Sequential and Quantitative Analysis of the Impact of Financial Signals on Company Performance

This thesis investigates the impact of financial signals—specifically share buybacks, directors’ dealings, and investor transactions—on company performance. The study employs a comprehensive sequential and quantitative analysis to understand the effects of these signals individually and, more importantly, in combination over short, medium, and long-term horizons. The research is structured into several key components: the examination of individual signals, the analysis of multiple signal types occurring within a defined timeline with and without granularity, and the application of Bayesian probabilities to assess performance outcomes. Empirical investigations reveal that the ordered sequence of signals composed of investor transactions followed by share buybacks and directors' dealings results in the highest or one of the highest median performances and probability of exceeding 100\% performance across all company sizes. Additionally, share buyback signals serve as strong indicators of positive company performance, with their size correlated with improved performance. However, the size of investor transactions and directors' dealings is mostly negatively correlated with company performance. Furthermore, the thesis develops an automated codebase framework to facilitate real-time analysis of new signals, enabling investors to make informed decisions promptly. The framework's practical implementation demonstrates its potential to significantly enhance investment strategies by effectively leveraging financial signals. Overall, the data-driven findings indicate that while using signal occurrences and their sizes can provide insights into company performance, investment decisions cannot rely solely on these signals. This supports the "no free lunch" theorem, highlighting that there is no one-size-fits-all strategy for investment success.