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1 – 10 of over 8000Given the importance of panel datasets in contemporary accounting and managerial finance research, the objective of this paper is to provide practical guidance for researchers who…
Abstract
Purpose
Given the importance of panel datasets in contemporary accounting and managerial finance research, the objective of this paper is to provide practical guidance for researchers who are inexperienced in dealing with panel estimation methodologies.
Design/methodology/approach
The paper presents and implements a set of procedures designed to establish whether or not pooled estimation is a viable proposition in a given setting. The paper also explores the suitability of alternative estimation methodologies given the results from these test procedures. To illustrate the key concepts the paper utilises a simple model of the relationship between UK directors' cash compensation and three explanatory variables: accounting earnings; stock returns; and firm growth. This model is used solely for illumination purposes and the paper does not seek to contribute to the compensation literature.
Findings
The results demonstrate the potentially misleading inference in panel settings, which can arise from: pooled OLS, where there is parameter heterogeneity; and firm‐specific OLS, when the impact of unobservable factors is likely to cause omitted variables difficulties.
Practical implications
The paper provides practical insights to researchers with respect to the appropriate ways of utilising the considerable benefits of panel estimation methodologies while simultaneously avoiding common errors.
Originality/value
This study presents guidance in a relatively non‐technical manner on an issue which has not received sufficient attention in the accounting and managerial finance literature to date, namely the procedures to follow in order to choose appropriate estimation methodologies when dealing with panel datasets.
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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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Practical implications
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.
Originality/value
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.
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Dante Amengual, Enrique Sentana and Zhanyuan Tian
We study the statistical properties of Pearson correlation coefficients of Gaussian ranks, and Gaussian rank regressions – ordinary least-squares (OLS) models applied to those…
Abstract
We study the statistical properties of Pearson correlation coefficients of Gaussian ranks, and Gaussian rank regressions – ordinary least-squares (OLS) models applied to those ranks. We show that these procedures are fully efficient when the true copula is Gaussian and the margins are non-parametrically estimated, and remain consistent for their population analogs otherwise. We compare them to Spearman and Pearson correlations and their regression counterparts theoretically and in extensive Monte Carlo simulations. Empirical applications to migration and growth across US states, the augmented Solow growth model and momentum and reversal effects in individual stock returns confirm that Gaussian rank procedures are insensitive to outliers.
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ROBERT BROOKS, ROBERT FAFF and TOM JOSEV
In this paper we empirically investigate the tendency for beta risk to mean‐revert across industries. Using a sample of Australian stocks over the ten‐year period 1989 to 1998…
Abstract
In this paper we empirically investigate the tendency for beta risk to mean‐revert across industries. Using a sample of Australian stocks over the ten‐year period 1989 to 1998, our key results are as follows. We generally observe evidence of a mean reversion tendency — in particular, this seems most appropriate for the Gold, Energy, Finance and Consumer industry groupings. Moreover, there is some evidence that the mean reversion of beta is different across industries. Furthermore, we see that the maximum mean reversion beta occurs for the Gold industry — a value of approximately 1.4 (1.6) for the OLS (Scholes‐Williams) beta analysis. On the other hand, the minimum mean reversion beta based on the ‘All Stocks’ OLS analysis occurs for Miscellaneous Industries with a value of 0.4, while a similar minimum mean reversion beta based on the Scholes‐Williams analysis occurs for the Consumer industry grouping.
The purpose of this paper is to enlarge the exposure of the Theil–Sen (TS) methodology to the academic, analyst and practitioner communities using an earnings forecast setting…
Abstract
Purpose
The purpose of this paper is to enlarge the exposure of the Theil–Sen (TS) methodology to the academic, analyst and practitioner communities using an earnings forecast setting. The study includes an appendix that describes the TS model in very basic terms and SAS code to assist readers in the implementation of the TS model. The study also presents an alternative approach to deflating or scaling variables.
Design/methodology/approach
Archival in nature using a combination of regression analysis and binomial tests.
