An Examination of the Empirical Relationship between the Dividend and Investment Decisions: A Note

This paper empirically examines the separation principle, which asserts that investment decisions are not influenced by dividend decisions. Existing empirical evidence on this proposition is inconclusive. In this paper, we employ causality tests to examine whether investment decisions are, in fact, statistically exogenous with respect to dividend decisions. These tests are undertaken using both firm-specific and aggregate data. The results indicate no causal relationship from dividends to investment, which provides support for the separation principle as an empirical proposition.     

Price discovery analysis of green equity indices using robust asymmetric vector autoregression

Covering the first commitment period of the Kyoto Protocol (2008–2012), we perform a price discovery analysis to determine Granger causality relationships for a range of prominent green equity indices with the broader equity and commodity markets. Three pivotal contributions are made. Firstly, an expanded database is used that gives greater depth to the price discovery analysis relative to previous literature. Prominent global, regional and sectoral green equity indices are considered, as well as a broader set of commodities including crude oil, natural gas and emissions. The inclusion of natural gas recognises its role as the transition fossil fuel to a low carbon economy. In addition to the main European Union Allowance traded under the EU Emissions Trading Scheme, Certified Emissions Reduction (CER) prices are also included in the emissions database to capture activities under the global Clean Development Mechanism. Secondly, a problem with conventional symmetric vector autoregression is that its implementation commonly leads to large occurrences of insignificant parameters. Therefore, as a first layer of robustness, we utilise an asymmetric vector autoregression model to perform the Granger causality testing, which addresses this limitation by means of allowing different lag specifications among the system variables. Thirdly, explicit recognition is made in our study of the multiple comparisons bias inherent in our high-dimensional testing framework, which is the non-negligible likelihood of identifying statistically significant results by pure chance alone. As a second layer of robustness, we utilise a generalised Holm correction method to control this source of bias. At conventional statistical significance levels, we find that the FTSE 100 and FTSE Global Small Cap equity indices have a causal effect on all of the green equity indices, with limited evidence of causality in the opposite direction. Within the green equity markets, we find evidence that the chosen sectoral index has a Granger causal effect on one of the two global indices considered and also the regional index. This price transmission provides modest evidence that the global green economy is becoming ever more integrated. NBP gas is shown to have a causal effect on all of the green equity indices, whereas we find no such evidence for Brent oil. The former observation may reflect the increasing role of gas as the transition fuel to a low carbon economy, playing a key role in decisions on power generation mix and associated capital investment. Finally, we find no evidence that EUA or CER prices have a causal effect on green stocks, consistent with previous findings and likely reflecting the excessively low prices being commanded for compliance permits in the European emissions markets.     

Time-varying pattern causality inference in global stock markets

Causality analysis can reveal the intrinsic interactions in financial markets. Though Granger causality test and transfer entropy method have successfully determined positive and negative causal interactions, they fail to reveal a more complex causal interaction, dark causality. Moreover, the causal relationship between variables may be time-varying. Thus, in this work, we are dedicated to determining the nature of causal interaction and explore the time-varying causality in global stock markets. To achieve this goal, pattern causality (PC) theory, cross-convergent mapping (CCM) theory, the sliding window method and complex networks are applied. By them, three causal interactions with different strength are revealed in global stock markets, and the causal strength is time-varying in different periods both in simulated systems and financial markets. While the dominant causal interaction is stable except for some stock pairs in frontier and emerging markets. In total, we determine the positive dominant causality in global stock markets; that is, the overall consistent trend among stocks can be explored. Additionally, we discover some exceptions that show negative dominant causality, where the reverse trend can be revealed among them; moreover, their dominant causality is time-varying. These uncertainties should receive great attention from investors and government managers.     

Short and long-term interactions between venture capital returns and the macroeconomy: evidence for the United States

The main purpose of this paper is to empirically model the influence of macroeconomic and financial variables on the performance of risk capital in the US. We start our investigation using a static long-run equilibrium model. In contrast to previous studies, we analyze the effect of several factors simultaneously within the framework of a vector error correction model (VECM). This allows us to study short- and long-term interactions to overcome the problem of endogeneity, and to discover causal mechanisms. The results show that the value of venture capital investments is positively related to industrial production, the exit channel Nasdaq, and the long-term interest rate. However, the value of venture capital investments is negatively related to the short-term interest rate. According to the short-term dynamics, VEC Granger causality confirms that only industrial production influences venture capital performance, while venture capital returns Granger causes Nasdaq performance.     

