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Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of...
Stock market is basically nonlinear in nature and the research on stock market is one of the most important issues in recent years. People invest in stock market based on some prediction. For predict, the stock market prices people search such methods and tools which will increase their profits, while minimize their risks. Prediction plays a very important role in stock market business which is very...
The people's living standards gradually increased, in addition to food and clothing problems, more and more people began to focus on the stock market investment, which also provides for the development of the stock market is a necessary condition. The stock market is not only the focus of investors, but also a valuable place for scholars to study and research. This paper presents a stock forecasting...
In order to explain the reason of the high underpricing rate of IPO and to judge whether there exists IPO underpricing, combined with China's specific policy system, this paper classified the IPO first-day underpricing or discount and constructed a classifier by using the decision tree modeling under the multiple influence factors. The numbers listed in this article refer to the data of initial public...
The objective of this paper is to construct a model to predict stock value movement using the opinion mining and clustering method to predict National Stock Exchange (NSE). We have used domain specific approach to predict the stocks from each domain we have taken some stock with maximum capitalization. Proposed Method is Not at all like past methodologies where the general states of mind or sentiments...
This article proposed a stochastic volatility model with T-distribution leveraged (ASV-T model), the model can be used to reflect the leverage effect and the fat-tail effect exist in stock market. Through the statistics analysis of the model, it is proved that the model is workable and is best for the fitting of historical data. With empirical research on Chinese GEM index, it is further proved that...
The behaviors of Price manipulation in stock markets have largely distorted stock prices, undermining the interests of small and medium investors. It is of great importance to the future development of stock markets that such activities be discriminated and prevented. After analyzing the characteristics of stock price manipulations, this paper establishes a logistic regression model for the discrimination...
The paper describes a new method of combining Artificial Neural Networks (ANN), technical analysis and fractal analysis for predicting share prices on the Warsaw Stock Exchange. The proposed hybrid model consists of two consecutive modules. In the first step share prices are preprocessed and calculated into moving averages and oscillators. Then, in the next step, they are given to the ANN inputs,...
In this paper, a forecasting-mean-correlation-entropy portfolio optimization model (FMCE) is developed by using the fuzzy time series techniques to predict securities' future returns distribution and employing entropy as risk measurement. Traditional portfolio models such as MV model have stringent conditions to returns distribution, while entropy as a new risk measurement is free from these restrictions...
In this study, support vector regression (SVR) analysis is used as a machine learning technique in order to predict the stock market price as well as to predict stock market trend. Moreover, different types of windowing operators are used as data preprocess or input selection technique for SVR models. This is a new approach which uses different types of windowing functions as data preprocess for predicting...
There are some volatility clustering in the time series, especially in the financial time series, from the proposition of ARCH model to the later development and reproduction, it has resolved many such problems in a lot of fields extensive involves: funds, stock prices, futures, crude oil prices, GDP, foreign exchange administration in bank, inflation rate, foreign exchange rate, etc. This paper mainly...
The listed companies will be specially treated when their business performance is bad or there are serious accidents, which is a rule to reveal the investment risk of stock market. So it is very important to study the performance of listed companies under special treatment (ST companies) for the development of stock market in China. We choose 50 companies as the samples in the ST plate of 2006 in...
Analyzing the latent relationship between parallel news articles and stock prices has become an important research issue which attracts more and more researchers' attention. It is believed that news articles have impact on prices. Many approaches address this issue either from the documents' sentiment point of view or from the word frequency point of view. In this paper, we propose a new model which...
With the development of nonlinear science, the existence of long memory in the financial market volatility has been found, which is inconsistent with the weak form of Efficient Market Hypothesis. With the brief introduction of Heterogeneous Market Hypothesis and Fractal Market Hypothesis, the long memory behavior of Shanghai Stock Index's returns volatility is tested, based on H/S analysis and GPH...
In this paper, we analyzed the highly nonlinear characteristics of the stock market and proposed a novel approach for time series analysis. This method is the use of RBF neural network analysis of time series and analysis of the initial analysis of the error also, and then combined with the analysis of two results to obtain new results. Using this method, we forecasted the trend of shares of China...
This paper applies a simple multiple regression based model to analyze financial data. The model uses a dummy variable as the dependent variable which could be interpreted as a predictor. The independent variables of the model are quantitative as well as qualitative. The results of the analysis support the predictive capability of the model.
This paper studies the relationship between amounts of new stockholders and share index in Chinese stock market. We analyze behavior characters of investors, construct a model to forecast the amounts based on grey model GM(1,1), and give the forecasting function. The function is tested by error analysis and sensitivity analysis, and proved to be an effective tool to forecast the accounts. The computing...
In this paper, based on the analysis of the Altman Z-score model, a new research instrument was proposed for the research method. In this paper, 41 ST companies and 41 non-ST companies were taken as samples, seven of the 20 financial indices two year before the financial distress of ST companies were selected as prediction variables and finally the financial distress model was set up with logistic...
Stock index in security market directly reflects the trend and level of the overall market stock price. Therefore, the price prediction directly affects investment decisions and is closely related to economic interest of investors. However, with specific volatility and uncertainty in stock market, changes in stock price index are influenced by many factors, which make it very difficult for the traditional...
This paper presents a modified model for Chinese credit risk management. The model is based on KMV model with consideration of Generalized Autoregressive Conditional Heteroskedasticit (GARCH). Data used in this research are from the balance sheet and the Chinese stock market. T-tests and ROC curves are employed to analyze the data, examining the model. It is shown that the model can be applied to...
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