ORCID

Abstract

Research problem - The literature suggests that the market either does not react or reacts with a delay to the content of annual reports, concerning notion given their importance for investment decisions due to the value relevance of the information they convey. According to the Efficient Market Hypothesis, the market should exhibit a prompt reaction to their release. Rationale - This study aims to uncover the underlying reasons for investors' lack of response to annual report filings. It predicts that the reaction depends, among other factors, on the release of preliminary disclosures of financial results, the method of distribution of annual reports and the length of an annual disclosure. The study examines the market reaction to FTSE100 companies' annual reports from 2006 to 2016. It also considers investor response to preliminary statements of annual. Specifically, it investigates whether investors react more promptly to preliminary disclosures compared to the more detailed annual reports, and how the absence of such preliminary reports might shift attention and reaction time to the information content of annual reports. Furthermore, the study examines changes in the market reaction associated with the shift from a paper-based submission system to the electronic National Storage Mechanism (NSM). This comparison aims to assess whether technological advancements in the dissemination method of corporate disclosure have improved market efficiency and accelerated investors’ response time. Lastly, the study investigates the effect of report length on the market, specifically looking at whether the market reacts differently to lengthier reports compared to those that are more concise. Research methods - To test the efficiency of market reactions, the researcher employs a short-run event study methodology to assess the impact of filings of annual reports on companies’ returns. The study determines whether the filing events captured abnormal returns compared to what would be expected if no filings occurred. If such abnormal returns are observed, it is concluded that the event impacted the companies' returns and that the market reacted efficiently to these filings. The event study, applied to all five enquiries, is followed by further tests on the impact of lengthy reporting on the market reaction using regression analysis. This additional analysis allows for the validation of findings by accounting for time, industry trends, and companies' specific characteristics. The model is tested for potential statistical issues, including non-stationarity, heteroscedasticity, multicollinearity and endogeneity, to ensure the reliability and consistency of the coefficient estimates. Main results - The study's results are mixed. It reveals no immediate abnormal reaction to the information content of annual reports. In contrast, the market shows a strong response to preliminary statements of annual reports. Additionally, contrary to the prediction, there is no evidence of the market responding to the content of annual reports in the absence of preliminary statements. Regarding dissemination methods, an abnormal price change is noted, but only in the first year following the National Storage Mechanism (NSM) adoption. Lastly, the study finds evidence supporting the impact of reporting length; investors tend to discount lengthy annual reports while reacting positively to more succinct ones. Conclusion - The study’s findings challenge the Efficient Market Hypothesis by showing a lack of market reactions to the information content of annual reports. This suggests that policies should emphasise the importance of report readability and simplicity to prompt quicker and more efficient investor responses. Specifically, the market tends to react negatively to lengthy reports, while succinct reports cause a positive response, indicating a policy need for guiding companies towards more concise reporting, including setting standards for report length. In the final section, the study points out the potential of artificial intelligence AI and machine learning in improving market efficiency by condensing reports, processing complex information effectively and analysing underlying sentiments and patterns in financial reporting, which can be important for detecting anomalies or obfuscation. This can aid in developing more informed and timely trading strategies, especially when the market underreacts or reacts with a delay to new information. Overall, the study advocates for policy changes that promote report clarity, technological advancement for better access to corporate disclosures and the use of AI to enhance market efficiency and investor protection.

Document Type

Thesis

Publication Date

2024-01-01

DOI

10.24382/5181

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