Application of Chaos Theory in Incomplete Randomized Financial Analysis

Abstract

In the new economy, some tech companies have rapidly built market power and modernization led to the unpredictable bank-to-client relationship. Moreover, financial markets are confronted with big data and as a result, digitization and further introduction of mathematical techniques and new models were brought into the financial industry. Uncertainty has increased markedly in the macroeconomic risk, payment systems, capital accumulation and investment. But so far, timid attempts are made to elucidate the possibilities of the chaos theory application in finance. To verify a theoretical model whether or not is an accurate representation of an empirically observed phenomenon is one of the most challenging investigations in the scientific field. The following study explores the problem related to incomplete randomized financial analysis. The behavior of financial market relates to the circumstances that are both internal and external. Chaos mathematics is an acute methodology to be applied in the analysis of the randomness in financial markets instead of completely randomized design. The completely randomized design places the emphasis on which the factor effects are constant and assumes the observation from experiments to be statistically independent. However, this hypothesis is often not realistic and practical. The correlated impact should not be ignored. This article attempts to clarify some points related to the possibility of using chaos theory in finance.