Identifying explosive behavioral trace in the CNX Nifty Index: a quantum finance approach

  • Received December 28, 2017;
    Accepted February 19, 2018;
    Published March 3, 2018
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/imfi.15(1).2018.18
  • Article Info
    Volume 15 2018, Issue #1, pp. 208-223
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The financial markets are found to be finite Hilbert space, inside which the stocks are displaying their wave-particle duality. The Reynolds number, an age old fluid mechanics theory, has been redefined in investment finance domain to identify possible explosive moments in the stock exchange. CNX Nifty Index, a known index on the National Stock Exchange of India Ltd., has been put to the test under this situation. The Reynolds number (its financial version) has been predicted, as well as connected with plausible behavioral rationale. While predicting, both econometric and machine-learning approaches have been put into use. The primary objective of this paper is to set up an efficient econophysics’ proxy for stock exchange explosion. The secondary objective of the paper is to predict the Reynolds number for the future. Last but not least, this paper aims to trace back the behavioral links as well.

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    • Figure 1. The Reynolds number over a period of 16 years, clearly coming down in amplitude and frequency
    • Figure 2. The Reynolds number is plotted with CNX Nifty and the volatility indicator CBOE VIX in the second zone (2007 to 2011); local maxima is observed at higher levels of CNX Nifty
    • Figure 3. The Reynolds number is plotted with CNX Nifty and the volatility indicator CBOE VIX in the second and third zone combined (2007 to 2015); local minimum is observed at higher levels of CNX Nifty
    • Figure 4. GARCH forecasting of the Reynolds number in Nifty
    • Table 1. The augmented Dickey-Fuller test
    • Table 2. Robustness measures
    • Table 3. Martingale test
    • Table 4. Individual tests
    • Table 5. Lag equation
    • Table 6. Variance equation
    • Table 7. Neural network output
    • Table 8. Comparative intercepts of GARCH and neural network