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FORECASTING FINANCIAL MARKETS

FORECASTING FINANCIAL MARKETS. 1. Forecasting and Efficient Markets Theory 2. Technical Analysis ( 2 session tutorial with applications ) 3. Behavioral Finance 4. Empirical (Scientific) Research on Return Predictability. The key variable we want to forecast is : future returns.

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FORECASTING FINANCIAL MARKETS

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  1. FORECASTING FINANCIAL MARKETS 1. Forecasting and Efficient Markets Theory 2. Technical Analysis (2 session tutorial with applications) 3. Behavioral Finance 4. Empirical (Scientific) Research on Return Predictability

  2. The key variable we want to forecast is: future returns Return in period t : (% return) Return per trade: • adjust for interim cash flows: where V is the value (price+any cashflow) • Average % return is biased upward, hence we should use log return in statistical analysis:

  3. In financial markets, there are 3 approaches to forecasting future returns: 1) Fundamental Analysis (FA): Analyzing all possible factors that affect the value of a financial asset 2) Technical Analysis (TA) (chartism): Analyzing past price action (using price/time charts) in order to forecast future price changes. 3) Efficient Markets Theory (academic view): Future returns are unpredictable, both Fundamental Analysis and Technical Analysis are useless.

  4. EFFICIENT MARKETS THEORY (EMT) The same core idea of “efficient markets” can be expressed in 3 ways: • Security prices in financial markets reflect all available information. • Future abnormal returns are unpredictable because they result from unexpected information. • It is not possible to earn persistent positive abnormal returns using any information set.

  5. Abnormal return: Part of the realized return that is not compensation for expected inflation, timepreference andsystematic risks. Ri,tABN = Ri,t – E(Ri,t) Ri,t= E(Ri,t) + ui,t Et-1(Ri,t) = E(e) + time preference + risk premium = Rf+ risk premium(i) Risk premium is security specific and estimated by an asset pricing model (for example, according to CAPM: rp(i) = i(Rm–Rf). “Beating the market” means earning positive abnormal returns. Random Walk Theory: Ri,tABN i.i.d.(0, ) Testing EMT: H0: E(Ri,tABN  It-1) = 0 where t-1 is the information set (available at the end of the previous period) utilized to predict future returns.

  6. Based on 3 possible information sets It-1, Fama (1970) defines 3 forms of market efficiency: weak: information about past prices is useless semi-strong: publicly available fundamental information is useless. strong: private information is useless. If all information is instantaneously incorporated intomarket prices, then one should not be able to earn positive abnormal returns in period t using any information set It-1. The Prescription of EMT: Investors should not try to predict and beat the market, rather they should hold well-diversified portfolios (with zero unique risk) in line with their risk preferences.

  7. Empirical Evidence on Return Distributions: • Empirical evidence rejects the hypothesis of normal distribution for abnormal returns. • Two main findings are excess kurtosis and volatility clustering. Excess Kurtosis: Observations of extreme returns. Volatility clustering: High (low) volatility periods are followed by high (low) volatility. This inspired ARCH models. Hence, the distribution is not i.i.d., and we allow changing variance of returns t2. • However, these do not violate EMT as long as the mean abnormal return is zero.

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