The Study on Digital Marketing Influences on Sales for B2B Start-Ups in South Asia

South Asia has seen a digital revolution in recent years. The number of persons who use the internet has risen drastically. They use it for shopping, social media and online sales. However, there exists a literature gap as far as the effect of outbound digital marketing in B2B markets is concerned. The research builds a model based on brand and consumer interactions in Indian B2B markets using a vector autoregressive model to systemically analyze the cost and outcome of digital marketing efforts by the start-ups operating in South Asia. The multivariate time series analyzed in identifying simultaneous and consistent impacts by the start-ups. We use Vector autoregressive model as it allows us to analyse the relationship among the factors as it changes over time. The research finds evidence for the conceptual framework in South Asian markets. The results prove that sales are greatly influenced by digital media, and outbound marketing efforts, predominantly word of mouth, has a huge impact in building a brand image as it spread over in the social media platforms. It is observed that the digital marketing strategies and consumer interaction are the same across South Asia, but its effect varies from country to country within South Asia thus suggesting a need of developing a new strategy in digital marketing for B2B markets.

Improved prediction of new COVID-19 cases using a simple vector autoregressive model: Evidence from seven New York State counties

With the rapid spread of COVID-19, there is an urgent need for a framework to accurately predict COVID-19 transmission. Recent epidemiological studies have found that a prominent feature of COVID-19 is its ability to be transmitted before symptoms occur, which is generally not the case for seasonal influenza and SARS. Several COVID-19 predictive epidemiological models have been proposed; however, they share a common drawback-they are unable to capture the unique asymptomatic nature of COVID-19 transmission. Here, we propose vector autoregression (VAR) as an epidemiological county-level prediction model that captures this unique aspect of COVID-19 transmission by introducing newly infected cases in other counties as lagged explanatory variables. Using the number of new COVID-19 cases in seven New York State counties, we predicted new COVID-19 cases in the counties over the next 4 weeks. We then compared our prediction results with those of 11 other state-of-the-art prediction models proposed by leading research institutes and academic groups. The results showed that VAR prediction is superior to other epidemiological prediction models in terms of the root mean square error of prediction. Thus, we strongly recommend the simple VAR model as a framework to accurately predict COVID-19 transmission.

Inference With Cross-Lagged Effects - Problems in Time and New Interpretations

The interpretation of cross-effects from vector autoregressive models to infer structure and causality amongst constructs is widespread and sometimes problematic. I first explain how hypothesis testing and regularization are invalidated when processes that are thought to fluctuate continuously in time are, as is typically done, modeled as changing only in discrete steps. I then describe an alternative interpretation of cross-effect parameters that incorporates correlated random changes for a potentially more realistic view of how process are temporally coupled. Using an example based on wellbeing data, I demonstrate how some classical concerns such as sign flipping and counter intuitive effect directions can disappear when using this combined deterministic / stochastic interpretation. Models that treat processes as continuously interacting offer both a resolution to the hypothesis testing problem, and the possibility of the combined stochastic / deterministic interpretation.

Correlation Analysis between Stock Price and Accounting Profit Based on a Vector Autoregressive Model

The study of accounting profitability was initiated by the famous American scholars Ball and Brown in the 1960s. In recent years, with the continuous development of market economy, the continuous improvement of the accounting legal system and accounting standards for enterprises has promoted the research on accounting profit in capital market in China. Due to the restriction of some objective conditions, there are not many valuable research results on the relationship between accounting earnings and stock price changes, and the research methods suitable for the study of accounting earnings still need to be explored and summarized. The China Securities Regulatory Commission (CSRC) has required listed companies to publish quarterly financial and accounting reports since 2002, and the condition of using the regression analysis method to study the accounting profit of listed companies is available. In this context, this paper designs a vector autoregressive model to study the correlation between stock price and accounting profit. First, combining the literature and the research results of accounting profit at home and abroad, this paper expounds the statistical analysis of accounting profit. Then, this paper analyzes the accounting profitability of listed companies in China from static and dynamic perspectives. Finally, according to the accounting profit status and profitability statistical analysis of accounting information, accounting profit and growth relationship, and accounting profit information and the relationship between stock prices, this paper is concluded. Also, this paper shows how to improve the profitability of listed companies and how can investors effectively use the accounting earnings information of listed companies for stock investment and put forward corresponding policy suggestions.

