![]() Tirea and Negru create an optimized portfolio through the combination of text mining, sentiment analysis, and risk models on the Bucharest Stock Exchange. Among the earlier papers, Engelberg demonstrates that soft information, although more difficult to calculate, offers greater predictability on asset prices in particular at a longer horizon. The use of textual analysis in the financial sector is relatively recent but constantly growing. It measures the information transferred between the two variables or equivalently the reduction in uncertainty uniquely caused by a variable on the another. The transfer entropy is a conditional mutual information between the past of a variable and the future of another variable conditioned to the past of this second variable. ![]() The first represents the uncertainty related to a variable’s possible outcomes and quantifies its information content, the second one measures the information that two variables share. Causality is quantified through tools of information theory using entropy and mutual information. We analyze the causality between some of the most important worldwide companies using both hard (prices) and soft (social media sentiment) information and investigate their interrelations. In this paper, we further investigate such relationship by means of information theoretic tools, with the aim of understanding the manifest and latent dynamics of h a r d and s o f t information within the US market. On the other hand, swings in social opinions have their independent dynamics and sometimes follow and other times anticipate market movements. On the one hand, an efficient market hypothesis would suggest that all information must be comprised into the prices. The relation between the two is still a domain in exploration. Therefore, a current study of market behaviour cannot be limited to the h a r d evidence related to the financial metrics but must also dig into the s o f t metrics of social media and news. This has probably always been true, but it has become even more crucial in the present world where social-media has a pervasive role. Reputation is playing a major role in economics. Indeed, the economic value of things and firms is both material and immaterial. In finance, the relationships between companies are usually analyzed considering the so-called “hard” information such as stock prices, trade volumes, the quantity of output, but, in recent years, there has been an increase in the use of “soft” information including textual data, opinions, news, and sentiment. In its original formulation, this corresponds to an additional term in a linear regression for financial forecasting, but the idea is general and requires the quantification of information flow between variables. In 1969, Granger first proposed to look at causality in terms of the amount of extra information that the observation of a variable provides about another variable. ![]() This is because the occurrence of two events, one after the other, does not necessarily imply that the first caused the second. Causality is hard to detect from observations.
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