Equity book-to-market ratios (BTM) should not exceed one if a firm’s return on equity exceeds its cost of capital or it employs conservative accounting. Yet, BTM is above one for many firms. We address whether BTM above one reflects macroeconomic risk, which could explain this apparent incongruity. We find BTM above one generates larger hedge returns than BTM below one, but HML—the BTM-based return prediction factor—does not explain the returns for BTM above one. We also find that hedge returns for BTM above one are concentrated in recession years and likely reflect risk rather than mispricing. In addition, we find BTM above one reflects potentially overstated equity book values, but only in non-recession years. In contrast, high BTM below one does not generate hedge returns and reflects potentially overstated equity book values in recession and non-recession years. Together, our findings reveal that BTM above one reflects macroeconomic risk, which means that BTM above one has implications for risk assessment, return prediction, and asset under-impairment identification. Our study calls into question using HML as a return prediction factor for BTM above one and using BTM as a generic measure of conservative accounting or as the key indicator of overstated asset book values.
{"title":"Equity Book-to-Market Ratios Above One and Macroeconomic Risk","authors":"Mary E. Barth, D. Israeli, Suhas A. Sridharan","doi":"10.2139/ssrn.3306503","DOIUrl":"https://doi.org/10.2139/ssrn.3306503","url":null,"abstract":"Equity book-to-market ratios (BTM) should not exceed one if a firm’s return on equity exceeds its cost of capital or it employs conservative accounting. Yet, BTM is above one for many firms. We address whether BTM above one reflects macroeconomic risk, which could explain this apparent incongruity. We find BTM above one generates larger hedge returns than BTM below one, but HML—the BTM-based return prediction factor—does not explain the returns for BTM above one. We also find that hedge returns for BTM above one are concentrated in recession years and likely reflect risk rather than mispricing. In addition, we find BTM above one reflects potentially overstated equity book values, but only in non-recession years. In contrast, high BTM below one does not generate hedge returns and reflects potentially overstated equity book values in recession and non-recession years. Together, our findings reveal that BTM above one reflects macroeconomic risk, which means that BTM above one has implications for risk assessment, return prediction, and asset under-impairment identification. Our study calls into question using HML as a return prediction factor for BTM above one and using BTM as a generic measure of conservative accounting or as the key indicator of overstated asset book values.","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87227023","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}
While assessments of the Gig Economy vary in terms of size, growth, and heterogeneity, most studies suggest that this segment of the economy is sizeable, growing, and diverse in terms of types of work. Some concerns in the literature include both the present and future welfare of workers in the Gig Economy. More granular, temporal reframing of savings (e.g., save $5 a day versus $150 a month) has been shown to improve participation in recurring savings programs, which can be an important tool for Gig Economy workers since they often face do-it-yourself financial savings. This paper extends prior work on temporal reframing to examine in a lab setting the psychology of temporal reframing and non-retirement savings decisions of workers. Key findings include a replication of main effects relative to daily framing increasing savings intentions. Additionally, evidence of reduced psychological pain as evidenced by both subjective feelings and objective thoughts (e.g., through a memory recall task consistent with Query Theory) about affordability is demonstrated. Evidence for granular framing helping those with lower financial literacy is also provided whereas subjective numeracy does not appear to moderate outcomes.
{"title":"Temporal Reframing of Recurring Savings Reduces Perceived Pain and Helps Those with Lower Financial Literacy to Save","authors":"Stephen Shu, Steve Thomas, David A. Smith","doi":"10.2139/ssrn.3923852","DOIUrl":"https://doi.org/10.2139/ssrn.3923852","url":null,"abstract":"While assessments of the Gig Economy vary in terms of size, growth, and heterogeneity, most studies suggest that this segment of the economy is sizeable, growing, and diverse in terms of types of work. Some concerns in the literature include both the present and future welfare of workers in the Gig Economy. More granular, temporal reframing of savings (e.g., save $5 a day versus $150 a month) has been shown to improve participation in recurring savings programs, which can be an important tool for Gig Economy workers since they often face do-it-yourself financial savings. This paper extends prior work on temporal reframing to examine in a lab setting the psychology of temporal reframing and non-retirement savings decisions of workers. Key findings include a replication of main effects relative to daily framing increasing savings intentions. Additionally, evidence of reduced psychological pain as evidenced by both subjective feelings and objective thoughts (e.g., through a memory recall task consistent with Query Theory) about affordability is demonstrated. Evidence for granular framing helping those with lower financial literacy is also provided whereas subjective numeracy does not appear to moderate outcomes.","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81370370","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}
In a world of increasingly extensive information, rational investors can make better decisions. However, reinforcement-oriented investors are also more likely to observe preferred signals close to their own perception. A focus on these signals distorts the perceived aggregate signal in the direction of the prior estimate. This reduces belief adaptation. Hence, the empirically well-documented selective exposure / reinforcement theory reduces the impact of greater information availability on price efficiency. Additional information can sometimes even decrease perception correctness. In a market with biased investors, managers have an incentive to announce more, diffuse (fewer, precise) signals in case of negative (positive) information. This results in post-earnings-announcement drift and dispersion anomaly. Also, the distribution shape matters for information processing. For unimodal, symmetric distributions, agents' perceptions converge to the fundamental -- even though at a reduced speed. For multimodal signal distributions, the estimate can diverge from the fundamental.