New evidence of extreme risk transmission between financial stress and international crude oil markets

We aim to analyze the risk transmission between financial stress and crude oil under different shocks, with applying a novel Granger causality test. Recent works suggest that this risk transmission is mixed, however, scholars mainly focus on their average causality but neglect the extreme causality and its time-varying characteristic. Using the weekly data of the financial stress index and WTI prices spanning from 1994 to 2020, we employ the extreme time-domain and frequency-domain Granger causality test to conduct our research. Results obtained from the time-domain test imply that their causality generally originates from extreme shocks rather than non-extreme shocks, which hasn’t been found before. For further distinguishing the long-run and short-run effects of these shocks, we apply the frequency-domain test and discover that these causalities are mainly found for long the run. Thus, investors and policy-makers may benefit from monitoring financial stress, especially under long-term extreme conditions.     

The bivariate GARCH approach to investigating the relation between stock returns,trading volume,and return volatility

We use a bivariate GJR-GARCH model to investigate simultaneously the contemporaneous and causal relations between trading volume and stock returns and the causal relation between trading volume and return volatility in a one-step estimation procedure, which leads to the more efficient estimates and is more consistent with finance theory. We apply our approach to ten Asian stock markets: Hong Kong, Japan, Korea, Singapore, Taiwan, China, Indonesia, Malaysia, the Philippines, and Thailand. Our major findings are as follows. First, the contemporaneous relation between stock returns and trading volume and the causal relation from stock returns and trading volume are significant and robust across all sample stock markets. Second, there is a positive bi-directional causality between stock returns and trading volume in Taiwan and China and that between trading volume and return volatility in Japan, Korea, Singapore, and Taiwan. Third, there exists a positive contemporaneous relation between trading volume and return volatility in Hong Kong, Korea, Singapore, China, Indonesia, and Thailand, but a negative one in Japan and Taiwan. Fourth, we find a significant asymmetric effect on return and volume volatilities in all sample countries and in Korea and Thailand, respectively.     

Sovereign-bank linkages: Quantifying directional intensity of risk transfers in EMU countries

This study attempts to identify and trace inter-linkages between sovereign and banking risk for each main country in the euro area. To this end, we use an indicator of banking sector risk in each country based on the Contingent Claim Analysis literature, and 10-year government yield spreads over Germany as a measure of sovereign risk. We apply a dynamic approach to test for Granger causality between the two measures of risk in each country, allowing us to check for episodes of significant and abrupt increase in short-run causal linkages. The empirical results indicate that episodes of causality intensification vary considerably in both directions over time and across the different EMU countries. The directionality suggests the presence of causality intensification, mainly from banks to sovereigns in crisis periods.     

Are there reliable determinant factors of capital structure decisions? Empirical study of SMEs in different regions of Portugal

On the basis of the empirical literature review about the capital structure decisions in Portuguese SMEs, this study analyses the relationships between the determinants – profitability, size, age, asset structure and growth, identified as reliable determinants in the empirical literature, and debt for SMEs located in different regions of Portugal (NUTS II). The global sample is made up of 11.016 SMEs and covers the period between 2007 and 2011. Overall, the results suggest that those determinants are reliable in explaining Portuguese SME capital structure decisions, suggesting that these decisions are closer to the predictions of Pecking Order Theory in comparison to the assumptions of Trade-off Theory. However, both financial theories are not enough to explain SME capital structure decisions.Furthermore, our results suggest that SMEs’ capital structure differs across regions and that, concerning the impact of profitability, size, age, asset structure and growth on firm debt, there are some differences across regions, which could be explained by regional heterogeneity. However, we do not find statistically significant differences in the kind (signs) of the relationships between those determinants and debt in Portuguese SMEs across the various regions.     

