We aim to achieve this goal by embedding sentiment into the view distribution.
The sentiment strategy uses lag (tau =12), a uniform view distribution with (U -0.005 0.03)), and the 'negative pick matrix'.
The results show that the superior performance of the sentiment strategy does not depend on a specific parameterization of the view distribution and remains robust for alternative specifications.
The sentiment strategy generates positive Sharpe ratios for parameter combinations that result in a positive mean for the view distribution.
Ahead of election day, Cognovi Labs were identifying presidential candidate sentiment and volume distribution leaning toward Clinton on November 5 – 6 (controlled for tweets identified and located in the battleground state of Florida (346,837 tweets) normalized to account for bots, RT, and other distortions).
Starting with the 3D plots of the test period in Fig. 8b, d, for the normal distribution, the sentiment strategy exhibits positive Sharpe ratios for positive values of (mu).
However, given the start of our sentiment indexes, the initial market distribution in (t=1) is based only on market data from February 1987 to July 1993 ((t=1)).
The new model, called BayesAct, can maintain multiple hypotheses about sentiments simultaneously as a probability distribution, and can make use of an explicit utility function to make value-directed action choices.
A significant proportion of tweets did contain non-neutral sentiment about MS treatments, and the distribution of sentiment score was different between treatments.
For the uniform distribution, the performance of the sentiment strategy follows a similar pattern.
