Practical Machine Learning Approach to Capture the Scholar Data Driven Alpha in AI Industry AI technologies are helping more and more companies leverage their resources to expand business, reach higher financial performance and become more valuable for investors. However, it is difficult to capture and predict the impacts of AI technologies on companiesxe2x80x99 stock prices through traditional financial factors. Moreover, common information sources such as companyxe2x80x99s earnings calls and news are not enough to quantity and predict the actual AI premium for a certain company. In this paper, we utilize scholar data as alternative data for trading strategy development and propose a practical machine learning approach to quantity the AI premium of a company and capture the scholar data driven alpha in the AI industry. First, we collect the scholar data from the Microsoft Academic Graph database, and conduct feature engineering based on AI publication and patent data, such as conferencejournal publication counts, patent counts, fields of studies and paper citations. Second, we apply machine learning algorithms to weight and rebalance stocks using the scholar data and traditional financial factors every month, and construct portfolios using the xe2x80x9cbuyandholdlong onlyxe2x80x9d strategy. Finally, we evaluate our factor and portfolio in terms of factor performance and portfolioxe2x80x99s cumulative return. The proposed scholar data driven approach achieves a cumulative return of 1029.1% during our backtesting period, which significantly outperforms the Nasdaq 100 indexxe2x80x99s 529.5% and Su0026P 500xe2x80x99s 222.6%. The traditional financial factors approach only leads to 776.7%, which indicates that our scholar data driven approach is better at capturing investment alpha in AI industry than traditional financial factors.

A DivideandConquer Framework for Attentionbased Combination of Multiple Investment Strategies In order to maximize the profit, investors usually examine diverse investment strategies based on various information when constructing their portfolios. However, they can hardly always construct a profitable portfolio due to the dynamic performance of the strategies and the dynamics of the market state along with the time. To address this challenge, we propose a 2Dattention framework to capture the dynamics of the above two factors in this paper. To capture the dynamic of the first factor, we design a strategywise attention model to dynamically combine multiple strategies according to their respective effectiveness. To deal with the second factor, we design a divideandconquer framework to learn multiple strategywise attention models, which categorizes the whole market periods into a few stable states and jointly learn respective models for each state and then build a statewise attention model to combine them dynamically for the final task. Extensive experiments on realworld data demonstrate that our 2Dattention framework can significantly outperform several widelyused baselines.