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On an average 9 out of 10 startups fail(industry standard). Several reasons are responsible for the failure of a startup including bad management, lack of funds, etc. This work aims to create a predictive model for startups based on many key things involved at various stages in the life of a startup. It is highly desirable to increase the success rate of startups and not much work have been done to...
During the last decades and recession of 2007–2009 witnessed many global financial crises. Consequently, this research represents a proactive study via introducing new modeling tool; in order to diagnose the financial distress and assess its probability of occurrence. The Neuro-Logit is a new approach for diagnosis, prediction and forecasting corporate financial distress. This tool acts as Logit (Logistic...
Data mining has been recently used in the field of car insurance to help the insurance companies in predicting the customers' choices in order to provide more competitive services. In this composition, the random forest was used to develop a classification model that could be applied in predicting which of the insurance policies would likely to be chosen by the customers. The performance of the developed...
Background: In order to address the challenges in companies having no or limited effort datasets of their own, cross-company models have been a focus of interest for previous studies. Further, a particular domain of investigation has been Web projects. Aim: This study investigates to what extent effort predictions obtained using cross-company (CC) datasets are effective in relation to the predictions...
Q-Gaussian function has the extensive scope of application compared with Gaussian function. It can become many different radial basis functions when we choice the different parameters. Q-Gaussian is chose as kernel to establish the financial early warning model of listing Corporation in this paper. Through the contrast of the Fisher model based on Gaussian kernel, polynomial kernel and the linear...
It is very important for the investors to correctly predict the listing status of listed companies (LSLC) in China. This paper is the first to format the problem of predicting LSLC as a multiclass classification problem, while almost all preliminary research considered it as a binary classification problem. Adaboost method is introduced to solve the problem and the experiment result shows that it...
With the rise of e-commerce business, sales forecasting plays an increasingly important role, for accurate and speedy forecasting can help e-commerce companies solve all the uncertainty associated with demand and supply and reduce inventory cost. As the rapid growth in the amount of data, traditional intelligence models like Neural Networks have weakness in terms of speed. In this paper, we introduce...
With the economy developing, effective financial distress prediction methods of artificial intelligence have got more and more attention of the academia. Concept drift in a data flow is another hot research topic. This paper firstly introduces several kinds of existing batch weighted methods for financial distress prediction modeling, and analyzes their shortages. To find a solution to deal with them,...
A PT X is a state-owned enterprise that provides the largest telecommunications services and network in Indonesia. By the growing challenges in the telecommunications industry, PT X must carefully take care of their customers by improving its services in order to make them satisfied and loyal. One of the effort that can be done by PT X is determining and predicting their customer's category, so that...
Corporate financial planning relies on thousands of financial forecasts generated by human forecasters with varying performance (forecast errors). Previous work proposes ARIMA prediction as a competitive benchmark for manual forecasts. However, ARIMA can also produce large errors, and a company needs to understand sensitivity of ARIMA-outcome to time series characteristics before ARIMA-benchmarks...
A food-processing-and-distribution company typically stores products in a warehouse before shipping them to customers. Inventory management is therefore important to the food-processing-and-distribution industry because of the large amount of products typically stored. Large amounts of stored products increase inventory cost and management cost and can reduce warehouse efficiency. This study is important...
This paper proposes a novel method of corporate financial risk prediction (FRP) modeling called the adaptive and dynamic ensemble (ADE) of support vector machine (SVM) (ADE-SVM), which integrates the inflow of new data batches for FRP with the process of time. Namely, the characteristic change of corporate financial distress hidden in the data flow is considered as the concept drift of financial distress,...
Research on the temporary staffing industry discusses different topics ranging from workplace safety to the internationalization of temporary labor. However, there is a lack of data mining studies concerning this topic. This paper meets this void and uses a financial dataset as input for the estimated models. Bagged decision trees were utilized to cope with the high dimensionality. Two bagged decision...
The subjects of the study are listed petrochemical companies in China. We regard ST as a symbol of financial crisis for an enterprise. T-test and relevant linear test are applied to determine the model variables and Logistic regression to build the forecasting model of financial crisis, then the data of ST enterprise samples and non-ST enterprise samples are used for analysis. With the forecasting...
Corporation financial distress has been an important issue for study in the financial fields. This paper uses traditional BP neural network model and proposes PNN model to predicate financial distress. The sample consists of 276 companies listed on the Shanghai Stock Exchange and Shenzhen Stock Exchange over the period 2001–2010. Factor analysis is used to lower correlation and reduce dimensionality...
This research made an early investigation on firm failure prediction (FFP) in hospitality industry of China using support vector machine (SVM) and classical statistical models, and an early comparison on difference of identified significant variables in FFPs for developed and developing countries. The findings indicate that: (a) working capital turnover, equity turnover, ratio of owners' equity, equity...
Fraud in public companies has a large financialimpact, and yet is only weakly detected by those who look for it, many incidents have been detected only when whistleblowers have come forward. We examine the problem of detecting fraud from the textual component of the quarterly and annual reports that public companies are required to file. Using an empirically derived set of words, we achieve prediction...
Data concerning of the IC work in process (WIP) can be classified into paired and unpaired, according to whether the corresponding input and output times of the WIP are correlated with each other. To handle these different classes of data, this study presents a production output forecast scheme that can predict the distributions in the next-period and simulate the production outputs of a semiconductor...
The development of business failure prediction system to prevent the significant loss of social costs caused by the companies' unexpected bankruptcy is a popular investigation issue. Because of the constraint on the statistic assumptions, the forecasting models established by traditional statistic methods have some limits in its identity. Therefore, in recent years various algorithms imitating of...
Prevention of financial risk is one of the major tasks that construction companies have to pay attention to. Using derivatives to avoid such risks is a practical strategy, but is heavily dependent on the traders' skills and accuracy of predictions. The purpose of this study is to develop an automatic expert model using a rule extraction based approach that provides practitioners with a prediction...
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