FINANCIAL MARKET PREDICTION AND
PORTFOLIO OPTIMIZATION USING FUZZY
DECISION TREES
ABHRA BASAK
KRISHNA KARNANI
Security Screening and Selection

SECURITY SCREENING AND SELECTION
Stock
Classification

Stock
Selection
Stock
Ranking
STOCK CLASSIFICATION
•

Security Evaluation using Technical Indicators
• Moving Average Convergence Divergence (MACD)
• Re...
STOCK RANKING
•

Corporate Evaluation using Fundamental Indicators
• Profitability – Returns on Assets and Equity
• Manage...
STOCK SELECTION
•

Select 3 different stocks – one each showing uptrend, downtrend, and steady
state

•

Attempt to displa...
Training Phase

TRAINING PHASE
TRAINING PHASE
•

Gather Historical Stock data

•

Obtain financial time series and price charts from data

•

Determine t...
Historical
Data

Financial
Time Series

Price Charts
Training Phase

PIECEWISE LINEAR REPRESENTATION
METHOD
PIECEWISE LINEAR REPRESENTATION
METHOD
•

Mining of trading points
• Points of begin (P) and end (Q) on a term of closing ...
PIECEWISE LINEAR REPRESENTATION
METHOD
•

Trading signals transformation
• Convert PLR segments into trading signals
• Upt...
Training Phase

STEPWISE REGRESSION ANALYSIS
METHOD
STEPWISE REGRESSION ANALYSIS METHOD
•

Data Preprocessing for Feature Selection
• Used to select important factors which a...
Training Phase

FUZZY RULES AND DECISION TREES
FUZZY RULES AND DECISION TREES
•

Fuzzification
• Set of indicators selected by SRA fed into data fuzzification module
• T...
I1

I2
I3

Fuzzy Inference
FUZZY RULES AND DECISION TREES
•

Defuzzification
• Output from fuzzy inference scheme is transformed into a meaningful
de...
FUZZY RULES AND DECISION TREES
•

Examples of Fuzzy decision rules
• If MACD above signal line, then BUY
• If RSI increase...
Training Phase

GENETIC ALGORITHMS AND
REFINEMENT
GENETIC ALGORITHMS AND REFINEMENT
•

Evolving the decision tree using GA

•

Fitness function set as forecasting accuracy ...
RESULT
•

Decision of Stock price and transaction will be determined by the decision
tree on the basis of trends and indic...
CREDITS
•

A Collaborative Trading Model by Support Vector Regression and TS Fuzzy
Rule for Daily Stock Turning Points Det...
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FINANCIAL MARKET PREDICTION AND PORTFOLIO OPTIMIZATION USING FUZZY DECISION TREES

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FINANCIAL MARKET PREDICTION AND PORTFOLIO OPTIMIZATION USING FUZZY DECISION TREES

  1. 1. FINANCIAL MARKET PREDICTION AND PORTFOLIO OPTIMIZATION USING FUZZY DECISION TREES ABHRA BASAK KRISHNA KARNANI
  2. 2. Security Screening and Selection SECURITY SCREENING AND SELECTION
  3. 3. Stock Classification Stock Selection Stock Ranking
  4. 4. STOCK CLASSIFICATION • Security Evaluation using Technical Indicators • Moving Average Convergence Divergence (MACD) • Relative Strength Indicator (RSI) • Commodity Channel Index (CCI) • Bollinger Bands • Momentum Oscillators
  5. 5. STOCK RANKING • Corporate Evaluation using Fundamental Indicators • Profitability – Returns on Assets and Equity • Management Performance – Assets and Inventories Turnover • Capital Structure – Assets to Liabilities, Liabilities to Equity • Sales, Profit, Transaction Volume, Marginal Account
  6. 6. STOCK SELECTION • Select 3 different stocks – one each showing uptrend, downtrend, and steady state • Attempt to display different profit making strategies in stock trading • All subsequent processes are applied on these 3 stocks
  7. 7. Training Phase TRAINING PHASE
  8. 8. TRAINING PHASE • Gather Historical Stock data • Obtain financial time series and price charts from data • Determine technical indicators and momentum oscillators from charts
  9. 9. Historical Data Financial Time Series Price Charts
  10. 10. Training Phase PIECEWISE LINEAR REPRESENTATION METHOD
  11. 11. PIECEWISE LINEAR REPRESENTATION METHOD • Mining of trading points • Points of begin (P) and end (Q) on a term of closing prices in the ascending order of dates • Point K having longest straight line distance between P and Q • K is the turning point resulting in 2 segments. • Apply recursively in the resulting segments till minimum distance threshold
  12. 12. PIECEWISE LINEAR REPRESENTATION METHOD • Trading signals transformation • Convert PLR segments into trading signals • Uptrend segment • I <= L/2 : 0.5 – (I – 1) / L • I <= L/2 : I / L – 0.5 • Downtrend segment • I <= L/2 : 0.5 + (I – 1) / L • I <= L/2 : 1.5 – I / L • Ranges from 0 to 1 • Can also act as a potential technical indicator
  13. 13. Training Phase STEPWISE REGRESSION ANALYSIS METHOD
  14. 14. STEPWISE REGRESSION ANALYSIS METHOD • Data Preprocessing for Feature Selection • Used to select important factors which affect forecasting results • Sort out affecting variables to leave more influential ones in the model • Adding or removing factors to find the fittest combination, decided by Ftest statistical value (takes into account the PLR)
  15. 15. Training Phase FUZZY RULES AND DECISION TREES
  16. 16. FUZZY RULES AND DECISION TREES • Fuzzification • Set of indicators selected by SRA fed into data fuzzification module • This module transforms technical indicators to fuzzy values • Adopt triangular and trapezoidal membership functions for the module • Output decision is obtained as a Gaussian membership function
  17. 17. I1 I2 I3 Fuzzy Inference
  18. 18. FUZZY RULES AND DECISION TREES • Defuzzification • Output from fuzzy inference scheme is transformed into a meaningful decision • Implemented using the popular Center of Area (COA) methods in the Fuzzy Control Module’s algorithm
  19. 19. FUZZY RULES AND DECISION TREES • Examples of Fuzzy decision rules • If MACD above signal line, then BUY • If RSI increases above 70, then market is BULLISH • If Price increases above BB upper then market is BULLISH • If MACD is LOW and RSI upper goes HIGH to LOW, then SELL • If MACD is HIGH and CCI upper goes LOW to HIGH, then BUY
  20. 20. Training Phase GENETIC ALGORITHMS AND REFINEMENT
  21. 21. GENETIC ALGORITHMS AND REFINEMENT • Evolving the decision tree using GA • Fitness function set as forecasting accuracy of the model Selection Crossover Mutation Replace Termination
  22. 22. RESULT • Decision of Stock price and transaction will be determined by the decision tree on the basis of trends and indicators • Uptrend if hike in price is greater than 0.5% • Downtrend if fall in price is less than -0.5% • Steady state / hold if y is between -0.5% and 0.5%
  23. 23. CREDITS • A Collaborative Trading Model by Support Vector Regression and TS Fuzzy Rule for Daily Stock Turning Points Detection – Wu, Chang, Chang, Zhang • Evolving and Clustering Fuzzy Decision Trees for Financial Time Series Data Forecasting – Lai, Fan, Huang, Chang • A Fuzzy Logic Based Trading System – Chueng, Keymak • Nigerian Stock Market Investment using a Fuzzy Strategy – Neenwi, Kabari, Asagba • Common Stock Portfolio Selection: A multiple criteria Decision making Methodology and an application to the Athens Stock Exchange – Xidonas, Askounis, Psarras

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