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A observação simultânea das notícias e dos mercados financeiros sugere que estão interrelacionados. Nesta dissertação, exploramos esta observação ao nível empresarial, analisando as interações entre o sentimento das notícias, avaliado utilizando Machine Learning, e os retornos. A análise baseia-se nos preços das ações de todos os constituintes do S&P 500 e nas manchetes de notícias publicadas pela "Reuters Newswire" entre 1 de Março de 2019 e 30 de Junho de 2020. Estimando modelos dinâmicos de painel, concluímos que a relação causal entre o sentimento jornalístico diário de uma empresa e o retorno excessivo de uma empresa é mútua. O sentimento noticioso prevê retornos no dia seguinte, que não se invertem numa semana de negociação. Provou-se que os noticiários contêm informações fundamentais. Adicionalmente, o excesso de retorno prevê o sentimento, indicando que os noticiários também relatam eventos passados. Estes resultados alinham-se com pesquisas anteriores de Ahmad et al. (2016). Também investigamos a precisão fora da amostra dos modelos de painéis dinâmicos ajustados pela indústria, o nível de cobertura mediática e num conjunto de testes caracterizado pelo surto de Covid-19. Através destas análises, obtemos que a cobertura da indústria e dos meios de comunicação social não estão relacionadas com a precisão da previsão, confirmando os resultados de Hendershott, Livdan e Schürhoff (2015) e Tetlock (2010). Contrariamente às descobertas de Antweiler e Frank (2006), e de García (2013), que sugerem uma maior precisão na previsão dos sentimentos durante as recessões, verificamos que a precisão do modelo reduz após o surto de Covid-19. Simultaneously observing the news and the development of financial markets suggests that both are interrelated in some way. In this dissertation, we explore this casual observation on the firm level by analysing the interactions between news sentiment, which we assess by Machine Learning techniques, and returns. Thereby, we base our analysis on all S&P 500 constituents’ stock prices and news headlines published by the ‘Reuters Newswire’ between 1 st of March 2019 and 30 th of June 2020. Estimating dynamic panel models, we conclude that the causal relationship between the firm-specific daily news sentiment and a firms’ excess returns is mutual. News sentiment predicts next day returns that are not reversed within a trading week. Thus, we find evidence that newswires contain fundamental information. Further, excess returns predict sentiment, which indicates that newswires report about past events as well. These findings are in line with previous research of Ahmad et al. (2016). In addition, we investigate the out-of-sample accuracy of the fitted dynamic panel models by industry, level of media coverage and in a test set characterised by the outbreak of Covid-19. From these analyses, we obtain that industry and media coverage are not related to the prediction accuracy, which confirms the results of Hendershott, Livdan and Schürhoff (2015) and Tetlock (2010) respectively. Contrastingly to findings of Antweiler and Frank (2006) as well as García (2013), that suggest improved sentiment prediction accuracies during recessions, we find that the accuracy of our model is reduced following the outbreak of Covid-19.

Over the covid-19 lockdown period, Dr James Rogers worked with history teachers and academics to keep the learning going in lockdown. He now has a fantastic range of videos and podcasts available. All content is free to access and is explicitly designed to help teachers and students undertake A-Level and GCSE history revision.Podcast - Slavery and Emancipation in the United States, with Dr Cathrine Armstrong.Podcast - The History of Terrorism - The IRA, with Professor Caroline Kennedy-Pipe.Video - The Rise of Hitler - Hitler, Power, and War, with Ms Laurie Matthews.Video - The Home Front in WW2 - The Butterfly Bombing of Grimsby, with Dr James Rogers.