唧唧堂：JAR 会计研究杂志 2022年5月刊论文摘要7篇
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1.风险信息的衡量
本文开发了一种衡量信息事件如何影响投资者风险预期的方法。该衡量方式适用范围广，实施简单。本文从期权定价模型中推导出该衡量方式，在该模型中，投资者预期的公告会同时传达有关公告的预期未来现金流和风险状况的信息。本文使用公司的盈余公告实证检验该衡量方式，表明它与本文模型的预测密切相关，能够有效预测公司的风险状况、资本成本和未来投资。本文进一步表明，使用期权隐含波动率的简单变化来研究从盈余公告中收集信息的方法存在缺陷。最后，本文将开发出的衡量方式应用于研究披露监管、基于文本衡量的效果和市场范围内的事件，本文用这些来说明该衡量方式的用途，并阐明其潜在的局限性。
We develop a measure of how information events impact investors' expectations of risk. The measure is broadly applicable and simple to implement. We derive it from an option-pricing model, where investors anticipate an announcement that simultaneously conveys information on the announcer's expected future cash flows and risk profile. We empirically implement the measure using firms' earnings announcements, showing that it closely aligns with our model's predictions and offers strong forecasting power for firms' risk profiles, costs of capital, and future investments. We further highlight pitfalls of using simple changes in option-implied volatilities to study information gleaned from earnings announcements. Finally, we apply our measure to study disclosure regulation, the efficacy of text-based proxies, and market-wide events, which we use to illustrate our measure's uses, and illuminate its potential limitations.
论文原文：SMITH, K.C. and SO, E.C. (2022), Measuring Risk Information. Journal of Accounting Research, 60: 375-426. https://doi.org/10.1111/1475-679X.12413
2. 炸弹货币：链上转账行为对恐怖袭击的预测能力
本研究检验了以区块链的转账行为预测恐怖袭击融资情况的可能性。本文利用区块链交易的透明度来映射数百个大型链上服务提供商的数百万次转账。首先，本文分析了在大规模高度可见的恐怖袭击附近的异常转账量。本文记录了属于不受监管交易和混合服务的硬币钱包活动加剧的证据——这是恐怖组织和地面行动者之间洗钱的核心。其次，本文使用法证会计技术来追踪与斯里兰卡复活节爆炸案有关的资金轨迹。这一事件证实了本文的发现，同时有助于本文构建一个基于区块链的预测模型。最后，利用机器学习算法，本文证明了资金轨迹在样本外分析中具有预测能力。本文向研究人员、监管者和市场参与者提供了信息，构建了使用会计知识和技术检测基于区块链系统的恐怖主义资金流动的方法。
This study examines whether we can learn from the behavior of blockchain-based transfers to predict the financing of terrorist attacks. We exploit blockchain transaction transparency to map millions of transfers for hundreds of large on-chain service providers. The mapped data set permits us to empirically conduct several analyses. First, we analyze abnormal transfer volume in the vicinity of large-scale highly visible terrorist attacks. We document evidence consistent with heightened activity in coin wallets belonging to unregulated exchanges and mixer services—central to laundering funds between terrorist groups and operatives on the ground. Next, we use forensic accounting techniques to follow the trails of funds associated with the Sri Lanka Easter bombing. Insights from this event corroborate our findings and aid in our construction of a blockchain-based predictive model. Finally, using machine-learning algorithms, we demonstrate that fund trails have predictive power in out-of-sample analysis. Our study is informative to researchers, regulators, and market players in providing methods for detecting the flow of terrorist funds on blockchain-based systems using accounting knowledge and techniques.
论文原文：AMIRAM, D., JØRGENSEN, B.N. and RABETTI, D. (2022), Coins for Bombs: The Predictive Ability of On-Chain Transfers for Terrorist Attacks. Journal of Accounting Research, 60: 427-466. https://doi.org/10.1111/1475-679X.12430
3. 使用机器学习和详细财务数据预测未来收益变化
本文使用机器学习方法和高维详细财务数据来预测未来一年盈余的变化方向。本文模型显示出显著的样本外预测能力：ROC曲线下面积范围为67.52% 到68.66%，显著高于随机游走模型的50%。根据本文模型预测形成的对冲投资组合的年度规模调整回报率在5.02%到9.74%之间。本文模型比使用逻辑回归的模型、使用少数会计指标的模型、专业分析师的预测更具有预测能力。进一步分析表明，与传统模型相比，机器学习模型的出色表现源于使用了传统回归所没有考虑的变量之间的非线性交互关系，以及机器学习使用了更详细的财务数据。
We use machine learning methods and high-dimensional detailed financial data to predict the direction of one-year-ahead earnings changes. Our models show significant out-of-sample predictive power: the area under the receiver operating characteristics curve ranges from 67.52% to 68.66%, significantly higher than the 50% of a random guess. The annual size-adjusted returns to hedge portfolios formed based on the prediction of our models range from 5.02% to 9.74%. Our models outperform two conventional models that use logistic regressions and small sets of accounting variables, and professional analysts’ forecasts. Analyses suggest that the outperformance relative to the conventional models stems from both nonlinear predictor interactions missed by regressions and the use of more detailed financial data by machine learning.
