A second-order stock market model (original) (raw)
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2015
Abstract. We study a mean-field version of rank-based models of equity markets such as the Atlas model introduced by Fernholz in the framework of Stochastic Portfolio Theory. We obtain an asymptotic description of the market when the number of companies grows to infinity. Then, we discuss the long-term capital distribution. We recover the Pareto-like shape of capital distribution curves usually derived from empirical studies, and provide a new description of the phase transition phenomenon observed by Chatterjee and Pal. Finally, we address the performance of simple portfolio rules and highlight the influence of the volatility structure on the growth of portfolios. 1.
Pattimura Proceeding: Conference of Science and Technology
Technological developments are getting faster, as is the dissemination of existing information, especially on the capital market. In order for investors to avoid losses from the capital market, a method is needed that is able to analyze the movement of the stock price. This study focuses on the application of the Data Scales Analysis (DSA) method which uses a formula with the concept of the same amount of data between the first and second order average differences at levels n and n + 1 for predicting the stock price of issuers, in predicting stock prices in the capital market. The resulting formula is named JIC-FLY 2 which is a new formula used to predict stock prices in the capital market. The population used in this study are issuers who are members of IDX 30 from the banking sub-sector with the sample used is the issuer of BBCA (PT Bank Central Asia Tbk). The results of this study note that the DSA method with this formula is able to produce the best predictive value, namely DSA ...
Modelling of Market Stock Using the Normal Variance Models
2019
Great work has been done on modeling of nancial instruments namely,shares,equities, stocks and many more.The focus of this thesis is mainly modeling of stocks based on normal mixtures.The essence of this work is to do a comparison between the Normal Variance Mean Model and Normal Variance model and determine which of the two is best for modeling stocks. Normal mixtures is a combination of two distributions where the normal distribution is the conditional distribution and is mixed with another distribution as the mixing distribution.The two mixing distribution discussed in this thesis are both Gamma and Inverse Gaussian distributions ,out of which we get the Variance Gamma and Normal Inverse Gaussian distributions respectively.Data is tted on the distributions, Normal variance model and the Normal Variance Mean Model and a comparison is done to ascertain which model gives the best goodness of t and is the best model. Construction of the two distributions based on Normal Variance is d...
The Decision-making Model for the Stock Market under Uncertainty
International Journal of Electrical and Computer Engineering (IJECE), 2017
The main purpose of this research is developing methods and models of decision-making to assess the stock market state, and predict the possible changes in the RTS index value. This article shows that the analytical models for assessing the stock market state do not give reliable results. The absence of the reliable estimates associated with the high degree of uncertainty, random, nonlinear and non-stationary process with a significant degree of aftereffect. In this paper, to formalize the securities market parameters it's proposed the fuzzy sets method. To assess the stock market current state and make decisions the fuzzy situational analysis model (situational model) is applied. The analytical prediction results of the stock market and graph of the RTS index expected return changes in 2014-2016 are showed. The model of calculating the fuzzy inference rules truth degree to predict the RTS index is developed. The market parameters linguistic definition is given and the expert's rules construction to predict the RTS index growth is shown. The program in Matlab environment is designed to perform research. The study result showed that the model allows for the RTS index prediction in the condition of incomplete initial data with a confidence level about 90%. 1. INTRODUCTION The stock market or Securities Market reflects the economy state in the world, frequently works with the changing rules (in their application) and reacts as economic nature events and political. Particularly it illustrates well the effect of the events in recent years, in particular with regard to political decision-making in a number of countries. These political decisions show instability of the stock market. Changes in the world economy, politics of leading banks, making decisions about changing the main interest rates and others are showed the effect on the stock market state. There are a large number of players in the stock market, a variety of unforeseen factors make the non-stationary and random market state. The market processes show the presence of aftereffect and nonlinear. The observing practice for the stock market state has shown that the linear and steady changes in market parameters can exist only in small time intervals. The stock market works in conditions of uncertainty, appropriate analytical models to determine the stock value at any time does not exist. There are not analytical models to determine the change in the stock market state sufficiently.