{"title":"When Beliefs Influence the Perceived Signal Precision: Information Provision Style and the Impact of News","authors":"Stefanie Schraeder","doi":"10.2139/ssrn.3263246","DOIUrl":"https://doi.org/10.2139/ssrn.3263246","url":null,"abstract":"In a world of increasingly extensive information, rational investors can make better decisions. However, reinforcement-oriented investors are also more likely to observe preferred signals close to their own perception. A focus on these signals distorts the perceived aggregate signal in the direction of the prior estimate. This reduces belief adaptation. Hence, the empirically well-documented selective exposure / reinforcement theory reduces the impact of greater information availability on price efficiency. Additional information can sometimes even decrease perception correctness. In a market with biased investors, managers have an incentive to announce more, diffuse (fewer, precise) signals in case of negative (positive) information. This results in post-earnings-announcement drift and dispersion anomaly. Also, the distribution shape matters for information processing. For unimodal, symmetric distributions, agents' perceptions converge to the fundamental -- even though at a reduced speed. For multimodal signal distributions, the estimate can diverge from the fundamental.","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88374519","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}
Agents frequently interact with multiple principals at the same time. An employee may have multiple supervisors, firms may engage in public-private partnerships or firms and individuals may borrow from multiple banks/peers. Digitalization further fuels the possibilities to simultaneously engage in economic transactions with multiple others around the globe (e.g., FinTechs offering financial advice or brokerage). This paper provides experimental evidence on reciprocity in interactions between multiple principals and one agent. In the experiments, one agent interacted with either one or multiple principals and needed to decide whether or not to reciprocate trust. An increase in the number of principals who trusted significantly undermined reciprocity. Principals, however, did not anticipate the decrease in reciprocity. Compared with bilateral principal - agent interactions, trust increased if principals had the possibility to pool risk. Observational field data from 113,000 loans funded by a varying number of investors on a crowdlending platform provides suggestive empirical evidence supporting the experimental results on reciprocity. The propensity of loan default is associated with the number of investors providing the credit. Situations in which multiple principals interact with a single agent may be considerably more risky compared to bilateral interactions. These findings have important implications for understanding, designing and structuring interactions in which multiple principals are involved.
{"title":"Reciprocity in Multiple Principals - Agent Interactions: Experimental Evidence","authors":"J. Schmitz","doi":"10.2139/ssrn.3913976","DOIUrl":"https://doi.org/10.2139/ssrn.3913976","url":null,"abstract":"Agents frequently interact with multiple principals at the same time. An employee may have multiple supervisors, firms may engage in public-private partnerships or firms and individuals may borrow from multiple banks/peers. Digitalization further fuels the possibilities to simultaneously engage in economic transactions with multiple others around the globe (e.g., FinTechs offering financial advice or brokerage). This paper provides experimental evidence on reciprocity in interactions between multiple principals and one agent. In the experiments, one agent interacted with either one or multiple principals and needed to decide whether or not to reciprocate trust. An increase in the number of principals who trusted significantly undermined reciprocity. Principals, however, did not anticipate the decrease in reciprocity. Compared with bilateral principal - agent interactions, trust increased if principals had the possibility to pool risk. Observational field data from 113,000 loans funded by a varying number of investors on a crowdlending platform provides suggestive empirical evidence supporting the experimental results on reciprocity. The propensity of loan default is associated with the number of investors providing the credit. Situations in which multiple principals interact with a single agent may be considerably more risky compared to bilateral interactions. These findings have important implications for understanding, designing and structuring interactions in which multiple principals are involved.","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77264416","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}
Quasi-hyperbolic discounting is one of the most well-known and widely-used models to capture selfcontrol problems in the economics literature. The underlying assumption of this model is that agents have a “present bias” toward current consumption such that all future rewards are downweighed relative to rewards in the present (in addition to standard exponential discounting for the length of delay). We report a meta-analytic dataset of estimates of the present bias parameter 𝛽 based on searches of all major research databases (62 papers with 81 estimates in total). We find that the literature shows that people are on average present biased for both monetary rewards (𝛽 = 0.82%-|-| confidence interval of [0.74, 0.90]) and non- monetary rewards (𝛽 = 0.66, 95% confidence interval of [0.51, 0.85]) but that substantial heterogeneity exists across studies. The source of this heterogeneity comes from the subject pool, elicitation methodology, geographical location, payment method, mode of data collection (e.g. laboratory or field), and reward type. There is evidence of selective reporting and publication bias in the direction of overestimating the strength of present-bias (making 𝛽 estimates smaller), but present bias still exists after correcting for these issues (for money 𝛽 = 0.87 with 95% confidence interval of [0.82, 0.92] after correcting for selective reporting).
