Does local political support influence financial markets? A study on the impact of job approval ratings of political representatives on local stock returns

Using data on job approval ratings of governors, U.S. senators, and the president, we find that firms located in states with high approval ratings outperform firms located in states with low approval ratings by .64% per month. Furthermore, this relationship is stronger when investors are actively involved in politics, when local politicians are closer to the center of political power, for small firms that have a larger proportion of local investors, and for financially strong areas where investors are ready to execute investments in local stocks. Overall, our study shows that investors’ political sentiment is important in determining stock returns.     

Determinants of workers’ remittances: An empirical investigation for a panel of eleven developing Asian economies

We explore the key motives of migrant workers’ remittances from abroad for 11 major Asian migrant‐sending countries. Using panel regressions, we find that relative higher growth rate, interest rate and capital market returns of home over the host, investment, financial deepening at home have significant impact on remittance inflows into Asia, along with higher per capita incomes and international crude oil prices. With incorporation of per capita incomes and lagged impact of remittances, we observe an emergence of consumption motives to remit. Therefore, we conclude that both investment and altruistic motives are the driving forces for remittances inflows into the Asian economies.     

Market Reaction to Corporate Social Responsibility Announcements： Evidence from China

This study uses an event study methodology to examine how the Chinese market reacts to announcements of involvement in corporate social responsibilitY （CSR） by Southern Weekend （a Chinese newspaper）for Chinese firms from 2008 to 2012. Our results show significant and pcsitive market reactions, supporting the instrumental stakeholder theory. We attribute the positive market response to social capital development and real growth options related to the CSR involvement by the Chinese firms.     

Abnormal Returns from Takeover Prediction Modelling: Challenges and Suggested Investment Strategies

While takeover targets earn significant abnormal returns, studies tend to find no abnormal returns from investing in predicted takeover targets. In this study, we show that the difficulty of correctly identifying targets ex ante does not fully explain the below‐expected returns to target portfolios. Target prediction models’ inability to optimally time impending takeovers, by taking account of pre‐bid target underperformance and the anticipation of potential targets by other market participants, diminishes but does not eliminate the potential profitability of investing in predicted targets. Importantly, we find that target portfolios are predisposed to underperform, as targets and distressed firms share common firm characteristics, resulting in the misclassification of a disproportionately high number of distressed firms as potential targets. We show that this problem can be mitigated, and significant risk‐adjusted returns can be earned, by screening firms in target portfolios for size, leverage and liquidity.     

Flight-to-quality and correlation between currency and stock returns

We document that capital flows in and out of emerging or developed markets are sensitive to global equity market conditions. Capital tends to move out of emerging into developed countries in global down markets, leading to depreciation (appreciation) of emerging (developed) currencies. This generates a positive (negative) correlation between currency and equity in emerging (developed) markets which is amplified by the magnitude of the capital movement. We also verify that hedging currency risks may undo the natural hedge and increase the total return volatility under negative correlation.     

A nesting framework for Markov-switching GARCH modelling with an application to the German stock market

In this paper, we establish a generalized two-regime Markov-switching GARCH model which enables us to specify complex (symmetric and asymmetric) GARCH equations that may differ considerably in their functional forms across the two Markov regimes. We show how previously proposed collapsing procedures for the Markov-switching GARCH model can be extended to estimate our general specification by means of classical maximum-likelihood methods. We estimate several variants of the generalized Markov-switching GARCH model using daily excess returns of the German stock market index DAX sampled during the last decade. Our empirical study has two major findings. First, our generalized model outperforms all nested specifications in terms of (a) statistical fit (when model selection is based on likelihood ratio tests) and (b) out-of-sample volatility forecasting performance. Second, we find significant Markov-switching structures in German stock market data, with substantially differing volatility equations across the regimes.     

