On XLE index constituents’ social media based sentiment informing the index trend and volatility prediction

Collective intelligence represented as sentiment extracted from social media mining found applications in various areas. Numerous studies involving machine learning modelling have demonstrated that such sentiment information may or may not have predictive power on the stock market trend. This research investigates the predictive information of sentiment regarding the Energy Select Sector related XLE index and of its constituents, on the index and its volatility, based on a novel robust machine learning approach. While we demonstrate that sentiment does not have any impact on any of the trend prediction scenarios investigated here related to XLE and its constituents, the sentiment’s impact on volatility predictions is significant. The proposed volatility prediction modelling approach, based on Jordan and Elman recurrent neural networks, demonstrates that the addition of sentiment or sentiment moment reduces the prediction root mean square error (RMSE) to about one third. The experiments we conducted also demonstrate that the addition of sentiment reduces the RMSE for 24 out of the 36 stocks/constituents, representing 87.9% of the index weight. This is the first study in the literature relating to the prediction of the market trend or the volatility based on an index and its constituents’ sentiment

Amyotrophic lateral sclerosis-associated mutant VAPBP56S perturbs calcium homeostasis to disrupt axonal transport of mitochondria

A proline-to-serine substitution at position 56 in the gene encoding vesicle-associated membrane protein-associated protein B (VAPB; VAPBP56S) causes some dominantly inherited familial forms of motor neuron disease, including amyotrophic lateral sclerosis (ALS) type-8. Here, we show that expression of ALS mutant VAPBP56S but not wild-type VAPB in neurons selectively disrupts anterograde axonal transport of mitochondria. VAPBP56S-induced disruption of mitochondrial transport involved reductions in the frequency, velocity and persistence of anterograde mitochondrial movement. Anterograde axonal transport of mitochondria is mediated by the microtubule-based molecular motor kinesin-1. Attachment of kinesin-1 to mitochondria involves the outer mitochondrial membrane protein mitochondrial Rho GTPase-1 (Miro1) which acts as a sensor for cytosolic calcium levels ([Ca2+]c); elevated [Ca2+]c disrupts mitochondrial transport via an effect on Miro1. To gain insight into the mechanisms underlying the VAPBP56S effect on mitochondrial transport, we monitored [Ca2+]c levels in VAPBP56S-expressing neurons. Expression of VAPBP56S but not VAPB increased resting [Ca2+]c and this was associated with a reduction in the amounts of tubulin but not kinesin-1 that were associated with Miro1. Moreover, expression of a Ca2+ insensitive mutant of Miro1 rescued defective mitochondrial axonal transport and restored the amounts of tubulin associated with the Miro1/kinesin-1 complex to normal in VAPBP56S-expressing cells. Our results suggest that ALS mutant VAPBP56S perturbs anterograde mitochondrial axonal transport by disrupting Ca2+ homeostasis and effecting the Miro1/kinesin-1 interaction with tubulin

Forecasting exchange rate volatility: GARCH models versus implied volatility forecasts

This study investigates whether different specifications of univariate GARCH models can usefully forecast volatility in the foreign exchange market. The study compares in-sample forecasts from symmetric and asymmetric GARCH models with the implied volatility derived from currency options for four dollar parities. The data set covers the period 2002 to 2012. We divide the data into two periods one for the period 2002 to 2007 which is characterised by low volatility and the other for the period 2008 to 2012 characterised by high volatility. The results of this paper reveal that the implied volatility forecasts significantly outperforms the three GARCH models in both low and high volatility periods. The results strongly suggest that the foreign exchange market efficiently prices in future volatility

Inhibition of IL-34 Unveils Tissue-Selectivity and Is Sufficient to Reduce Microglial Proliferation in a Model of Chronic Neurodegeneration

The proliferation and activation of microglia, the resident macrophages in the brain, is a hallmark of many neurodegenerative diseases such as Alzheimer’s disease (AD) and prion disease. Colony stimulating factor 1 receptor (CSF1R) is critically involved in regulating microglial proliferation, and CSF1R blocking strategies have been recently used to modulate microglia in neurodegenerative diseases. However, CSF1R is broadly expressed by many cell types and the impact of its inhibition on the innate immune system is still unclear. CSF1R can be activated by two independent ligands, CSF-1 and interleukin 34 (IL-34). Recently, it has been reported that microglia development and maintenance depend on IL-34 signaling. In this study, we evaluate the inhibition of IL-34 as a novel strategy to reduce microglial proliferation in the ME7 model of prion disease. Selective inhibition of IL-34 showed no effects on peripheral macrophage populations in healthy mice, avoiding the side effects observed after CSF1R inhibition on the systemic compartment. However, we observed a reduction in microglial proliferation after IL-34 inhibition in prion-diseased mice, indicating that microglia could be more specifically targeted by reducing IL-34. Overall, our results highlight the challenges of targeting the CSF1R/IL34 axis in the systemic and central compartments, important for framing any therapeutic effort to tackle microglia/macrophage numbers during brain disease

Reduction of systemic risk by means of Pigouvian taxation

We analyze the possibility of reduction of systemic risk in financial markets through Pigouvian taxation of financial institutions, which is used to support the rescue fund. We introduce the concept of the cascade risk with a clear operational definition as a subclass and a network related measure of the systemic risk. Using financial networks constructed from real Italian money market data and using realistic parameters, we show that the cascade risk can be substantially reduced by a small rate of taxation and by means of a simple strategy of the money transfer from the rescue fund to interbanking market subjects. Furthermore, we show that while negative effects on the return on investment (ROI) are direct and certain, an overall positive effect on risk adjusted return on investments (ROIRA) is visible. Please note that the taxation is introduced as a monetary/regulatory, not as a _scal measure, as the term could suggest. The rescue fund is implemented in a form of a common reserve fund

Daily volume, intraday and overnight returns for volatility prediction: profitability or accuracy?

This article presents a comprehensive analysis of the relative ability of three information sets—daily trading volume, intraday returns and overnight returns—to predict equity volatility. We investigate the extent to which statistical accuracy of one-day-ahead forecasts translates into economic gains for technical traders. Various profitability criteria and utility-based switching fees indicate that the largest gains stem from combining historical daily returns with volume information. Using common statistical loss functions, the largest degree of predictive power is found instead in intraday returns. Our analysis thus reinforces the view that statistical significance does not have a direct mapping onto economic value. As a byproduct, we show that buying the stock when the forecasted volatility is extremely high appears largely profitable, suggesting a strong return-risk relationship in turbulent conditions