论文原文：CHEN, X., CHO, Y.H., DOU, Y. and LEV, B. (2022), Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data. Journal of Accounting Research, 60: 467-515. https://doi.org/10.1111/1475-679X.12429
4. 经理人的身体扩张、投资者认知、公司预测误差和估值
本文研究了衡量经理人的体态——身体扩张——与有利的报告做法（在公司预测和估值信息中）和绩效（生存和融资成功）之间的关系。本文录制了154位企业家宣传其商业理念的视频，并使用计算机视觉软件获取有关演讲者动作的信息。本文表明，身体的扩张程度与较高的预测误差和拟议的公司估值相关，且也与较低的生存率以及较高的融资成功可能性相关。本文认为，投资者在评估企业家时可能会错误地解读非语言交流，并提出行为解释。本文通过研究投资者对企业家个人特征的看法进一步证实了所提出的机制。总的来说，本文揭示了一个被忽视的信息来源——非语言行为——并将其与公司预测、估值、生存和融资成功联系起来，这些都是评估投资机会、交易结构和监督的重要因素。
We examine the relation between a measure of managers’ physical display—body expansiveness—and favorable reporting practices (in firm forecasts and valuation information) and performance (survival and funding success). We videotape 154 entrepreneurs pitching their business ideas, and use computer vision software to obtain information about speakers’ movements. We show that physical expansiveness correlates with higher forecast errors and proposed firm valuations and lower survival rates yet higher likelihood of funding success. We argue that investors may incorrectly interpret nonverbal communication in their assessments of entrepreneurs and propose a behavioral explanation. We further corroborate the proposed mechanism by studying investor perceptions of entrepreneurs’ personal characteristics. Overall, we shed light on an overlooked source of information—nonverbal behavior—and relate it to firm forecasting, valuation, survival, and financing success, which are important factors in the assessment of investment opportunities, deal structure, and monitoring.
论文原文：DÁVILA, A. and GUASCH, M. (2022), Managers’ Body Expansiveness, Investor Perceptions, and Firm Forecast Errors and Valuation. Journal of Accounting Research, 60: 517-563. https://doi.org/10.1111/1475-679X.12426
5.利用大数据研究重大公司事件的信息传递
实时大数据能否帮助解开重大的公司事件？与公司披露和传统媒体相比，在及时性和完整性方面如何？大数据能否为投资者提供增量价值相关信息？考虑到这些问题，本文使用手机“pings”数据集（即来自移动设备的地理定位信号）来跟踪生产中断（停电）——美国炼油厂的重大事件。本文首先通过检验停电对当地天然气价格和公司会计业绩的影响来验证该结构。本文主要分析表明(1)炼油企业不会资源披露通过手机“pings”识别的炼油厂停运情况；(2)传统媒体仅报道一小部分基于“ping”的停工事件；(3)股市发现基于“ping”的中断与价值相关，但整合信息存在延迟。进一步分析表明，鉴于媒体报道不完整且缺乏公司披露，投资者似乎通过随后的收益公告了解了此类中断的财务影响。本文的证据对美国能源信息署和证券交易委员会等监管机构有影响，因为它们将继续评估对重大公司事件（如生产中断）披露的合规性和有用性。
Could real-time big data help unravel material firm events? How would it compare with firm disclosure and traditional media in terms of timeliness and completeness? Could big data provide incremental value-relevant information for investors? With these questions in mind, we use a novel data set of cell phone “pings” (i.e., geolocation signals from mobile devices) to track production disruptions (outages)––material events for U.S. oil refineries. We first validate the construct by examining the effects of outages on local gas prices and firms’ accounting performance. Our main analyses show that (1) refining firms do not voluntarily disclose refinery outages identified by cell phone pings; (2) traditional media cover only a small portion of ping-based outages; (3) the stock market finds ping-based outages to be value relevant but incorporates the information with delay. Further analysis suggests that given the incomplete media coverage and lack of firm disclosure, investors appear to learn the financial impact of such outages through subsequent earnings announcements. Our evidence has implications for regulators such as the U.S. Energy Information Administration and the Securities and Exchange Commission as they continue to evaluate both the compliance and usefulness of disclosures for material firm events such as production disruptions.
