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There is a tremendous need for the valuation of small businesses. Oftentimes, small businessowners do not have the wherewithal to gather the data and keep it up to date for use in situations that require valuation. Formal valuations are necessary because they provide objective evidence of value, in contrast to value set by markets on which public companies are traded. This article focuses on some factors that impact the valuation of the business and will help small businessowners feel more comfortable talking with financial professionals about how the business might be valued.

The purpose of this paper is to develop a method to quantify the digital economy using a representative measurement approach and use it to analyze the USA, Germany, the Republic of Korea and Sweden.

The research approach of this paper is based on a developed methodology to identify firms of the digital economy by measuring the market capitalization of selected countries in comparison over time using financial databases.

Comparing the market capitalization of the digital economy, the USA lead both in absolute as well as in relative terms. The 11 firms with the largest market capitalization are all American. For Germany, the results show that policy measures should be undertaken to ameliorate competitiveness in the field.

This current measurement only includes public firms. An interesting avenue for future research would be to transfer the approach to investigate private firms.

Previous research has focused on comparing information and communication technologies adoption and infrastructure as well as innovation hubs between countries. The authors are not aware of any paper to date which has compared market capitalization in the digital economy between countries using a representative sample. This paper offers a research approach to measure and compare the digital economy between countries. The methodology could be applied to other countries which seek to benchmark their performance and derive policy measures to be able to compete with jurisdictions leading in the digital economy.

Fama–French model (FFM) has been successful in helping to predict the financial markets, but investors have been interested in creating more sophisticated models to better predict the performance of the stock market. The objective of the extended version is to create a more robust econometric model to better predict the performance of the Mexican Stock Market.

The study divides the Mexican Stock Market into six different portfolios. The criteria to build those portfolios are the same one used in Fama–French (1992). The study comprises 78 stocks listed in the Mexican Stock Market that are analyzed monthly during 1997–2018. The study analyzes the period before and after the 2008–2009 financial crisis to identify whether there are important changes. The estimation applies the traditional and an extended version of the FFM that include macroeconomic variables such as country risk, economic activity, inflation rate, and exchange rate and some financial variables recommended in the literature.

Results indicate that classic FFM variables are statistically significant in most cases, but relevant macroeconomic variables such as the interest rate, exchange rate and country risk stand out for being weakly relevant in most of the portfolios. However, it is noticed that some of these macroeconomic variables became relevant for different portfolios only after the 2008–2009 crisis, especially in portfolios which include small market capitalization firms.

The study includes the stocks listed in the Mexican Stock Market. One limitation is the small number of stocks available, which reduces the possibility of creating well diversified portfolios. This study includes 78 stocks. The stocks removed from the sample are from firms that were not listed during six consecutive months or whose market capitalization did not change in the same period. Outlier data were removed from the sample to capture in better way the general performance of the stock market.

The objective of the extended version is to create a more robust econometric model than the traditional model. It is expected that such estimations can be helpful to investors to make better decisions when they try to predict performance in the stock market.

An extended version of the FFM can be helpful to investors to make better decisions when they try to predict performance in the stock market.

To the best of our knowledge there are no more studies in the literature of the Mexican financial market that apply the same methodology.

This research investigates the bond between stock market development and agricultural growth in African emerging economies from 1990 to 2020.

Agricultural value added to the gross domestic product measures agricultural growth and market capitalization and stock value traded measure stock market development.

The findings disclose that market capitalization negatively affects agricultural growth while stock value traded positively affects agricultural growth in the fully modified and dynamic ordinary least square techniques. The findings unveil bidirectional causality between labour and agricultural value added with unidirectional causality flow from agricultural value added to market capitalization and stock value traded.

The governments should promote agricultural growth initiatives which stimulate stock market development. Effective methods required to encourage credit flow to the agricultural enterprises through the stock markets' intermediation should be promoted using aggressive policies which eliminate credit flow bottlenecks. Policy makers and regulatory authorities should implement policies which attract investors to the agricultural sector and encourage companies' listing in the stock markets. The capital market funding should be expanded to boost economic growth through agricultural value added.

Literature reveals divergent results on the relationship between stock market development and agricultural growth. Earlier studies provide conflicting findings on the bond between stock market development and agricultural growth. Some findings indicate positive link between stock market development and agricultural growth, while others show a negative association. Studies' results reveal opposing directions of causality between stock market development and agricultural growth.

