Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/18704
Title: Evaluation of moving average convergence divergence MACD technique for stock price trend determination and assessment of its applicability to based on real data set
Authors: Adatia, Chirag
Meshram, Nilesh
Keywords: Moving Average Convergence Divergence Technique
Issue Date: 2007
Publisher: Indian Institute of Management Ahmedabad
Series/Report no.: SP;001491
Abstract: MACD stands for Moving Average Convergence Divergence,is a technical analysis indicator extensively used in financial markets either as a trend or momentum indicator. In this technique instrument’s 26- day and 12-day exponential moving average (EMA) and a 9-day EMA of MACD are used.The use of moving averages ensures that the indicator will eventually follow the movements of the underlying security.Thus,as a momentum indicator,MACD has the ability to foreshadow moves in the underlying security.However, Moving averages, be they simple, exponential, weighted or simple, are lagging indicators.Owing to this lag,the effectiveness of the MACD will vary for different securities and markets.This report judges the effectiveness of this model by applying this tool on the data of various equity securities and sectors. In this report, the following two questions are addressed: Despite being a lagging model, how good it can act as a momentum indicator, to foreshadow moves in the underlying stock prices and for which type of firms and sectors, Moving Average Convergence Divergence model gives the best accuracy and predictability for stock pricing estimation. For this, the model is analyzed considering the underlying assumptions, applicability,limitations and the various conditions under which it produces. Bullish and Bearish signals are determined.The model is applied to real data set to estimate the prices and compare it with the actual prices and based on results the accuracy and predictability of the model with reference to the type of the firm, nature of industry and influence of the external environment are inferred.
URI: http://hdl.handle.net/11718/18704
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