This thesis involved an empirical investigation of the predictability of Australian industrial stock returns using a dynamic state-space framework. The systematic risks of industrial portfolios were examined in a stochastic market- model. The systematic risks of industry portfolios are found to be stochastic processes. Most of the industry groups have time-varying systematic risks that are mean-reverting to their stable or moving long-term mean. However, the investment and financial services, alcohol and tobacco, gold, insurance and media industry groups have rather random systematic risks. The time-varying market model provides a better explanation of the portfolio returns than the single-index model since it captures the stochastic properties of market risk. Further, a Bayesian dynamic-forecasting model was employed to examine the explanatory power of a set of economic and financial variables. The unanticipated components of the term-structure variable, the interest-rate variable and the aggregate-dividend-yield variable were shown to be significant in explaining the industry portfolio excess returns. The comparison between multivariate analysis and univariate analysis strongly indicates that the correlations within industries are critical in the investigation of the predictability of returns. In the out-of-sample analysis, a maximally predicted portfolio (MPP) was constructed based on the updated economic and financial information; however, the predictability of the MPP did not exceed that of a naive forecast. / Furthermore, the market timing ability associated with the predictability of the MPP was insignificant. The industry-group-rotation strategy is able to enhance the industry portfolio performance, but the predictability only contributes a small proportion of the profits. The results indicate that the industry returns contain predictive components; however, investors are less likely to exploit the existing predictability to gain excess profit. The level of predictability discovered here does not contradict market-efficiency theory.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-06-12T13:47:55Z No. of bitstreams: 1 JOSÉ GILDO DE ARAÚJO JÚNIOR - DISSERTAÇÃO PPGCC 2016..pdf: 44985365 bytes, checksum: 7cf3e353444964c334025b7c6f4f6df5 (MD5) / Made available in DSpace on 2018-06-12T13:47:55Z (GMT). No. of bitstreams: 1 JOSÉ GILDO DE ARAÚJO JÚNIOR - DISSERTAÇÃO PPGCC 2016..pdf: 44985365 bytes, checksum: 7cf3e353444964c334025b7c6f4f6df5 (MD5) Previous issue date: 2016-12-13 / CNPq / Há algum tempo pesquisadores e analistas de mercado vêm apresentando indícios da previsibilidade de mercados acionários. Embora acredite-se que o mercado de ações seja imprevisível, análises de previsibilidade realizadas em bolsas da China, Turquia, Hong Kong, Itália, Teerã e EUA vêm mostrando o contrário. O fato é que a hipótese de eficiência de mercado foi planteada em 1970, e não se poderia prever as mudanças culturais e tecnológicas que impactaram o mundo, como o aumento da capacidade de processamento dos computadores, o desenvolvimento de técnicas de aprendizagem de máquina, a publicação de notícias online e a exposição em tempo real da opinião de investidores em redes sociais, por exemplo. A combinação destes elementos passaram a potencializar o lucro de investidores à medida que simplificaram o monitoramento e a gestão do risco, a compreensão do cenário econômico e até a realização de análises complexas sobre setores, índices e ações em poucos minutos. Este trabalho se propôs a lançar luz sobre relações e impactos que as notícias econômicas publicadas em jornais brasileiros, online, mantêm com o mercado acionário nacional em dois níveis de análise: índice Bovespa e setores. Inicialmente, foram coletadas notícias econômicas publicadas em jornais de alta circulação no Brasil entre 2000 e 2015, seus comentários e suas repercussões nas redes sociais Twitter, Facebook, Linkedln e GooglePlus. A análise de correlação entre o índice Bovespa e a quantidade de compartilhamento de notícias em redes sociais revelam uma correlação negativa de 48%. Além disso, por meio da análise de sentimento das notícias coletadas, verificou-se que a quantidade de notícias positivas publicadas é, em média, 4.5 vezes superior ao de negativas, e que, apesar disso, as notícias negativas são mais repercutidas nas redes sociais que as positivas. Para os setores, verificou-se que o setor mais previsível apenas por meio de notícias econômicas é o setor de Petróleo, Gás e Biocombustíveis enquanto o menos previsível é o setor Bens Industriais. Por fim, as variáveis extraídas das notícias foram utilizadas como base no desenvolvimento de modelos de predição tanto para o índice Bovespa quanto para os setores da BM&FBOVESPA. De forma geral, os resultados encontrados superaram estatisticamente baselines comumente utilizados em ~ 20%. / For some time researchers and market analysts have shown evidence of predictability of stock markets. Although many investors believe that the stock market is unpredictable, predictability analysis in China, Turkey, Hong Kong, Italy, Tehran and the US stock markets has shown the opposite situation. The Efficient-Market Hypothesis (EMH) was designed in 1970 and could not anticipate the cultural and technological changes that affected the world, such as the increased processing power of computers, the development of machine learning techniques, real time publication of news and opinions of investors in social media platforms, such as twitter and facebook, for example. The combination of these elements enabled investors to perform more complex analysis of sectors, índices and stocks in almost real time, thus increasing their understanding of the stock market dynamics and improving their likelihood of success. his study aimed to shed light on the relationships and impacts that economic news published in online Brazilian newspapers, have with the national stock market in two leveis of analysis: Bovespa Index and sectors. Initially, we collected economic news published in high-circulation newspapers in Brazil between 2000 and 2015, their comments and their repercussions on social medias like Twitter, Facebook, Linkedln and GooglePlus. The correlation analysis between the Bovespa index and the amount of news shared on social networks showed a negative correlation of 48%. Furthermore, using sentrment analysis it was found that the amount of positive news reported is in average of 4.5 times higher than the negative, and, nonetheless, the negative news are more rebound on the social media than positive news. For the sectors, it was found that the most predictable sector by economic news is the Oil, Gas and Biofuels while the less predictable is the Industrial Goods sector. Finally, the variables drawn from the news were used as as input for the definition of prediction models for both the Bovespa Index and for the sectors of BM& FBOVESPA. In general, the results overperformed baselines such as the random classifier in ~ 20%.