Cosmic constraint on the unified model of dark sectors with or without a cosmic string fluid in the varying gravitational constant theory

Observations indicate that most of the universal matter are invisible and the gravitational constant $G(t)$ maybe depends on the time. A theory of the variational $G$ (VG) is explored in this paper, with naturally producing the useful dark components in universe. We utilize the observational data: lookback time data, model-independent gamma ray bursts, growth function of matter linear perturbations, type Ia supernovae data with systematic errors, CMB and BAO to restrict the unified model (UM) of dark components in VG theory. Using the best-fit values of parameters with the covariance matrix, constraints on the variation of $G$ are $(\frac{G}{G_{0}})_{z=3.5}\simeq 1.0015^{+0.0071}_{-0.0075}$ and $(\frac{\dot{G}}{G})_{today}\simeq -0.7252^{+2.3645}_{-2.3645}\times 10^{-13} yr^{-1}$, the small uncertainties around constants. Limit on the equation of state of dark matter is $w_{0dm}=0.0072^{+0.0170}_{-0.0170}$ with assuming $w_{0de}=-1$ in unified model, and dark energy is $w_{0de}=-0.9986^{+0.0011}_{-0.0011}$ with assuming $w_{0dm}=0$ at prior. Restriction on UM parameters are $B_{s}=0.7442^{+0.0137+0.0262}_{-0.0132-0.0292}$ and $\alpha=0.0002^{+0.0206+0.0441}_{-0.0209-0.0422}$ with $1\sigma$ and $2\sigma$ confidence level. In addition, the effect of a cosmic string fluid on unified model in VG theory are investigated. In this case it is found that the $\Lambda$CDM ($\Omega_{s}=0$, $\beta=0$ and $\alpha=0$) is included in this VG-UM model at $1\sigma$ confidence level, and the larger errors are given: $\Omega_{s}=-0.0106^{+0.0312+0.0582}_{-0.0305-0.0509}$ (dimensionless energy density of cosmic string), $(\frac{G}{G_{0}})_{z=3.5}\simeq 1.0008^{+0.0620}_{-0.0584}$ and $(\frac{\dot{G}}{G})_{today}\simeq -0.3496^{+26.3135}_{-26.3135}\times 10^{-13}yr^{-1}$.Comment: 17 pages,4 figure

BERT-based Financial Sentiment Index and LSTM-based Stock Return Predictability

Traditional sentiment construction in finance relies heavily on the dictionary-based approach, with a few exceptions using simple machine learning techniques such as Naive Bayes classifier. While the current literature has not yet invoked the rapid advancement in the natural language processing, we construct in this research a textual-based sentiment index using a novel model BERT recently developed by Google, especially for three actively trading individual stocks in Hong Kong market with hot discussion on Weibo.com. On the one hand, we demonstrate a significant enhancement of applying BERT in sentiment analysis when compared with existing models. On the other hand, by combining with the other two existing methods commonly used on building the sentiment index in the financial literature, i.e., option-implied and market-implied approaches, we propose a more general and comprehensive framework for financial sentiment analysis, and further provide convincing outcomes for the predictability of individual stock return for the above three stocks using LSTM (with a feature of a nonlinear mapping), in contrast to the dominating econometric methods in sentiment influence analysis that are all of a nature of linear regression.Comment: 10 pages, 1 figure, 5 tables, submitted to NeurIPS 2019, under revie