Stock market indices serve as fundamental market measurement that quantify systematic market dynamics. However, accurate index price prediction remains challenging, primarily because existing approaches treat indices as isolated time series and frame the prediction as a simple regression task. These methods fail to capture indices' inherent nature as aggregations of constituent stocks with complex, time-varying interdependencies. To address these limitations, we propose Cubic, a novel end-to-end framework that explicitly models the adaptive fusion of constituent stocks for index price prediction. Our main contributions are threefold. i) Fusion in the latent space: we introduce the fusion mechanism over the latent embedding of the stocks to extract the information from the vast number of stocks. ii) Binary encoding classification: since regression tasks are challenging due to continuous value estimation, we reformulate the regression into the classification task, where the target value is converted to binary and we optimize the prediction of the value of each digit with cross-entropy loss. iii) Confidence-guided prediction and trading: we introduce the regularization loss to address market prediction uncertainty for the index prediction and design the rule-based trading policies based on the confidence. Extensive experiments across multiple stock markets and indices demonstrate that Cubic consistently outperforms state-of-the-art baselines in stock index prediction tasks, achieving superior performance on both forecasting accuracy metrics and downstream trading profitability.

Graduate School of Information Science and Technology Osaka University Osaka, Japan thanasutives@ai.sanken.osaka-u.ac.jp Ken-ichi Fukui SANKEN (The Institute of Scientific and Industrial Research) Osaka University Osaka, Japan fukui@ai.sanken.osaka-u.ac.jp The uncertainty-penalized information criterion (UBIC) has been proposed as a new model-selection criterion for data-driven partial differential equation (PDE) discovery. In this paper, we show that using the UBIC is equivalent to employing the conventional BIC to a set of overparameterized models derived from the potential regression models of different complexity measures. The result indicates that the asymptotic property of the UBIC and BIC holds indifferently.

We propose a new parameter-adaptive uncertainty-penalized Bayesian information criterion (UBIC) to prioritize the parsimonious partial differential equation (PDE) that sufficiently governs noisy spatial-temporal observed data with few reliable terms. Since the naive use of the BIC for model selection has been known to yield an undesirable overfitted PDE, the UBIC penalizes the found PDE not only by its complexity but also the quantified uncertainty, derived from the model supports' coefficient of variation in a probabilistic view. We also introduce physics-informed neural network learning as a simulation-based approach to further validate the selected PDE flexibly against the other discovered PDE. Numerical results affirm the successful application of the UBIC in identifying the true governing PDE. Additionally, we reveal an interesting effect of denoising the observed data on improving the trade-off between the BIC score and model complexity. Code is available at https://github.com/Pongpisit-Thanasutives/UBIC.

Workers who may be tempted to sell confidential corporate data should think twice about what they write in an email--an AI-based monitoring system could be watching. Tokyo-based data analysis company UBIC has developed an artificial intelligence system that scans messages for signs of potential plans to purloin data. A risk prediction function is being added to an existing product from the company that audits email for signs of activity such as price fixing. The Lit i View Email Auditor has been used in electronic discovery procedures in U.S. lawsuits. The artificial intelligence system, dubbed Virtual Data Scientist, can sift through messages and identify senders whose writing suggests they are in financial straits or disgruntled about how their employer treats them.

From robotics to deep learning and image recognition, a glimpse of science fiction-like technologies developed by the nation's artificial intelligence industry is on display at Tokyo Big Sight in Koto Ward. At the three-day AI World exhibition, a total of 15 companies exhibited cutting-edge AI technology in an attempt to realize a society which will make entertainment and business more interactive and efficient. Equipped with the company's original AI engine Kibit, the robot recommends new books based on what people have read in the past and other personal preferences. Unlike the recommendation system that is widely used by online shopping sites today, Ubic's AI technology analyzes book reviews and makes recommendations based on feedback from actual people instead of a computer, said Ubic's spokeswoman, Akane Hirose. Kibiro also has a function to guess a person's age and gender by using an in-built facial recognition camera.

TOKYO, May 16, 2016 (GLOBE NEWSWIRE) -- UBIC, Inc. (Nasdaq:UBIC) (TSE:2158), a leading provider of international litigation support and big-data analysis services, and Hearts United Group Co., Ltd. announced today that on June 1, they will launch DH-AI, a next-generation system designed to detect potential signs of risk contained in comments and other content posted on the Internet using UBIC's KIBIT artificial intelligence (AI) engine. Since UBIC started engaging in joint research with Hearts United Group in October 2015, both companies set out to develop cutting-edge debugging technologies and services using AI. By leveraging their technical expertise, the companies have made steady progress researching AI-based debugging and are now preparing the service for commercialization. In recent years, many firms have launched community and blog websites as a channel to communicate with end users, so as to promote their products and services. Increasingly, malicious comments have been posted on such websites, which often serve to incite hostile exchanges or mislead customers about products and services, resulting in damage to the companies' public images.