Investor attention drives analyst coverage. We find that, between 2012-2017, institutional investor attention explains 21.39% of the cross-sectional variation in analyst coverage, second only to market capitalization (22.09%). We build a model where limited investor attention drives information supply. Analysts compete for scarce investor attention to maximize volume for brokerage houses. In equilibrium, analysts cluster in riskier stocks, for which information is most valuable. However, relaxing investors' attention constraints can reinforce coordination motives for analysts and lead to even higher clustering. The results mirror "crowded" coverage in the U.S., where the most-covered 5% equities amount to 25% of earnings forecasts.
{"title":"Investor Attention and the Cross-Section of Analyst Coverage","authors":"Charles Martineau, M. Zoican","doi":"10.2139/ssrn.3376162","DOIUrl":"https://doi.org/10.2139/ssrn.3376162","url":null,"abstract":"Investor attention drives analyst coverage. We find that, between 2012-2017, institutional investor attention explains 21.39% of the cross-sectional variation in analyst coverage, second only to market capitalization (22.09%). We build a model where limited investor attention drives information supply. Analysts compete for scarce investor attention to maximize volume for brokerage houses. In equilibrium, analysts cluster in riskier stocks, for which information is most valuable. However, relaxing investors' attention constraints can reinforce coordination motives for analysts and lead to even higher clustering. The results mirror \"crowded\" coverage in the U.S., where the most-covered 5% equities amount to 25% of earnings forecasts.","PeriodicalId":224430,"journal":{"name":"Decision-Making in Economics eJournal","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122435476","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}
The adoption of a photovoltaic system has positive environmental effects, but the main driver of the choice in the industrial and commercial sector is economic profitability. Switching from acquisition of energy to production of energy is an investment with costs (e.g. leasing annual payment, O&M costs, capital expenditure) and benefits (e.g. savings in the electric bill, sale of the energy exceeding consumptions). In this work, we use an accounting-and-finance model to calculate the Equity Net Present Value in different scenarios and a sensitivity-analysis method (Finite Change Sensitivity Index) to explain the reasons for differences in results. This technique enables identifying the contribution of any input factor in the output value variation. In this way, the investor can draw attention on the most significant critical variables in the initial estimations to ensure success in forecasting.
{"title":"Comprehensive Financial Modeling of Solar PV Systems","authors":"Davide Baschieri, C. Magni, A. Marchioni","doi":"10.2139/ssrn.3722442","DOIUrl":"https://doi.org/10.2139/ssrn.3722442","url":null,"abstract":"The adoption of a photovoltaic system has positive environmental effects, but the main driver of the choice in the industrial and commercial sector is economic profitability. Switching from acquisition of energy to production of energy is an investment with costs (e.g. leasing annual payment, O&M costs, capital expenditure) and benefits (e.g. savings in the electric bill, sale of the energy exceeding consumptions). In this work, we use an accounting-and-finance model to calculate the Equity Net Present Value in different scenarios and a sensitivity-analysis method (Finite Change Sensitivity Index) to explain the reasons for differences in results. This technique enables identifying the contribution of any input factor in the output value variation. In this way, the investor can draw attention on the most significant critical variables in the initial estimations to ensure success in forecasting.","PeriodicalId":224430,"journal":{"name":"Decision-Making in Economics eJournal","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130360749","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}
We consider a joint inventory and pricing problem with one warehouse and multiple stores, in which the retailer needs to make a one-time decision on the amount of inventory to be placed at the warehouse at the beginning of the selling season, followed by periodic joint replenishment and pricing decisions for each store throughout the season. Unmet demand at each store is immediately lost. The retailer incurs the usual variable ordering costs, inventory holding costs and lost sales costs, and his objective is to maximize the expected total profits. The optimal control (or policy) for this problem is unknown and numerically challenging to compute. To deal with this, we propose a heuristic control based on the optimal solution of a deterministic relaxation of the original stochastic problem. The construction of our heuristic combines four ideas: (1) order-up-to control, (2) dynamic pricing with linear rate adjustment, (3) replenishment batching, and (4) random errors averaging. We show for a particular choice of control parameters that the heuristic is close to optimal when demand is Poisson and the annual market size is large. In addition to analyzing our proposed heuristic, we also analyze the performance of some popular and simple heuristics that directly implement the solution of the deterministic approximation. We show that simple re-optimization of deterministic problem may yield a very poor performance by causing a ``spiraling down" movement in price trajectory, which in turn yields a ``spiraling up" movement in expected lost sales quantity (i.e., the expected lost sales quantity keeps increasing as we re-optimize more frequently). This cautions against the use of simple re-optimizations in the joint inventory and pricing setting with lost sales.
