200DMVA and 50DMVA are better predictors of stock price changes (R-squared is. Results indicate that none of PE, PB, PS, PB ratios is a good predictor of stock price changes (R-squared is less than.
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The method used to calculate the return is the difference in stock price in August 2015 and October 2014, plus any dividends paid in the time period, the result divided by the stock price in October 2014.The calculated return is regressed against the variables PE, PEG, PB, PS, 200DMVA, 50DMVA, 52WH, 52WL, TGTP. Short term returns are assumed to be share price returns for the 10 month period between October 2014 and August 2015.
Finally the yahoo finance 1 year target price (TGTP) is used as a predictor variable for the short term appreciation or depreciation of the stock price. The independent variables used are Price to Earnings (PE), Price to Sales (PS), Price to Earnings Growth (PEG), Price to Book (PB), Deviation from 200 day moving average (200D MVA), deviation from 50 day moving average (50D MVA), deviation from 52 week hi (52WH), deviation from 52 week low (52WL). This paper uses data from 500 stocks in the Standards and Poor's (S&P) 500 and checks the level of prediction of the selected variables and actual stock price movements, appreciation/depreciation against the predictor variables. Several research papers exist on whether stock market movement can be predicted or not. The best market timing strategy occurred when using 131 daily fractal data with a Sharpe/Total trades ratio of 0.31%. Findings strongly support the use of the adaptive fractal moving average model over the naïve buy-and-hold strategy where the former yielded higher annualized returns, lower annualized risk, a higher Sharpe value, although it was subject to more trades than the buy-and-hold strategy. Due to the existence of different number of long and short trades in every trading scenario, this paper proposes the use of a new measure called the Sharpe/Total trades ratio which takes into account the number of trades when evaluating the different trading strategies. The optimized strategy is evaluated against a buy-and-hold strategy over the 2000- 2015 period, using annualized returns, annualized risk and Sharpe performance measure. This study looks at the performance of an optimized fractal adaptive moving average strategy over different frequency intervals, where the Euro/US Dollar currency pair is analyzed due to the increased correlation between the Euro Index and EUR/USD, and the Dollar Index and EUR/USD over the last year compared to the last 15 years. Most technical analysis tools focus traditionally on the simple and exponential moving average technique.
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