Pattern Discovery of Fuzzy Time Series for Financial Prediction IEEE Transaction of Knowledge and Da

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Pattern Discovery of Fuzzy Time Series for
Financial Prediction-IEEE Transaction of
Knowledge and Data Engineering

• Presented by Hong Yancheng
• For COMP630P, Spring 2009

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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

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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

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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

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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

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Japanese Candlestick Theory

• Four general ways of represent stock price
  fluctuation
• Original daily fluctuation
• Single close price
• Bar chart
• Candlestick chart
• More visual information

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Fuzzy Time Series

• Fuzzy time series
• Assume U is the universe of discourse, where U
  x1, x2,, xn. A fuzzy set Ai of U is defined by
• Ai µAi (x1)/x1 µAi (x2)/x2 µAi
  (xn)/xn
• where µAi (xk) is membership function of the
  fuzzy set Ai , µAi U -gt 0,1

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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

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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

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Candlestick line modeling

• Modeling the length of shadow and body
• Four linguistic variables EQUAL, SHORT, MIDDLE
  and LONG indicate the fuzzy sets of length
• Lupper (high MAX(open, close)/open) 100
• Llower (MIN(open, close) - low/open) 100
• Lbody (MAX(open, close) MIN(open,
  close)/open) 100

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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

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Candlestick line modeling

• The body color is defined by three terms BLACK,
  WHITE and CROSS
• If openclose gt 0 then body color is BLACK
• If openclose lt 0 then body color is WHITE
• If openclose 0 then body color is CROSS

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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

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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

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Trend modeling

• Following trend is derived from the variation of
  close price
• (Closetn Closet)/ Closet 100
• Closetn and Closet mean the close price at day
  tn and day t respectively
• n is a user-defined parameter

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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

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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

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Forecast procedure

• Step 1
• Calculate the variation percentage between two
  close prices.
• Use the minimum increase Imin and maximum
  increase Imax to define the universe of discourse
• UoD Imin D1, Imax D2
• E.g. Imin -5.83, Imax 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

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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 ? ux, and there is Ay in which maximum
  membership function occurs at ux, v is translate
  to Ay

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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(Ax Py )
• Statistic T Count(Py n Ax)/Count(Py) as the
  threshold to select the patterns

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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 ? (Forecasti - Actuali)2 / N

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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

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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

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Experiments and Applications

• Experiment for TAIEX index

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Experiments and Applications

• Experiment results for TAIEX

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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

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Experiments and Applications

• Experiment with 2330 (TSMC)
• The focus is to find the buying time of the stock
• The rule is IF Tgt0.5 and the following trend is
  STRONG_INCREASE or EXTREME_INCREASE THEN select
  the pattern
• 5-day return is 2.9 on average

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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

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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

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Conclusion and Discussion

• 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

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Thanks for listening
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Q A