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Purpose: The purpose of this research was to provide a model for predicting time series of financial information based on the Lyapunov representation of information using chaos theory.Method: This research is applied in its purpose, which is conducted using a quantitative approach. The research ranks as descriptive-causal accounting research based on actual information in companies' financial statements. The research method is the "post-event" type and was carried out using chaos theory and Saida's method based on the Lyapunov view.Findings: The findings showed that during the ADF test, the null hypothesis was rejected at a level of less than 5% type 1 error and 95% confidence, and it shows that the data is not static. During the substitution analysis test and its significance level, the behavior of the time series of the main financial information is significantly different compared to their substitutes. The obtained value was calculated to describe the production process of all data sets for μ = 2, ApEnMax equal to 0.65 and rMax equal to 0.32, and for μ = 3, ApEnMax equal to 0.6 and rMax equal to 0.44. The value of the Lyapunov profile in stability at a certain point is less than zero and in the limited cycle of stability is equal to zero and in chaos, it is greater than zero and smaller than ∞, and in noise it is equal to ∞.Conclusion: The results show that higher returns, encourage investors to invest and increase the flow of capital. It is believed that companies' stock returns are a function of systematic risk, and systematic risk represents the changes in the return rate of a share compared to the changes in the return rate of the entire stock market.

Purpose: Portfolio optimization is one of the important issues in the field of financial sciences and investment, which has many applications in financial planners and decisions. By choosing a suitable stock portfolio, it is possible to greatly increase the efficiency of investment (in terms of increasing returns and reducing risk). Methodology: In this paper, by presenting a model of liquidity risk, using the concept of diversification in the form of Shannon's entropy and econometric approach, an optimal portfolio of investment with the lowest risk and the highest return has been presented in the form of a portfolio. To calculate the liquidity risk, using multivariable methods, the variance-covariance matrix of price index returns and price gap was calculated and used in the presented model, and finally, the optimal weight was used using the optimization method and meta-heuristic algorithm of non-dominant ranking of the second version., calculated for selected industries. Findings: The output results of the model show that the optimal weight of the groups that have less variance in the optimal portfolio is higher. Originality/Value: Besides, the effect of removing the concept of liquidity from the model leads to an increase in the weight of industries that have less liquidity, and along with the increase in risk, the return of the optimal portfolio also increases in this case. [ABSTRACT FROM AUTHOR]