论会计信息相关性和可靠性的关系

相关性和可靠性作为会计信息的两大主要质量特征,一直备受人们的关注。相关性和可靠性到底孰轻孰重,在不同的时期人们有不同的认识。本文认为,在我国现阶段,可靠性应作为第一位的质量特征,并重于相关性。     

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.     

DSGE forecasts of the lost recovery

The years following the Great Recession were challenging for forecasters. Unlike other deep downturns, this recession was not followed by a swift recovery, but instead generated a sizable and persistent output gap that was not accompanied by deflation as a traditional Phillips curve relationship would have predicted. Moreover, the zero lower bound and unconventional monetary policy generated an unprecedented policy environment. We document the actual real-time forecasting performance of the New York Fed dynamic stochastic general equilibrium (DSGE) model during this period and explain the results using the pseudo real-time forecasting performance results from a battery of DSGE models. We find the New York Fed DSGE model’s forecasting accuracy to be comparable to that of private forecasters, and notably better for output growth than the median forecasts from the FOMC’s Summary of Economic Projections. The model’s financial frictions were key in obtaining these results, as they implied a slow recovery following the financial crisis.     

Forecasts in times of crises

Financial crises pose unique challenges for forecast accuracy. Using the IMF’s Monitoring of Fund Arrangements (MONA) database, we conduct the most comprehensive evaluation of IMF forecasts to date for countries in times of crises. We examine 29 macroeconomic variables in terms of bias, efficiency, and information content to find that IMF forecasts add substantial informational value, as they consistently outperform naive forecast approaches. However, we also document that there is room for improvement: two-thirds of the key macroeconomic variables that we examine are forecast inefficiently, and six variables (growth of nominal GDP, public investment, private investment, the current account, net transfers, and government expenditures) exhibit significant forecast biases. The forecasts for low-income countries are the main drivers of forecast biases and inefficiency, perhaps reflecting larger shocks and lower data quality. When we decompose the forecast errors into their sources, we find that forecast errors for private consumption growth are the key contributor to GDP growth forecast errors. Similarly, forecast errors for non-interest expenditure growth and tax revenue growth are crucial determinants of the forecast errors in the growth of fiscal budgets. Forecast errors for balance of payments growth are influenced significantly by forecast errors in goods import growth. The results highlight which macroeconomic aggregates require further attention in future forecast models for countries in crises.     

Optimizing the credit term decisions in supply chain finance

We study a credit term determination problem in the context of a supplier-buyer supply chain. The supplier's credit term decision is simultaneously made with its production and inventory decisions, and most importantly, it is impacted by the buyer's order quantity. We present a new game-theoretic framework to model this problem, which captures the interaction between the supplier's credit term decision and the buyer's order decision in a multi-period setting. An exact method based on nonlinear programming is implemented to obtain the optimal solutions. We apply our methodologies on a real world case. The computational results show that our approach significantly outperforms the heuristics with fixed credit terms, and either a short or a long credit term can be sub-optimal for the supplier in profitability. Our work offers the first data-driven model and solution approach that assists purchasing and supply managers to make optimal dynamic credit term decision in conjunction with production, ordering and inventory decisions in a game-theoretic setting.     

信贷供给、资产配置及企业绩效——来自中国制造业上市公司的证据

近些年,中国“金融与实体经济”的研究愈发成为热点。宏观层面,资金“脱实入虚”“金融空转”的讨论亦此起彼伏。然而,针对微观层面的企业资金“脱实入虚”的研究则寥寥无几。本文选取2003—2016年中国A股制造业上市公司的年度报告数据,利用2009年宏观调控措施营造的“准自然实验”环境,采用双重差分方法探究在资金资源充足的前提下,企业配置金融资产的倾向性,并进一步研究企业绩效的后续变化情况。实证结果表明,当制造业企业获得更多的信贷资金支持,同时缺乏有效的外部监管时,企业更倾向于配置金融资产,金融活动也会在企业的日常生产经营中扮演愈发重要的角色。在此基础上,企业的主营业务却未得到发展,反而盈利能力、成长能力均受到损害,且后者受到的影响更大,即进行过多金融投资对中国A股制造业上市公司的远期影响更为深重。基于上述结论,本文从实体经济从业者、资金提供方以及监管层三个角度提出了相关政策建议。     

Existence of financial equilibria with restricted participation

We consider a two-date model of a financial exchange economy with finitely many agents having nonordered preferences and portfolio constraints. There is a market for physical commodities at any state today or tomorrow and financial transfers across time and across states are allowed by means of finitely many nominal assets or numéraire assets. We prove a general existence result of equilibria for such a financial exchange economy in which portfolios are defined by linear constraints, extending the framework of linear equality constraints by Balasko et al. (1990), and the existence results in the unconstrained case by Cass (1984, 2006), Werner (1985), Duffie (1987), and Geanakoplos and Polemarchakis (1986). Our main result is a consequence of an auxiliary result, also of interest for itself, in which agents’ portfolio constraints are defined by general closed convex sets and the financial structure is assumed to satisfy a “nonredundancy-type” assumption, weaker than the ones in Radner (1972) and Siconolfi (1989).     

金融危机下东北地区中小企业发展现状与分析

目前,中小企业已经成为东北地区经济社会发展的重要组成部分,但发展水平还有待进一步提升。金融危机下,东北地区中小企业应通过加快实现产业升级、完善融资渠道、提高自主创新来提升发展水平。     

The effects of bargaining power on trade credit in a supply network

We develop a multi-tier supply network model, rooted in social network theory, to evaluate the effect of bargaining power on trade credit and to track the effect of buyers' trade credit on suppliers' trade credit. We apply social network analysis to measure companies' bargaining power in the supply network of Hennes & Mauritz AB (H&M, the Swedish clothing retailer). The results show that the buyer's bargaining power significantly affects the choice of trade credit, and that the supplier's “upstreamness” is significantly associated with its trade credit. We find limited evidence to support the notion of a financial bullwhip effect, a result that merits further research, since this study is limited to the network of one company up to its fourth tier of suppliers in one financial year. Our results can be applied by companies seeking to control their cash flow and, therefore, the financial pressure within their supply network. This study contributes to the literature by bringing social network measures into the buyer–supplier financial flow, as well as offering one of the first empirical examinations of the propagation of financial pressure in a multi-tier supply network.     

Industries' heterogeneous reactions during the COVID-19 outbreak: Evidence from Chinese stock markets

This study examines the heterogeneous effects of the COVID-19 outbreak on stock prices in China. We confirm what is already known, that the pandemic has had a significant negative impact on stock market returns. Additionally, we find, this effect is heterogeneous across industries. Second, fear sentiment can directly cause stock prices to fall and panic exacerbates the negative impact of the pandemic on stock returns. Third, and most importantly, we demonstrate the underlying mechanisms of four firm characteristics and find that those with high asset intensity, low labor intensity, high inventory-to-revenue ratio, and small market value are more negatively affected than others. For labor-intensive state-owned firms, in particular, stock performance worsened because of higher idle labor costs. Finally, we created an index to measure the relative position of an industry in the supply chain, which shows that downstream companies were more vulnerable to the effects of the pandemic.