Real income convergence and the patterns of financial integration in the EU

We analyse real income convergence among the EU28 countries throughout 1995–2017 and the relationship with the convergence patterns of financial systems. We apply the nonlinear latent factor model of Phillips and Sul (2007, 2009) to real incomes and the IMF financial development indices for financial markets and financial institutions (Svirydzenka, 2016), and identify convergence clubs endogenously. We have several results. First, income disparities narrowed significantly over the last twenty years; yet, the growth convergence process lost momentum triggered by the global financial crisis and countries legacies shaped up asymmetries that have crystallised. Second, countries' financial systems exhibit high fragmentation, especially for financial markets, with the new EU member states at a lower financial development, confirming the existence of a two-tier Europe. Overall, the convergence patterns for real incomes and financial development are strongly correlated. Finally, the financial structure matters and market-oriented economies feature higher long-run growth, indicating the need to implement pan-European policy actions that increase the opportunities of risk diversification, enhance capital raising and channel large-scale financing to firms.     

经济开放与货币需求:国际金融风险及持币成本的测度

在中国开放经济体制下的基准货币需求模型中,本文将源于国际金融市场的持币成本设为遗漏潜变量,并构建特定的国际金融综合指数(CIFI)作为该潜变量的测度。借鉴机器学习与测度理论,本文利用对数误差修正模型提出了分步降维的CIFI构造算法,构造了长期CIFI和短期CIFI。结果表明,CIFI构造中的无监督降维步骤有助于减少高维金融数据中的冗余信息。实证分析发现,国际机会成本对中国货币需求具有规律性的前导影响,而在2007至2008年国际金融危机期间,央行的应急措施对长期CIFI所代表的非均衡冲击起到明显的阻截效果,对短期CIFI的影响基本是持续不变的。通过综合指数构造与宏观货币需求模型的算法连接,可以利用CIFI的构成结构从前导时间与影响强度两方面追踪冲击货币需求的国际金融风险的具体来源,这为宏观决策者监测国际金融市场提供了颇有规律的信息。在方法论上,本研究为如何利用模型监测国际金融市场影响宏观经济开辟了一条新路。     

Exploring the links among corporate social responsibility,reputation, and performance from a multi-dimensional perspective

This study examines the links among corporate social responsibility (CSR), reputation, and performance in hotel companies from a multidimensional perspective. Data were collected from 322 hotels in China and partial least squares equation modelling (PLS-SEM) was employed for a causal-predictive analysis. Study results reveal that CSR practices influence hotel reputation as seen from both international and local perspectives. CSR and reputation influence performance dimensions (accounting-based performance, market-based performance, and non-financial performance) differently. The research findings offer specific theoretical and practical implications for hotel managers.     

Data vs. information: Using clustering techniques to enhance stock returns forecasting

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms.We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models.These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.     

How do valuations impact outcomes of asset sales with heterogeneous bidders?

Differences among bidder type-specific outcomes of asset sales are theoretically related to differences in bidders’ valuations and participation. The lead application to quantify these relations is takeover auctions: bidders are classified into strategic and financial, and bids are available. I structurally estimate valuations from all bids. The positive difference in premiums between strategic and financial acquirers is driven by the difference in dispersions of valuations (e.g., strategic bidders’ synergies are more dispersed) and the set of auction participants. The difference in average valuations is relatively unimportant. My approach can help explain outcomes of asset sales, even in settings with limited bidder data.     

Is it U.S. GAAP or IFRS? Understanding how R&D costs affect ratio analysis

While financial reporting standards under U.S. GAAP and IFRS are fundamentally similar, differences do exist that may affect our analysis of company financial statements. This is particularly true when comparing a U.S. company following U.S. GAAP to a firm that uses IFRS. To illustrate, we compare research and development (R&D) accounting methods under both sets of standards and illustrate how they affect the analysis of financial results of firms in a specific industry—automotive manufacturers. Our results provide insight into settings in which differences in R&D accounting may have the greatest impact on financial analysis.     

Improving forecasting performance using covariate-dependent copula models

Copulas provide an attractive approach to the construction of multivariate distributions with flexible marginal distributions and different forms of dependences. Of particular importance in many areas is the possibility of forecasting the tail-dependences explicitly. Most of the available approaches are only able to estimate tail-dependences and correlations via nuisance parameters, and cannot be used for either interpretation or forecasting. We propose a general Bayesian approach for modeling and forecasting tail-dependences and correlations as explicit functions of covariates, with the aim of improving the copula forecasting performance. The proposed covariate-dependent copula model also allows for Bayesian variable selection from among the covariates of the marginal models, as well as the copula density. The copulas that we study include the Joe-Clayton copula, the Clayton copula, the Gumbel copula and the Student’s $t$-copula. Posterior inference is carried out using an efficient MCMC simulation method. Our approach is applied to both simulated data and the S&P 100 and S&P 600 stock indices. The forecasting performance of the proposed approach is compared with those of other modeling strategies based on log predictive scores. A value-at-risk evaluation is also performed for the model comparisons.     

Does ethics improve stock market resilience in times of instability?

This paper compares the resilience of ethical (Islamic and socially responsible) indexes among five developed (US, UK, Japan, Canada, Australia) and three emerging markets (Brazil, India, South Africa) during the period following the 2008 subprime crisis. It relies on a multivariate CAPM-EGARCH model that accounts for sudden changes in volatility through the application of an iterated cumulative sums of squares (ICSS) algorithm on daily data over the sample period 2008–2014 to model time-varying volatility and ensure reliable estimates. The study confirms the lower systemic risk associated with Islamic indexes during the bearish period and reports that SRI, despite being more subject to systemic risk, offered higher alphas in highly integrated markets, while Islamic indexes performed better in less integrated ones. The evidence also reveals a very limited increase in the models’ predictability power from the integration of sudden changes in volatility into the EGARCH models during the full sample period. This limit is more marked during the bearish sub-period. Our findings have important implications for international investment and portfolio diversification perspectives in times of financial downturn.     

Classification of intraday S&P500 returns with a Random Forest

Stock markets can be interpreted to a certain extent as prediction markets, since they can incorporate and represent the different opinions of investors who disagree on the implications of the available information on past and expected events and trade on their beliefs in order to achieve profits. Many forecast models have been developed for predicting the future state of stock markets, with the aim of using this knowledge in a trading strategy. This paper interprets the classification of the S&P500 open-to-close returns as a four-class problem. We compare four trading strategies based on a random forest classifier to a buy-and-hold strategy. The results show that predicting the classes with higher absolute returns, ‘strong positive’ and ‘strong negative’, contributed the most to the trading strategies on average. This finding can help shed light on the way in which using additional event outcomes for the classification beyond a simple upward or downward movement can potentially improve a trading strategy.     

The impact on stock prices of deferral and elimination of internal control audit requirement for small firms

This paper examines the cumulative market reaction to the events related to deferral of internal control audit requirement under the Sarbanes-Oxley Act of 2002 and its elimination under the Dodd-Frank Act of 2010 for nonaccelerated filers (small firms). We document that small firms experienced negative cumulative abnormal returns around these events; and the differences between the cumulative abnormal returns for small firms and the two control groups (accelerated and large accelerated filers) were negative and significant at the 1% level. These results support the notion that market participants value the reliability of financial information irrespective of the firm size. Within the small firms, we find no firm characteristic significantly explains the market reaction to the events considered. That is, all small firms lost market value in reaction to the events that delayed and eliminated their internal control audit requirement.