{"title":"Quasi-hyperbolic Present Bias: A Meta-analysis","authors":"S. Cheung, Agnieszka Tymula, Xueting Wang","doi":"10.2139/ssrn.3909663","DOIUrl":"https://doi.org/10.2139/ssrn.3909663","url":null,"abstract":"Quasi-hyperbolic discounting is one of the most well-known and widely-used models to capture selfcontrol problems in the economics literature. The underlying assumption of this model is that agents have a “present bias” toward current consumption such that all future rewards are downweighed relative to rewards in the present (in addition to standard exponential discounting for the length of delay). We report a meta-analytic dataset of estimates of the present bias parameter 𝛽 based on searches of all major research databases (62 papers with 81 estimates in total). We find that the literature shows that people are on average present biased for both monetary rewards (𝛽 = 0.82%-|-| confidence interval of [0.74, 0.90]) and non- monetary rewards (𝛽 = 0.66, 95% confidence interval of [0.51, 0.85]) but that substantial heterogeneity exists across studies. The source of this heterogeneity comes from the subject pool, elicitation methodology, geographical location, payment method, mode of data collection (e.g. laboratory or field), and reward type. There is evidence of selective reporting and publication bias in the direction of overestimating the strength of present-bias (making 𝛽 estimates smaller), but present bias still exists after correcting for these issues (for money 𝛽 = 0.87 with 95% confidence interval of [0.82, 0.92] after correcting for selective reporting).","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83817882","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}
Francesco Carli, S. Suetens, Burak R. Uras, Philine Visser
We design an experiment to study the role of (a)symmetry in the context of group lending with joint liability. The performance of joint-liability contracts crucially hinges on borrowers engaging in peer monitoring. We find that asymmetric contracts, in which monitoring is a dominant strategy for one borrower, increase the monitoring rate, and thus the repayment rate and performance. Moreover, asymmetric contracting also increases expected profits of the lending institution. Overall, our results suggest that asymmetric joint-liability contracts are worth considering as part of a policy to maintain financial stability.
{"title":"Asymmetric Group Loan Contracts: Experimental Evidence","authors":"Francesco Carli, S. Suetens, Burak R. Uras, Philine Visser","doi":"10.2139/ssrn.3909867","DOIUrl":"https://doi.org/10.2139/ssrn.3909867","url":null,"abstract":"We design an experiment to study the role of (a)symmetry in the context of group lending with joint liability. The performance of joint-liability contracts crucially hinges on borrowers engaging in peer monitoring. We find that asymmetric contracts, in which monitoring is a dominant strategy for one borrower, increase the monitoring rate, and thus the repayment rate and performance. Moreover, asymmetric contracting also increases expected profits of the lending institution. Overall, our results suggest that asymmetric joint-liability contracts are worth considering as part of a policy to maintain financial stability.","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76563717","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}
Equity market seems to go viral. The recent epic surge in AMC stock price just three-month following the GameStop short squeeze episode might indicate that meme stocks are unlikely to be an epiphenomenon. This letter explores the investment behavior of meme stock traders, particularly concerning their tendency to exhibit herd behavior. We define the meme stock market as an equally weighted index of stocks whose purchase the Robinhood app restricted during the GameStop short squeeze episode. Over the last two years, using hourly data, we provide robust evidence that herding in meme stocks occurs beyond the brief short squeeze episodes.
{"title":"Meme Stocks and Herd Behavior","authors":"A. Aloosh, Hyungeun Choi, Samuel Ouzan","doi":"10.2139/ssrn.3909945","DOIUrl":"https://doi.org/10.2139/ssrn.3909945","url":null,"abstract":"Equity market seems to go viral. The recent epic surge in AMC stock price just three-month following the GameStop short squeeze episode might indicate that meme stocks are unlikely to be an epiphenomenon. This letter explores the investment behavior of meme stock traders, particularly concerning their tendency to exhibit herd behavior. We define the meme stock market as an equally weighted index of stocks whose purchase the Robinhood app restricted during the GameStop short squeeze episode. Over the last two years, using hourly data, we provide robust evidence that herding in meme stocks occurs beyond the brief short squeeze episodes.","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74529716","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}
In this paper, we provide a system of equations to measure investor impatience in financial markets. As in physics, we propose that there exists a measurable force created by external market factors, including investor impatience, which we equate with gravitational force. Using a physics-based Eulerian fluid flow system of equations, we model this force and associated energy conservation equation. We test this hypothesis using minute-by-minute data from meme stocks during a unique market event, the GameStop short squeeze of January 2021. Aside from the effect created by intrinsic market forces, the resulting parameters provide evidence of external force acting on stock prices. We further extend our research to the 2010 flash crash, showing that the system captures external influence on market behavior.
