Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods

Financial distress prediction (FDP) has been widely considered as a promising approach to reducing financial losses. While financial information comprises the traditional factors involved in FDP, nonfinancial factors have also been examined in recent studies. In light of this, the purpose of this study is to explore the integrated factors and multiple models that can improve the predictive performance of FDP models. This study proposes an FDP framework to reveal the financial distress features of listed Chinese companies, incorporating financial, management, and textual factors, and evaluating the prediction performance of multiple models in different time spans. To develop this framework, this study employs the wrapper-based feature selection method to extract valuable features, and then constructs multiple single classifiers, ensemble classifiers, and deep learning models in order to predict financial distress. The experiment results indicate that management and textual factors can supplement traditional financial factors in FDP, especially textual ones. This study also discovers that integrated factors collected 4 years prior to the predicted benchmark year enable a more accurate prediction, and the ensemble classifiers and deep learning models developed can achieve satisfactory FDP performance. This study makes a novel contribution as it expands the predictive factors of financial distress and provides new findings that can have important implications for providing early warning signals of financial risk.     

The industrial asymmetry of the stock price prediction with investor sentiment: Based on the comparison of predictive effects with SVR

As a representative emerging financial market, the Chinese stock market is more prone to volatility because of investor sentiment. It is reasonable to use efficient predictive methods to analyze the influence of investor sentiment on stock price forecasting. This paper conducts a comparative study about the predictive performance of artificial neural network, support vector regression (SVR) and autoregressive integrated moving average and selects SVR to study the asymmetry effect of investor sentiment on different industry index predictions. After studying the relevant financial indicators, the results divide the Shenwan first-class industries into two types and show that the industries affected by investor sentiment are composed of young companies with high growth and high operative pressure and there are a great number of investment bubbles in those companies.     

Curcin和curcin C与腺嘌呤互作位点的比较分析

麻疯树核糖体失活蛋白curcin和curcin C均具有抗肿瘤活性，但后者的活性水平显著高于前者，为了探索造成这一差异的结构基础，本文采用在线的同源建模预测软件SWISSMODEL，对两种麻疯树核糖体失活蛋白curcin、curcin C的三维结构模型进行预测，采用SYBYL对预测模型进行能量最小化优化，采用PROCHECK、VERIFY 3D和ERRAT软件对优化前后的模型进行质量评估，随后采用AutoDock软件将预测的模型与腺嘌呤进行分子对接分析.结果显示，两种蛋白采用与Ricin A类似的方式与腺嘌呤相互作用，但在相互作用的氨基酸残基种类、数量以及形成的氢键和疏水相互作用上还是存在差异，其中，curcin C与腺嘌呤具有最强的结合能力，curcin则最低.     

The Predictive Performance Evaluation of Biased Regression Predictors With Correlated Errors

When the interdependence of disturbances is present in a regression model, the pattern of sample residuals contains information which is useful in the prediction of post‐sample drawings and when multicollinearity among regressors is also present, it is useful to use biased regression estimators. This information is exploited in the biased predictors derived here. Also, the predictive performance of various biased predictors with correlated errors is discussed and all pair‐wise comparisons are made among these predictors. The theoretical results are illustrated by a numerical example. Copyright © 2015 John Wiley & Sons, Ltd.     

Dynamic Latent Class Model Averaging for Online Prediction

We consider the problem of online prediction when it is uncertain what the best prediction model to use is. We develop a method called dynamic latent class model averaging, which combines a state‐space model for the parameters of each of the candidate models of the system with a Markov chain model for the best model. We propose a polychotomous regression model for the transition weights to assume that the probability of a change in time depends on the past through the values of the most recent time periods and spatial correlation among the regions. The evolution of the parameters in each submodel is defined by exponential forgetting. This structure allows the ‘correct’ model to vary over both time and regions. In contrast to existing methods, the proposed model naturally incorporates clustering and prediction analysis in a single unified framework. We develop an efficient Gibbs algorithm for computation, and we demonstrate the value of our framework on simulated experiments and on a real‐world problem: forecasting IBM's corporate revenue. Copyright © 2014 John Wiley & Sons, Ltd.     

