Political booms and currency crises

This paper shows evidence that political booms, defined as the rise in governments’ popularity, are associated with a higher likelihood of currency crises. The reasoning behind this finding is that prudent economic policies to address underlying weaknesses in the economy may be political costly for incumbent governments in the short-term. Hence, popularity-concerned governments may not have enough incentives to take such corrective actions in a timely manner. This approach, in turn, can deteriorate economic fundamentals and increase related risks in the economy which can eventually lead to crises. This paper sheds light on this phenomenon in the case of currency crises, suggesting that currency crises can be viewed as “political booms gone bust” events. Moreover, it finds that higher international reserves, higher exports, and a higher degree of financial openness alleviate the effect of political booms on currency crises.     

金融资产配置与违约风险:蓄水池效应,还是逐利效应？

本文以2007—2016年中国A股上市公司为研究对象,检验了企业金融资产配置对违约风险的影响。实证研究发现:金融资产持有量越多,企业的违约风险越低,金融资产配置的“蓄水池效应”显著;在货币政策宽松时期,金融资产配置导致的代理冲突显现,宽松的货币政策会抑制金融资产投资对违约风险的降低作用。政府规制也会有一定的公司治理作用,将产业政策纳入讨论发现:对于产业政策支持的行业来说,企业金融资产配置能够降低违约风险,但是宽松的货币政策会刺激管理层的短视投资行为,抑制政府规制的公司治理作用。进一步地,本文提出会计稳健性的提升是企业金融资产配置降低违约风险的重要路径。本文的研究结论丰富了企业金融资产配置动机和违约风险影响因素的讨论,能够为政府部门防范经济运行中的内在风险提供有益借鉴。     

委托理财导致上市公司脱实向虚吗?——基于企业创新的视角

依据2008-2017年上市公司委托理财与专利数据,本文实证研究了委托理财对企业创新的影响。研究发现随着委托理财规模与理财收益对公司业绩贡献的增加,上市公司总体上创新数量变化不明显,但创新质量却显著下降。对于不同的委托理财而言,上市公司购买银行与非银行委托理财对创新质量的影响均为负面,但对创新数量的影响却正好相反。两种类型委托理财对创新数量的影响可能相互抵消,从而导致总体上委托理财对创新数量的影响不显著。进一步研究发现,购买更多的委托理财导致上市公司更加倾向于进行风险低的开发性创新,减少风险高的探索性创新。购买银行理财的上市公司更倾向于机会主义创新,而购买非银行理财的上市公司更可能消极创新。总之,基于企业创新视角的分析发现大规模和投机性的委托理财会干扰上市公司创新行为,导致其经营"脱实向虚"。     

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.     

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。     

Reverse Payments and Risk of Bankruptcy Under Private Information

Abstract

In the pharmaceutical industry, a reverse payment (pay-for-delay) is a payment from an originator to a generic producer to delay her entry. In some recent cases, the US and EU antitrust authorities have banned these agreements per se, while in others they have used a rule of reason. This paper analyzes their dynamic effects and shows that a ban per se may reduce consumer surplus when the generic producer may go bankrupt and her financial situation is private information. Reverse payments are more beneficial when competition among few players is soft, the economy is in a downturn, and the period of drug usage after patent expiry is long. Results suggest that a rule of reason is more suited than a ban per se.     

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.     

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.     

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.     

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.