论文原文：LI, B. and VENKATACHALAM, M. (2022), Leveraging Big Data to Study Information Dissemination of Material Firm Events. Journal of Accounting Research, 60: 565-606. https://doi.org/10.1111/1475-679X.12419
6. 评估信贷决策中的人类信息处理：一种机器学习方法
有效的财务报告需要有效的信息处理。本文研究了决定有效信息处理的因素。本文利用了一个独特的小企业贷款环境，从而能够观察到信贷员用来做出决策的整个编纂的人口统计和会计信息集。本文将信贷员的决策分解为由编码硬信息驱动的部分和由未编码软信息驱动的部分。本文发现，在处理硬信息方面，机器学习模型大大优于信贷员。信贷员只能处理机器学习模型识别的一组稀疏有用的硬信息，并将注意力集中在现金流量大幅跳跃等显著的信号上。然而，信贷员使用显著的硬信息作为“危险信号”，以强调从何处获取更多软信息。这一结果表明，显著信息是一种注意力分配工具：它引导人类分配有限的认知资源来获取软信息，在这一任务中，人类比机器具有优势。
Effective financial reporting requires efficient information processing. This paper studies factors that determine efficient information processing. I exploit a unique small business lending setting where I am able to observe the entire codified demographic and accounting information set that loan officers use to make decisions. I decompose the loan officers’ decisions into a part driven by codified hard information and a part driven by uncodified soft information. I show that a machine learning model substantially outperforms loan officers in processing hard information. Loan officers can only process a sparse set of useful hard information identified by the machine learning model and focus their attention on salient signals such as large jumps in cash flows. However, the loan officers use salient hard information as “red flags” to highlight where to acquire more soft information. This result suggests that salient information is an attention allocation device: It guides humans to allocate their limited cognitive resources to acquire soft information, a task in which humans have an advantage over machines.
论文原文：LIU, M. (2022), Assessing Human Information Processing in Lending Decisions: A Machine Learning Approach. Journal of Accounting Research, 60: 607-651. https://doi.org/10.1111/1475-679X.12427
7. 面部价值:金融分析师的特质印象、绩效特征和市场结果
使用基于机器学习的算法，本文使用卖方分析师的LinkedIn照片来衡量他们的关键印象。本文发现，对分析师可信度(TRUST)和支配力(DOM)的印象与预测准确性正相关，特别是在分析师和公司经理最近面对面会议之后。高度信任也增强了股票回报对预测修正的敏感性，尤其是对于机构持股比例较高的股票。相比之下，分析师的吸引力印象(ATTRACT)只与新分析师的准确性或当公司有新的首席执行官或首席财务官时正相关。此外，虽然高DOM有助于男性分析师获得全明星地位，但它降低了女性分析师的准确性和赢得全明星奖的可能性。此外，信任和准确性之间的关系受到披露环境的调节，并受到监管公平披露的削弱。本文的结果表明，面部印象影响分析师对信息的获取和他们报告的可信度。
Using machine learning–based algorithms, we measure key impressions about sell-side analysts using their LinkedIn photos. We find that impressions of analysts’ trustworthiness (TRUST) and dominance (DOM) are positively associated with forecast accuracy, especially after recent in-person meetings between analysts and firm managers. High TRUST also enhances stock return sensitivity to forecast revisions, especially for stocks with high institutional ownership. In contrast, the impression of analysts’ attractiveness (ATTRACT) is only positively associated with accuracy for new analysts or when a firm has a new CEO or CFO. Furthermore, while high DOM helps male analysts’ chances of attaining All-Star status, it reduces female analysts’ accuracy and the likelihood of winning the All-Star award. In addition, the relation between TRUST and accuracy is modulated by the disclosure environment and is attenuated by Regulation Fair Disclosure. Our results suggest that face impressions influence analysts’ access to information and the perceived credibility of their reports.
论文原文：PENG, L., TEOH, S.H., WANG, Y. and YAN, J. (2022), Face Value: Trait Impressions, Performance Characteristics, and Market Outcomes for Financial Analysts. Journal of Accounting Research, 60: 653-705. https://doi.org/10.1111/1475-679X.12428
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