{"title":"On the Joint Inventory and Pricing Control for a One-Warehouse Multi-Store Problem with Lost Sales: Spiraling Phenomena and a Near-Optimal Heuristic","authors":"Y. Lei, Sheng Liu, Stefanus Jasin, A. Vakhutinsky","doi":"10.2139/ssrn.3688561","DOIUrl":"https://doi.org/10.2139/ssrn.3688561","url":null,"abstract":"We consider a joint inventory and pricing problem with one warehouse and multiple stores, in which the retailer needs to make a one-time decision on the amount of inventory to be placed at the warehouse at the beginning of the selling season, followed by periodic joint replenishment and pricing decisions for each store throughout the season. Unmet demand at each store is immediately lost. The retailer incurs the usual variable ordering costs, inventory holding costs and lost sales costs, and his objective is to maximize the expected total profits. The optimal control (or policy) for this problem is unknown and numerically challenging to compute. To deal with this, we propose a heuristic control based on the optimal solution of a deterministic relaxation of the original stochastic problem. The construction of our heuristic combines four ideas: (1) order-up-to control, (2) dynamic pricing with linear rate adjustment, (3) replenishment batching, and (4) random errors averaging. We show for a particular choice of control parameters that the heuristic is close to optimal when demand is Poisson and the annual market size is large. In addition to analyzing our proposed heuristic, we also analyze the performance of some popular and simple heuristics that directly implement the solution of the deterministic approximation. We show that simple re-optimization of deterministic problem may yield a very poor performance by causing a ``spiraling down\" movement in price trajectory, which in turn yields a ``spiraling up\" movement in expected lost sales quantity (i.e., the expected lost sales quantity keeps increasing as we re-optimize more frequently). This cautions against the use of simple re-optimizations in the joint inventory and pricing setting with lost sales.","PeriodicalId":224430,"journal":{"name":"Decision-Making in Economics eJournal","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114386797","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}
The most used methods for valuing companies by Cash Flow Discounting are equity cash flow, free cash flow, capital cash flow and APV (Adjusted Present Value). Only APV does not require iteration All four methods, if properly applied, always give the same value. This result is logical, as all the methods analyze the same reality under the same hypotheses; they differ only in the cash flows or parameters taken as the starting point for the valuation. Many valuations are incorrect because the authors do not iterate and, therefore, the four methods do not provide the same value.
{"title":"Valuing Companies by Cash Flow Discounting: Only APV Does Not Require Iteration","authors":"Pablo Fernández","doi":"10.2139/ssrn.3682128","DOIUrl":"https://doi.org/10.2139/ssrn.3682128","url":null,"abstract":"The most used methods for valuing companies by Cash Flow Discounting are equity cash flow, free cash flow, capital cash flow and APV (Adjusted Present Value). Only APV does not require iteration All four methods, if properly applied, always give the same value. This result is logical, as all the methods analyze the same reality under the same hypotheses; they differ only in the cash flows or parameters taken as the starting point for the valuation. Many valuations are incorrect because the authors do not iterate and, therefore, the four methods do not provide the same value.","PeriodicalId":224430,"journal":{"name":"Decision-Making in Economics eJournal","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121047911","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}
We consider a model of Bertrand competition where consumers are uncertain about the qualities and prices of firms' products. Consumers can inspect all products at zero cost. A share of consumers is expectation-based loss averse. For these consumers, a purchase plan, which involves buying products of varying quality and price with positive probability, creates scale-dependent dis-utility from gain-loss sensations. Even if their degree of loss aversion is modest, they may refrain from inspecting all products and choose an individual default that is first-order stochastically dominated. Firms' strategic behavior can exacerbate the scope for this "uncertainty effect'", and sellers of inferior products may earn positive profits despite Bertrand competition. We find suggestive evidence for the predicted association between consumer behavior and loss aversion in new survey data.
