Stock recommendation plays a critical role in modern quantitative trading. The large volumes of social media information such as investment reviews that delegate emotion-driven factors, together with price technical indicators formulate a “snapshot” of the evolving stock market profile. However, previous studies usually model the temporal trajectories of price and media modalities separately while losing their interrelated influences. Moreover, they mainly extract review semantics via sequential or attentive models, whereas the rich text associated knowledge is largely neglected. In this paper, we propose a novel heterogeneous interactive snapshot network for stock profiling and recommendation. We model investment reviews in each snapshot as a heterogeneous document graph, and develop a flexible hierarchical attentive propagation framework to capture fine-grained proximity features. Further, to learn stock embedding for ranking, we introduce a novel twins-GRU method, which tightly couples the media and price parallel sequences in a cross-interactive fashion to catch dynamic dependencies between successive snapshots. Our approach excels state-of-the-arts over 7.6% in terms of cumulative and risk-adjusted returns in trading simulations on both English and Chinese benchmarks.

Stock investment selection is a hard issue in the Fintech field due to non-stationary dynamics and complex market interdependencies. Existing studies are mostly based on RNNs, which struggle to capture interactive information among fine granular volatility patterns. Besides, they either treat stocks as isolated, or presuppose a fixed graph structure heavily relying on prior domain knowledge. In this paper, we propose a novel Adaptive Long-Short Pattern Transformer (ALSP-TF) for stock ranking in terms of expected returns. Specifically, we overcome the limitations of canonical self-attention including context and position agnostic, with two additional capacities: (i) fine-grained pattern distiller to contextualize queries and keys based on localized feature scales, and (ii) time-adaptive modulator to let the dependency modeling among pattern pairs sensitive to different time intervals. Attention heads in stacked layers gradually harvest short- and long-term transition traits, spontaneously boosting the diversity of representations. Moreover, we devise a graph self-supervised regularization, which helps automatically assimilate the collective synergy of stocks and improve the generalization ability of overall model. Experiments on three exchange market datasets show ALSP-TF’s superiority over state-of-the-art stock forecast methods.

Lacking in sequence preserving mechanism, existing heterogeneous information network (HIN) embedding discards the essential type sequence information during embedding. We propose a Type Sequence Preserving HIN Embedding model (SeqHINE) which expands the HIN embedding to sequence level. SeqHINE incorporates the type sequence information via type-aware GRU and preserves representative sequence information by decay function. Abundant experiments show that SeqHINE can outperform state-of-the-art even with 50% less labeled data.

Currently, a cloud database is employed to serve on-line query-intensive applications. It inevitably happens that some cloud data nodes storing hot records are facing high frequent query requests while others are rarely visited or even idle. Therefore, how data are dynamically allocated and migrated at runtime has significant impact on query load distribution and system performance. Existing system adopt centralized approaches, and they face two main challenges: (1) Query load on individual node cannot be always balancing even if the data are fairly distributed; (2) For each node, the dynamic changes of configuration resources cannot be captured during the runtime. To this end, this paper presents an environment-aware market strategy based system, named e-MARS, for reasonable data migration to achieve query load balance in cloud database. In e-MARS, cloud database is modeled as a cloudDB market, while data nodes are regarded as intelligent traders and the query load as commodity. Each trader is aware of its local environmental re-sources, such as computing capacity, disk volume, based on which the trader itself decides how to trade the query load and migrates the corresponding data. In this way the cloudDB market will achieve equilibrium. Experiments are conducted on the real communication data, and e-MARS significantly enhances the efficiency. Compared with HBase Balancer, more than 65% improvement is achieved in terms of query response time.

Topic modeling on phrases is important in understanding documents by providing interpretable topics. But existing methods are not as efficient as the topic modeling methods on words, which may limit their potential application.Towards providing a more efficient method, we propose a novel topic model SparseTP, which (1) models the words and phrases by linking them in Markov Random Field when necessary; (2) provides a well-formed lower bound of the model for Gibbs sampling; (3) utilizes the sparse distribution of words and phrases on topics to speed up the inference. The experiments demonstrate that it can achieve the high efficiency without sacrificing the effectiveness.

