FINANCIAL MARKET PREDICTION AND PORTFOLIO OPTIMIZATION USING FUZZY DECISION TREES
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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 FINANCIAL MARKET PREDICTION AND PORTFOLIO OPTIMIZATION USING FUZZY DECISION TREES Presentation Transcript

    • 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) • Relative Strength Indicator (RSI) • Commodity Channel Index (CCI) • Bollinger Bands • Momentum Oscillators
    • 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
    • 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
    • Training Phase TRAINING PHASE
    • TRAINING PHASE • Gather Historical Stock data • Obtain financial time series and price charts from data • Determine technical indicators and momentum oscillators from charts
    • 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 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
    • 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
    • Training Phase STEPWISE REGRESSION ANALYSIS METHOD
    • 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)
    • Training Phase FUZZY RULES AND DECISION TREES
    • 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
    • I1 I2 I3 Fuzzy Inference
    • 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
    • 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
    • Training Phase GENETIC ALGORITHMS AND REFINEMENT
    • GENETIC ALGORITHMS AND REFINEMENT • Evolving the decision tree using GA • Fitness function set as forecasting accuracy of the model Selection Crossover Mutation Replace Termination
    • 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%
    • 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