Information transmission among energy markets,cryptocurrencies, and stablecoins under pandemic conditions

This paper investigates the directional causal relationship and information transmission among the returns of West Texas Intermediate (WTI), Brent, major cryptocurrencies, and stablecoins by drawing on daily data from July 2019 to July 2020. Applying effective transfer entropy, a non-parametric statistic, the results show that the direction of the causal relationship and the nature of information spillovers changed after the COVID-19 pandemic. More precisely, our findings reveal that WTI and Brent are leading the prices of Bitcoin and Bitcoin Cash. Conversely, Bitcoin futures and stablecoins (TrueUSD and USD Coin) are leading WTI and Brent prices. In addition, the stablecoin Tether became a leader against Brent prices after the pandemic, although it is still following WTI prices. Moreover, Ethereum and USD coin preserved their position as leaders against Brent prices. Interestingly, our results also reveal that Ethereum, Litecoin, and Ripple preserved their position as leaders of WTI prices. The change in the nature of directional causality and the spillover effect after the COVID-19 crisis provide valuable information for practitioners, investors, and policymakers on how the ongoing pandemic influences the connection and network correlation among the energy, cryptocurrency, and stablecoin markets.     

Big data tools for Islamic financial analysis

Behavioural science states that emotions, principles and the manner of thinking can affect the behaviour of individuals and even investors in their decision making on financial markets. In this paper, we have tried to measure the investor sentiment by three means of big data. The first is based on a search query of a list of words related to Islamic context. The second is inferred from the engagement degree on social media. The last measure of sentiment is built, based on the Twitter API classified into positive and negative directions by a machine learning algorithm based on the naive Bayes method. Then, we investigate whether these sensations and emotions have an impact on the market sentiment and the price fluctuations by means of a vector autoregression model and Granger causality analysis. In the final step, we apply the agent‐based simulation by means of the sequential Monte Carlo method with the control of our Twitter measure on Islamic index returns. We show, then, that the three social media sentiment measures present a remarkable impact on the contemporaneous and lagged returns of the different Islamic assets studied. We also give an estimation of the parameters of the latent variables relative to the agent model studied.     

The Causality Effects of the Federal Reserve's Monetary Policy on U.S. and Eurodollar Interest Rates

Previous studies argue that U.S. interest rates will become more sensitive to changes in eurodollar rates as international financial-market integration increases. However, the empirical results of these studies are suspect because they select their subperiods in an ad hoc manner and ignore the different trading hours of the U.S. and London markets. This study adjusts for the markets' different trading hours and uses Goldfeld and Quandt's switching regression technique to show that the causal relation between U.S. CD rates and eurodollar rates is impacted by the Federal Reserve's monetary policy. Because the latest subperiod exhibits uni-directional causality (i.e., U.S. interest rates cause changes in eurodollar rates), the results cast doubt on the implicit assumption made in the literature that interest-rate causality is only affected by increasing levels of financial-market integration.     

Do major customers promote firms’ innovation?

This study examines whether and how a concentrated supply chain relationship affects a firm’s innovation decisions. Using data from Chinese listed firms in the manufacturing industry, we find that a concentrated customer base constrains a firm’s R&D investment, where a 1% increase in customer concentration is associated with a 0.011% decrease in R&D investment. To establish causality, we use the instrumental variable method, the reverse causality model, and the Granger causality test to re-examine the relationship and arrive at a consistent conclusion. Results from mechanism analysis suggest that a concentrated customer base constrains the internal fund availability and that the negative relationship between customer concentration and firms’ innovation is less pronounced for firms with more external financial support. Additional analysis reveals that the negative effect of customer concentration mainly affects R&D investment expenditure and that customer concentration also constrains innovation output in China. Overall, our paper reveals the dark side of close customer-supplier relationships and provides new insights into how supply chain relationships affect firms’ innovation decisions.     

Is there a symmetric nonlinear causal relationship between large and small firms?

This paper uses both linear and nonlinear causality tests to reexamine the causal relationship between the returns on large and small firms. Consistent with previous results, we find that large firms linearly lead small firms. We also find a significant linear causality in the direction from small firms to large firms, particularly in the more recent time period where the impact from small firms to large firms is greater than from large to small. More important, in contrast to the received literature, we find significant nonlinear causality that is bi-directional and of the same duration in either direction. Using the BEKK asymmetric GARCH model we are able to capture most of the detected nonlinear relationship. This indicates that volatility spillovers are largely responsible for the observed nonlinear Granger causality.     

