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Chinese beer consumption has undergone major changes within the last decade. The combination of a growing middle class and greater exposure to foreign products has resulted in a significant increase in beer imports. The authors examined transformations in this market and how beer preferences have changed over time. This study focuses on changes is origin-specific preferences (e.g. German beer and Mexican beer) as reflected by habit formation (i.e. dynamic consumption patterns) and changes in demand sensitivity to expenditure and prices.

The authors estimated Chinese beer demand – differentiated by source – using a generalized dynamic demand model that accounted for habit formation and trends, as well as the immediate and long-run effects of expenditures and prices on demand. The authors employed a rolling regression procedure that allowed for model estimates to vary with time. Preference changes were inferred from the changing demand estimates, with a particular focus on changes in habit formation, expenditure allocating behaviour, and own-price responsiveness.

Results suggest that Chinese beer preferences have changed significantly over the last decade, increasing for Mexican beer, Dutch beer and Belgian beer. German beer once dominated the Chinese market. However, all indicators suggest that German beer preferences are declining.

Although China is the world's third largest beer importing country behind the United States and France. Few studies have focused on this market. While dynamic analyses of alcoholic beverage demand are not new, this is the first study to examine the dynamics of imported beer preferences in China and implications for exporting countries.

The authors examine the contemporaneous and causal association between tweet features (bullishness, message volume and investor agreement) and market features (stock returns, trading volume and volatility) using 140 South African companies and a dataset of firm-level Twitter messages extracted from Bloomberg for the period 1 January 2015 to 31 March 2020.

Panel regressions with ticker fixed-effects are used to examine the contemporaneous link between tweet features and market features. To examine the link between the magnitude of tweet features and stock market features, the study uses quantile regression.

No monotonic relationship is found between the magnitude of tweet features and the magnitude of market features. The authors find no evidence that past values of tweet features can predict forthcoming stock returns using daily data while weekly and monthly data shows that past values of tweet features contain useful information that can predict the future values of stock returns.

The study is among the earlier to examine the association between textual sentiment from social media and market features in a South African context. The exploration of the relationship across the distribution of the stock market features gives new insights away from the traditional approaches which investigate the relationship at the mean.

This study empirically investigates the relationship between goodwill assets and capital structure (i.e. debt ratio) of firms and the moderating effect of financial market development on the relationship between goodwill assets and capital structure.

This research applied a quantitative method. The article collects large samples of listed firms from 23 developing and nine developed countries and applied the panel data techniques. This research used firm-level data from the DataStream database for both developed and developing countries. The study uses 4,912 firm-level data from 23 developing countries and 4,303 firm-level data from nine developed countries.

The findings reveal a significant positive relationship between goodwill assets and capital structure in developing countries, but goodwill assets have a significant negative relationship with capital structure in developed countries. Moreover, financial market development positively moderates the relationship between goodwill assets and the capital structure of firms in developing countries. The results inform firm managers that goodwill assets serve as additional collateral to secure debt financing. Moreover, policymakers should formulate a debt market policy that recognizes goodwill assets as additional collateral for the purpose of obtaining debt capital.

The study has several implications. First, goodwill assets are identified as a factor of capital structure in this study. Fixed assets have been identified as one of the drivers of capital structure in previous research, although goodwill assets are seldom included. Second, this article shows that along with demand-side determinants, supply-side determinants also play an important role in terms of the firms' choice about the capital structure. Therefore, firms should take both the demand-side and supply-side factors into consideration when sourcing for external financing (i.e. debt capital).

The study considered goodwill as a component of capital structure. The study analysis includes a large sample of enterprises, including 4,912 big firms from 23 developing countries and 4,303 large firms from nine industrialized or developed countries, which adds to the current capital structure information. Furthermore, a large sample size increases the results' robustness and generalizability.

This article examines the links between average city size, fiscal decentralisation, and national economic growth in 33 Organisation for Economic Co-operation and Development (OECD) countries.

The data in this paper comprise an unbalanced panel dataset which contains economic growth indicators, average city size, fiscal decentralisation indicators and control variables in 33 OECD member countries from 1975 to 2015 in five-year intervals. Fixed-effects (FE) estimators are used for the analysis.

This research finds i) countries with larger weighted average city sizes have higher economic growth, ii) countries with greater fiscal decentralisation have higher economic growth, but iii) countries with larger weighted average city sizes with greater decentralisation have lower rates of economic growth.

The research highlights the importance of agglomerations and decentralised governance and management for economic growth. While the findings are consistent with previous evidence that larger city sizes and fiscal decentralisation are separately associated with higher rates of economic growth, the authors find countries which have larger cities and greater fiscal decentralisation experience lower rates of economic growth highlighting a need for caution on decentralisation agendas in such cases. The implications of this suggest policymakers should proceed with caution on decentralisation agendas in countries with large cities.

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.

The purpose of this paper is to examine the effect of financial derivatives usage and country’s tax environment characteristics on the relationship between financial derivatives and tax avoidance.

This study uses a cross-country analysis with the scope of ASEAN (Association of Southeast Asian Nations) countries which consists of the Philippines, Indonesia, Malaysia, and Singapore.

The level of financial derivatives usage positively affects the level of tax avoidance. This finding indicates that financial derivatives can be used as tax avoidance tool. Furthermore, the positive effect of the level of financial derivatives usage on the level of tax avoidance is lower in countries with a competitive tax environment than in countries with an uncompetitive tax environment. This finding indicates that in country with a competitive tax environment, the use of financial derivatives as a tax avoidance tool can be replaced by the tax facilities provided by that country.

This study uses four countries in the Association of Southeast Asian Nations region and does not test the sample based on the financial derivative types.

Tax authorities need to establish a clear tax regulation in regard to the tax treatment of financial derivatives transactions, e.g. define the definition of financial derivatives for hedging purposes and financial derivatives for speculative purposes; and define specific criteria to separate financial derivatives for hedging purposes from financial derivatives for speculative purposes. It is necessary to determine whether losses arising from derivative transactions are classified as deductible expenses or non-deductible expenses.

To the best of the authors’ knowledge, this study is also the first that provide empirical evidence that the relationship between financial derivatives and tax avoidance activities depends on a country’s tax environment.

Students’ assignments are often much better in style and organisation than the email messages they send to theirteachers. Some teachers, including myself, often ‘covertly’ correct students’ email messages for style, organisation,content, or correctness. While some students appreciate this extra effort from the teachers, others see it as an inhibitingintrusion. However, I have frequently noticed that students who are corrected repeatedly improve in writing emails. Myresearch concerns both the use of academic email writing and the correction of errors in student emails, and concludesthe following: students usually write only formal emails to their teachers; those instructors who correct email errors do notoffer explicit error correction; and if email writing were taught to the students, it would offer variety in the writing genresstudents currently compose