Measuring asymmetric information and error with technical analysis indicator based on fuzzy logic

This thesis examines three widely used oscillators, the Exponential Moving Average which provides an average price, the Moving Average Convergence Divergence which is a momentum indicator that shows the strength of price movement in a market, and the Relative Strength Index which aims to indicate overbought or oversold market condition in relation to recent price levels. We must also understand indices, which include daily closing values which are compared for their asymmetric information and error, and help for better understanding on price movements of financial markets by the right decision percentage of the same indicator signal, which comes from different trading strategies. To compare the values of oscillators equally, we define output values of these three oscillators by giving terms such as Buy-Buy, Buy-Sell, Sell-Buy and Sell-Sell. However, outputs of these three oscillators were different from each other most of the time, for a given period. The uncertainty which emerges in this case can therefore be concluded by using the Fuzzy Logic approach, which sets for one resolution instead of three to solve the problem. In addition, Fuzzification also results in a better ability to forecast and decide the timing of stock market entry and exit. In order to test this approach's validation and examine the correlation between oscillators and indices, one could compare the signals that have been generated by Fuzzification and the signals that have been generated individually from technical indicators.