Return and volatility spillovers between South African and Nigerian equity markets

PurposeThe paper evaluates the cross-transmission of returns and volatility shocks between Nigeria and South Africa stock markets to infer the extent of interdependence between the two markets. The paper also makes inference to optimal portfolio weights of holding assets in the two markets.Design/methodology/approachThe paper uses an asymmetric vector autoregressive-exogenous generalised autoregressive conditional heteroscedasticity (VAR-X GARCH) model to assess the extent of returns and volatility spillovers between Nigeria and South Africa.FindingsThe results of the empirical analysis show evidence of shock spillovers from the South African stock market to the Nigerian stock market. Moreover, based on the dynamic Sharpe ratio and portfolio weight optimisation, the results indicate the possibility of portfolio diversification when holding simultaneous positions in the two stock markets.Practical implicationsThe results imply the possibility of economic profit for investors who take positions in the two stock markets. The lack of synchronisation of stock markets in the two largest economies in Africa is in contrast with the situations in other regions where stock markets returns of large economies often co-move.Originality/valueThe paper is the first to use the asymmetric VAR-X GARCH model to assess the cross-transmission of shocks between stock markets.

Multivariate Hyper-Rotated GARCH-BEKK

For large multivariate models of generalized autoregressive conditional heteroskedasticity (GARCH), it is important to reduce the number of parameters to cope with the ‘curse of dimensionality’. Recently, Laurent, Rombouts and Violante (2014 “Multivariate Rotated ARCH Models” Journal of Econometrics 179: 16–30) developed the rotated multivariate GARCH model, which focuses on the parameters for standardized variables. This paper extends the rotated multivariate GARCH model by considering a hyper-rotation, which uses a more flexible structure for the rotation matrix. The paper shows an alternative representation based on a random coefficient vector autoregressive and moving-average (VARMA) process, and provides the regularity conditions for the consistency and asymptotic normality of the quasi-maximum likelihood (QML) estimator for VARMA with hyper-rotated multivariate GARCH. The paper investigates the finite sample properties of the QML estimator for the new model. Empirical results for four exchange rate returns show the new specifications works satisfactory for reducing the number of parameters.

Regional economic structure and heterogeneous effects of monetary policy: evidence from Indonesian provinces

We analyze how regional economic structures affect the impact of monetary policy on rates of inflation across 34 Indonesian provinces. The paper first applies structural factor augmented vector autoregressive model (SFAVAR) to all the 34 provinces based on monthly provincial data in order to measure the length and magnitude of responses of regional inflation to monetary policy shock, derived from the consequential impulse response functions of 34 provinces. In the second step, we analyze the impact of economic structures on the length and magnitude of regional inflationary responses of 34 provinces. We find that the impacts of monetary policy across regions are significantly influenced by economic structural variables such as manufacturing sector share to GDP, mining sector share to GDP, bank lending share to GDP and export share to GDP. In addition, we found the spatial lag, rate of inflation of neighboring provinces, is also statistically significant. In a similar fashion, economic structural variables such as manufacturing sector share to GDP, construction sector share to GDP and investment share to GDP are found statistically significant in explaining regional differences of monetary policy efficiency. Our findings imply economic structures of provinces have to be incorporated to designing monetary policy in Indonesia.

Cannabis Stocks Returns: The Role of Liquidity and Investors’ Attention via Google Metrics

This paper studies one of the most popular investment themes over recent years, investing in the cannabis industry. In particular, it investigates relationships between investor attention, as proxied by Google Trends, and stock market activities, i.e., return, volatility, and liquidity. To this end, in the empirical analysis we study how liquidity and investors’ attention affect the return dynamics of an investment in cannabis stocks by augmenting the three-factor Fama–French model. In addition, we use a vector autoregressive approach and the impulse response function to measure shock transmission between the variables under consideration. Our empirical findings show that there is a statistically positive relationship between cannabis stock returns and liquidity. We also find that increased investors’ attention results in higher returns.

Government domestic debt arrears and private investment in Sub-Saharan Africa

PurposeThis study investigates the effect of government domestic payment arrears on private investment. The authors argue that an increase in government domestic arrears can reduce private sector investment owing to the competition for credit.Design/methodology/approachThe prediction is empirically tested using data for 33 Sub-Saharan Africa (SSA) countries for the period 2007–2018 using a panel general methods of moment estimation technique. This is also complemented with impulse responses derived from the standard vector autoregressive model.FindingsThe results show that an increase in government domestic arrears adversely affects private investment in SSA and most subregional communities within SSA. It also revealed that private investment negatively responds to shocks in government domestic arrears.Originality/valueThis is the first study that attempts to investigate the effect of government domestic borrowing arrears on private investment. It seeks to serve as a guide to governments in their domestic borrowing decisions to ensure timely servicing.