FINANCIAL MARKET PREDICTION AND PORTFOLIO OPTIMIZATION USING FUZZY DECISION TREES
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Transcript of FINANCIAL MARKET PREDICTION AND PORTFOLIO OPTIMIZATION USING FUZZY DECISION TREES
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ABHRA BASAK
KRISHNA KARNANI
FINANCIAL MARKET PREDICTION AND PORTFOLIO OPTIMIZATION USING FUZZY
DECISION TREES
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SECURITY SCREENING AND SELECTIONSecurity Screening and Selection
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Stock Classification
Stock Ranking
Stock Selection
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STOCK CLASSIFICATION
• Security Evaluation using Technical Indicators
• Moving Average Convergence Divergence (MACD)
• Relative Strength Indicator (RSI)
• Commodity Channel Index (CCI)
• Bollinger Bands
• Momentum Oscillators
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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
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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
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TRAINING PHASETraining Phase
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TRAINING PHASE
• Gather Historical Stock data
• Obtain financial time series and price charts from data
• Determine technical indicators and momentum oscillators from charts
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Historical Data
Financial Time Series
Price Charts
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PIECEWISE LINEAR REPRESENTATION METHOD
Training Phase
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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
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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
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STEPWISE REGRESSION ANALYSIS METHOD
Training Phase
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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 F-test statistical value (takes into account the PLR)
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FUZZY RULES AND DECISION TREESTraining Phase
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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
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Fuzzy Inference
I3
I2I1
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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
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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 BBupper then market is BULLISH
• If MACD is LOW and RSIupper goes HIGH to LOW, then SELL
• If MACD is HIGH and CCIupper goes LOW to HIGH, then BUY
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GENETIC ALGORITHMS AND REFINEMENT
Training Phase
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GENETIC ALGORITHMS AND REFINEMENT
• Evolving the decision tree using GA
• Fitness function set as forecasting accuracy of the model
Selection
Crossover
Mutation
Replace
Termination
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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%
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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