How much do online consumers really value free product returns? Evidence from eBay

Consumer return rates have been steadily rising in recent years, resulting in growing costs for retailers who must manage the returns process and the disposition of returned products. This cost pressure is driven in part by extremely generous return policies, such as giving consumers a full refund upon return. Interestingly, this common retail practice of full refunds is inconsistent with the recommendations of many analytical models of returns, which nearly always show that a partial refund is optimal. Such inconsistencies between theory and practice might arise when the decision drivers included in the analytical models do not match the decision drivers in practice. It might also be the case that retailers are overly optimistic about the value that consumers assign to a full refund, and thus assume that the value of such a policy outweighs its costs. In this paper, we use data collected from eBay, where identical products are sold with different return policies, to investigate these open questions in the literature. We analyze both the return policy drivers from the retailer's perspective and the return policy value from the consumer's perspective. Our results suggest that the value of a full refund policy to consumers may not be as large as one might expect, and it also exhibits a large heterogeneity across buyers with different levels of online purchase experience. In addition, we provide empirical evidence for what has long been suspected by online retailers – that a non-refundable forward shipping charge quickly erodes any value that consumers assign to return policies. The generality of our results is limited by the fact that eBay differs from traditional retail contexts in many respects, including the fact that eBay buyers may not be representative of the general buyer population. However, our study of how eBay consumers value free returns provides new insights into an understudied area, and it can serve as a starting point for future studies of the value of return policies in other retail contexts.     

The Cross‐Section of Expected Stock Returns: What Have We Learnt from the Past Twenty‐Five Years of Research?

I review the recent literature on cross-sectional predictors of stock returns. Predictive variables used emanate from informal arguments, alternative tests of risk-return models, behavioural biases, and frictions. More than fifty variables have been used to predict returns. The overall picture, however, remains murky, because more needs to be done to consider the correlational structure amongst the variables, use a comprehensive set of controls, and discern whether the results survive simple variations in methodology .     

Comparing linear and nonlinear forecasts for stock returns

We compare the out-of-sample performance of monthly returns forecasts for two indices, namely the Dow Jones (DJ) and the Financial Times (FT) indices. A linear and a nonlinear artificial neural network (ANN) model are used to generate the out-of-sample competing forecasts for monthly returns. Stationary transformations of dividends and trading volume are considered as fundamental explanatory variables in the linear model and the input variables in the ANN model. The comparison of out-of-sample forecasts is done on the basis of forecast accuracy, using the Diebold and Mariano test [J. Bus. Econ. Stat. 13 (1995) 253.], and forecast encompassing, using the Clements and Hendry approach [J. Forecast. 5 (1998) 559.]. The results suggest that the out-of-sample ANN forecasts are significantly more accurate than linear forecasts of both indices. Furthermore, the ANN forecasts can explain the forecast errors of the linear model for both indices, while the linear model cannot explain the forecast errors of the ANN in either of the two indices. Overall, the results indicate that the inclusion of nonlinear terms in the relation between stock returns and fundamentals is important in out-of-sample forecasting. This conclusion is consistent with the view that the relation between stock returns and fundamentals is nonlinear.     

递增阶梯定价:一个综述

作为较独特的非线性定价,递增阶梯定价使消费者预算凸边界非光滑,进而导致某些消费者消费决策异度集中而难以甄别。此特征致使递增阶梯定价机制的理论和实证研究非常复杂。鉴于公共资源的特殊性,公共资源定价需要合理地兼顾效率、社会公正和成本完全补偿原则。在多元化目标角度上,本文初步验证了递增阶梯定价机制的最优性,虽然此最优性受制于接入率和特征信息等假定。递增阶梯定价下的需求设定趋于统一但估计方法却趋于复杂化和多样化。关于递增阶梯定价下的(价格和收入)需求效应明显与否的问题存在完全相反的结果,实证研究和理论对其不一致性给出多种解释和探讨。这种差异性可能由于消费者对价格、价格设计者对家庭特征等信息不完全,还可能因为各实证分析结果中的时间、市场或产品特征维度含义差异所致。基于多元化目标和资源特性的递增阶梯定价机制的设计和执行问题,将成为递增阶梯定价理论研究发展的一大方向。