Influential factors on Chinese airlines’ profitability and forecasting methods

We establish profit models to predict the performance of airlines in the short term using the quarterly profit data collected on the three largest airlines in China together with additional recent historical data on external influencing factors. In particular, we propose the application of the LASSO estimation method to this problem and we compare its performance with a suite of other more modern state-of-the-art approaches including ridge regression, support vector regression, tree regression and neural networks. It is shown that LASSO generally outperforms the other approaches in this study. We concluded a number of findings on the oil price and other influential factors on Chinese airline profitability.     

Moral hazard and subsidized crop insurance

Along with adverse selection, moral hazard is one of the major hurdles that private and public insurance plans must contend with. Moral hazard occurs if risks are endogenous to a producer's behavior and if the insurer is unable to properly monitor the insured. We review the role of moral hazard in the US crop insurance program. We conduct an empirical analysis of one important aspect of the US crop insurance program—prevented planting. This provision provides indemnity payments if conditions are not suitable for planting. The program has been the subject of considerable controversy, especially during 2019, when the rate of claims is expected to be especially high. Because loss adjustors may encounter difficulties in assessing the weather conditions associated with prevented planting claims, the program is susceptible to moral hazard. We consider the extent to which prevented planting claims may be endogenous to prices. We find significant evidence of moral hazard. The likelihood of prevented planting claims increases as the expected market price decreases or as fertilizer costs increase for corn and soybeans in the Prairie Pothole Region and for grain sorghum and cotton in all states.     

Investor Pessimism and the German Stock Market: Exploring Google Search Queries

We analyze the relationship of retail investor sentiment and the German stock market by introducing four distinct investor pessimism indices (IPIs) based on selected aggregate Google search queries. We assess the predictive power of weekly changes in sentiment captured by the IPIs for contemporaneous and future DAX returns, volatility and trading volume. The indices are found to have individually varying, but overall remarkably high explanatory power. An increase in retail investor pessimism is accompanied by decreasing contemporaneous market returns and an increase in volatility and trading volume. Future returns tend to increase while future volatility and trading volume decrease. The outcome is in line with the conjecture of correction effects. Overall, the results are well in line with modern investor sentiment theory.     

基于LASSO变量选择方法的投资组合及实证分析

由于传统的Markowitz均值方差模型具有很强的不稳定性,且容易产生较大的空头头寸,因此,资产选择成为构建投资组合的一个研究热点。针对以风险分散和指数跟踪为目的的资产组合构建提出了基于变量选择观点的LASSO选择方法,说明了LASSO方法在资产选择中的优势,并应用LASSO方法使用2010年3月到2012年2月的日收益率数据对沪深300指数的指数跟踪做了实证检验,结果表明LASSO方法在组合构建和预测中都有很好的效果。     

农业产业化联合体实现机制及其决定因素——基于家庭农场调查数据的实证分析

[目的]农业产业化联合体是我国乡村产业融合发展的高级形态,对农业现代化起关键作用。探究家庭农场对联合体的满意情况及其影响因素,对于联合体的持续稳定发展有重要的现实意义。[方法]文章利用2020年对安徽合肥、亳州、六安和宿州等9个地市的农业产业化联合体调查数据,分析了安徽省各地区、各生产类型的家庭农场对农业产业化联合体满意情况,并对满意度影响因素采用多分类有序Logistic回归模型进行了实证分析。[结果]各地区的家庭农场对联合体的满意情况存在一定差异,但不同类型的家庭农场满意差距不大。家庭农场满意度受到结构因素的影响,其中种养规模、共同目标制定、沟通程度对满意度产生正向影响,而文化程度与机会主义行为对满意度产生了负向影响。过程因素中的合同履行与监督制度对满意度产生正向影响,结果因素中体现经济与社会效益的利润率增长和劳动力带动对满意度有正向影响。[结论]积极扩大家庭农场的生产规模、完善联合体的监督管理制度、加强利益联结等措施是实现农业产业化联合体可持续发展的关键。     

A novel LASSO – TLBO – SVR hybrid model for an efficient portfolio construction

In this study a LASSO – TLBO – SVR hybrid model is used for portfolio construction. Relevant economic parameters are determined and used for stock selection. Along with stock selection, weights for the stocks are obtained by solving a portfolio optimization problem using three methods: GRG Nonlinear, Evolutionary method based on Genetic Algorithm, and Equal weight method. The portfolio return in the proposed model is compared with the return of the Indian market portfolio (NSE and BSE). It is observed that the proposed model outperforms the market portfolio.     

Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives

Despite the clear success of forecast combination in many economic environments, several important issues remain incompletely resolved. The issues relate to the selection of the set of forecasts to combine, and whether some form of additional regularization (e.g., shrinkage) is desirable. Against this background, and also considering the frequently-found good performance of simple-average combinations, we propose a LASSO-based procedure that sets some combining weights to zero and shrinks the survivors toward equality (“partially-egalitarian LASSO”). Ex post analysis reveals that the optimal solution has a very simple form: the vast majority of forecasters should be discarded, and the remainder should be averaged. We therefore propose and explore direct subset-averaging procedures that are motivated by the structure of partially-egalitarian LASSO and the lessons learned, which, unlike LASSO, do not require the choice of a tuning parameter. Intriguingly, in an application to the European Central Bank Survey of Professional Forecasters, our procedures outperform simple average and median forecasts; indeed, they perform approximately as well as the ex post best forecaster.     

Determinants of dividend payout and dividend propensity in an emerging market,Iran: an application of the LASSO

Accurate prediction of dividends is important for market participants such as investors, firm managers, and monitoring authorities, as they can, respectively, invest, manage dividend decisions, and monitor dividend policies more effectively. We identify the most relevant variables for predicting the dividend payout of the firms in an emerging market, Iran, using the least absolute shrinkage and selection operator (LASSO). The advantages of the LASSO include: enhancing the prediction accuracy of the dividend model, improving interpretation of the results, and applicability to high-dimensional data. We obtain several results. First, some fundamental determinants of dividends in the industrialized economies such as market-to-book ratio and current ratio, do not play a role in deciding dividends in Iran. Second, LASSO-selected variables outperform the variables commonly used in the literature in terms of model fit and prediction accuracy. Third, business risk, leverage, return on assets and effective tax rate are the most important predictors of dividend propensity of the Iranian firms. Fourth, if the support vector machine algorithm, an often-used classification method, is combined with LASSO-selected variables, it can better discriminate between dividend-paying and dividend non-paying firms than other methods such as logistic regression and linear discriminant analysis.

Abbreviations: LASSO: Least Absolute Shrinkage and Selection Operator; TSE: Tehran Stock Exchange; RMSE: Root Mean Squared Errors; MAE: Mean Absolute Errors; ROC: Receiver Operating Characteristics; GMM: Generalized Method of Moments; MENA: Middle East and North Africa region; AIC: Akaike Information Criterion; BIC: Bayesian Information Criterion; LARS: Least Angel Regression; OLS: Ordinary Least Squares; AUC: Area Under Curve; BS: Brier Score ; OA: Overall Accuracy; LDA: Linear Discriminant Analysis; SVM: Support Vector Machine algorithm; LR: Logistic Regression.     

具有自回归误差的变系数模型的变量选择

基于局部线性估计方法研究误差具有自回归结构的变系数回归模型,讨论利用估计的残差和LASSO方法对误差部分进行定阶和估计的统计推断问题.通过蒙特卡洛模拟方法对比研究,揭示具有良好性质的局部多项式估计方法的有效性.