C12 - Hypothesis Testing: GeneralReturn

The study aimed to determine the impact of m-banking apps’ ease of use on brand trust, including the moderating effect of users’ demographic factors on that relationship. The research was quantitative, where 400 samples were selected based on simple random sampling. The participants were m-banking app users based on their availability and popularity and concentrated on the Dhaka division. The collected data were processed through the SPSS V23 and SmartPLS. Structural equation modeling (SEM) was run to test the hypothesis. The study revealed a positive significant impact of m-banking apps’ ease of use on brand trust. In addition, gender and age had a moderating effect on the relationship between ease of use and brand trust, especially the male and older class groups who had strong brand trust at the high level of m-banking apps’ ease of use.

Following the landmark ruling by the German Federal Constitutional Court in Karlsruhe on 7th February 2014 in which they endorsed the efficient market hypothesis, we present evidence on the efficiency of the German financial market. Introducing a new variance bound test based on the Component-GARCH model of volatility to analyse the long- and short-runs effects on the efficiency of the German financial market, we test the price volatility of four markets: DAX stock index, German sovereign debt index as provided by Barclays and Bloomberg, Euro gold index by the World Gold Council and Euro currency index by the Bank of England. Our use of the Component-GARCH-T model highlight two key contributions, the first being the analysis of the efficiency of the market in the long and short runs. However, a more important contribution is the result of our variance bound test highlight the relatively strong acceptance of the efficient market hypothesis in both the short and long runs in all the observed financial markets. It must be stated our research is of importance to researches in both applied finance and portfolio management. The influencing question of what moves specific markets is crucial to market participants seeking market alpha for their investments strategies and portfolio optimisations.

The paper deals with the impact of the economic agent sentiment on the return for Apple and Microsoft stocks. We employed text mining procedures to analyze Twitter messages with either negative or positive sentiment towards the chosen stock titles. Those sentiments were identified by developed algorithms which are capable of identifying sentiment towards companies and also counting the numbers of tweets in the same group. This resulted in counts of tweets with positive and negative sentiment. Then we ran analysis in order to find causality between sentiment levels and the stock price of companies. To identify causal effects we applied Granger causality tests. We found bilateral causality between the risk premium and the amount of news distributed by Twitter messages.