Identifying individual's political ideology from their speeches and written texts is important for analyzing political opinions and user behavior on social media. Traditional opinion mining methods rely on bag-of-words representations to classify texts into different ideology categories. Such methods are too coarse for understanding political ideologies. The key to identify different ideologies is to recognize different opinions expressed toward a specific topic. To model this insight, we classify ideologies based on the distribution of opinions expressed towards real-world entities or topics. Specifically, we propose a novel approach to political ideology detection that makes predictions based on an opinion-aware knowledge graph. We show how to construct such graph by integrating the opinions and targeted entities extracted from text into an existing structured knowledge base, and show how to perform ideology inference by information propagation on the graph. Experimental results demonstrate that our method achieves high accuracy in detecting ideologies compared to baselines including LR, SVM and RNN.

Microblogs contain the most up-to-date and abundant opinion information on current events. Aspect-based opinion mining is a good way to get a comprehensive summarization of events. The most popular aspect based opinion mining models are used in the field of product and service. However, existing models are not suitable for event mining. In this paper we propose a novel probabilistic generative model (ASEM) to simultaneously discover aspects and the specified opinions. ASEM incorporate a sequence labeling model(CRF) into a generative topic model. Additionally, we adopt a set of features for separating aspects and sentiments. Moreover, we novelly present a continuously learning model. It can utilize the knowledge of one event to learn another, and get a better performance. We use five real world events to do experiment. The experimental results show that ASEM extracts aspects and sentiments well, and ASEM outperforms other state-of-art models and the intuitive two-step method.

Microblog has become a major platform for information about real-world events.Automatically discovering realworld events from microblog has attracted the attention of many researchers.However, most of existing work ignore the importance of emotion information for event detection.We argue that people's emotional reactions immediately reflect the occurring of real-world events and should be important for event detection.In this study, we focus on the problem of communityrelated event detection by community emotions.To address the problem, we propose a novel framework which include the following three key components: microblog emotion classification, community emotion aggregation and community emotion burst detection.We evaluate our approach on real microblog data sets.Experimental results demonstrate the effectiveness of the proposed framework.

Bayesian Network (BN) is one of the most popular models in data mining technologies. Most of the algorithms of BN structure learning are developed for the centralized datasets, where all the data are gathered into a single computer node. They are often too costly or impractical for learning BN structures from large scale data. Through a simple interface with two functions, map and reduce, MapReduce facilitates parallel implementation of many real-world tasks such as data processing for search engines and machine learning. In this paper, we present a parallel algorithm for BN structure leaning from large-scale dateset by using a MapReduce cluster. We discuss the benefits of using MapReduce for BN structure learning, and demonstrate the performance of this approach by applying it to a real world financial factor relationships learning task from the domain of financial analysis.

This paper addresses a new uncertainty set - interval random uncertainty set for robust Value-at-Risk optimization. The form of interval random uncertainty set makes it suitable for capturing the downside and upside deviations of real-world data. These deviation measures capture distributional asymmetry and lead to better optimization results. We also apply our interval random chance-constrained programming to robust Value-at-Risk optimization under interval random uncertainty sets in the elements of mean vector. Numerical experiments with real market data indicate that our approach results in better portfolio performance.

This paper addresses the portfolio selection problem in a robust manner. In practice, it is difficult to collect all information to determine the precise bounds of the box uncertainty set used in robust portfolio optimization. To solve this problem, we introduce a novel uncertainty set: interval random uncertainty. We apply our interval random chance-constrained programming to robust semi-absolute deviation portfolio selection under interval random uncertainty in the element of mean vector. The method for generating the uncertainty set from historical data is discussed. An hybrid-intelligent algorithm is applied to solve the robust portfolio model. Finally, we compare the potentially significant economic benefits of investing in portfolios computed using classical model and the model introduced here. And the robustness is achieved at relatively high performance and low cost.

Uncertainty over exit time is an important practical issue faced by most investors. In this paper, we introduce the notations of the possibilistic mean, variance and covariance of fuzzy numbers to generalize Markowitz analysis. In the first time, we consider the uncertain investment period from the point of view of possibilistic analysis, and build the possibilistic models of portfolio selection under the situations involving uncertainty over the time horizon. The exit time can be either independent or dependent of asset price behavior. Moreover, an numerical example is presented to show the application of our results.

When employing fuzzy random variable in some real programming problems, it is not easy to specify the fuzzy values of random variables. But it is relatively easy to obtain the boundaries of the values of random variables. Hence, it is a good idea for people to determine the values of random variables as intervals. In this paper, we introduce the framework of interval random variable and interval random dependent-chance programming model. To pay attentions to both randomness and incompleteness of financial environment, we build the portfolio selection model by quantifying the stock return as interval random variable under this framework. Some computational results are discussed that demonstrate the potentially significant economic benefits of investing in portfolios computed using classical models and the model introduced here. The benefits are achieved at relatively high performance and low cost.