p redicting the d aily r eturns using f inancial q uantitative d ata and asx a nnouncements zhendong...
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PREDICTING THE DAILY RETURNS USING FINANCIAL QUANTITATIVE DATA AND ASX ANNOUNCEMENTS
Zhendong Zhao (4238 8910)
Supervisor: Mark Johnson
MOTIVATIONS – TO PREDICT THE DAILY RETURNS Previous works
Use either textual or financial quantitative features
Our workUse 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
THE DAILY RETURNS (LOG)
Where is the close price of today, and is the close price of yesterday.
For example: = 1$ = 1.2$
PROPOSED FRAMEWORK
A regression model using heterogeneous features
Textual Features(Announcements,
Financial News, etc.)
Regression Algorithms
The dailyReturns
Financial Quantitative Features(past daily returns,
past trading value, etc.)
DATASET & FEATURES
Corpus:Half year (2010) ASX announcements, 27,580 in total. 80% for developing algorithms and 20% for testing.
Features: Textual features:
o Unigram of announcements
Quantitative features:• Past daily returns (~• Stock price;• price sensitive label;• whether published in trading time;• Past trading value;• Decile by capital
EXPERIMENTS
Objectives: to find1. 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.
OBJECTIVE 1 -- THE BEST FEATURES COMBINED VS. INDIVIDUAL FEATURES
Textual Features(Announcements,
Financial News, etc.)
Quantitative Features(Panel data)
Regression Algorithms
The Stock Returns
OBJECTIVE 2 -- THE BEST TEXTUAL FEATURESUNIGRAM 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
Vs.
OBJECTIVE 3 -- THE BEST REGRESSION SOLVER EQUAL VS. UNEQUAL PENALTY FACTORS
Quantitative Features (dense)
Textual Features (sparse)
Equal penalty factors
Vs.Quantitative
Features (dense)
Textual Features (sparse)
Quantitative penalty factors
Textual penalty factors