Findings
The binomial test results support the hypothesis that the forecasting performance of the naïve no-change model is at least equal to or better than the ordinary least squares (OLS) model when earnings volatility is low. However, the results do not support the same hypothesis for the TS model nor do the results support the hypothesis that the OLS and TS models will outperform the naïve no-change model when cash flow volatility is high. Nevertheless, the study makes notable contributions to the literature, as the results indicate that the performance of the naïve model is at least as good as the OLS and TS models across 18 of the 20 binomial tests. Moreover, the results indicate that the performance of the TS model is always superior to the OLS model.
Research limitations/implications
The results are generalizable to US firms and may not extend to non-US firms.
Practical implications
The TS methodology is advantageous to OLS in that the results are robust to outlier observations, and there is no heteroscedasticity. Researchers will find this study to be useful given the use of a model (i.e. TS) which has to date received little attention, and the provision of the details for the mechanics of the model. A bonus for researchers is that the study includes SAS code for implementing the procedure.
Social implications
Awareness of alternative forecast methodologies could lead to improved forecasting results in certain contexts. The study also helps the financial community in general, as improved forecasting abilities are important for all capital market participants as they improve market efficiency.
Originality/value
Although a healthy literature exists for examining out-of-sample forecasts for earnings, the literature lacks an answer for a simple question before pursuing additional analyses: Are the results any better than those from a naive no-change forecast? The current study emphasizes the idea that the naïve no-change forecast is the most elementary model possible, and the researcher must first establish the superiority of a more complex model before conducting further analyses.
The purpose of this paper is to investigate the relationship between infrastructure development, rural–urban income inequality and poverty for BRICS economies.
Abstract
Purpose
The purpose of this paper is to investigate the relationship between infrastructure development, rural–urban income inequality and poverty for BRICS economies.
Design/methodology/approach
Pedroni’s panel co-integration test and panel dynamic ordinary least squares (PDOLS) have been used to carry out the analysis.
Findings
The empirical findings confirm a long-run relationship among infrastructure development, poverty and rural–urban inequality. The PDOLS results suggest that both infrastructure development and economic growth lead to poverty reduction in BRICS. However, rural–urban income inequality aggravates poverty in these nations. The paper advocates for adopting policies aimed at strengthening infrastructure and achieving economic growth to reduce the current levels of poverty prevailing in the BRICS nations.
Originality/value
Significant limitations exist in the literature in terms of not clearly defining the nature of relationship and interlinkages between infrastructure development, poverty and inequality, with regard to the BRICS nations. The available studies mainly focus on the relationship between infrastructure and growth, with the universal agreement being that these two are positively related. However, it is still not right to assume that economic growth attributable to infrastructure development will, therefore, subsequently lead to a reduction in inequality. This forms the basis for this study, that is, to critically examine the relationship between infrastructure development, inequality and poverty for BRICS nations.
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This study aims to examine the influence of socioeconomic development on inflation in South Asia using the foreign exchange rate and money supply as control variables.
Abstract
Purpose
This study aims to examine the influence of socioeconomic development on inflation in South Asia using the foreign exchange rate and money supply as control variables.
Design/methodology/approach
The study uses annual panel data for five South Asian economies, namely, Bangladesh, India, Nepal, Pakistan and Sri Lanka over the period 1990–2018, applies cointegrating regression techniques, namely, the panel dynamic ordinary least square (OLS) and fully modified OLS estimators to examine the long-run relations and conducts the Toda-Yamamoto Granger causality test to detect the direction of causality among variables.
Findings
The cointegrating regression estimations have documented that the socioeconomic development proxied by the human development index (HDI) has no significant impact on inflation. Although economic development represented by gross domestic product (GDP) growth causes inflation, socioeconomic development represented by HDI has no impact on inflation and has demonstrated as a better macroeconomic indicator, and thus creates no inflationary pressure in the economy. The foreign exchange rate has a positive impact on inflation. The broad money supply has the usual positive effect on domestic inflation that endorses the monetarist view about prices. The Toda-Yamamoto Granger causality test has confirmed several unidirectional causalities: inflation causes HDI, money supply causes both inflation and HDI and the foreign exchange rate causes HDI.
