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【Workshop for MLinEcon第2期】2020年4月7日

来源:南风金融网 作者:南风金融网 人气: 发布时间:2020-04-02 16:06:07
【题目1】:Text-Based Network Industries and Endogenous Product Differentiation
【汇报人】:尹兴强(上海财经大学会计学院博士研究生)
【时间】:2020年4月7日(周二)20:00-20:50
【地点】:Zoom在线

【论文摘要】:We study how firms differ from their competitors using new time-varying measures of product differentiation based on text-based analysis of product descriptions from 50,673 firm 10-K statements filed yearly with the Securities and Exchange Commission. This year-by-year set of product differentiation measures allows us to generate a new set of industries and corresponding new measures of industry competition where firms can have their own distinct set of competitors. Our new sets of industry competitors better explain specific discussion of high competition by management, rivals identified by managers as peer firms and firm characteristics such as profitability and leverage than do existing classifications. We also find evidence that firm R&D and advertising are associated with subsequent differentiation from competitors, consistent with theories of endogenous product differentiation.
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【文献信息】Hoberg G., and Phillips, G., "Text-Based Network Industries and Endogenous Product Differentiation", Journal of Political Economy, 2016, 124(5), 1423-1465.
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【题目2】:Human Decisions and Machine Predictions
【汇报人】:孟庆玺(上海财经大学会计学院博士研究生)
【时间】:2020年4月7日(周二)20:50-21:40
【地点】:Zoom在线

【论文摘要】:Can machine learning improve human decision making? Bail decisions provide a good test case. Millions of times each year, judges make jail-or-release decisions that hinge on a prediction of what a defendant would do if released. The concreteness of the prediction task combined with the volume of data available makes this a promising machine-learning application. Yet comparing the algorithm to judges proves complicated. First, the available data are generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the variable the algorithm predicts; for instance, judges may care specifically about violent crimes or about racial inequities. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: one policy simulation shows crime reductions up to 24.7% with no change in jailing rates, or jailing rate reductions up to 41.9% with no increase in crime rates. Moreover, all categories of crime, including violent crimes, show reductions; these gains can be achieved while simultaneously reducing racial disparities. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals.
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【文献信息】Kleinberg, J., Lakkaraju, H. Leskovec, J., Ludwig, J., and Mullainathan, S., “Human Decisions and Machine Predictions”, Quarterly Journal of Economics, 2018, 133(1), 237-293.
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排? ? 版 | 石庆宇
审?? ?核 |?郭?? ?峰
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