{"title":"Consumer Search and the Uncertainty Effect","authors":"H. Karle, Heiner Schumacher, Rune Vølund","doi":"10.2139/ssrn.3678667","DOIUrl":"https://doi.org/10.2139/ssrn.3678667","url":null,"abstract":"We consider a model of Bertrand competition where consumers are uncertain about the qualities and prices of firms' products. Consumers can inspect all products at zero cost. A share of consumers is expectation-based loss averse. For these consumers, a purchase plan, which involves buying products of varying quality and price with positive probability, creates scale-dependent dis-utility from gain-loss sensations. Even if their degree of loss aversion is modest, they may refrain from inspecting all products and choose an individual default that is first-order stochastically dominated. Firms' strategic behavior can exacerbate the scope for this \"uncertainty effect'\", and sellers of inferior products may earn positive profits despite Bertrand competition. We find suggestive evidence for the predicted association between consumer behavior and loss aversion in new survey data.","PeriodicalId":224430,"journal":{"name":"Decision-Making in Economics eJournal","volume":"278 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123370876","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}
Game-theoretic models of learning are hard to study even in the laboratory setting due to econometric and practical concerns (like the limited length of an experimental session).
In particular, as the simulations by (Salmon, 2001) show, in a cross-model (or "blind'') testing of several models, the data generated by those models does not correspond to the estimated parameters correctly.
Thus, even when the real data generation process is known we cannot distinguish correct models from incorrect ones by looking at the estimates.
However, we demonstrate that under the same conditions, models are clearly distinguishable if we compare predictions that the models make instead of comparing the model parameters.
We also provide a rationale for why this cross-model predictive quality is a particularly relevant way for improving learning models.
{"title":"Evaluation of Econometric Models of Adaptive Learning by Predictive Measures","authors":"G. Chernov, I. Susin, Sergey Cheparuhin","doi":"10.2139/ssrn.3658087","DOIUrl":"https://doi.org/10.2139/ssrn.3658087","url":null,"abstract":"Game-theoretic models of learning are hard to study even in the laboratory setting due to econometric and practical concerns (like the limited length of an experimental session).<br><br>In particular, as the simulations by (Salmon, 2001) show, in a cross-model (or \"blind'') testing of several models, the data generated by those models does not correspond to the estimated parameters correctly.<br><br>Thus, even when the real data generation process is known we cannot distinguish correct models from incorrect ones by looking at the estimates.<br><br>However, we demonstrate that under the same conditions, models are clearly distinguishable if we compare predictions that the models make instead of comparing the model parameters.<br><br>We also provide a rationale for why this cross-model predictive quality is a particularly relevant way for improving learning models.","PeriodicalId":224430,"journal":{"name":"Decision-Making in Economics eJournal","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114620598","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}
The active-versus-passive asset debate falsely forces investors into an all-or-nothing decision between the two. Instead, following Margrabe, we use a “better-of-two” option to:
(1) calculate the value of active management and,
(2) find the optimal weights for an active-plus-passive portfolio that dynamically replicates this option through time.