考虑车辆间交互作用的驾驶意图预测方法

准确的意图预测可以帮助智能车辆更好地了解周围环境并做出更加安全的决策，从而提高自动驾驶的安全性，促进人机协同驾驶。为了对驾驶员未来的意图做出更加精准的预测，提出了一种交互式意图预测方法。首先，通过将隐马尔可夫模型（HMM）与高斯混合模型（GMM）相结合，在充分考虑周围场景信息后建立了行为识别模型，用于对当前的驾驶行为做出准确的判断。然后，考虑到交通场景复杂多变的特点，提出基于意图的轨迹预测方法规划出一条最佳的行驶轨迹，并采用最大期望效用理论对未来的驾驶行为进行推理。由于行为识别和意图推理模型综合考虑了交通态势的演变过程和车辆之间的交互作用，所以将两个模型得到的结果相结合可得到车辆最终预测出的驾驶意图。最后，在NGSIM数据集对所提出的方法进行验证，结果表明提出的行为识别模型能够提前0.2~0.3 s识别出车辆的换道意图，结合未来意图推理模型，能够更加准确地预测出车辆未来的驾驶行为，由此可提高车辆驾驶的安全性。     

保定‒雄安地区近地面大气流动与轨迹输送特征

基于2016—2017年22个地面气象站点的观测资料和NECP-fnl数据, 使用CALMET风场诊断模式, 计算保定?雄安地区两个年份的逐时风场和每日输送轨迹。根据风场与流动轨迹特性, 从大气扩散输送的角度, 将当地大气流动划分为系统大风型、局地环流型与弱系统影响型 3 类。统计分析结果表明: 大气流动3个类型的大致比例为10%, 50%和40%; 系统大风型流动的占比较低, 且春夏季较多, 秋冬季较少; 局地环流型流动以秋冬季较多, 春夏季较少; 弱系统影响型的出现频率在全年变化不大, 但在秋冬季出现频率稍低; 大气流动受西北部山地影响, 山地-平原风可控制山前约100 km的范围, 覆盖保定市和雄安新区大部; 山地-平原风的转换可在山前区域形成部分时段的小风状况; 该地区大气输送的主要路径是西南-东北方向, 对应大尺度背景流动情况; 西北-东南方向的横向输送距离较短, 对应山地-平原风地形环流的影响; 在系统大风和弱系统影响下, 冬春季会出现部分直接向东南方向的输送轨迹。     

由粗到精和特征筛选的精确回归预测方法及其在二语习得中的应用

针对数据分布不均匀且因素多而容易造成预测不精确的问题,提出一种结合由粗到精与特征筛选的精确回归预测方法.首先,由于数据分布不均匀且预测区间大,直接预测难以精确地拟合,提出一种由粗到精的预测方法,并使用决策树进行粗分类,预测目标所在的子区间,然后在子区间内实现精确的回归预测.其次,如果数据量少且特征因素多会引起过拟合,而且部分冗余特征会影响模型的预测精度,因此,提出一种基于特征筛选的回归预测方法以提高预测精度.在大学生的英语成绩与其人格因素数据集上进行相关实验,结果证明了由粗到精和特征筛选方法与传统回归模型相比精度更高且稳定性更好.通过提出的人格因素与英语成绩回归预测模型,可以制定合理的培养方案弥补学生人格因素中的短板,提升学生的自身竞争能力,从而更好地推动中国的英语教育.     

利用反预报方法从截面预言近滴线丰质子核的结合能

在Origin等数据处理软件中,当实验数据较少时,自由参数的不同初始化设置会导致较大的结果差异,这为物理结果的确定带来较大不确定性.通过最小二乘法分析了345MeV/u ~(78) Kr+~9Be反应中产生的丰质子同位素的截面和结合能,并得到线性回归方程.通过回归方程,利用结合能预报部分丰质子核素的截面,以及通过实验截面对近质子滴线核素的结合能进行反预报测量.这对于近质子滴线的丰质子核素实验测量具有较好的借鉴意义.     

基于自适应变异PSO-SVM的APU性能参数预测模型

为提高辅助动力装置(auxiliary power unit, APU)性能参数预测的精度,针对支持向量机(support vector machine, SVM)模型在实际使用中遇到的参数选择问题,采用自适应变异粒子群优化(particle swarm optimization, PSO)算法实现对SVM惩罚参数和核参数的优化选择,提出一种基于自适应变异PSO算法优化SVM的APU性能参数预测模型。进一步分析了预测模型不同预测步长对短期预测精度的影响。利用某型APU性能参数数据进行了验证,并与多种预测模型进行了对比实验。实验结果表明,对于排气温度(exhaust gas temperature, EGT)的预测,自适应变异PSO-SVM模型的平均绝对百分比误差(mean absolute percentage error, MAPE)比标准PSO-SVM模型低47%;对于滑油温度(oil temperrature, OT)的预测,自适应变异PSO-SVM模型的MAPE比标准PSO-SVM低29%,为短期APU性能变化趋势预测提供了一定的参考。