Practical implications
The study has practical implications for policymakers in South Asia, to improve HDI, particularly GDP per capita, education and health-care facilities to realize continuous socioeconomic development, which will take care of inflation. Moreover, these counties may follow a conservative monetary policy to control inflationary pressure in their economies.
Originality/value
The study is original and claims to be the first to examine the impact of socioeconomic development on inflation. The findings have socioeconomic values regarding controlling inflation in South Asia.
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Sinclair Davidson and Thomas Josev
We investigate the effect standard time series β‐adjustments have on the OLS‐β. We report that most changes are not statistically significant and the β‐adjustments appear to have…
Abstract
We investigate the effect standard time series β‐adjustments have on the OLS‐β. We report that most changes are not statistically significant and the β‐adjustments appear to have no relationship to the extent of thin trading. Researchers using β face the difficult choice of using an estimate known to be biased by thin trading, or making an adjustment that may not be statistically significant.
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In the finance literature, fitting a cross-sectional regression with (estimated) abnormal returns as the dependent variable and firm-specific variables (e.g. financial ratios) as…
Abstract
Purpose
In the finance literature, fitting a cross-sectional regression with (estimated) abnormal returns as the dependent variable and firm-specific variables (e.g. financial ratios) as independent variables has become de rigueur for a publishable event study. In the absence of skewness and/or kurtosis the explanatory variable, the regression design does not exhibit leverage – an issue that has been addressed in the econometrics literature on the finite sample properties of heteroskedastic-consistent (HC) standard errors, but not in the finance literature on event studies. The paper aims to discuss this issue.
Design/methodology/approach
In this paper, simulations are designed to evaluate the potential bias in the standard error of the regression coefficient when the regression design includes “points of high leverage” (Chesher and Jewitt, 1987) and heteroskedasticity. The empirical distributions of test statistics are tabulated from ordinary least squares, weighted least squares, and HC standard errors.
Findings
None of the test statistics examined in these simulations are uniformly robust with regard to conditional heteroskedasticity when the regression includes “points of high leverage.” In some cases the bias can be quite large: an empirical rejection rate as high as 25 percent for a 5 percent nominal significance level. Further, the bias in OLS HC standard errors may be attenuated but not fully corrected with a “wild bootstrap.”
Research limitations/implications
If the researcher suspects an event-induced increase in return variances, tests for conditional heteroskedasticity should be conducted and the regressor matrix should be evaluated for observations that exhibit a high degree of leverage.
Originality/value
This paper is a modest step toward filling a gap on the finite sample properties of HC standard errors in the event methodology literature.
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Papar Kananurak and Aeggarchat Sirisankanan
There are several different factors that can influence self-employment. However, there is little evidence stemming from direct examination of the impact of financial development…
Abstract
Purpose
There are several different factors that can influence self-employment. However, there is little evidence stemming from direct examination of the impact of financial development (FD) on self-employment. This study aims to formulate empirical specification models to examine the effect of FD on self-employment.
Design/methodology/approach
Panel data analysis of 136 sample countries was performed during the period from 2000 to 2017. This study initially implemented the new financial index developed by the International Monetary Fund (IMF) to examine the impact of FD on self-employment. Panel data analysis including the pooled model, fixed effect and random effect model has been carried out.
Findings
The empirical results show that the financial institutions index has a negative significant impact on self-employment by a considerable magnitude, whereas the financial markets index does not show any statistical significance. The results also find that the government effectiveness index is negative and statistically significant on self-employment.
Originality/value
There are several different factors which can influence self-employment. Nevertheless, there is little evidence for the direct examination of the impact of FD on self-employment. This study investigated the impact of FD on self-employment by using the new FD index created by the IMF. The finding may help policymakers to implement FD along with other institutional policies to control self-employment.
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