Application of Textual Feature Extraction to Corporate Bankruptcy Risk Assessment

The inception of the Internet in the late twentieth century has established the ability to generate a huge volume of data from multitudinous sources in a very short period of time. However, most of this data is presented in an unstructured format. According to the latest research, unstructured data contains more comprehensive, effective and practical information when compared to structured data due to its descriptive characteristics, especially in finance, healthcare, manufacturing and other domains. It is anticipated that the effective use of data mining technology can be applied to the development of more accurate predictive models, decision-support platforms and man-machine interactive systems on unstructured data. This thesis focuses on the application of a text mining system known as TP2K which stands for Text Pattern to Knowledge System, developed by my supervisor Professor Andrew K.C. Wong, to the finance industry. More specifically, the text mining system I proposed in this thesis is a concept-based textual feature extraction based on TP2K for corporate bankruptcy risk assessment. Bankruptcy risk assessment is to assess the bankruptcy risk of a corporation in the finance industry. It is linked to enterprise sustainability assessment, investment portfolio optimization and corporate management. Throughout the years, various models have been built using numerical and structured data (e.g. financial indicators and ratios). Yet no model has adequately leveraged the textual data for quantitative analysis in corporate bankruptcy risk assessment. Note that certain critical information such as strategic future directions and cooperate governance of an enterprise can only be reflected through textual data (e.g. annual financial reports). Recently, it has been reported that the combination of textual and numeric features will render a more accurate assessment of corporate bankruptcy. Nevertheless, extracting features from textual data remains difficult since it still requires considerable human efforts. According to the existing literature, there is no obvious criteria for textual feature mining and extraction in finance due to the diversity of objectives and interests. From a general perspective, there is no simple criteria for textual feature mining and extraction in finance according to existing literature. Thus, domain experts still remain essential in the industry. The current textual feature extraction methods in finance can be categorized into two distinct types. The first type is based on a comprehensive handcrafted dictionary of proper keywords with continuous manual updating. The second type is based on data mining technology (e.g. high-frequency words). The former is time-consuming, while the latter usually produces results which are ambiguous, irrelevant or hard to be interpreted by industry in practice. In this thesis, we (my supervisor and I) proposed a method known as concept-based textual feature extraction based on TP2K for corporate bankruptcy risk assessment. Compared to existing methods, this method can extract and mine textual features more accurately and succinctly from financial reports, allowing industrial interpretation in practice with limited human participation. It is semi-automatic and interactive. Its algorithmic procedure is briefly described as follows: (1) apply a linear-time and language-independent TP2K system to discover the “Word, Term and Phrase” (WTP) patterns from text data without relying on explicit prior knowledge or training; (2) apply a WTP-directed search algorithm in TP2K to find appropriate financial attribute names and their attribute values from the text context to obtain relevant attribute and attribute value pairs (AVPs) to build part of the Domain Knowledge Base (DKB) in support of predictive analysis of corporate bankruptcy risk. At the onset, domain experts will still play a major role in building the DKB. As more user-inputted domain information is integrated into the DKB, the system will become more automated to extract and validate related information for bankruptcy risk assessment with limited involvement from domain experts. In this thesis, AVPs have been used in corporate risk assessment to render more robust and less biased textual features. This allows experts to reasonably acquire and assist with the organization of individual selection rules in a comprehensive manner using traditional machine learning processing. To validate the proposed method, experiments on financial data have been conducted. A collection of corporate annual reports containing textual and numeric information were adopted to evaluate the corporate risk assessment in a semi-automatic manner. Initially the extracted AVPs data was converted to binarized textual features in accordance with certain finance field criteria. It was then integrated with related numerical features (financial ratios) for traditional machine learning technologies to construct a predictive model for corporate bankruptcy assessment. The experimental results demonstrated an effective two-year ahead (T-2) prediction, outperforming prediction models based on only numeric features under 10-fold cross-validation. At the same time, we observed that all features discovered, numeric or textual, were consistent to the industry standard. Hence, we believe the proposed method has achieved an important milestone for assessing bankruptcy assessment in practice, and is potentially useful for providing trading advice for investors in the future

Driving Risk Detection Model of Deceleration Zone in Expressway Based on Generalized Regression Neural Network

Drivers’ mistakes may cause some traffic accidents, and such accidents can be avoided if prompt advice could be given to drivers. So, how to detect driving risk is the key factor. Firstly, the selected parameters of vehicle movement are reaction time, acceleration, initial speed, final speed, and velocity difference. The ANOVA results show that the velocity difference is not significant in different driving states, and the other four parameters can be used as input variables of neural network models in deceleration zone of expressway, which have fifteen different combinations. Then, the detection model results indicate that the prediction accuracy rate of testing set is up to 86.4%. An interesting finding is that the number of input variables is positively correlated with the prediction accuracy rate. By applying the method, the dangerous state of vehicles could be released through mobile internet as well as drivers' start of risky behaviors, such as fatigue driving, drunk driving, speeding driving, and distracted driving. Numerical analyses have been conducted to determine the conditions required for implementing this detection method. Furthermore, the empirical results of the present study have important implications for the reduction of crashes. Document type: Articl

