![]() ![]() Therefore Financial Markets appear as a good playground where models and theories can be tested. In fact since twenty years there exists a huge amount of high frequency data from stock exchanges which permit to perform experimental analyses as in Natural Sciences. In the last two decades Financial Markets have appeared as natural candidates for this inter-disciplinary application of methods deriving from Physics because a systematic approach to the issues set by this field can be undertaken. Physical and mathematical methods derived from Complex Systems Theory and Statistical Physics have been shown to be effective tools 1, 2, 3, 4 to provide a quantitative description and an explanation of many social 5, 6, 7 and economical phenomena 8, 9, 10, 11. Such an effect is a quantitative evidence of the so-called self-fulfilling prophecy, that is the self-reinforcement of agents' belief and sentiment about future stock prices' behavior. In fact we show that prices more likely re-bounce than cross these values. Then we show that memory effects in the price dynamics are associated to these selected values. We first develop a criterion to detect the potential values of supports and resistances. In particular, we focus on a specific figure called supports and resistances. ![]() Quantitative technical analysis pdf series#In this paper we investigate if technical trading produces detectable signals in price time series and if some kind of memory effects are introduced in the price dynamics. On the contrary, it is well-known that technical traders exist and operate on different time scales. According standard economical theories these strategies should not be used because they cannot be profitable. Technical trading represents a class of investment strategies for Financial Markets based on the analysis of trends and recurrent patterns in price time series. ![]()
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