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.     

Short‐term salmon price forecasting

This study establishes a benchmark for short‐term salmon price forecasting. The weekly spot price of Norwegian farmed Atlantic salmon is predicted 1–5 weeks ahead using data from 2007 to 2014. Sixteen alternative forecasting methods are considered, ranging from classical time series models to customized machine learning techniques to salmon futures prices. The best predictions are delivered by k‐nearest neighbors method for 1 week ahead; vector error correction model estimated using elastic net regularization for 2 and 3 weeks ahead; and futures prices for 4 and 5 weeks ahead. While the nominal gains in forecast accuracy over a naïve benchmark are small, the economic value of the forecasts is considerable. Using a simple trading strategy for timing the sales based on price forecasts could increase the net profit of a salmon farmer by around 7%.     

A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.     

Forecasting the Daily Time‐Varying Beta of European Banks During the Crisis Period: Comparison Between GARCH Models and the Kalman Filter

This intention of this paper is to empirically forecast the daily betas of a few European banks by means of four generalized autoregressive conditional heteroscedasticity (GARCH) models and the Kalman filter method during the pre‐global financial crisis period and the crisis period. The four GARCH models employed are BEKK GARCH, DCC GARCH, DCC‐MIDAS GARCH and Gaussian‐copula GARCH. The data consist of daily stock prices from 2001 to 2013 from two large banks each from Austria, Belgium, Greece, Holland, Ireland, Italy, Portugal and Spain. We apply the rolling forecasting method and the model confidence sets (MCS) to compare the daily forecasting ability of the five models during one month of the pre‐crisis (January 2007) and the crisis (January 2013) periods. Based on the MCS results, the BEKK proves the best model in the January 2007 period, and the Kalman filter overly outperforms the other models during the January 2013 period. Results have implications regarding the choice of model during different periods by practitioners and academics. Copyright © 2016 John Wiley & Sons, Ltd.     

The US Dollar/Euro Exchange Rate: Structural Modeling and Forecasting During the Recent Financial Crises

The paper investigates the determinants of the US dollar/euro within the framework of the asset pricing theory of exchange rate determination, which posits that current exchange rate fluctuations are determined by the entire path of current and future revisions in expectations about fundamentals. In this perspective, we innovate by conditioning on Fama–French and Carhart risk factors, which directly measures changing market expectations about the economic outlook, on new financial condition indexes and macroeconomic variables. The macro‐finance augmented econometric model has a remarkable in‐sample and out‐of‐sample predictive ability, largely outperforming a standard autoregressive specification. We also document a stable relationship between the US dollar/euro Carhart momentum conditional correlation (CCW) and the euro area business cycle. CCW signals a progressive weakening in economic conditions since June 2014, consistent with the scattered recovery from the sovereign debt crisis and the new Greek solvency crisis exploded in late spring/early summer 2015. Copyright © 2016 John Wiley & Sons, Ltd.     

欧美股票市场对我国股票市场的传染效应研究——基于次贷与欧债危机背景

近年来,金融危机频频爆发且易表现出传染性,这使得金融传染引起国内外学者的高度关注.本文选取合适的动态条件相关模型研究欧美市场与A股、港股市场的条件相关性,结合内生多重结构突变模型与T检验方法划分危机传染期与平稳期,选用考虑外部影响的CCK模型研究A股、港股市场的羊群行为,随后,引入收益率分散度指标,研究两次危机的羊群行为传染渠道.研究结果表明:港股市场受两次危机传染的速度均快于A股市场,受传染的持续时间均长于A股市场,但受传染的程度均弱于A股市场:次贷危机传染程度强于欧债危机的传染程度,但传染的持续时间短于欧债危机:羊群行为传染渠道是两次危机对A股、港股市场的传染渠道之一.     

公司层次信息技术投资生产率的实证研究

随着公司将大量资金投入信息技术,研究人员和企业界对于信息技术投资价值问题给予越来越多的关注.研究表明,信息技术投资并未导致公司绩效的提高,存在所谓的信息技术投资"生产率悖论"问题.利用中国上市公司的公开数据,研究了信息技术投入对公司主营收入和净利润等产出的贡献;搜集了200多家上市公司的财务数据,时间从1999～2004年;建立了三要素的经典生产函数,利用广义最小二乘法对回归方程进行估计.结果表明:公司的信息技术投资,对主营收入、净利润等绩效指标具有积极的影响.信息技术投资是一种生产性投资,对产出具有积极的贡献.     

金融系统的资源配置功能与经济增长

建立内生增长模型研究金融系统资源配置功能如何促进经济增长 .结论 :通过提高“创新平均成功概率”,增加“创新投资比例”、“储蓄转化比例”和储蓄率 ,金融系统能够促进经济快速稳定增长 .另外本文还得到产出 -资本比“阶跃”增长图象.     

非国有企业收购国有控股权绩效实证分析

选取了1997年到2004年中国证券市场上的非国有企业收购国有控股权的72个案例为样本,通过财务数据法以及事件分析法,对目标公司并购前后的财务绩效以及股票价格的变化进行了实证分析.研究发现,目标公司在被收购前业绩普遍比较差,与同行业平均水平相比,被收购前的财务指标均显著地小于0.并购后目标公司的财务指标有明显的好转,可见通过转让国有股权完成非国有企业并购国有企业是比较有效的.然而在证券市场上并没有得到充分的反映,股价仅表现为短期的投机性上涨.     

金融危机背景下影响购房者消费心理之因素探析

分析了金融危机大背景下影响我国消费者购房心理的主要因素,阐述了目前国家推动房地产行业健康发展所出台的优惠政策,提出从消费者层面恢复购房者信心的一些想法。