1. P REDICTING THE D AILY R ETURNS USING F INANCIAL Q UANTITATIVE D ATA AND ASX A NNOUNCEMENTS Zhendong Zhao (4238 8910) Supervisor: Mark Johnson

2. O UTLINE Motivations Framework & formulation Dataset & features Experiments

3. M OTIVATIONS – T O PREDICT THE DAILY RETURNS Previous works Use either textual or financial quantitative features Our work Use heterogeneous features (both textual and financial quantitative features) Textual Features (Announcements, Financial News, etc.) Financial Quantitative Features (past daily returns, past trading value, etc.) The daily Returns

4. E XAMPLES

5. T HE DAILY RETURNS ( LOG )

6. O UTLINE Motivations Framework & formulation Dataset & features Experiments

7. P ROPOSED F RAMEWORK A regression model using heterogeneous features Textual Features (Announcements, Financial News, etc.) Regression Algorithms The daily Returns Financial Quantitative Features (past daily returns, past trading value, etc.)

8. F ORMULATION

9. R EGRESSION S OLVER

10. O UTLINE Motivations Framework & formulation Dataset & features Experiments

11. D ATASET & F EATURES

12. O UTLINE Motivations Framework & formulation Dataset & features Experiments

13. E XPERIMENTS Objectives: to find 1. The best features; Combined vs. individual features; 2. The best textual features; Unigram vs. sentiment features; 3. The best regression solver; Equal vs. unequal penalty factors on quantitative features.

14. O BJECTIVE 1 -- T HE BEST FEATURES C OMBINED VS. INDIVIDUAL FEATURES Textual Features (Announcements, Financial News, etc.) Quantitative Features (Panel data) Regression Algorithms The Stock Returns

15. O BJECTIVE 1 -- T HE BEST FEATURES C OMBINED VS. INDIVIDUAL FEATURES

16. O BJECTIVE 2 -- T HE BEST TEXTUAL FEATURES U NIGRAM VS. SENTIMENT FEATURES Unigram features (all words in corpus) Huge size of vocabulary (100,000 features) but sparse for each document Sentiment features (negative, positive, uncertainty) Smaller size of features But may loss information

17. O BJECTIVE 2 -- T HE BEST TEXTUAL FEATURES U NIGRAM VS. SENTIMENT FEATURES

18. O BJECTIVE 3 -- T HE BEST REGRESSION SOLVER E QUAL VS. UNEQUAL PENALTY FACTORS Quantitative Features (dense) Textual Features (sparse) Quantitative Features (dense) Textual Features (sparse)

19. O BJECTIVE 3 -- T HE BEST REGRESSION SOLVER E QUAL VS. UNEQUAL PENALTY FACTORS

20. Q & A Thanks!