A Novel Distributed Representation of News (DRNews) for Stock Market Predictions

In this study, a novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions. With the merit of integrating contextual information and cross-documental knowledge, the DRNews model creates news vectors that describe both the semantic information and potential linkages among news events through an attributed news network. Two stock market prediction tasks, namely the short-term stock movement prediction and stock crises early warning, are implemented in the framework of the attention-based Long Short Term-Memory (LSTM) network. It is suggested that DRNews substantially enhances the results of both tasks comparing with five baselines of news embedding models. Further, the attention mechanism suggests that short-term stock trend and stock market crises both receive influences from daily news with the former demonstrates more critical responses on the information related to the stock market {\em per se}, whilst the latter draws more concerns on the banking sector and economic policies.Comment: 25 page

CRISIS TRANSMITTING EFFECTS DETECTION AND EARLY WARNING SYSTEMS DEVELOPMENT FOR CHINA’S FINANCIAL MARKETS

In the background of China’s economic development mode being focused the worldwide attention, there is a growing trend to study the risk transmission pattern and the crisis forecasting mechanism for China’s financial markets by domestic and global academics. The study progress, however, is observed to be affected by two gaping research problems: 1) few studies construct comparative contagion models and integrated crisis forecasting systems for China’s financial markets and 2) current econometric models hired to the risk spreading effects detection and the financial crisis forecasts are yet deterministically investigated in terms of the effectiveness on China. To fill the gaps, this research proposes two hybrid contagion models and prototypes the early warning systems with motivations of first analyzing the crisis linkages and transmission channels across domestic markets in hierarchical frameworks, and then predicting the market turbulence by integrating the crisis identifying techniques and time-dependent deep learning neuron networks. To accomplish our aims, the full project is progressed in phases by solving four technical challenges that portray two literature gaps of A) the crisis identification on the basis of price volatility state distinction, B) the decomposition for multivariate correlated patterns to infer the interdependence structure and risk spillover dynamics respectively, C) the real-time warning signals generation in comparison of between traditional and stylized predictive models and D) the contagion information fusion in the EWS frameworks to distinguish the leading indicators from between internal macroeconomic factors and external risk transmitters in statistical validation metrics. The research mainly contributes to the comparative analysis on financial contagion effects detection and market turbulence prediction through the hybrid model innovations for CM and EWS development, and meanwhile brings practical significance to improve the risk management in investing activities and support the crisis prevention in policy-making. In addition, the model experimented results corroborate the China-characterized mode on risk transmissions and crisis warnings that 1) the stocks and real estate markets are verified to play the central role among risk transmitters, while the managed floating foreign exchange rate and the non-fully liberalized bond market are peripheral during the crisis; and 2) the all-round opening up policy increases the possibility of domestic security markets being exposed to external risk factors, especially relating to the cash flows, energy commodities and precious metals

Recent advances in high-βN experiments and magnetohydrodynamic instabilities with hybrid scenarios in the HL-2A Tokamak

Over the past several years, high-βN experiments have been carried out on HL-2A. The high-βN is realized using double transport barriers (DTBs) with hybrid scenarios. A stationary high-βN (>2) scenario was obtained by pure neutral-beam injection (NBI) heating. Transient high performance was also achieved, corresponding to βN≥3, ne/neG∼0.6, H98∼1.5, fbs∼30%, q95∼4.0, and G∼0.4. The high-βN scenario was successfully modeled using integrated simulation codes, that is, the one modeling framework for integrated tasks (OMFIT). In high-βN plasmas, magnetohydrodynamic (MHD) instabilities are abundant, including low-frequency global MHD oscillation with n = 1, high-frequency coherent mode (HCM) at the edge, and neoclassical tearing mode (NTM) and Alfvénic modes in the core. In some high-βN discharges, it is observed that the NTMs with m/n=3/2 limit the growth of the plasma energy and decrease βN. The low-n global MHD oscillation is consistent with the coupling of destabilized internal (m/n = 1/1) and external (m/n = 3/1 or 4/1) modes, and plays a crucial role in triggering the onset of ELMs. Achieving high-βN on HL-2A suggests that core-edge interplay is key to the plasma confinement enhancement mechanism. Experiments to enhance βN will contribute to future plasma operation, such as international thermonuclear experimental reactor