Using simulations, we found that for an at-the-money exchange option with a 10-year horizon, an investor would pay about 5.5% of the passive portfolio’s value. To replicate this option, the investor would allocate roughly 40% the active asset. We also accounted for borrowing costs, tracking error and active alpha, finding that replication cost and portfolio turnover decrease as time horizon increases and that results are robust to errors in expected volatility. Finally, we replaced hypothetical return distributions with historical mutual fund returns used in 60/40 equity/fixed income portfolios rebalanced monthly. Excess ending wealth distributions using the better-of-two strategy exceeded those of an all-active strategy with better downside protection. These values also came close or, in the case of funds that produced excess return, exceeded the ending value of the all-passive strategy while limiting downside relative risk. Gains from the dynamic option replication portfolio are particularly noticeable during periods when active excess returns are negative. Turnover for long-horizon portfolios is less than 4% per month and our results are statistically significant.
{"title":"The Better-of-Two Strategy for Active Management: Dynamically Combining (and Valuing) Active and Passive Portfolios Through Time","authors":"S. Fox, P. Hammond","doi":"10.2139/ssrn.3667838","DOIUrl":"https://doi.org/10.2139/ssrn.3667838","url":null,"abstract":"The active-versus-passive asset debate falsely forces investors into an all-or-nothing decision between the two. Instead, following Margrabe, we use a “better-of-two” option to:<br><br>(1) calculate the value of active management and,<br><br>(2) find the optimal weights for an active-plus-passive portfolio that dynamically replicates this option through time. <br><br>Using simulations, we found that for an at-the-money exchange option with a 10-year horizon, an investor would pay about 5.5% of the passive portfolio’s value. To replicate this option, the investor would allocate roughly 40% the active asset. We also accounted for borrowing costs, tracking error and active alpha, finding that replication cost and portfolio turnover decrease as time horizon increases and that results are robust to errors in expected volatility. Finally, we replaced hypothetical return distributions with historical mutual fund returns used in 60/40 equity/fixed income portfolios rebalanced monthly. Excess ending wealth distributions using the better-of-two strategy exceeded those of an all-active strategy with better downside protection. These values also came close or, in the case of funds that produced excess return, exceeded the ending value of the all-passive strategy while limiting downside relative risk. Gains from the dynamic option replication portfolio are particularly noticeable during periods when active excess returns are negative. Turnover for long-horizon portfolios is less than 4% per month and our results are statistically significant.","PeriodicalId":224430,"journal":{"name":"Decision-Making in Economics eJournal","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128468370","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}
The goal of this essay with practical applications is to address and develop visual models to discuss and ease the understanding of the communication process in finance, taking into account impacts from feedback and the surrounding environment. This work also targets the relationship between a client and a financial practitioner. Besides discussing the visual models, this work describes how these models may be used to improve financial human communication. In the end of this article are key points and suggestions for future work in the field of human behavior, communication, and interaction when implementing financial decisions.
{"title":"Communication Affects Financial Decisions and Outcomes","authors":"Marcelo Henriques-de-Brito","doi":"10.2139/ssrn.3728921","DOIUrl":"https://doi.org/10.2139/ssrn.3728921","url":null,"abstract":"The goal of this essay with practical applications is to address and develop visual models to discuss and ease the understanding of the communication process in finance, taking into account impacts from feedback and the surrounding environment. This work also targets the relationship between a client and a financial practitioner. Besides discussing the visual models, this work describes how these models may be used to improve financial human communication. In the end of this article are key points and suggestions for future work in the field of human behavior, communication, and interaction when implementing financial decisions.","PeriodicalId":224430,"journal":{"name":"Decision-Making in Economics eJournal","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116255232","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}
Ever since the late 1990s, China has experienced explosive growth in consumer lending, especially in short-term consumer loans, among which, the growth rate of non-bank lending has surpassed bank lending due to the development in financial technology. On the other hand, China does not have a universal credit scoring and registration system that can guide lenders during the processes of credit evaluation and risk control, for example, an individual’s bank credit records are not available for online lenders to see and vice versa. Given this context, the purpose of this paper is three-fold. First, we explore if and how alternative digital footprint data can be utilized to assess borrower’s creditworthiness. Then, we perform a comparative analysis of machine learning methods for the canonical problem of credit default prediction. Finally, we analyze, from an institutional point of view, the necessity of establishing a viable and nationally universal credit registration and scoring system utilizing online digital footprints, so that more people in China can have better access to the consumption loan market. Two different types of digital footprint data are utilized to match with bank’s loan default records. Each separately captures distinct dimensions of a person’s characteristics, such as his shopping patterns and certain aspects of his personality or inferred demographics revealed by social media features like profile image and nickname. We find both datasets can generate either acceptable or excellent prediction results, and different types of data tend to complement each other to get better performances. Typically, the traditional types of data banks normally use like income, occupation, and credit history, update over longer cycles, hence they can’t reflect more immediate changes, like the financial status changes caused by business crisis; whereas digital footprints can update daily, weekly, or monthly, thus capable of providing a more comprehensive profile of the borrower’s credit capabilities and risks. From the empirical and quantitative examination, we believe digital footprints can become an alternative information source for creditworthiness assessment, because of their near-universal data coverage, and because they can by and large resolve the "thin-file" issue, due to the fact that digital footprints come in much larger volume and higher frequency.
