Chaos theory

For many investors, it is important to predict the future trend of abnormal stock returns. Thus, in this research, the abnormal stock returns of the listed companies in Tehran Stock Exchange were tested since 2008- 2017 using three hypotheses. The first and second hypotheses examined the non-linearity and non-randomness of the abnormal stock returns ′ trend around the release date of annual financial statements, respectively. While, the third hypothesis tested the potential of the chaos model in explaining future abnormal returns based on the past abnormal returns around the release date of the annual financial statements. For this pur-pose, BDS, Teraesvirta Neural Network, and White Neural Network tests were used to investigate its non-linearity. In addition, Lyapunov exponent, correlation dimension, Dickey-Fuller, and Hurst exponent tests were used for testing non-randomness and the fitness of AR, SETAR, and LSTAR models to determine the optimal model in explaining the abnormal returns utilizing R software. Results of these tests represented a non-linear and non-random process and chaos in the abnormal stock returns, implying the predictability of abnormal stock returns. Also, among three used chaos models, the LSTAR model had lower error and more predictability than the other two models.

Having its roots in science, “chaos theory” provides a new strategy to examine the disordered world of postmodern novels to find the hidden order underlying the apparent chaos. This study tries to clarify the ambiguous relation between Oedipa Maas, the “Tristero system” and the “Maxwell’s Demon” in Thomas Pynchon’s The Crying of Lot 49. Oedipa Maas, the main character of the novel is regarded as an “everyman” type character who tries desperately to make sense of the signs and mysteries proliferating around her, but she can never fulfill her quest because she is trapped in a chaotic world where there are no stable values, friends or meanings. She feels separated from the world around her and longs to regain the stability she used to have before she started her task as the “executrix” of her former lover’s vast estate. She does not manage to find “order” in the surrounding chaos, but applying the major tenets of chaos theory and examining concepts such as “the butterfly effect”, “bifurcations”, “strange attractors”, “recursive symmetry” and “entropy” , we can come to a better understanding of the order underlying the disordered world of the novel.

The Forex market is known as the strongest and richest financial market in the world that has been operating continuously. This market is formed based on the exchange rates of different countries, as well as the prices of oil and gold. The price of currency pairs in the Forex market as the largest market for financial transactions has always been of great importance. The purpose of this article is to study the Forex market based on chaos theory. Its statistical population includes three currency pairs Euro / Dollar, Pound / Dollar and Dollar / Yen. The period of prices of the surveyed currency pairs is from January 2017 to December 2021 and its time range includes daily prices. The results showed that after fitting the model and performing the BDS test, on the GARCH model residues in different dimensions and ε of all 6 groups, which are IID residues and there is no dependence on them. The results of Lyapunov's maximalism test showed that for all dimensions, and all time intervals (n), the value of Lyapunov's exponent is a positive and small number, indicating that the time series of the triple currency pair follows a chaotic process.

Stock prices are influenced by economic, technological, psychological and geopolitical factors. A review of the literature in this field shows that stochastic approaches, trend analysis and econometrics have been used to demonstrate stock market dynamics and price forecasting. However, these techniques cannot provide a comprehensive overview of market dynamics. Because they ignore the temporal relationship between these factors and are unable to understand their cumulative effects on prices. By integrating chaos theory and continuous data mining based on price acceleration, this study has eliminated these gaps by inventing a new price forecasting method called dynamic stock market recognition simulator and combining two methods: one is delay structures. Or gives time intervals to the data set, and the other is the method of selecting new variables for the market environment. The results showed that the method used can be used to predict the long-term stock price using a small data set with small dimensions.

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.