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In financial analysis, forecasting often involves regressing one time series variable on another. However, to ensure that the models are correctly specified, one needs to first test for stationarity, co‐integration and causality. In testing for causality, the variables should be stationary. If non‐stationary, one can estimate the model in difference form, unless the variables are co‐integrated. This article determines whether cash flow and earnings variables are stationary, and which variable causes the other, using econometric analysis. In most cases, cash flow variables are found to cause earnings variables. This is so when the models are estimated in levels. However, when estimated in first differences, the causal relationship tends to be reversed such that earnings cause cash flows. Further study is recommended, whereby panel data could be used to improve the power of the tests.

Wealth Management Products (WMPs) are the largest and most crucial component of China's Shadow banking, which are off the balance sheet and considered as a substitute for deposits. Commercial banks in China are involved in the issuance of WMPs mainly to; evade the regulatory restrictions, move non-performing loans away from the balance sheet, chase the profits and take advantage of yield spread (the difference between WMPs yield and deposit rate).

In this study, the authors investigate what bank related characteristics and needs; influenced and prompted the issuance of WMPs. By using a quarterly panel data from 2010 to 2019, this study performed the fixed effects approach favored by the Hausman specification test, and a feasible generalized least square (FGLS) estimation method is employed to deal with any issues of heteroscedasticity and auto-correlation.

This study found that there is a positive and significant association between the non-performing loan ratio and the issuance of WMPs. Moreover, profitability and spread were found to play an essential role in the issuance of WMPs. The findings of this study suggest that WMPs are issued for multi-purpose, and off the balance sheet status of these products makes them very lucrative for regulated Chinese commercial banks.

Non-guaranteed WMPs are considered as an item of shadow banking in China, as banks do not consolidate this type of WMPs into their balance sheet; due to that reason, there is no individual bank data available for the amount of WMPs. The authors use the number of WMPs issued by banks as a proxy for the bank's exposure to the WMPs business.

From a regulatory perspective, this study helps regulators to understand the risk associated with the issuance of WMPs; by providing empirical evidence that Chinese banks issue WMPs to hide the actual risk of non-performing loans, and this practice could mislead the regulators to evaluate the bank credit risk and loan quality. This study also identifies that Chinese banks issue WMPs for multi-purpose; this can help potential investors to understand the dynamics of WMPs issuance.

This research is innovative in its orientation because it is designed to investigate the less explored wealth management products (WMPs) issued by Chinese banks. This study's content includes not only innovation but also contributes to the existing literature on the shadow banking sector in terms of regulatory arbitrage. Moreover, the inclusion of FGLS estimation models, ten years of quarterly data, and the top 30 Chinese banks (covers 70% of the total Chinese commercial banking system's assets) make this research more comprehensive and significant.

The purpose of this paper is to analyze different seasonal anomalies for the Israeli securities markets for the pre- and post-global financial crisis periods.

The closing values of six indices of the Tel Aviv Stock Exchange (TASE) of Israel have been considered. The time frame ranges from 2000 to 2018. Further, the overall time frame has been segregated into pre- and post-financial crisis periods. The study employs dummy variable regression technique for assessing different calendar anomalies.

The results show evidence pertaining to different seasonal anomalies for the Israeli markets. The results specifically show that the anomalies change considerably across the pre- and post-financial crisis periods. The results are more apparent for three anomalies including the day of the week effect, the month of the year effect and the holiday effect. However, anomalies including the Halloween effect and the trading month effect are found to be insignificant across both pre- and post-financial crisis periods.

The study is first of its kind that analyzes different seasonal anomalies across pre- and post-financial crisis periods for the Israeli markets. The study provides newer insights about the overall return patterns observed in different indices of the TASE.

The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data.

Using a multiplicative error model (hereinafter MEM) to describe the margins in volatility of China’s Shanghai and Shenzhen stock market, this study adopts static and time-varying copulas, respectively, estimated by maximum likelihood estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China.

This paper has identified the asymmetrical dependence structure in financial market volatility more precisely. Gumbel copula could best fit the empirical distribution as it can capture the relatively high dependence degree in the upper tail part corresponding to the period of volatile price fluctuation in both static and dynamic view.

Previous scholars mostly use GARCH model to describe the margins for price volatility. As MEM can efficiently characterize the volatility estimators, this paper uses MEM to model the margins for the market volatility directly based on high-frequency data, and proposes a proper distribution for the innovation in the marginal models. Then we could use copula-MEM other than copula-GARCH model to study on the dependence structure in volatility between Shanghai and Shenzhen stock market in China from a microstructural perspective.

Shadow banking has been evolving rapidly in China, with banks actively using wealth management products (WMPs) to evade regulatory restrictions. These products are the largest constituent of China's shadow banking sector. A large number of these products are off-balance-sheet and considered a substitute for bank deposits. China's banking sector, especially the small and medium-sized banks (SMBs), uses these products to avoid regulatory restrictions and sustainability risk in the deposit market.

This study empirically examined how banks in China, specifically SMBs, utilize these products on a short and long-run basis to manage and control their deposit levels. This study utilized a quarterly panel dataset from 2010 to 2019 for the top 30 Chinese banks, by first implementing a Panel ARDL-PMG model. For cross-sectional dependence, this study further executed a cross-sectional augmented autoregressive distributive lag model (CS-ARDL).