The study seeks to find the effect of financial leverage on the firm performance of non-financial companies listed in the Tokyo stock market.

The study collected data from 263 companies in the automobile and industrial producer sectors listed on the Tokyo stock exchange between 2001 and 2021. The generalized method of moments was used to estimate the effect of leverage on financial performance due to its ability to overcome the problems of endogeneity and autocorrelation.

The study found that the equity multiplier has a positive and statistically significant effect on return on assets (ROA), return on equity (ROE) and earning per share (EPS). The study discovered that the interest coverage ratio has a positive and statistically significant effect on ROA, ROE, EPS and Tobin’s Q. The results revealed that the degree of financial leverage and debt to earnings before interest, taxes, depreciation and amortization (EBITDA) have a negative and statistically significant effect on ROE, EPS and Tobin’s Q. The study also found that the capitalization ratios of the firms have a negative and statistically significant effect on ROA, ROE, EPS and Tobin’s Q.

The use of debt financing, which presents financial leverage, indicates that the companies can make enough earnings to pay off the interest and principal (debt service obligations), which were shown by the interest coverage ratio, as well as to pay all the long-term fixed expenses, which were shown by the fixed charge coverage ratio. Interest and fixed charge coverage have a positive statistically significant effect on the financial performance of automobile and industrial producer companies.

The study focused on the effect of financial leverage on financial performance by relying on pecking and trade-off theories to contribute to the existing body of literature in finance.

There are a number of differences in the current Sharīʿah screening guidelines formulated by Sharīʿah scholars associated with world-renowned index providers and financial institutions. The purpose of this study is to highlight the consequences of such differences on the portfolio level outcomes for Sharīʿah-compliant investors. This study also investigates the cost of adopting an alternative stock selection methodology.

Seven Sharīʿah-compliant equity portfolios (SCEPs) are created from the active constituents of the S&P 500. Size, sector allocation and financial performance of the resulting seven portfolios are evaluated for the period 1984–2019. Style analysis is performed to attribute the difference in financial performance caused by the choice of selection criteria to different risk factors. The cost of switching the selection criteria is evaluated with turnover analysis and break-even transaction cost.

The choice of stock selection criteria has a significant effect on the size, sector bets and financial performance of the portfolios. Those portfolios which are constructed with market capitalization-based screens outperform portfolios constructed with total assets-based screens. The turnover analysis revealed that SCEPs are relatively costly in practice.

This study investigates the performance of Sharīʿah-compliant portfolios in the context of seven different screening guidelines. The effects of transaction cost and performance attribution to different risk factors represent the key contributions of this study.

This study aims to analyse the effect of gun-related violence on housing values, controlling for the area's crime levels and locational factors. Previous studies that aimed to find a causal connection between crime and housing values used instrument variables to solve the endogeneity problem. Here, the authors have instead been able to take advantage of the fact that shootings have occurred in random time and space. This has made it possible to estimate models to create windows around the shooting (event) and to estimate the causal effects of the shootings. Thus, the authors aim to contribute to the regression discontinuity design method in this context to estimate the short-term effects.

Using the regression discontinuity design method, the authors can estimate the short-term effects of shootings.

Findings from the analysis indicate that shootings directly affect those who are impacted by shootings and indirectly affect the environments where shootings occur. The indirect effect of shootings is momentary as it is capitalised directly in housing values in the immediate area. The effect also appears to be relatively long-term and persistent as housing values have not returned to the price level before the shooting 100–200 days after the shooting. The capitalisation effect is higher the closer one gets to the central parts of the city. On the other hand, the capitalisation effect is not higher or lower in areas with a higher crime rate per capita.

The article contributes to the previous literature in several ways. First and foremost, it provides an explicit analysis of shootings in built-up areas and their hypothesised effect on property prices through the impact on attractiveness and perceived safety. As far as the authors know, no study has analysed this issue on the international level or in Sweden. In this way, the authors aim to develop a study that can provide critical knowledge about one of the adverse effects of shootings. The authors also contribute to the literature by utilising unique data material, which allows the authors to merge information from the police about the exact location of shootings in the Stockholm area with data on sales of apartments in the same residential areas. In addition to the exact location of the shootings (coordinates), the authors also have access to data about whether the shootings led to injuries or deaths. Thus, the authors have separated the effect of shootings and fatal shootings, which has not been done before. Finally, the authors set out to highlight the results as a contribution to the debate on shootings.

