The Information of Spam

This paper explores the value of information contained in spam tweets as it pertains to prediction accuracy. As a case study, tweets discussing Bitcoin were collected and used to predict the rise and fall of Bitcoin value. Precision of prediction both with and without spam tweets, as identified by a naive Bayesian spam filter, were measured. Results showed a minor increase in accuracy when spam tweets were included, indicating that spam messages likely contain information valuable for prediction of market fluctuations

Sentiment analysis in the stock market based on Twitter data

In this dissertation, we discuss how Twitter can help detecting public sentiment towards companies listed in the stock market, in particular listed in the S&P 500 index (S&P 500). The collection of data is done through a web scrapper that collects tweets from Twitter, using advanced search features based on queries related to the companies under scrutiny. The content of tweets are classified as positive, neutral or negative sentiments and the outcome is then compared against stock market prices. To do so, it is proposed and implemented a framework with different Sentiment Analysis (SA) models and Machine Learning (ML) techniques. Also, to establish which models are more appropriate in detecting and classifying sentiments, a series of visual representations were created to evaluate and compare results. As a conclusion, the results obtained show that an increase in the volume of tweets leads to oscillations in both stock price and trading volume. Furthermore, the data analysis performed in relation to some companies under scope shows that the use of moving averages of sentiment scores makes the analysis clearer and more insightful, which is particular useful when measuring the strength or weakness of the price of a stock. In the end, it can be perceived as a momentum indicator for the stock market.Nesta dissertação, é analisada a forma como a plataforma Twitter pode ajudar a detectar sentimento público relativamente a empresas cotadas em bolsa, com foco em empresas que fazem parte do indíce americano S&P 500. A obtenção de dados é feita através de um web scrapper, que recolhe tweets através de funções de pesquisa avançada, baseada em queries associadas às empresas em análise. O conteúdo dos tweets são classificados como positivos, neutros ou negativos, sendo os resultados comparados de seguida com os preços das ações. Nesse sentido, é proposta um arquitectura de trabalho, com a respetiva implementação, que inclui vários modelos de análise de sentimento e técnicas de Machine Learning. Por outro lado, de modo a estabelecer quais são os modelos mais adequados para detectar e classificar sentimentos, são criados várias representações visuais para avaliar e comparar resultados. Como conclusão, os resultados obtidos mostram que um aumento do número de tweets conduz a oscilações, quer no preço, quer na quantidade de ações transacionadas. Além disso, a análise de dados levada a cabo relativamente a algumas empresas em estudo, mostra que a utilização de médias móveis de resultados de sentimento torna a leitura da análise mais clara e evidente, o que é bastante útil para medir a força ou fraqueza do preço de determinada ação. Acima de tudo, tal poderá ser percecionado como um indicador de momento para o mercado de capitais

The relevance of coarse thinking for investors' willingness to pay: An experimental study

People tend to think by analogies. We investigate whether thinking-by-analogy matters for investors’ willingness to pay for a risky asset in a laboratory experiment. We find that thinking-by-analogy has a strong influence when the assets in question have similar (but not identical) payoffs. The hypothesis of thinking-by-analogy or coarse thinking clearly outperforms other hypotheses including the hypothesis of arbitrage-free or rational pricing. When the similarity between the payoffs is reduced, the risk neutral hypothesis outperforms the hypothesis of thinking-by-analogy. Regardless of the similarity between the payoffs, the arbitrage-free or rational pricing remains the hypothesis with the worst performance.Coarse Thinking; Thinking-by-Analogy; Asset Pricing; Call Option

Forecasting of commercial sales with large scale Gaussian Processes

This paper argues that there has not been enough discussion in the field of applications of Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be important as it e.g., can provide automatic feature relevance determination and the posterior mean can unlock insights on the data. Significant challenges are the large size and high dimensionality of commercial data at a point of sale. The study reviews approaches in the Gaussian Processes modeling for large data sets, evaluates their performance on commercial sales and shows value of this type of models as a decision-making tool for management.Comment: 1o pages, 5 figure