Under regulations avoidance theory, the findings revealed that WMPs and deposits have a stable long-run substitute relationship. Furthermore, the WMP–Deposit substitute relationship was only significant and consistent for SMBs, but not for large four banks. The findings further revealed that the WMP–Deposit substitute relationship existed, even after the removal of the deposit rate limit imposed by the People's Bank of China (PBOC) to control the deposit rates.

The individual bank-issued WMPs' amount data is not available in any database. Therefore, this study utilized the number of WMPs as a proxy for China's banking sector's exposure to the wealth management business.

This research helps policymakers to understand the Deposit–WMP relationship from the off-balance-sheet perspective. During the various stages of interest rate liberalization, banks were given more control to establish their deposit and loan interest rates. However, the deposit rates are still way below the WMP returns, making WMPs more competitive. This research suggests that policymakers should formulate a more balanced strategy regarding deposit rates and WMPs returns.

This study contributes to the existing literature on China's shadow banking by concentrating on the WMPs. This research represents one of the few studies that analyze regulatory arbitrage in terms of the WMP–Deposit relationship. Moreover, the implementation of CS-ARDL panel data models and multiple data sources makes this study's findings more reliable and significant.

The purpose of this paper is to empirically analyze the determinants of capital structure and their long-run equilibrium relationships with firm-specific and macroeconomic indicators for Indian manufacturing firms.

The study is conducted using the panel semi-parametric and non-parametric regression models to identify the key determinants of capital structure. Panel cointegration models are also employed for analyzing the long-run equilibrium association of capital structure with its determinants.

The study finds that each manufacturing sector has unique determinants of capital structure. The debt level is significantly affected by asset tangibility, growth opportunity, effective tax rate, non-debt tax shield, cash flow, profitability, firm size, foreign investment, government borrowing, economic growth, and interest rate. All these firm-specific and macroeconomic variables have strong long-run equilibrium relationship with capital structure as a whole.

The study analyzes the determinants of capital structure for eight manufacturing sectors of India, which helps firm managers and policy-makers to identify appropriate factors that maximize firm value. The sector-specific features of firms may lead to a new path with regard to corporate governance and ownership structure to enhance stakeholder's satisfaction.

The use of semi-parametric and non-parametric panel regression models to analyze the determinants of capital structure, and the use of panel cointegration approach to explore the long-run equilibrium relationship between the determinants and its factors are the unique contributions of the present research.

The purpose of this paper is to examine the short-term and long-term interdependence among the stock markets of Africa and Middle East region. It also attempts to analyze the pattern of volatility spillover among the regional stock markets.

The study has used Granger causality test, variance decomposition test of vector auto-regression (VAR) model, vector error correction model (VECM), multivariate generalized conditional heteroskedasticity (MGARCH-BEKK) models and Johansen and Juselius multivariate cointegration techniques.

The study finds that the interlinkages of the stock markets are not uniform across all the countries of the region. The stock market of Israel, South Africa and Jordan are found to be highly connected stock market of the region followed by Egypt and Botswana. The study also finds significant spillover of lagged standardized volatility across the stock markets of the region. But the magnitude of the response of volatility spillover and its persistence is very minimum. However, the stock markets are found to be co-integrated and expected to share long-run equilibrium relationships among each other.

The study has the scope to be extended to capture the time-varying integration of market returns with transmission of monetary policy and exchange rate changes within the region. The results obtained from this study may assist the firm managers and international investors to understand the key drivers of market connectedness.

Empirically investigating the pattern of stock market connectedness in Africa and Middle East region with advanced methodology over a long study period is the originality of this study.

The purpose of this paper is to empirically investigate the factors deriving effective tax rate (ETR) for Indian manufacturing firms in different sectors. The study also tries to analyze the sensitiveness of ETR because of shocks on its key determinants.

The study is using Arellano–Bond dynamic panel regression model to identify the key drivers of ETR, and impulse response functions of panel vector auto-regression model to analyze the response of ETR because of one standard deviation (SD) shock to its key determinants.

This study concludes that ETR is significantly explained by firm size, profitability, growth rate and non-debt tax shield in most of the sectors, and debt ratio, asset tangibility and age of the firms are impacting ETR differently across sectors. In case of entire manufacturing sector, firm size, profitability, growth and non-debt tax shield are driving ETR positively and asset tangibility is driving ETR negatively. Interest coverage ratio (ICR) and firm age are not significant drivers of ETR. ETR is positively related with firm size, but responses negatively when there is an immediate shock to firm size. Similarly, ETR is negatively related with asset tangibility, but responds positively following an immediate shock to it. Overall, ETR is more sensitive and responses significantly because of shocks in firm size, profitability, growth, asset tangibility and non-debt tax shield whereas, the response is very marginal following shocks to debt ratio, ICR and age of the firm.

Firm managers may find the study useful to understand the receptiveness of ETRs at each sector level. The empirical findings are not only validating the theoretical developments but also providing a root cause analysis to the firm managers to understand the cause and consequence of ETRs for firms at different sectors.

Empirically investigating the factors driving ETR and analyzing its sensitiveness because of one SD shock on its key determinants for Indian manufacturing firms from different sectors is the originality of this study. Developing a strong theoretical background and empirically validating it through advanced methodology makes the study unique.