This research is related to the usefulness of different machine learning methods in forecasting time series on financial markets. The main issue in this field is that economic managers and scientific society are still longing for more accurate forecasting algorithms. Fulfilling this request leads to an increase in forecasting quality and, therefore, more profitability and efficiency. In this pa...

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support ve...

EDDIE is a genetic programming based decision support tool for financial forecasting. EDDIE itself does not replace forecasting experts. It serves to improve the productivity of experts in searching the space of decision trees, with the aim to improve the odds in its user's favour. The efficacy of EDDIE has been reported in the literature. However, discovering patterns in historical data is onl...

The performance of financial forecasting with neural networks dependents on the particular training set. We design mean-change-point test to divide the original dataset into different training sets. The experiment results show that the larger training set does not necessarily produce better forecasting performance. Although the original datasets are different, the change-points to produce the o...

This paper examines financial professionals’ overconfidence in their forecasting performance. We are the first to compare individual financial professionals’ self-ratings with their true forecasting performance. Data spans several years at monthly frequency. The forecasters in our sample do not provide feasible self-ratings compared to their true performance but show overconfidence on average. ...

A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of SVM in financial time series forecasting. The feasibility of applying SVM in...

There has been an increasing attention on the influences online financial information has on the financial markets. In the meanwhile, the volatility of trading volumes, just as the volatility of stock returns, has an inseparable association with financial risks. It has been considered that there might exist some direct or indirect correlations between online financial information volumes and fi...

Essays on Financial Risk Modeling and Forecasting

Neural networks demonstrate great potential for discovering non-linear relationships in time-series and extrapolating from them. Results of forecasting using financial data are particularly good [LapFar87, Schöne9O, ChaMeh92]. In contrast, traditional statistical methods are restrictive as they try to express these non-linear relationships as linear models. This thesis investigates the use of t...

We propose a forecasting procedure based on multivariate dynamic kernels, with the capability of integrating information measured at different frequencies and at irregular time intervals in financial markets. A data compression process redefines the original financial time series into temporal data blocks, analyzing the temporal information of multiple time intervals. The analysis is done throu...