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We analyze cross-correlation between return fluctuations of stocks of an emerging market by using random matrix theory (RMT). We test the statistics of eigenvalues of cross-correlation (C) between stocks of the Tehran Price Index (TEPIX) as an emerging market and compare these with a mature market (US market). According to the "null hypothesis," a random correlation matrix constructed from mutually uncorrelated time series, the deviation from the Gaussian orthogonal ensemble of RTM is a good criterion. We find that a majority of the eigenvalues of C fall within the bulk (RMT bounds between λ+and λ-) for the eigenvalues of the random correlation matrices. Further, we find that the distribution of eigenvector components for the eigenvectors corresponding to the largest deviating eigenvalues, display systematic deviations from the RMT prediction. Analyzing the components of the deviating eigenvectors by Inverse Participation Ratio, leads us to know that the largest eigenvalue corresponds to an influence common to the whole market. Our analysis of the other deviating eigenvectors shows distinct industries, whose identities corresponds to the structure of the Iran business environment.

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We unveil secrets of the financial markets that prove very effective on shaping their future. The question to be answered is why instant high amplitude variations of price returns are never experienced. We deduce that the key to shedding light on this issue is the quantum potential whose existence is due to the entanglement between a price and its prior-day price. Implementing the quantum potential would enable us to sketch a robust pattern for the price return fluctuations of a financial market. As such, we model real markets by the Bohmian quantum approach bearing a quantum potential that guides the price return fluctuations. Strictly speaking, we show that this quantum potential confines the price returns of real markets in a scale-invariant manner, which proves to be different for emerging and efficient markets. By modelling the oil and gold markets we see that a 20 day time scale is enough to exhibit the class difference of the scaling behaviour. The appearance of this characteristic time scale lies in the fact that oil and gold markets are influenced by short- and long-term programs. This statement is supported by the fact that short-term programs are due to the market supply and demand, while long-term programs are due to political and natural factors. In short times the potential is very efficiently controlling the market showing a big margin against a white noise, while in the long run the potential is not as efficient as before tending to look more like a white noise. This is due to the widening of the boundaries disabling the quantum potential efficiency on controlling the market, and hence justifying the model.

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