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We propose a novel rule-based mechanism that identifies Horizontal Support And Resistance (HSAR) levels. The novelty of this system resides in the manner it encloses principles, found in well known technical manuals, used for the identification via visual assessment. The drawing of these levels derives from historical locals, rather than denoting support (resistance) levels from the lowest (highest) price levels of precedent constant time intervals. We further proceed in evaluating whether these levels are efficient trend-reversal predictors, and if they can generate systematic abnormal returns. The dataset used includes adjusted for dividends and splits, daily closing prices of stocks listed on National Association of Securities Dealers Automated Quotation (NASDAQ) and New York Stock Exchange (NYSE) for the last 2 decades. Our results are aligned with the efficient market hypothesis. More concretely, support levels outperform resistance ones in predicting trend interruptions but they fail to generate excess returns when they are compared with simple buy-and-hold strategies.
