analysis of technical trends
DESCRIPTION
Analysis of Technical Trends. Ryan Weikert. Asset Valuation. Pricing, Buying , and Selling of Assets Methods of Appraisal What stocks, when? Fundamental Analysis Technical Analysis. Fundamental Analysis. Quality Sector/Industry Financial Statements Peer and Historical Comparisons - PowerPoint PPT PresentationTRANSCRIPT
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Analysis of Technical Trends
Ryan Weikert
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Asset Valuation
• Pricing, Buying, and Selling of Assets• Methods of Appraisal• What stocks, when?• Fundamental Analysis• Technical Analysis
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Fundamental Analysis
• Quality• Sector/Industry• Financial Statements• Peer and Historical Comparisons• Earnings• Management• Fair Value
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Technical Analysis
• Study trends and the state of the market• Behavioral Indicators• Anticipate price movements
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Examples
• Support/Resistance Levels• Moving Averages and Momentum• Overbought and Oversold Signals• Buy and Sell
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Is it possible to earn greater profits using pure technical analytics?
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Process
• Generate Random Walks• Geometric Brownian Motion• Apply certain technical indicators to these
random walks• Generate buy and sell signals• Record asset price at the time of those signals• Calculate profit
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Moving Average Convergence Divergence (MACD)
• Signals upward and downward momentum• Exponential Moving Average (EMA)
• Xi=(Pi - Xi-1)*[2/(# of periods + 1)]+Xi-1
• MACD=12 day EMA – 26 day EMA• Signal Line=9 day EMA MACD• MACD Histogram = MACD – Signal Line• Buy if MACD Histogram > 0 (upward momentum)• Sell if MACD Histogram < 0 (downward momentum)
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Apple MACD Chart
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MACD Results
• E(profit) ≈ ½ (mu)(S0)
• SD ≈ 2/3 (sigma)(S0)
• If mu = .08, S0 = 100, sigma=.3– E(profit)=3.95– SD = 22.15
• ½ expected profit of a long position• Only slightly less risk
• Inferior
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DistributionHistogram of profvec
profvec
Frequency
-50 0 50 100 150
0500
1000
1500
2000
2500
3000
3500
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Relative Strength Index (RSI)
• Overbought and Oversold signals• RSI = 100 – 100/(1+Relative Strength)• Relative Strength = Average Gain/Average Loss• Agi = [Agi-1 * 13 + current gain] / 14
• 0<RSI<100• Oversold if RSI <30 Buy• Overbought if RSI >70 Sell
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BBVA RSI Chart
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RSI Results – Perfect Timing
S0 100 100 100 100mu 0.08 0.04 -0.02 0.08sigma 0.3 0.3 0.3 0.1mean 29.08 29.17 28.63 9.28sd 21.37 20.97 20.64 7.42lb 28.66 28.66 28.23 9.13ub 29.50 29.50 29.04 9.43
E(profit) ≈ sigma* S0
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DistributionHistogram of profit
profit
Frequency
-50 0 50 100 150
0500
1000
1500
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RSI Results – Quick Trigger
E(profit) ≈ 0
S0 100 100 100 100mu 0.08 0.02 -0.02 0.08sigma 0.3 0.3 0.3 0.1mean -1.32 -0.02 0.23 -2.68sd 25.69 23.68 22.46 9.836lb -1.83 -0.48 -0.20 -2.87ub -0.82 0.44 0.67 -2.49
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Distribution
Histogram of profit
profit
Frequency
-200 -150 -100 -50 0 50
01000
2000
3000
4000
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Overall RSI Results
• Further technical analysis would be required• Impossible to receive returns of Perfect
Investor– If it were possible, technical indicators wouldn’t be
needed• Expected profit won’t be as high• Closer to 0
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Commodity Channel Index (CCI)
• Overbought and Oversold signals• CCI = (Typical Price – 20 day SMA of TP) (.015 * Mean Deviation)TP = (High + Low + Close)/3
Overbought if CCI > 100 SellOversold if CCI < -100 Buy
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Apple CCI Chart
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Results
• Similar to RSI
• Perfect Timing– E(profit) ≈ 2*sigma*S0
– SD ≈ 2/3 * sigma• Quick Trigger– E(profit) ≈ 0– SD ≈ 2/3 * sigma
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Histogram of profit
profit
Frequency
-150 -100 -50 0 50 100
01000
2000
3000
Histogram of profit
profit
Frequency
0 50 100 150 200 250
01000
2000
3000
4000
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CCI Conclusion
• Still impossible to attain perfect timing• Expected profit likely to be closer to 0
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Conclusion
• Technical analysis• Geometric Brownian Motion• Not useful
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Upcoming
• What must be true for these indicators to yield a statistically significant profit?
• Other Distributions– How are real world returns distributed?– How do they vary in different situations?
• Other Processes– Autoregressive process– Lévy Process
• Applied to actual graphs
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Questions?