Abstract
We offer a new dimension for measuring monthly stock market cycle fluctuations using p-values derived from linear regression analysis. Like any other non-stationary macroeconomic variables, we propose that stock market indices do have their own intrinsic cyclical dynamicity that represents as wave-like characteristic patterns which can be interpreted to identify market trends in the short-run. Using regression statistics, this paper helps to develop a real framework for identifying linear waves of conditional volatility that can be conceptualized to construct a novel indicator-the ‘p-value’ indicator to decipher the boom-bust cycles periodically. We find that our modeled approach being effectual in enumerating index cyclical dynamicity as such.
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