The statistical shape of the market spectrum is often considered "pink noise" because it exhibits a power law relationship between the frequency and amplitude of fluctuations in prices. Pink noise is a type of signal where the power spectral density (PSD) is inversely proportional to the frequency, which means that as the frequency increases, the power decreases at a rate of approximately 1/f, where f is the frequency. In the context of financial markets, this means that there is a higher probability of observing larger price movements over longer time horizons compared to shorter time horizons. This pattern of fluctuations is often referred to as long-range dependence or long memory, and is observed in a wide variety of financial time series data. The pink noise characteristic of financial markets is thought to arise from the complex interactions between traders with different investment horizons, strategies, and risk preferences, as well as the underlying economic and political factors that affect market sentiment. The fractal nature of market data also contributes to this pink noise property, as fluctuations at different time scales exhibit similar statistical properties. Overall, the pink noise characteristic of financial markets has important implications for risk management and trading strategies, as it suggests that there may be persistent patterns in market data that can be exploited by traders with the right tools and techniques.