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TECHNICAL ANALYSIS

TECHNICAL ANALYSIS. Charts Trend Theory Support and Resistance Moving Averages Elliot wave theory Indicators Chart Patterns Trading Volume. Chart Types : Line charts Bar charts (open-high-low-close bars) Candlestick charts (also, volume-adjusted bar size)

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TECHNICAL ANALYSIS

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  1. TECHNICAL ANALYSIS Charts Trend Theory Support and Resistance Moving Averages Elliot wave theory Indicators Chart Patterns Trading Volume

  2. Chart Types: • Line charts • Bar charts (open-high-low-close bars) • Candlestick charts (also, volume-adjusted bar size) • Point and Figure Charts (X up, O down) Trend Theory: A local maximum is called High, and a local minimum is called Low. Uptrend = higher High’s and higher Low’s Downtrend = lower High’s and lower Low’s “Trend is your friend”

  3. Trendlines: Uptrendline: connect consecutive Low’s once uptrend is confirmed. Downtrendline: connect consecutive High’s once downtrendis confirmed. Channel Lines: parallels of trendlines. Support lines: connect consecutive Low’s when no uptrend is evident. Resistance lines: connect consecutive High’s when no downtrend is evident.

  4. SUPPORT: A level where buyers are expected to dominate sellers. RESISTANCE: A level where sellers are expected to dominate buyers. What acts as S or R ? • Previous H’s and L’s • Uptrendlines act as S, Downtrendlines act as R • Upchannellines act as R, Downchannellines act as S • S and R lines • Moving Averages • Fibonacci Retracement Levels

  5. MOVING AVERAGES Simple, Exponential, Weighted. Use simple. Moving Average Crossover Rules: Buy when the price (which is MA(1)) or MA(s) crosses the MA(l) up from below. Sell when the price (which is MA(1)) or MA(s) crosses the MA(l) down from above.

  6. A strategic question: Can you predict whether a S or R will hold or be breached? Yes, to some extent. The significance of a S or R depends on: 1) how many times it has been tested and has held 2) for how long it has held 3) slope 4) chart frequency The strength of a S or R depends on: 1) previous move before the test 2) type, slope 3) chart frequency

  7. INDICATORS Derived from the price, using complex mathematical calculations. Help visual decision making. MACD: practically, MACD(l,s,t) = EMA(s)−EMA(l) where the trigger line is a t-period mov.avg. of the MACD, Stochastic Oscillator: %K = (C – Ln) / (Hn−Ln) %D = MA(%K) %D is the t-period mov.avg. of %D and acts as the trigger line

  8. CHART PATTERNS Reversal Patterns • Head-and-Shoulder and Inverse H-S • Double top and double bottom • Diamond Consolidation Patterns • Ascending Triangle, Descending Triangle • Flag, Pennant • Rectangle • Rising Wedge, Falling Wedge

  9. The Sources of TA’s predictive power • Asymmetric private information • (short-lag positive and long-lag negative) autocorrelation in macroeconomic data • Gradual revision of beliefs Underreaction and overreaction 4) Self-fulfilling prophecy

  10. How to benefit from TA? * In social sciences there are no absolute truths. So, the efficacy of TA is not stable. * For an experienced professional trader, it is at least: a tool to read the mind of others, to mechanize buy-sell decisions, to detect asymmetric information. A smart-technical system can make net money on the average (combine it with “buy-low-sell-high” principle). TA is an inevitable part of full toolkit. * For an amateur investor: it is probably a cheaper and usually better guide than most alternative guides (provided that it is employed properly recalling its limitations). * For a corporate financial manager, it is a complementary in exploiting financial information or maximizing market value of your company.

  11. ACADEMIC WORK ON RETURN PREDICTABILITY Two Tasks in Investing: Timing and Stock Selection Strategy Direction: Momentum Strategies:Positive Feedback Trading Contrarian Strategies: Negative Feedback Trading Autocorrelation in Returns: Rt = 0 + 1Rt-1 + 2Rt-2 + 3Rt-3 + …+et

  12. History of Academic Thinking on Predictability in Financial Markets 1870-1920: Public participation in stock markets begins, TA develops even before Financial analysis. 1930-50: The idea of efficient (unpredictable) markets emerges. 1952: Markowitz’ portfolio theory 1964: CAPM (Sharpe, Merton) 1970: Efficient Markets Theory (Fama) 1970-1980’s: Efficient Markets Theory firmly dominates academic thinking. 1985: the first studies on Behavioral Finance (1979 Kahneman) 1990’s: Rain of evidence against efficient markets Hot debate between proponents and opponents of EMT 2000’s: Behavioral Finance gains widespread acceptance However, markets are more unpredictable. 2008: New Paradigm: Reflexivity

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