Entropy, the Information Processing Cycle, and the Forecasting of Bull and Bear Market Peaks and Troughs

Entropy, the Information Processing Cycle, and the Forecasting of Bull and Bear Market Peaks and Troughs

Edgar Parker
DOI: 10.4018/IJPMAT.2019010105
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Many econophysics applications have modeled financial systems as if they were pure physical systems devoid of human limitations and errors. On the other hand, traditional financial theory has ignored limits that physics would impose on human interactions, communications, and computational abilities. The entropic yield curve blends the physical and human financial worlds in a new, powerful, and surprisingly simple way. This article uses this information theoretic perspective to provide a new explanation of the dynamics and timing of financial cycles. Additionally, the entropic yield curve offers a new method of forecasting market peaks and troughs.
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Materials And Methods

Parker (2017) developed an alternative derivation of the yield curve. This derivation is based on Shannon type entropy or information loss as described by Ben-Naim (2017), and combined with Burnashev’s formula for the error exponent of communication systems (Burnashev, 1976). An estimate of the information processing efficiency of the economy (R/C) could found using actual yield rates.

Using this alternative derivation, Parker (2017) demonstrated that differing levels of R/C could generate the different regimes of the entropically derived yield curve. These regimes have an equivalent representation in the popular Nelson-Siegel specification of the yield curve (Nelson & Siegel, 1987). The current paper extends the previous research by more closely examining the time evolution R/C during bull and bear markets. As demonstrated empirically R/C rises, reaches a maximum, and then falls in a cyclical pattern. The evolution of this information process provides a new and intuitive explanation of the boom and bust financial cycles as seen from an information theoretic perspective.

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