Indiana University Kelley School of Business Research Paper Series最新文献

Price-Path Convexity, Extrapolation, and Short-Horizon Return Predictability 价格路径的凸性、外推和短期收益的可预测性

Indiana University Kelley School of Business Research Paper Series Pub Date : 2021-06-24 DOI: 10.2139/ssrn.3800419

Zhi Da, Huseyin Gulen, Michael Woeppel

引用次数: 2

Disentangling the Effects of Changes in Book-Tax Conformity Related to Mandatory IFRS Adoption on Earnings Quality 解读与强制性采用国际财务报告准则相关的帐面税务一致性变动对盈余质量的影响

Indiana University Kelley School of Business Research Paper Series Pub Date : 2021-04-30 DOI: 10.2139/ssrn.3837467

Fabio Moraes da Costa, Anne C. Ehinger, Wolfgang Schultze, Samuel L. Tiras

引用次数: 0

Managerial Control Benefits and Takeover Market Efficiency 管理层控制收益与收购市场效率

Indiana University Kelley School of Business Research Paper Series Pub Date : 2019-08-01 DOI: 10.2139/ssrn.3213483

Wenyu Wang, Yufeng Wu

引用次数: 8

Equity Exchange Fees and Revenues 股权交换费用及收入

Indiana University Kelley School of Business Research Paper Series Pub Date : 2019-06-26 DOI: 10.2139/ssrn.3391834

Ike Brannon, R. Jennings

引用次数: 2

Target Price Optimism, Investor Sentiment, and the Informativeness of Target Prices 目标价乐观、投资者情绪与目标价的信息量

Indiana University Kelley School of Business Research Paper Series Pub Date : 2019-05-31 DOI: 10.2139/ssrn.3398387

Markus Buxbaum, Wolfgang Schultze, Samuel L. Tiras

{"title":"Target Price Optimism, Investor Sentiment, and the Informativeness of Target Prices","authors":"Markus Buxbaum, Wolfgang Schultze, Samuel L. Tiras","doi":"10.2139/ssrn.3398387","DOIUrl":"https://doi.org/10.2139/ssrn.3398387","url":null,"abstract":"This paper provides large sample evidence that financial analysts’ target prices are more informative in periods of low investor sentiment compared to high sentiment. While prior research argues that target prices have differential investment value depending on analysts’ use of sophisticated or heuristic valuation models, we find that these differences are largely explained by investor sentiment. We find that the investment value of target prices based on both sophisticated and heuristic valuation models is close to zero in periods of high sen- timent. Return predictability is highest for target prices based on sophisticated valuation models during periods of low sentiment, but only slightly higher than for heuristics. Also, target price errors are lower for low sentiment. In periods of low sentiment, when concurrent market prices are below intrinsic values and target prices are above market prices due to analysts’ optimism, target prices are more reflective of intrinsic values and hence more informative. In addition, we find that investors do not understand the differential informativeness of target prices. Market reactions to target price revisions do not reflect the higher informativeness in low sentiment. Rather, investors overreact to target price revisions in high sentiment, potentially fueling the build-up of bubbles.","PeriodicalId":412480,"journal":{"name":"Indiana University Kelley School of Business Research Paper Series","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116967467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

The Effects of Openness of Internal Reporting and Shared Interest with an Employee on Managerial Collusion and Subsequent Cooperation 内部报告公开性与员工利益共享对管理层合谋及后续合作的影响

Indiana University Kelley School of Business Research Paper Series Pub Date : 2019-05-01 DOI: 10.2139/ssrn.3233079

Dan Way

{"title":"The Effects of Openness of Internal Reporting and Shared Interest with an Employee on Managerial Collusion and Subsequent Cooperation","authors":"Dan Way","doi":"10.2139/ssrn.3233079","DOIUrl":"https://doi.org/10.2139/ssrn.3233079","url":null,"abstract":"Collusion between managers who share private information represents a significant control concern for firms. Prior research suggests that mutual monitoring contracts that incentivize honest reporting and whistleblowing do not prevent all collusion, making it important to understand how elements of the control environment facilitate or prohibit collusion, as well as how these control choices – and the act of collusion itself – affect subsequent behavior within the firm. In a two-stage experiment, I predict and find that the frequency of collusion between managers is greatest when one can view the other’s reports to the firm before making their own (“open internal reporting”) and any slack obtained from misreporting is shared with an employee (“shared interest”). Evidence suggests this stems from managers’ less positive perceptions of the firm and the reporting environment, which also result in decreased cooperation on a subsequent task. I further predict and find that successful collusion improves managers’ perceptions of trust, control in reporting decisions, and group identification, resulting in greater cooperation on a subsequent task and potentially reducing the costs of some collusion. The implications of my findings for management accounting research and practice are discussed.","PeriodicalId":412480,"journal":{"name":"Indiana University Kelley School of Business Research Paper Series","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124413246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem 用数据挖掘变量实现可靠的因果推理:测量误差问题的随机森林方法