Using bank-level data over the 1994–2015 period, the authors aim to investigate the role of bank-specific factors on credit risk in Italy by considering two different groups of banks, namely, cooperative and non-cooperative (commercial and popular), in different local markets.

Relying on highly territorially disaggregated data at labour market areas’ level, the authors estimate the impact of the role of bank-specific factors on credit risk in Italy from the estimation of a fixed-effect estimator. Non-performing loans to total loans has been used as a proxy of credit risk; the bank-specific factors are as follows: growth of loans, reflecting credit policy; log of total assets, controlling for banks’ size; loans to total assets, reflecting the volume of credit market; equity to total assets, capturing the solvency of banks and reflecting their capital strength; return on assets, reflecting the profitability of banks; deposits to loans, reflecting the intermediation cost; cost of total assets, reflecting the banks’ efficiency or volume of intermediation cost.

The empirical findings suggest that regulatory credit policy, capitalisation, volume of credit and volume of intermediation costs are the main bank-specific factors affecting non-performing loans. Nevertheless, the present analysis suggests that the behaviour of cooperative banks’ behaviour seems to be in line with that of commercial rather than popular banks, casting doubts about the feasibility of their credit policies. It turns out that recent reforms involving popular and cooperative banks represent the first step toward the enhancement of the stability and efficiency of the Italian banking system. While the present study’s benchmark results are not particularly affected by the degree of competition in the banking sector and by banks’ size, it shows that both cooperative and non-cooperative banks have undertaken more prudent credit policies after the advent of the financial crisis and the introduction of the Basel regulation.

The relationship between bank-specific factors and credit risk has been analysed using a rich sample of cooperative, commercial and popular banks in Italy over the 1994–2015 period. The authors rely on labour market areas being sub-regional geographical areas where the bulk of the labour force lives and works. The contribution is motivated by the financial distress experienced after the 2008 financial crisis, which has significantly hit the Italian banking system and cooperative banks in particular.

The purpose of this paper is to contribute to empirical evidence by recognizing the importance of stock markets in the financial system and consequently its causality to economic growth and vice versa.

The study used the autoregressive distribute lag model (ARDL) with bound testing procedures, the sample covered quarterly time-series data from 2001q1 to 2019q2 in Tanzania.

The results suggest that stock market development have both negative and positive causality for both short-run dynamics and long-run relationship with economic growth. Economic growth is found to only cause and relate negatively to liquidity both in the short-run and in the long-run. The results show predominantly a unidirectional causality flow from stock market development to economic growth and finds partial causality flow from economic growth to stock market development, as represented by stock market turnover which proxied liquidity.

The use of quarterly data to reflect more realistically the dynamics of the variables because yearly data may sometimes cover-up specific dynamics that may be useful for prediction and policy planning. The study uses indices to capture general aspects within the stock market against economic growth as an intuitive way to aggregate the stock market development effects.

Motivated by the negative effect of external shocks on the domestic economy, this study explores the role of financial sector development in absorbing the effect of external shocks on macroeconomic volatility in Nigeria.

Autoregressive distributed lag and fully modify ordinary least square are used to examine the moderating effect of financial development in the link between external shocks and macroeconomic volatilities in Nigeria between 1986Q1 and 2019Q4. External shock is proxy using oil price shock, and financial development is proxy by domestic credit to the private sector and market capitalisation. At the same time, macroeconomic volatility is proxy by output and inflation volatilities. Macroeconomic volatilities are generated using generalised autoregressive conditional heteroskedasticity (GARCH 1,1).

The results indicate that domestic credit to the private sector significantly reduces output and inflation volatilities in Nigeria in the short and long run. However, market capitalisation promotes macroeconomic volatility. More specifically, financial development indicators play different roles in curtaining macroeconomic volatilities. The results also reveal that external shocks stimulate macroeconomic volatility in Nigeria in the short and long run. Nevertheless, the effects of external shocks on macroeconomic volatilities are reduced when the role of financial development is incorporated.

This study, therefore, concludes that strong financial sector development serves as a significant shock absorber in reducing the adverse effect of external shock on the domestic economy.

This study contributes to the extant studies by introducing a country-specific analysis into the empirical examination of how financial development can moderate the influence of external shock on macroeconomic volatilities.