This study focuses on examining the relationship between stock prices and earnings surprises in quoted companies of Nigeria. This study applied a longitudinal research design which studies the effect of earnings surprises on stock prices using panel data. A sample of 64 companies was chosen to study in all sectors of the Nigerian Stock Exchange. The research data were obtained from secondary sources of the annual reports for the selected companies covering the period from 2013 to 2017. The measurement for earnings surprises used in the study is the residual or unexplained component of earnings persistence model commonly referred to as first-order autoregressive AR (1) regression of reported earnings. Were, the data analysis was carried out by regression using the generalised least squares technique. The regression results for positive earnings surprise shows that share prices react negatively to positive surprises with a coefficient of (-2.4109) in tandem with the return news hypothesis which suggests that positive earnings news results in a negative stock-price reaction. The negative earnings surprise results show that stock prices react positively to negative earnings surprises with a positive coefficient of (0.1136). This is in line with the premise of return news, which indicates that negative earnings news leads to a positive reaction to the share price. The study recommends that there is a need to regulate the stock market to improve the level of market efficiency in stock markets. This will improve the rate at which earnings news will be reserved at stock prices. Secondly, there is a need to improve investor confidence in the disclosed profits made by companies.

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This study compared the weaknesses identified in the audit engagement performance and quality control (that have been made public by the PCAOB after a lack of progress report within 12 months by the firms) sections of the PCAOB's published reports. It also, examined the relationship between firms' characteristics (i.e. size of workforce, number of clients, number of audit offices, and number of audit engagement deficiencies) and PCAOB's report, to achieve these, 108 PCAOB Inspection reports for inspected firms were analyzed. The reports were grouped into two panels (i.e. clean reports-panel A and unresolved reports-panel B); 52 and 56 firms' reports respectively were analyzed for each group. The OLS regression results indicate that clean reports are positively and significantly associated with the size of work force, while, it is negatively associated with the number of clients, number of audit offices and number of audit engagement deficiencies. Unresolved reports increase as number of audit offices, and number of audit engagements increase, but it decreases as size of workforce and audit clients increase. We conclude that the number of audit clients is a key quality control determinant as well as the size of work force. We therefore recommend that the PCAOB should encourage firms to increase workforce because, the result of this study has shown that, the number of employees and partners impact greatly on the quality of report by audit firms.

The objective of this study is to examine whether companies’ life cycle stages follow a random or sequential developmental pattern using their cash flow patterns. That is to ascertain the optimum life cycle stage of Nigerian companies. Data were obtained from the sampled firms annual reports and accounts, which comprises 79 listed companies on the Nigerian Stock Exchange (NSE) from 2009 to 2013 financial years. The cash flow patterns of the firms were thematically analysed as a proxy of developmental patterns, and transition rates between developmental stages were determined. The study reveals that Introduction firms at T0 transited quickly to the Mature stage (70% in T1 through T3), whereas Growth firms developed most rapidly into Shakeout firms (38% at T1). The Mature stage was most stable; 57–65% of firms in this stage at T0 remained so. By contrast, 60% of Decline firms remained in this stage at T1 before transiting to the Mature and Growth stages at T3 and then ultimately fading away at T4, leaving only the Introduction (20%) and Decline (20%) stages. Thus, the development of firms from one life cycle stage to another is random and not sequential. The study, therefore, recommends that Nigerian companies experience their optimum life cycle stage at the matured stage and firms should employ the use of cash flow patterns to identify their business life cycle stage as this will enable companies to apply strategies to sustain themselves at a target stage of the life cycle.