An Improved Stock Price Prediction using Hybrid Market Indicators

In this paper the effect of hybrid market indicators is examined for an improved stock price prediction. The hybrid market indicators consist of technical, fundamental and expert opinion variables as input to artificial neural networks model. The empirical results obtained with published stock data of Dell and Nokia obtained from New York Stock Exchange shows that the proposed model can be effective to improve accuracy of stock price prediction

Digital Modeling of Economic Processes and Supply Chain Management in the Formation of Cooperative Relations in the Petrochemical Cluster of the Region

Abstract- A study of the state of the theory of fuzzy sets shows that until recently in Russia there were almost no studies in the chemical sector of the economy and finance using fuzzy analysis and forecasting, although by that time all necessary prerequisites for modeling financial systems had been created. The current situation in Russia is characterized by a high degree of science lagging behind the requests of state and commercial supply chain management.Fuzzy sets have not been used to date for financial analysis and planning of chemical corporations, evaluating the investment attractiveness of securities, optimizing the stock portfolio, forecasting stock indices and macroeconomic indices. You can also talk about the lack of software based on fuzzy models, although abroad, such software products and information technologies that solve economic problems using fuzzy-plural and related descriptions already exist.In the 80's, software solutions and information technologies began to emerge, solving economic problems using fuzzy-plural and related descriptions. Thus, under the leadership of C. Zopounidis at the Technical University on the island of Crete, an expert system was developed for detailed financial analysis of corporations. A little earlier in Germany, a group led by H. Zimmerman developed a strategic planning system, in which the positioning of the corporation's business is based on fuzzy descriptions of the competitiveness and attractiveness of the business.As an example of such software, you can use expensive complex systems, which include fuzzy logic, which bankers and financiers use to solve the most complicated problems of forecasting financial indicators. The beginning of this process was laid by the Japanese financial corporation. Having set out to automate the game on the securities market, this company attracted about 30 specialists in artificial intelligence

Predicting the Effects of News Sentiments on the Stock Market

Stock market forecasting is very important in the planning of business activities. Stock price prediction has attracted many researchers in multiple disciplines including computer science, statistics, economics, finance, and operations research. Recent studies have shown that the vast amount of online information in the public domain such as Wikipedia usage pattern, news stories from the mainstream media, and social media discussions can have an observable effect on investors opinions towards financial markets. The reliability of the computational models on stock market prediction is important as it is very sensitive to the economy and can directly lead to financial loss. In this paper, we retrieved, extracted, and analyzed the effects of news sentiments on the stock market. Our main contributions include the development of a sentiment analysis dictionary for the financial sector, the development of a dictionary-based sentiment analysis model, and the evaluation of the model for gauging the effects of news sentiments on stocks for the pharmaceutical market. Using only news sentiments, we achieved a directional accuracy of 70.59% in predicting the trends in short-term stock price movement.Comment: 4 page

Mary Douglas, risk and accounting failures.

Sociology and anthropology are especially valuable in providing a critical understanding of the risk-related implications of modernity. There has, however, been relatively little discussion of the work of Mary Douglas within accounting although her pioneering writings in the area of risk have been highly influential. This paper uses Douglas' cultural theory of risk to provide an alternative perspective on the demise of Enron and Andersen. The failure at Enron is interpreted through the grid-group model and analysed as a series of events that threaten to destabilize established cultures. Accounting is thus construed as an activity that exists on the margins of boundaries. There are two important conclusions drawn from the analysis. First, as the worldviews of both the individualist and hierarchical cultures became threatened by the ensuing crisis they collaborated to ensure their perpetuation. This also averted individuals from becoming susceptible to recruitment by subversive egalitarian groups. Second, the individualistic culture of Andersen shaped practices within the firm weakening its ability to act as a gatekeeper and therefore public accounting firms need to modify their cultures if they are to police the margins effectivel

"Can the neuro fuzzy model predict stock indexes better than its rivals?"

This paper develops a model of a trading system by using neuro fuzzy framework in order to better predict the stock index. Thirty well-known stock indexes are analyzed with the help of the model developed here. The empirical results show strong evidence of nonlinearity in the stock index by using KD technical indexes. The trading point analysis and the sensitivity analysis of trading costs show the robustness and opportunity for making further profits through using the proposed nonlinear neuro fuzzy system. The scenario analysis also shows that the proposed neuro fuzzy system performs consistently over time.

The Russian corporation: patterns of behavior during the crisis

This paper considers the behavior patterns of Russian firms before and during the financial crisis of 2008-2009. To facilitate comparison, we define three main groups of actors at the firm level in the Russian economy – large, politically connected companies; mid-size firms that expanded in the 2000s with the help of administrative support, and successful mid-size firms driven by market factors. Many of the large companies practiced highly risky financial policy and experienced a decrease in efficiency before the crisis, and the managers and owners of some Russian firms have been engaging in opportunistic behavior during the crisis; the forms and causes of this behavior are analyzed here. We conclude by proposing some policy implications with emphasis on supporting successful mid-size firms driven by market factors