Market efficiency, volatility behaviour and asset pricing analysis of the oil & gas companies quoted on the London Stock Exchange.

This research assessed market efficiency, volatility behaviour, asset pricing, and oil price risk exposure of the oil and gas companies quoted on the London Stock Exchange with the aim of providing fresh evidence on the pricing dynamics in this sector. In market efficiency analysis, efficient market hypothesis (EMH) and random walk hypothesis were tested using a mix of statistical tools such as Autocorrelation Function, Ljung-Box Q-Statistics, Runs Test, Variance Ratio Test, and BDS test for independence. To confirm the results from these parametric and non-parametric tools, technical trading and filter rules, and moving average based rules were also employed to assess the possibility of making abnormal profit from the stocks under study. In seasonality analysis, stock returns were tested for the day-of-the-week and month-of-the-year effects. Volatility processes, estimation, and forecasting were undertaken using both asymmetric and symmetric volatility models such as GARCH (1,1) and Threshold ARCH or TARCH (1,1,1) to investigate the volatility behaviour of stock returns. To determine the effect of an exogenous variable on volatility, Brent crude oil price was used in the models formulated as a variance regressor for the assessment of its impact on volatility. The models were then used to forecast the price volatility taking note of the forecasting errors for the determination of the most effective forecasting model. International oil price risk exposure of the oil and gas sector was measured using a multi-factor asset pricing model similar to that developed by Fama and French (1993). Factors used in the asset pricing model are assessed for statistical significance and relevance in the pricing of oil and gas stocks. Data used in the study were mainly the adjusted daily closing prices of oil and gas companies quoted on the exchange. Five indices of FTSE All Share, FTSE 100, FTSE UK Oil and Gas, FTSE UK Oil and Gas Producers, and FTSE AIM SS Oil and Gas were also included in the analysis. Our findings suggest that technical trading rules cannot be used to gain abnormal returns, which could be regarded as a sign for weak form market efficiency. The results from seasonality analysis have not shown any day-of-the-week or monthly effect in stock returns. The pattern of stock returns volatility can be estimated and forecasted, although the relationship between risk and return cannot be generalised. On a similar note, the relationship between volatility attributes and the efficient market hypothesis cannot be clearly established. However, we have established that volatility modelling can significantly measure the quantum of risk in the oil and gas sector. Market risk, oil price risk, size and book-to-market related factors in asset pricing models were found to be relevant in the determination of asset prices of the oil and gas companies

A critical overview of the transparency and competitiveness of the London stock exchange

The paper explores the operational activities of the London Stock Exchange in the 21st century to provide an overview of its operational transparency and competitiveness; the competition among its market participants and how it competes with other developed stock exchanges around the world. Evidence was found that suggests the manifestation of both competitive and uncompetitive practices in the London Stock Exchange. The presence of the key elements that enhance the competitiveness of the market, such as continued technology transformation, strategies that promote globalisation and regulatory flexibilities was observed. Simultaneously, signs of non-competitiveness such as high membership and annual fees, transaction costs and stamp duties were also observed

Action research to reassess the effectiveness of a blended learning approach in postgraduate business education using unified theory of acceptance and use of technology model

Although the pedagogy of blended learning in higher education has been well-accepted since its inception in 2000 particularly due to the incessant technological innovations, its impact on students’ experience has been reliant on various factors. This includes cultural diversity and background, technical abilities, level of organisational support, language difficulties, educational background, learning environment, instructional design, and many others. In this study, the effectiveness of the blended learning approach has been practically reassessed among the diverse cohorts of international students at Birmingham City University. The motivation for the selection of this sample was to enable the inclusion of diversity as one of the focal points of the study. Data was collected from the action research undertaken and analysed based on a survey research method. This was to test the significance of the hypotheses formulated and find answers to the research questions that were designed to portray the central intent of the study. Based on the action research, two-cycle model was adopted to reassess the effectiveness of blended learning in comparison to the traditional learning approach. In the first cycle, the effectiveness of traditional learning approach was tested. The mixed responses received had justified the implementation of the second cycle of the action research. In the second cycle, the blended learning approach was adopted in the class session and its effectiveness tested by administering questionnaires to the students under study. Furthermore, multiple regressions were employed using unified theory of acceptance and use of technology (UTAUT) to test the significance of each variable collected from the survey on the students’ learning experience and engagement. Our results have suggested that students’ engagement is determined by positive learning experience without any bias to traditional or blended learning approach. Students’ age group was found to be relevant in the determination of behavioural intention, social influence, effort expectancy, performance expectancy and facilitating conditions towards the effective use of technology and blended learning. Students’ gender was an irrelevant factor in the success of blended learning approach

Investigating the sources of Black’s leverage effect in oil and gas stocks

The Black’s leverage effect hypothesis postulates that a negative stock return innovation increases the financial leverage of a firm since the value of equity decreases at a given level of debt, which, in turn, creates a higher equity return volatility in the future. The paper is aimed at investigating the authenticity of the Black’s leverage effect hypothesis and the relationship between negative stock returns and the financial leverage of the UK oil and gas stocks from 2004 to 2015. For each stock, exponential generalised autoregressive conditional heteroscedasticity model was estimated using Fama–French–Carhart 4-factor asset pricing model to extract the difference between the effects of negative and positive stock return innovations, regarded as leverage effect. The leverage effect parameter was further regressed on the financial leverage ratios of the book value of long-term debt to total assets, interest expenses to total assets and long-term debt to market value of equity to examine whether variation in the leverage parameter was as a result of variation in the firm’s financial leverage. The findings of the study show that Fama-French-Carhart four risk factors of market, size effect, value and momentum were significant in the stock returns of most of the oil and gas companies. The mixed results in the significance level of the factors were attributed to the differences in individual firm characteristics. An evidence of leverage effect was also found in all the oil and gas stock returns but no evidence to suggest it was derived from the changes in the financial leverage of the companies. The implication of these findings for financial managers in the oil and gas industry was that while asset pricing frameworks such as CAPM and its extensions are relevant in determining oil stock returns, the level of gearing is irrelevant, albeit it has been recognised as one of the determinants of the firm’s level of risk

Measuring Predictability of Oil and Gas Stock Returns and Performance of Moving Average Trading Rules

The paper re-examines whether investors can predict oil and gas stock prices for abnormal returns using autocorrelation-based trading and filter rules and moving average strategies. In this paper, short and long lengths moving averages are employed and their performances are measured against the returns from simple buy and hold investment strategy. As a result, the paper finds that employed trading rules do not indicate that investors can make abnormal returns in oil and gas stocks. Moreover, the performances of short and long moving averages in predicting abnormal returns also do not suggest a conclusive evidence that any of the moving averages can result in more returns compared to others

Modelling oil and gas stock returns using multi factor asset pricing model including oil price exposure

Oil and gas is one of the most important sectors in every economy and the valuation of oil and gas companies becomes quite challenging due to the volatility of crude oil price. The paper investigates the determinants of the UK oil and gas stock returns using multi factor asset pricing model and the existence of asymmetric effects in the Brent crude oil price. Our results show that market risk, oil price risk, size and book-to-market related factors are all relevant in the determination of asset returns of the oil and gas companies quoted on the London stock exchange. Oil price increases and decreases decomposed separately have more effect on the oil companies’ stock returns than the normal log changes of the price which show the presence of asymmetric effect. However, the oil price shocks in general do not seem to strongly affect stock returns in oil and gas sector possibly due to horizontal and vertical integration of bigger companies in the sector