Abstract: Chinese segmentation has attracted amounts of attention in natural language processing in recent years and is the basis of web text mining. The article improved statistics-based method EMI, then we use improved approach to detect new words in tourism field. The result demonstrates that our method can detect new words significantly, especially in detecting proper nouns and sentiment words which will be helpful in subsequent tasks such as sentiment analysis and word embedding. In additional, this paper analyze parameters which are influential on the effects of new words detection. At last, the article discussed possible application of new word detection in sentiment analysis.

Abstract: Large-scale linear classification is widely used in many areas. Although SVM-based models for ordinal regression problem are considered as the popular leaning techniques, their performance with kernels are often suffering from time consuming. Recently, linear SVC without kernels not only is shown to obtain competitive performance in most of the cases, but also it is considerably faster in training and testing process. However, a few studies have focused on linear SVM-based ordinal regression models. In this paper, we proposed an efficient solver for training the linear Nonparallel Support Vector Ordinal Regression based on alternating direction method of multipliers (ADMM). Our experiments show that the proposed algorithm is suitable for training large document ordinal regression and efficiently obtained desired results.

Abstract: This paper analyzes the influence of capital operations on the performance of listed companies under different market conditions by combining various capital operation modes with the market value management. Random Forest algorithm is adopted and other machine learning methods are used to compare. We find that capital operation is significantly related to market value management and different capital operations have different effects on companies in different market conditions. In addition, Random Forest algorithm has the highest classification accuracy in different market environments and is more stable under different thresholds. Our findings will help to establish a market or industry benchmark which provides a scientific basis and decision support to the target companies when they operate their capitals.

Abstract: Recent years have witnessed more and more success of hash methods for building efficient classifiers, but less for prediction in machine learning. In this paper, we propose a hash based method for large scale nonparallel support vector machine prediction(HNPSVM). Our key idea of this method is that we use an approximal decision function instead of exact decision function by computing the Hamming distance between hashing the normal to the hyperplane of the classifier and the features. This method benefits nonparallel support vector(NPSVM) prediction in three aspects. First, it enhances the prediction accuracy using an flexible and general method. Second, the proposed HNPSVM reduce storage cost owing to the compact binary hash representation. Last, HNPSVM can speed up the computation of classification function. Moreover, we prove that the classification results of a hash based NPSVM classifier converge to the results of the exact NPSVM classifier as the number of binary hash functions tends to infinity. Several experiments on large scale data sets show the efficient of our method.

Abstract: Recently, two-sided best fitting hyperplane classifier (2S-BFHC) is proposed, which has several significant advantages over previous proximal hyperplane classifiers. Moreover, Concave-Convex Procedure (CCCP) has already been provided to solve the dual problem of 2S-BFHC. In this paper, we solve the primal problem of 2S-BFHC by the alternating direction method of multipliers (ADMM) which is well suited to solve the distributed optimization problem, and we also propose a robust L1-norm two-sided best fitting hyperplane classifier (L1-2S-BFHC) with ADMM, which aims at giving a robust performance for the problem with outliers. Priliminary numerical results demonstrate the effectiveness of proposed methods.

Abstract: In this paper, we first review some important aspects of asset allocation for some typical large Social Security Reserve Funds (SSRFs) in the world. Then we present the mean-variance model with CVaR constraints as asset allocation methodology. Concerning the real circumstance in China, we apply the model to pension fund asset allocation. The empirical results show that to maintain purchase power of pension fund, certain proportion should be invested in stocks as well as direct equity investments. We also find that time horizon significantly influence asset allocation of pension fund. If time horizon is longer, more allocations to stocks and equity investments help the pension fund to achieve better performance.

Abstract: Short-term electricity price forecasting is a critical issue for the operation of both electricity markets and power systems. An ensemble method composed of Empirical Mode Decomposition (EMD), Kernel Ridge Regression (KRR) and Support Vector Regression (SVR) is presented in this paper. For this purpose, the electricity price signal was first decomposed into several intrinsic mode functions (IMFs) by EMD, followed by a KRR which was used to model each extracted IMF and predict the tendencies. Finally, the prediction results of all IMFs were combined by an SVR to obtain an aggregated output for electricity price. The electricity price datasets from Australian Energy Market Operator (AEMO) are used to test the effectiveness of the proposed EMD-KRR-SVR approach. Simulation results demonstrated attractiveness of the proposed method based on both accuracy and efficiency.

Abstract: We propose an application of the metaheuristic algorithm to interbank market reconstruction. This is a simulated annealing algorithm that is considered, and it is Russian interbank market that this is applied to. We consider a network with the 504 largest Russian banks to be compared with corresponding empirical results obtained by Leonidov & Rumyantsev. The topological properties of a graph to be fitted was average in- and out- degree, density and average clustering coefficient. The proposed algorithm of network reconstruction is compared with maximum entropy, minimum density, low density methods. Results shown the efficiency of the approach.

Abstract: This paper presents experimental results of cluster analysis using self organising neural networks for identifying failing banks. The paper first describes major reasons and likelihoods of bank failures. Then it demonstrates an application of a self-organising neural network and presents results of the study. Findings of the paper demonstrate that a self-organising neural network is a powerful tool for identifying potentially failing banks. Finally, the paper discusses some of the limitations of cluster analysis related to understanding of the exact meaning of each cluster.

Abstract: This paper presents the performance of seven portfolios created using clustering analysis techniques to sort out assets into categories and then applying classical optimization inside every cluster to select best assets inside each asset category. The proposed clustering algorithms are tested constructing portfolios and measuring their performances over a two month dataset of 1-minute asset returns from a sample of 175 assets of the Russell 1000® index. A three-week sliding window is used for model calibration, leaving an out of sample period of five weeks for testing. Model calibration is done weekly. Three different rebalancing periods are tested: every 1, 2 and 4 hours. The results show that all clustering algorithms produce more stable portfolios with similar volatility. In this sense, the portfolios volatilities generated by clustering algorithms are smaller when compare to the portfolio obtained using classical Mean-Variance Optimization (MVO) over all the dataset. Hierarchical clustering algorithms achieve the best financial performance obtaining an adequate trade-off between accumulated financial returns and the risk-adjusted measure Omega ratio during the out of sample testing period.

Abstract: The purpose of this study was to detect latent periodicity in the presence of deletions or insertions in the analyzed data, when the points of deletions or insertions are unknown. A mathematical method was developed to search for periodicity in the numerical series, using dynamic programming and random matrices. The developed method was applied to search for periodicity in the Euro/Dollar (Eu/$) exchange rate. Period length equal to 24 and 25 h were found. The reasons for the existence of the periodicity in the financial time series are discussed. The results can find application in computer systems, for the purpose of forecasting exchange rates.