Indiana University Kelley School of Business Research Paper Series Pub Date : 2019-02-22 DOI: 10.2139/ssrn.3339983

Mochen Yang, E. McFowland, Gordon Burtch, G. Adomavicius

{"title":"Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem","authors":"Mochen Yang, E. McFowland, Gordon Burtch, G. Adomavicius","doi":"10.2139/ssrn.3339983","DOIUrl":"https://doi.org/10.2139/ssrn.3339983","url":null,"abstract":"Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy uses predictive modeling techniques to “mine” variables of interest from available data and then includes those variables into an econometric framework to estimate causal effects. However, because the predictions from machine learning models are inevitably imperfect, econometric analyses based on the predicted variables likely suffer from bias due to measurement error. We propose a novel approach to mitigate these biases, leveraging the random forest technique. We propose using random forest not just for prediction but also for generating instrumental variables for bias correction. The random forest algorithm performs best when comprised of a set of trees that are individually accurate in their predictions, yet which also make “different” mistakes, that is, have weakly correlated prediction errors. A key observation is that these properties are closely related to the relevance and exclusion requirements of valid instrumental variables. We design a data-driven procedure to select tuples of individual trees from a random forest, in which one tree serves as the endogenous covariate and the others serve as its instruments. Simulation experiments demonstrate its efficacy in mitigating estimation biases and its superior performance over alternative methods.","PeriodicalId":412480,"journal":{"name":"Indiana University Kelley School of Business Research Paper Series","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116014128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Turn Your Online Engagement in Chronic Disease Management from Zero to Hero: A Multi-Dimensional Continuous-Time Evaluation 将您在慢性病管理中的在线参与从零变为英雄:多维度连续时间评估

Indiana University Kelley School of Business Research Paper Series Pub Date : 2019-01-21 DOI: 10.2139/ssrn.3320076

Tongxin Zhou, L. Yan, Yingfei Wang, Yong Tan

{"title":"Turn Your Online Engagement in Chronic Disease Management from Zero to Hero: A Multi-Dimensional Continuous-Time Evaluation","authors":"Tongxin Zhou, L. Yan, Yingfei Wang, Yong Tan","doi":"10.2139/ssrn.3320076","DOIUrl":"https://doi.org/10.2139/ssrn.3320076","url":null,"abstract":"Individuals’ engagement in online healthcare communities (OHCs) has attracted a large body of research. However, prior research has mainly studied a single behavioral dimension of online engagement. Given that individuals’ online engagement is a complex process, overlooking certain aspects of engagement may lead to underestimating the effectiveness of OHCs. In addition, due to the progressive nature of chronic disease, chronic disease management needs to be evaluated continuously. These concerns motivate us to develop a framework that accounts for both engagement dimensions and engagement timing for studying chronic disease management in OHCs. Considering that the engagement level of disease management cannot be directly observed, in this study, we propose a multi-dimensional Continuous-Time Hidden Markov Model (CTHMM) that captures individuals’ engagement level as a latent state. We root our research in the context of weight management. Our main findings include: 1) The timing of engagement can affect an individual’s engagement level; for instance, individuals with higher participation frequency are more likely to shift to different engagement levels. 2) Participating in support-exchange activities can shift individuals’ focus from weigh-ins to journals, which are two distinct behavioral dimensions of self-monitoring. Thus, an incomplete characterization of engagement dimensions can underestimate individuals’ activeness in OHCs, which will further lead to underrating the role of OHCs in chronic disease control. 3) Different forms of social support can have statistically different effects on engagement, and these effects are mediated by individuals’ own engagement levels. Individuals need to “smartly” adopt social support tools to improve their health management.","PeriodicalId":412480,"journal":{"name":"Indiana University Kelley School of Business Research Paper Series","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121431771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Conforming Tax Avoidance and Capital Market Pressure 一致性避税与资本市场压力

Indiana University Kelley School of Business Research Paper Series Pub Date : 2018-12-17 DOI: 10.2139/ssrn.2619317

Brad A. Badertscher, Sharon P. Katz, S. Rego, R. Wilson

引用次数: 88

Do Portfolio Manager Contracts Contract Portfolio Management? 项目组合经理是否与项目组合管理签订合同?

Indiana University Kelley School of Business Research Paper Series Pub Date : 2018-12-03 DOI: 10.2139/ssrn.2844962

Jung Hoon Lee, Charles Trzcinka, Shyam Venkatesan

引用次数: 24