Crude oil is one of the strategic energies and plays an increasingly critical role effecting on the world economic development. The fluctuations of crude oil prices are caused by various extrinsic and intrinsic factors and usually demonstrate complex characteristics. Therefore, it is a great challenge for accurately forecasting crude oil prices. In this study, a self-optimizing ensemble learning model incorporating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sine cosine algorithm (SCA), and random vector functional link (RVFL) neural network, namely ICEEMDAN-SCA-RVFL, is proposed to forecast crude oil prices. Firstly, we employ ICEEMDAN to decompose the raw series of crude oil prices into a group of relatively simple subseries. Secondly, RVFL is used to forecast the target values for each decomposed subseries individually. Due to the complex parameter settings of ICEEMDAN and RVFL, SCA is introduced to optimize the parameters for ICEEMDAN and RVFL in the above decomposition and prediction stages simultaneously. Finally, we assemble the predicted values of all individual subseries as the final predicted values of crude oil prices. Our proposed ICEEMDAN-SCA-RVFL significantly outperforms the single and ensemble benchmark models, as demonstrated by a case study conducted using the time series of West Texas Intermediate (WTI) daily crude oil spot prices.

Determining the event type is one of the main tasks of event extraction (EE). The announcement news released by listed companies contains a wide range of information, and it is a challenge to determine the event types. Some fine-grained event type frameworks have been built from financial news or stock announcement news by domain experts manually or by clustering, ontology or other methods. However, we think there are still some improvements to be made based on the existing results. For example, a legal category has been created in previous studies, which considers violations of company rules and violations of the law the same thing. However, the penalties they face and the expectations they bring to investors are different, so it is more reasonable to consider them different types. In order to more finely classify the event type of stock announcement news, this paper proposes a two-step method. First, the candidate event trigger words and co-occurrence words satisfying the support value are extracted, and they are arranged in the order of common expressions through the algorithm. Then, the final event types are determined using three proposed criteria. Based on the real data of the Chinese stock market, this paper constructs 54 event types (p = 0.927, f = 0.946), and some reasonable and valuable types have not been discussed in previous studies. Finally, based on the unilateral trading policy of the Chinese stock market, we screened out some event types that may not be valuable to investors.

It is an important deployment of the Party Central Committee and the State Council to fully promote the employment of college graduates with higher quality, and salary is an important indicator of quality measurement. This paper takes the cross-sectional data of the employment of graduates from a financial and economic university in 2020 as the sample; whether the actual starting salary is a high salary as the dependent variable; and human capital, social capital, labor market as the explanatory variables and uses R to establish a logistic regression model to analyze the determinants of the high salary of graduates. Five machine learning methods, SVM, naive Bayes, CART, random forest, and XGBoost, are used to predict whether graduates can get a high starting salary, compare the advantages and disadvantages of various methods horizontally, optimize the parameters at the same time, and further enhance the performance of the model. Based on the employment data of graduate students in a university of finance and economics in 2020, this paper makes an empirical study. The study shows that academic qualifications, professional disciplines, employment regions, employment industries, the nature of employment units, gender, and whether they have served as student cadres have a significant impact on whether graduates can get “high salaries.” The main factors affecting the starting salary of graduates are the accumulation of human capital and social capital, but the segmentation of labor market is also the main reason affecting the starting salary of graduates. The prediction results of several models show that the integrated models have better performance than single models, and the XGBoost model is the best, which can help predict whether graduates get high salary.