Performance analysis of the plastic square plate based on the fiber reinforced PA66

As the plastic component has the advantage of the lightweight, they are used widely in the industry and many researches are conducted simultaneously; In this study, combined the elastic constitutive model and the fiber orientation, the simulations of the plastic square plate with two material definitions are carried out. They are the orthotropic material and the anisotropic material respectively. Through the simulation comparisons, the anisotropic behaviors on the thermal field, the pressure field and the vibration characteristics of the fiber reinforced PA66 are all analyzed. The results show that the different material definitions have important influence on the structure analysis. It offers the guidance for the design and analysis of the plastic component

Performance analysis of the plastic square plate based on the fiber reinforced PA66

As the plastic component has the advantage of the lightweight, they are used widely in the industry and many researches are conducted simultaneously; In this study, combined the elastic constitutive model and the fiber orientation, the simulations of the plastic square plate with two material definitions are carried out. They are the orthotropic material and the anisotropic material respectively. Through the simulation comparisons, the anisotropic behaviors on the thermal field, the pressure field and the vibration characteristics of the fiber reinforced PA66 are all analyzed. The results show that the different material definitions have important influence on the structure analysis. It offers the guidance for the design and analysis of the plastic component

Reliability analysis on high-pressure fuel pipe of diesel engine

High-pressure fuel pipe is an important component of the fuel injection system of diesel engine, it suffer complex loading from engine. The fatigue life of fuel pipe has great impact on reliability of engine. In this study, the fluid-solid coupling model which considers the action of fuel is established. Analysis on the effect of fuel’s pressure oscillation on the mode is carried out. Through the simulation comparisons, the mode frequency and mode shape of each model are all analysed. Base on the fluid-solid coupling model, frequency response analysis is done to study the reliability of high-pressure fuel pipe. The results show that the pressure oscillation of fuel has important influence on the mode frequency. The maximum Stress Tensor and Vibration Velocity appeared at the position of first order mode frequency. It offers the guidance for the design and analysis of High-pressure fuel pipe

Reliability analysis on high-pressure fuel pipe of diesel engine

High-pressure fuel pipe is an important component of the fuel injection system of diesel engine, it suffer complex loading from engine. The fatigue life of fuel pipe has great impact on reliability of engine. In this study, the fluid-solid coupling model which considers the action of fuel is established. Analysis on the effect of fuel’s pressure oscillation on the mode is carried out. Through the simulation comparisons, the mode frequency and mode shape of each model are all analysed. Base on the fluid-solid coupling model, frequency response analysis is done to study the reliability of high-pressure fuel pipe. The results show that the pressure oscillation of fuel has important influence on the mode frequency. The maximum Stress Tensor and Vibration Velocity appeared at the position of first order mode frequency. It offers the guidance for the design and analysis of High-pressure fuel pipe

Driving Risk Detection Model of Deceleration Zone in Expressway Based on Generalized Regression Neural Network

Drivers’ mistakes may cause some traffic accidents, and such accidents can be avoided if prompt advice could be given to drivers. So, how to detect driving risk is the key factor. Firstly, the selected parameters of vehicle movement are reaction time, acceleration, initial speed, final speed, and velocity difference. The ANOVA results show that the velocity difference is not significant in different driving states, and the other four parameters can be used as input variables of neural network models in deceleration zone of expressway, which have fifteen different combinations. Then, the detection model results indicate that the prediction accuracy rate of testing set is up to 86.4%. An interesting finding is that the number of input variables is positively correlated with the prediction accuracy rate. By applying the method, the dangerous state of vehicles could be released through mobile internet as well as drivers' start of risky behaviors, such as fatigue driving, drunk driving, speeding driving, and distracted driving. Numerical analyses have been conducted to determine the conditions required for implementing this detection method. Furthermore, the empirical results of the present study have important implications for the reduction of crashes

A novel measurement‐protection‐integrated current transformer based on hybrid core and magnetic field sensor

Abstract Current transformers (CTs) are widely used for energy metering, relay protection, condition monitoring and control circuits. However, CT saturation may lead to non‐negligible measurement errors and relay malfunctions, posing a threat to the stability and security of the power grid. In order to address the problem of CT saturation, a novel measurement‐protection‐integrated current transformer (MPICT) is proposed. First, the working states of the MPICT are summarised and the approximate expressions for the steady‐state measuring characteristics, the transient response characteristics, and the measurement errors are derived from the equivalent circuit model. Then, the feasibility of the MPICT and the theoretical analyses are verified by the 3D FEM simulation imitating the presented MPICT excited by diverse currents. Finally, a down‐scale prototype is fabricated and a series of tests are conducted in the laboratory to validate the effectiveness of the equipment. The simulation and experimental results suggest that the output of the MPICT can accurately reconstruct the primary current waveform, even if the primary current contains decaying or constant DC component