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Using features extracted from StockTwits messages between July 2009 and September 2012, we show through simulations that: (1) message volume and sentiment can be used as a risk factor in an asset pricing model framework; (2) message volume and sentiment help explain the diffusion of price information over several days, and (3) message volume and sentiment can be used as features to predict asset price directional moves. Our findings suggest statistics derived from message volume and sentiment can improve asset price forecasts and leads to a profitable trading strategy.

We examine whether a put-call ratio, derived from a unique set of market data, can be used to predict directional moves in asset prices during various market conditions between March 2005 and Decem...

This paper evaluates the question of whether sentiment extracted from social media and options volume anticipates future asset return. The research utilized both textual based data and a particular market data derived call-put ratio, collected between July 2009 and September 2012. It shows that: (1) features derived from market data and a call-put ratio can improve model performance, (2) sentiment derived from StockTwits, a social media platform for the financial community, further enhances model performance, (3) aggregating all features together also facilitates performance, and (4) sentiment from social media and market data can be used as risk factors in an asset pricing framework.

Mitochondrial diseases often feature early onset of dilated cardiomyopathy, due to the large energetic demand placed by the heart. Among regulators of mitochondrial respiration, we focus on calcium, which potently stimulates ATP synthesis. We assessed the hypothesis that cells boost mitochondrial calcium to stimulate respiration as this declines in mitochondrial cardiomyopathies. To this end, we studied mice that develop a neonatal, severe cardiomyopathy caused by cardiac-specific knockout (KO) of mitochondrial transcription factor A ( Tfam ). Such deletion impairs transcription of mitochondrial DNA, which encodes multiple subunits of the electron transport chain (ETC). Tfam KO mice exhibited a dilated cardiomyopathy, with decreased fractional shortening and increased LV diameters, and had marked inhibition of multiple ETC complexes. We determined that Tfam KO mitochondria took up calcium twice as fast as mitochondria from littermate controls, while calcium efflux was approximately 70% slower. Whole-mitoplast voltage clamp revealed that the enhanced calcium uptake was due to an increase in the current carried by the uniporter. Furthermore, the larger uniporter current reflected increased amounts of uniporter subunit proteins. This occurred despite a reduction in the transcripts of genes encoding these subunits, suggesting a post-translational mechanism for the enhanced uniporter stability. Finally, we found that the rate of ADP-stimulated oxygen consumption in calcium-free solution was 50% less in the Tfam KO mitochondria compared to controls, but increased substantially more, nearly to control levels, when calcium was present (2.5-fold increase for KO versus 1.7-fold for control). In conclusion, enhanced mitochondrial calcium signaling in a mitochondrial cardiomyopathy model may serve to compensate for energetic failure.

This research investigates the predictive capability of sentiment extrapolated from three dictionaries; financial, social media and mood states. Our findings show: 1) through the Fama-Macbeth regression method, social media based sentiment measures can be used as risk factors in an asset pricing framework; 2) these sentiment measures have predictive capability when used as features in a machine learning framework, and 3) adjusting returns for market effects result in positive alpha.

This paper evaluates if sentiment extracted from social media and options volume anticipates future asset return. Using both textual based data and a particular market data derived call-put ratio, between July 2009 and September 2012, this research shows that: 1) features derived from market data and a call-put ratio improve model performance, and 2) sentiment derived from StockTwits, a social media platform for the financial community, further improves model performance.

Using features extracted from StockTwits messages between July 2009 and September 2012, we show through simulations that: 1) both message volume and sentiment help explain the diffusion of price information; 2) both message volume and sentiment can be used as features to predict asset price directional moves, we show that positive and negative sentiment diffuses into an assets price over a period of days. Our findings suggest statistics derived from both message volume and message sentiment can improve asset price forecasts.

We examine whether a particular put-call ratio, derived from a unique set of market data, can be used to predict directional moves in asset prices during various market conditions between March 2005 and December 2012. Our findings show: 1) specific market participant option trading volume is shown to be a predecessor to asset price movements; 2) portfolios adjusted for risk, momentum and transaction costs exhibit abnormal excess returns. These findings suggest that short positions of a specific market participant improve the overall performance of a given portfolio.