{"title":"Investor Impatience and Financial Markets: the Case of the Short Squeeze of Meme Stocks","authors":"J. Choy, Ben Wang, Abdullah AlShelahi, R. Saigal","doi":"10.2139/ssrn.3908732","DOIUrl":"https://doi.org/10.2139/ssrn.3908732","url":null,"abstract":"In this paper, we provide a system of equations to measure investor impatience in financial markets. As in physics, we propose that there exists a measurable force created by external market factors, including investor impatience, which we equate with gravitational force. Using a physics-based Eulerian fluid flow system of equations, we model this force and associated energy conservation equation. We test this hypothesis using minute-by-minute data from meme stocks during a unique market event, the GameStop short squeeze of January 2021. Aside from the effect created by intrinsic market forces, the resulting parameters provide evidence of external force acting on stock prices. We further extend our research to the 2010 flash crash, showing that the system captures external influence on market behavior.","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87278564","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 reactions of investors to both good and bad news when it comes to the stock market are very diverse. Most professional investors will argue that their reactions to the stock market rises and falls are based on calculated and scientific investment strategies. There are even investors who choose to make investment choices based on computer programs or newly developed artificial intelligence (AI) algorithms to make decisions with regards to the price of stocks and the specific market futures. In reality this is not the case at all. While there are some investors who can leave their emotions at the door, the majority are making their decisions based on emotional responses to the ebbs and flows of stock prices. It does not matter if the information coming out of the stock market is good or bad, the reactions to it is often emotional. Fear, panic, worry, over-confidence, exuberance, and greed will cause an effect on both stock prices and valuations. Investing is a man-made ecosystem; it does not exist in nature. All humans are emotional at their core, therefore their emotional reactions to natural disasters, pandemics, presidential elections, and other political matters will affect their decision to buy or sell.
{"title":"Emotional Response in Investment Decision Making & Sentiment for Apple Inc. Stock","authors":"A. Aitken","doi":"10.2139/ssrn.3908148","DOIUrl":"https://doi.org/10.2139/ssrn.3908148","url":null,"abstract":"The reactions of investors to both good and bad news when it comes to the stock market are very diverse. Most professional investors will argue that their reactions to the stock market rises and falls are based on calculated and scientific investment strategies. There are even investors who choose to make investment choices based on computer programs or newly developed artificial intelligence (AI) algorithms to make decisions with regards to the price of stocks and the specific market futures. In reality this is not the case at all. While there are some investors who can leave their emotions at the door, the majority are making their decisions based on emotional responses to the ebbs and flows of stock prices. It does not matter if the information coming out of the stock market is good or bad, the reactions to it is often emotional. Fear, panic, worry, over-confidence, exuberance, and greed will cause an effect on both stock prices and valuations. Investing is a man-made ecosystem; it does not exist in nature. All humans are emotional at their core, therefore their emotional reactions to natural disasters, pandemics, presidential elections, and other political matters will affect their decision to buy or sell.","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79928303","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}
Tesla is an American electric vehicle and clean energy company. They are based in Palo Alto, California and their product base consists of electric cars, battery energy storage, solar panels, and solar roof tiles. On an average day in 2021, Tesla stock sells for $700/share. We will review historical Tesla data and examine whether this particular stock is worth the investment? In addition to historical data, this research reviews the effect of traditional news and social media on human behavior. Exploring social media analytics, investor sentiment and behavior in hopes to gauge how these factors can impact Tesla and whether this should be taken into consideration prior to the investment.
{"title":"Tesla: Is Now the Time to Invest? An examination of Tesla, social media, and its effect on stock","authors":"Michele Coiro","doi":"10.2139/ssrn.3908275","DOIUrl":"https://doi.org/10.2139/ssrn.3908275","url":null,"abstract":"Tesla is an American electric vehicle and clean energy company. They are based in Palo Alto, California and their product base consists of electric cars, battery energy storage, solar panels, and solar roof tiles. On an average day in 2021, Tesla stock sells for $700/share. We will review historical Tesla data and examine whether this particular stock is worth the investment? In addition to historical data, this research reviews the effect of traditional news and social media on human behavior. Exploring social media analytics, investor sentiment and behavior in hopes to gauge how these factors can impact Tesla and whether this should be taken into consideration prior to the investment.","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85590280","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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