{"title":"An Alternative Credit Scoring System in China's Consumer Lending Market: A System Based on Digital Footprint Data","authors":"G. Fu, Minjuan Sun, Qingyuan Xu","doi":"10.2139/ssrn.3638710","DOIUrl":"https://doi.org/10.2139/ssrn.3638710","url":null,"abstract":"Ever since the late 1990s, China has experienced explosive growth in consumer lending, especially in short-term consumer loans, among which, the growth rate of non-bank lending has surpassed bank lending due to the development in financial technology. On the other hand, China does not have a universal credit scoring and registration system that can guide lenders during the processes of credit evaluation and risk control, for example, an individual’s bank credit records are not available for online lenders to see and vice versa. Given this context, the purpose of this paper is three-fold. First, we explore if and how alternative digital footprint data can be utilized to assess borrower’s creditworthiness. Then, we perform a comparative analysis of machine learning methods for the canonical problem of credit default prediction. Finally, we analyze, from an institutional point of view, the necessity of establishing a viable and nationally universal credit registration and scoring system utilizing online digital footprints, so that more people in China can have better access to the consumption loan market. \u0000 \u0000Two different types of digital footprint data are utilized to match with bank’s loan default records. Each separately captures distinct dimensions of a person’s characteristics, such as his shopping patterns and certain aspects of his personality or inferred demographics revealed by social media features like profile image and nickname. We find both datasets can generate either acceptable or excellent prediction results, and different types of data tend to complement each other to get better performances. Typically, the traditional types of data banks normally use like income, occupation, and credit history, update over longer cycles, hence they can’t reflect more immediate changes, like the financial status changes caused by business crisis; whereas digital footprints can update daily, weekly, or monthly, thus capable of providing a more comprehensive profile of the borrower’s credit capabilities and risks. From the empirical and quantitative examination, we believe digital footprints can become an alternative information source for creditworthiness assessment, because of their near-universal data coverage, and because they can by and large resolve the \"thin-file\" issue, due to the fact that digital footprints come in much larger volume and higher frequency.","PeriodicalId":224430,"journal":{"name":"Decision-Making in Economics eJournal","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127711853","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}
Abstract Optimal monetary policy depends on whether agents have exogenous, endogenous–inelastic, or endogenous–elastic attention. Under elastic attention, optimal monetary policy induces equilibria that are not possible under the other two settings: no attention to any shocks that generate inefficient economic fluctuations.
{"title":"Elasticity of Attention and Optimal Monetary Policy","authors":"Shaowen Luo, K. Tsang","doi":"10.2139/ssrn.3623412","DOIUrl":"https://doi.org/10.2139/ssrn.3623412","url":null,"abstract":"Abstract Optimal monetary policy depends on whether agents have exogenous, endogenous–inelastic, or endogenous–elastic attention. Under elastic attention, optimal monetary policy induces equilibria that are not possible under the other two settings: no attention to any shocks that generate inefficient economic fluctuations.","PeriodicalId":224430,"journal":{"name":"Decision-Making in Economics eJournal","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128503877","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}
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