A Tensor-based eLSTM Model to Predict Stock Price Using Financial News

Stock market prediction has attracted much attention from both academia and business. Both traditional finance and behavioral finance believe that market information affects stock movements. Typically, market information consists of fundamentals and news information. To study how information shapes stock markets, common strategies are to concatenate various information into one compound vector. However, such concatenating ignores the interlinks between fundamentals and news information. In addition, the fundamental data are continuous values sampled at fixed time intervals, while news information occurred randomly. Such heterogeneity leads to miss valuable information partially or twist the feature spaces. In this article, we propose a tensor-based event-LSTM (eLSTM) to solve these two challenges. In particular, we model the market information space with tensors instead of concatenated vectors and balance the heterogeneity of different data types with event-driven mechanism in LSTM. Experiments performed on an entire year data of China Securities markets demonstrate the supreme of the proposed approach over the state-of-the-art algorithms including AZfinText, eMAQT, and TeSIA

Rumor Clarification, Digital Platform, and Stock Movement

Stock return is influenced by information release, dissemination, and acceptance. Rumor clarification is supposed to reduce asymmetric information and abnormal stock return. In this research, we extracted 4134 rumor-clarification pairs from 687,429 postings in social media, and quantified the language used in these messages, along with online firm behaviors, to study the effect of clarifications on stock returns. Our findings include (1) the digitalized rumor clarification messages affect the abnormal returns of the relevant stocks; (2) Such influence can be quantified and measured by the emotion polarity of rumor clarification; (3) Firm’s online clarification behaviors may have no influence on abnormal returns except for the total response number of rumor clarification for a listed company. In particular, investors prefer to trust the clarifications from the companies with frequent online interactive engagements

Active backstepping control of combined projective synchronization among different nonlinear systems

In this article, the authors have studied combination projective synchronization using active backstepping method. The main contribution of this effort is realization of the projective synchronization between two drive systems and one response system. We relax some limitations of previous work, where only combination complete synchronization has been investigated. According to Lyapunov stability theory and active backstepping design method, the corresponding controllers are designed to observe combination projective synchronization among three different classical chaotic systems, i.e. the Lorenz system, Rossler system and € Chen system. The numerical simulation examples verify the effectiveness of the theoretical analysis. Combination projective synchronization has stronger anti-attack ability and antitranslated ability than the normal projective synchronization scheme realized by one drive and one response system in secure communication

4-(2,4-Dichloro­phen­yl)-5,5-dimethyl-2-(3-silatranyl­propyl­mino)-1,3,2-dioxa­phospho­rinane 2-oxide

In the title compound, C20H31Cl2N2O6PSi, the dioxaphospho­rinane ring adopts a cis conformation. The silatrane fragment forms a cage-like structure in which there exists an intra­molecular Si—N donor–acceptor bond. In the crystal, centrosymmetrically related mol­ecules are linked by pairs of N—H⋯O hydrogen bonds into inversion dimers, generating rings with graph-set motif R 2 2(8). The dimers are further connected into ribbons parallel to the a axis by inter­molecular C—H⋯O hydrogen bonds

Blockchain Sharding and Incentive Mechanism for 6G Dependable Intelligence

The sixth generation(6G) wireless communication network will become the base of endogenous intelligence,ubiquitous connectivity,and full-scene interconnection.It is an important basis to realize dependable intelligence in the future.Blockchain is considered as the key decentralized-enabled technology to improve the performance of 6G networks.In the future,the consensus nodes of the blockchain will be composed of massive edge devices and connected through wireless networks.However,motivating self-interest edge devices to participate in the consensus process still faces the challenges of information asymmetry,resource constraints and heterogeneous wireless communication environment.To solve these challenges,a blockchain sharding framework and an incentive mechanism for trusted and dependable intelligence in 6G are proposed.Firstly,an incentive mechanism is presented based on contract theory,which aims to maximize the benefits and reliability of the blockchain sharding.By analyzing the practical byzantine fault tolerance (PBFT) based intrashard consensus mechanism,this paper design energy consumption model for auditing and transmitting the blocks in wireless networks.Secondly,in order to improve the system reliability,it proposes a reputation mechanism based on subjective logic.Finally,a set of optimal contracts under complete information and asymmetric information scnearios are abtained,which could optimize the block revenue for blockchain service requester,while ensuring some desired economic properties,i.e.,budget feasibility,individual rationality and incentive compatibility.Simulation results show that the proposed contract-based incentive mechanism can motivate edge devices to participate in the blockchain consensus process and maintain the operation of blockchain from the perspective of economics more efficiently

Nosocomial outbreak of KPC-2- and NDM-1-producing Klebsiella pneumoniae in a neonatal ward: a retrospective study

Sequencing for MLST. (DOCX 21 kb

China's low-emission pathways toward climate-neutral livestock production for animal-derived foods

Funding Information: This research was supported by the National Natural Science Foundation of China (Grant No. 31922080 and 31872403 ), China Agriculture Research System of MOF and MARA and the Hunan province science and technology plan (Grant No. 2022NK2021 ).Peer reviewedPublisher PD

OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling

Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data. Many algorithms are designed for online time series forecasting, with some exploiting cross-variable dependency while others assume independence among variables. Given every data assumption has its own pros and cons in online time series modeling, we propose \textbf{On}line \textbf{e}nsembling \textbf{Net}work (OneNet). It dynamically updates and combines two models, with one focusing on modeling the dependency across the time dimension and the other on cross-variate dependency. Our method incorporates a reinforcement learning-based approach into the traditional online convex programming framework, allowing for the linear combination of the two models with dynamically adjusted weights. OneNet addresses the main shortcoming of classical online learning methods that tend to be slow in adapting to the concept drift. Empirical results show that OneNet reduces online forecasting error by more than $\mathbf{50\%}$ compared to the State-Of-The-Art (SOTA) method. The code is available at \url{https://github.com/yfzhang114/OneNet}.Comment: 32 pages, 11 figures, 37th Conference on Neural Information Processing Systems (NeurIPS 2023