Tradition financial studies on asset pricing focused on the economic indicators and media information of a stock. Recent financial studies found that the momentum spillovers of relevant firms are salient as well for measuring asset risk. However, previous studies on asset pricing via machine learning only relied on partial of these market information types. In this study, a deep learning framework is proposed to combine these three market information types with different data structures, that is, numerical economic indicators represented as scalars, media represented as textual vectors, and the influences of related firms captured by graphs. More importantly, the unique data characteristics brought by such data fusion are well addressed in the proposed learning framework. Specifically, a matrix-based module is first proposed to fuse numerical economic data and textual media, which specifically considers the interactions of the fused features. Such fused information, along with the firm relevance represented in graphs, is further integrated by a novel self-adaptive graph neural network that can address the dynamic merging of multilinked listed firms. Experiments performed on real market data demonstrate the effectiveness of the proposed approach over state-of-the-art algorithms, including eLSTM, RGCN, and TGC.

A remote embedded power measuring and supervising system is developed based on the global network.The electric signal such as voltage, current and power for a single-phase two-wire circuit or a three-phase three-wire circuit can be measured by the power measurement device. The center embedded server composed of the embedded Linux OS (Operation System) called as TimeSys [1] and embedded framework with Hitachi microcomputer [2], works as the bridge between users and power measurement devices. RS485 communication bus is designed to connect at most 31 measurement devices to the center server. The web access service based on the network communication interface is provided to enable remote data supervising, comparing, downloading, and so on. The whole system achieves remote supervision and abundant data analysis for energy conversation.

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