Level-ARCH short rate models with regime switching: Bivariate modeling of US and European short rates

This paper introduces regime switching into level-ARCH models for the short rates of the US, the UK, and Germany. Once regime switching and level effects are included there are no gains from including ARCH effects. It is of secondary importance how the regime switching is specified. The estimated level parameters differ across countries. The corresponding new bivariate models show that the states of the US and UK short rate volatilities are not independent nor identical. There is Granger causality from the US to the UK short rate volatility state but not vice versa. There is no contagion between the US and UK volatility states. Equivalent results apply to the relation between the US and German volatility states.     

Short-sale constraints and stock price crash risk: Causal evidence from a natural experiment

We examine the relation between short-sale constraints and stock price crash risk. To establish causality, we take advantage of a regulatory change from the Securities and Exchange Commission (SEC)’s Regulation SHO pilot program, which temporarily lifted short-sale constraints for randomly designated stocks. Using Regulation SHO as a natural experiment setting in which to apply a difference-in-differences research design, we find that the lifting of short-sale constraints leads to a significant decrease in stock price crash risk. We further investigate the possible underlying mechanisms through which short-sale constraints affect stock price crash risk. We provide evidence suggesting that lifting of short-sale constraints reduces crash risk by constraining managerial bad news hoarding and improving corporate investment efficiency. The results of our study shed new light on the cause of stock price crash risk as well as the roles that short sellers play in monitoring managerial disclosure strategies and real investment decisions.     

Causality in quantiles and dynamic stock return–volume relations

This paper investigates the causal relations between stock return and volume based on quantile regressions. We first define Granger non-causality in all quantiles and propose testing non-causality by a sup-Wald test. Such a test is consistent against any deviation from non-causality in distribution, as opposed to the existing tests that check only non-causality in certain moment. This test is readily extended to test non-causality in different quantile ranges. In the empirical studies of three major stock market indices, we find that the causal effects of volume on return are usually heterogeneous across quantiles and those of return on volume are more stable. In particular, the quantile causal effects of volume on return exhibit a spectrum of (symmetric) V-shape relations so that the dispersion of return distribution increases with lagged volume. This is an alternative evidence that volume has a positive effect on return volatility. Moreover, the inclusion of the squares of lagged returns in the model may weaken the quantile causal effects of volume on return but does not affect the causality per se.     

DISAGREEMENTS ON CONDITIONS FOR UNANIMITY

This paper considers two articles which have appeared in this journal on the subject of shareholder unanimity. It argues that their differing conclusions arise in part from errors in the analysis and in part from radically different assumptions about the structure of the economies in which shareholder decisions are made.     

Causal relationship between spot and futures prices with multiple time horizons: A nonparametric wavelet Granger causality test

This study investigates the causal information flow between 45 major daily spot returns and their corresponding futures in developing, emerging, and commodity indices through a novel nonparametric wavelet Granger causality test (NWGC) that is capable of detecting causality patterns in various time scales without any stationarity assumption or multivariate autoregressive modeling requirement. We provide new evidence for a complex causality pattern phenomenon. First, there may not be just one dichotomous answer about the Granger causality test for each market data in a time domain, as markets exhibit different causal information flows for different time scales. Second, each market may show distinct causality patterns compared to other markets.     

The implied cost of equity capital,corporate investment and chief executive officer turnover

This study investigates how the cost of equity capital, along with corporate investment, affects chief executive officer (CEO) turnover decisions. We hypothesize that the cost of equity conveys information about firm performance uncertainty that is informative of CEO talent. Consistently, our empirical results show that the likelihood of CEO turnover is positively associated with the implied cost of equity, after controlling for earnings and stock performance measures and risk factors. Additional analysis of reverse causality supports the causal effect of the high cost of equity on CEO dismissals. We also find that the positive association is more pronounced for firms that are more likely to suffer from underinvestment problems. These results suggest that the cost of equity plays a more important role in assessing CEO performance when the firm needs more external equity capital to pursue investment opportunities.     

The Analysis of Poverty Data with Endogenous Transitions*

It is common to analyse poverty data broken down by household or economic status. Implicitly, it is assumed that people change state (for example, single, married, children, no children) for exogenous reasons. If we bring economic behaviour into the problem, then such transitions become endogenous. The data are then insufficient to identify the claims made from them. The distribution of the characteristics of the individuals in the states will be endogenous, and the state average poverty rate will depend on the composition of the individuals in the state as well as on the economic impact of being in that state per se. In this paper, we set out a simple model with endogenous transitions to make our point, and apply this to Family Expenditure Survey data for Britain. We show that our argument has empirical content for Britain.