using unique data from the Eurostat, World Bank and World Happiness Reports.

To examine the determinants of happiness index ratings for EU-27 countries over the period 2012–2019, panel ordinary least square and quantile regression model are used to data obtained from all sample.

Evidence from European data on happiness index generate some important key outcomes; economic outcomes levels with both current taxes and inflation rate have a positively relationship on happiness index ratings (HIR), while total employment rate has a significant negativity on HIR. Additionally, in a quantile panel regression of 27 countries, the impact of financial inclusion on happiness index looks to change with a country's level of income. On the macroeconomic level, gross domestic product (GDP) improves the happiness index for the individual under certain conditions. Thus, GDP on 0.25th quantile levels positively and significantly impacts the HIR for leader countries.

Empirical evidence suggests that macro-economic variables and the labor market proxies of the countries play a key role in determining HIR as well.

The study extends the literature on developed countries and suggestions a particular perspective on the relationship between economic outcomes and happiness index. This study offers two main originalities: it simultaneously examines the “happiness-macroeconomic level” and “happiness-employment status dimension”, and it uses a quantile regression approach, including financial inclusion variation.

The purpose of this paper is to investigate whether corporate dividend policy changed during the financial crisis.

For this study, a life‐cycle model is used to predict the probability that a firm pays a dividend. The data sample for this research follows that of Fama and French and of DeAngelo et al., for the time period of 2006‐2009. The panel logistic regression analysis considers the firm cluster effects and the autoregressive correlation of the firm clusters.

This study shows evidence that the probability that a firm paid a dividend declined in 2008 and 2009, even after taking the firm's financial condition into account. Furthermore, the analysis also shows that dividend policy did shift during the financial crisis.

The results of this study show that dividend policy did shift during the financial crisis. The research provides evidence that firms placed additional emphasis on financial viability after the financial crisis.

Intellectual capital (IC) has been considered as a valuable asset in the wealth creation and sustainability of the company; however, limited and mixed results are found on its impact on firm financial performance and market value (MV). This paper aims to investigate the influence of IC toward MV and financial performance of publicly listed firms in Indonesia. In addition, this research also presents the comparison of the high and low level of knowledge industries regarding IC performance.

A balanced panel data of 127 firms from 12 industries in Indonesia during 2010 until 2017 was evaluated using dynamic panel regression and administering a well-developed Blundell–Bond instrument (dynamic panel data estimator) to account for endogeneity problem.

The results of this study showed that IC had a significant and positive impact on firm performance. Specifically, structural capital efficiency and capital employed (CE) efficiency have been contributed to the value creation of the company, after controlling for firm size and type of industry. Different to the theoretical expectation, this research found no significant relationship between IC and MV of the firm. However, when the sample was clustered into high-level and low-level knowledge industry, CE displayed positive and significant relationship in high-level industry.

This research contributes to IC research by having a larger sample of Indonesian firms from all industries except banks and financial institutions and using Modified Value Added Intellectual Capital measurement model. To address the endogeneity problem, dynamic panel regression using system generalized method of moment was applied.

Debt sustainability and the growing level of external debt in sub-Saharan African (SSA) continue to be significant research priorities. This study aims to examine the corruption-external debt nexus in SSA economies and whether different levels of corruption better explain this relationship.

The panel quantile regression approach was applied to account for the heterogeneous effect of the exogenous variables on external debts. The research covers 30 years of panel data from 30 selected SSA economies for the period spanning from 2000 to 2021.

The empirical findings of the regression analysis demonstrate the heterogeneous influences of the exogenous variables on external debt. While there was a positive impact of foreign direct investment (FDI) inflows on external debts, corruption established a negative relationship with external debt from the 10th to the 80th quantile. The findings showed a positive link between trade openness and external debt, while they also showed a negative relationship between gross fixed capital formation and external debt.

It is implied that corruption “sands the wheels” of external debts in the selected SSA countries. Therefore, the amount of external debt that flows into SSA is inversely correlated with corruption activity.

To the best of the authors’ knowledge, this study is one of the first to use panel quantile regression to analyze how corruption affects debt dynamics across different levels of debt, allowing for a more nuanced understanding of how corruption affects debt dynamics. Based on the findings of this study, SSA countries should create enabling environments to attract FDI inflows and to continue to drive domestic revenue mobilization and capital so as to be less dependent on external debts.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

This paper aims to ask whether a range of stock market factors contain information that is useful to investors by generating a trading rule based on one-step-ahead forecasts from rolling and recursive regressions.

Using USA data across 3,256 firms, the authors estimate stock returns on a range of factors using both fixed-effects panel and individual regressions. The authors use rolling and recursive approaches to generate time-varying coefficients. Subsequently, the authors generate one-step-ahead forecasts for expected returns, simulate a trading strategy and compare its performance with realised returns.

Results from the panel and individual firm regressions show that an extended Fama-French five-factor model that includes momentum, reversal and quality factors outperform other models. Moreover, rolling based regressions outperform recursive ones in forecasting returns.

The results support notable time-variation in the coefficients on each factor, whilst suggesting that more distant observations, inherent in recursive regressions, do not improve predictive power over more recent observations. Results support the ability of market factors to improve forecast performance over a buy-and-hold strategy.

The results presented here will be of interest to both academics in understanding the dynamics of expected stock returns and investors who seek to improve portfolio performance through highlighting which factors determine stock return movement.

The authors investigate the ability of risk factors to provide accurate forecasts and thus have economic value to investors. The authors conducted a series of moving and expanding window regressions to trace the dynamic movements of the stock returns average response to explanatory factors. The authors use the time-varying parameters to generate one-step-ahead forecasts of expected returns and simulate a trading strategy.