1. Pattern Discovery of Fuzzy Time Series for Financial Prediction -IEEE Transaction of Knowledge and Data Engineering Presented by Hong Yancheng For COMP630P, Spring 2009

2. Outline Introduction and target problem Background knowledge and related work Modeling the candlestick pattern Candlestick pattern for financial prediction Experiments and applications Conclusion and Discussion

3. Problems with existing stock prediction tools A lot of tools exists for predicting stock price –Artificial Neural Network, SVM, NeuroFuzzy, Naïve Bayes and so on Three major problems with these tools –Training process is nontrivial and training result cannot be further used for other target –Prediction results are incomprehensible Hard for user to tuning the parameters –Gap exists between prediction result and investment decision Improving prediction VS buy/sell decision

4. Target problem Data preprocessing are needed before applying various of techniques –Data mining, machine learning & pattern recognition Good knowledge representation method can assist investors Knowledge-based method to transfer financial data to comprehensible rules and visual patterns

5. Outline Introduction and target problem Background knowledge and related work Modeling the candlestick pattern Candlestick pattern for financial prediction Experiments and applications Conclusion and Discussion

6. Japanese Candlestick Theory Four general ways of represent stock price fluctuation –Original daily fluctuation –Single close price –Bar chart –Candlestick chart More visual information

7. Fuzzy Time Series Fuzzy time series Assume U is the universe of discourse, where U = {x 1, x 2,…, x n }. A fuzzy set A i of U is defined by A i = µ A i (x 1 )/x 1 + µ A i (x 2 )/x 2 + … + µ A i (x n )/x n where µ A i (x k ) is membership function of the fuzzy set A i, µ A i : U -> [0,1]

8. Outline Introduction and target problem Background knowledge and related work Modeling the candlestick pattern Candlestick pattern for financial prediction Experiments and applications Conclusion and Discussion

9. Fuzzy candlestick pattern A fuzzy candlestick pattern is composed of related fuzzy candlestick lines in a period A fuzzy candlestick line has seven parts –Sequence, open style, close style, upper shadow, body, body color and lower shadow –Sequence defines the location of the candlestick –Open/Close style model the relationship between consecutive candlestick lines

10. Candlestick line modeling Modeling the length of shadow and body Four linguistic variables EQUAL, SHORT, MIDDLE and LONG indicate the fuzzy sets of length L upper = ([high – MAX(open, close)]/open) * 100 L lower = ([MIN(open, close) - low]/open) * 100 L body = ([MAX(open, close) – MIN(open, close)]/open) * 100

11. Candlestick line modeling The membership function of four fuzzy sets are shown as follows –The range is set to (0, 14) because the Taiwan stock price limitation

12. Candlestick line modeling The body color is defined by three terms BLACK, WHITE and CROSS –If open–close > 0 then body color is BLACK –If open–close < 0 then body color is WHITE –If open–close = 0 then body color is CROSS

13. Candlestick line modeling The open/close style is another important feature Five linguistic variables LOW, EQUAL_LOW, EQUAL, EQUAL_HIGH, HIGH indicate fuzzy sets of open/close style

14. Trend modeling Two linguistic variables are used to model the trends before and after the candlestick pattern previous trend is represented by weekly candlestick line Six fuzzy sets are used to define the trend –CROSS, EQUAL, WEAK, NORMAL, STRONG, and EXTREME BEARISH and BULLISH define the body color

15. Trend modeling Following trend is derived from the variation of close price (Close t+n – Close t )/ Close t * 100 –Close t+n and Close t mean the close price at day t+n and day t respectively –n is a user-defined parameter

16. Outline Introduction and target problem Background knowledge and related work Modeling the candlestick pattern Candlestick pattern for financial prediction Experiments and applications Conclusion and Discussion

17. Three major pattern recognition problems Sensing problem –Measured values are open, close, high, low Feature extraction problem –Fuzzy candlestick patterns Pattern classification problem –Can be determined by user

18. Forecast procedure Step 1 –Calculate the variation percentage between two close prices. –Use the minimum increase I min and maximum increase I max to define the universe of discourse –UoD = [I min –D 1, I max +D 2 ] –E.g. I min = -5.83, I max = 7.66 then UoD = [-6, 8] Step 2 –Partition UoD into several intervals –E.g. partition [-6, 8] into seven intervals [-6, -4], [- 4, -2], …, [6, 8]

19. Forecast procedure Step 3 –Define fuzzy sets on the UoD associate with the intervals in step 2 Step 4 –Fuzzifying the values calculated in step 1 –If v ∈ u x, and there is A y in which maximum membership function occurs at u x, v is translate to A y

20. Forecast procedure Step 5 –Calculate all the candlestick patterns Step 6 –Refine extracted patterns, identify important attributes Step 7 –Select pattern for forecasting based on probability P(A x |P y ) –Statistic T = Count(P y ∩ A x )/Count(P y ) as the threshold to select the patterns

21. Forecast procedure Step 8 –Forecast the trend follows –Rule 1: test pattern not found, set variation v to 0 –Rule 2: test pattern found, set variation v to arithmetic average of midpoints of matched patterns –Forecast = close + close * v Step 9 –Evaluate the forecasting –MSE = ∑ (Forecast i - Actual i ) 2 / N

22. Outline Introduction and target problem Background knowledge and related work Modeling the candlestick pattern Candlestick pattern for financial prediction Experiments and applications Conclusion and Discussion

23. Experiments and Applications The experiments are conducted based on TAIEX index from 2004-01-02 to 2005-01-31 and 2330(TSMC) from 1997-10-23 to 2002- 12-25

24. Experiments and Applications Experiment for TAIEX index

25. Experiments and Applications Experiment results for TAIEX

26. Problems with existing stock prediction tools Three major problems with these tools –Training process is nontrivial and training result cannot be further used for other target –Prediction results are incomprehensible Hard for user to tuning the parameters –Gap exists between prediction result and investment decision Improving prediction VS buy/sell decision

27. Experiments and Applications Experiment with 2330 (TSMC) –The focus is to find the buying time of the stock –The rule is: IF T>0.5 and the following trend is STRONG_INCREASE or EXTREME_INCREASE THEN select the pattern –5-day return is 2.9% on average

28. Experiments and Applications Fuzzy modifier can be implemented to help user tuning the parameters –ABOVE, BELOW, PLUS, VERY, EXTREMELY, MORE_OR_LESS, SOMEWHAT, and NOT –E.g. STRONG_BEARISH and EXTREME_BEARISH can be merged by ABOVE STRONG_BEARISH

29. Outline Introduction and target problem Background knowledge and related work Modeling the candlestick pattern Candlestick pattern for financial prediction Experiments and applications Conclusion and Discussion

30. Pros –Knowledge-based method to represent the financial time series and to facilitate the knowledge discovery –Comprehensible, computable and visual –Can be used directly or as data preprocess Cons –Time complexity –How many candlestick lines for a pattern

31. Thanks for listening

32. Q & A