The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Financial forecasting is the basis for budgeting activities and estimating future financing needs. Applying machine learning and data mining models to financial forecasting is both effective and efficient. Among different kinds of machine learning models, kernel methods are well accepted since they are more robust and accurate than traditional models, such as neural networks. However, learning from...
This paper focuses on the application of fuzzy correction model in financial forecasting. As the limitations of traditional fuzzy logic model, when forecasting the continuous trend of financial time series, the trend accuracy rate is low. The continuous predictive value of fluctuations is small and can not reflect the future market direction. In this paper, we presents Fuzzy Method of amendment. Test...
Financial distress is the most synthetic form of business crisis and financial distress prediction (FDP) has been a widely and continually studied topic in the field of corporate finance. This paper attempts to put forward OR-CBR in K-nearest neighbors model, which can be the implementation of corresponding algorithm.
Knowledge discovery in financial databases has important implications. Decision making process on financial datasets is known to be difficult because of the complex knowledge domain and specific statistical characteristics of the data. In this paper, we investigate the decision making problem on financial datasets such as stock market fluctuations by means of financial ratio measurements while maintaining...
Volatility is an important variable in financial forecasting. Forecasting volatility requires a development of a suitable model for it. In this paper, we examine different time series models for volatility modelling. Specifically, we will study the use of recurrent mixture density networks, GARCH and EGARCH models to model volatility. In addition, we demonstrate the impact of different factors on...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.