Knowledge Discovery and Data Visualization for Taiwan Stock Market: Using FScore Analysis This study examines the performance of stocks investment portfolio by using accounting information. The sample is selected from Taiwan Economic Journal to test FSCORE cooperating with Dividendtoprice ratio, Earningstoprice Ratio and BooktoMarket respectively. The sample period is from May, 2008 to May, 2018. The findings of this study are as follows. First, if we choose Dividendtoprice ratio, Earningstoprice Ratio or BooktoMarket to distinguish from the value and growth portfolio, the value portfolio is significantly better than the growth one. Second, if we choose FSCORE to distinguish from high and low score, the high score portfolio is significantly better than the low score one. The result showed that when the investor use Dividendtoprice ratio, Earningstoprice Ratio or BooktoMarket together with fundamental financial information, they could become more profitable or more effectively improve the performance.

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.