Pub Date : 2023-03-31DOI: 10.3905/joi.2023.32.3.001
Brian R. Bruce
{"title":"Editor’s Letter","authors":"Brian R. Bruce","doi":"10.3905/joi.2023.32.3.001","DOIUrl":"https://doi.org/10.3905/joi.2023.32.3.001","url":null,"abstract":"","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"1 - 2"},"PeriodicalIF":0.6,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49147088","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}
Most institutional investors, such as public pension funds and endowments, report their performance using biased benchmarks. The benchmarks are biased downwardly, meaning their returns tend to be less than a fair return for the market exposures and risk exhibited by the institutions’ portfolios. Significant samples of both fund types exhibit benchmark bias in the range of 1.4 to 1.7 percentage points per year. This bias enables a sizable majority of both types of funds to report outperforming their chosen benchmarks when, in fact, most underperform an appropriate passive-management benchmark by a wide margin. Benchmark bias masks serious agency problems in the management of institutional funds. For example, fund staff and consultants have strong incentives to justify complex, costly, multi-asset-class portfolios, for which they themselves are the benchmarkers. Trustees may feel they have no choice but to accept the benchmarking and reporting by staff and consultants, but this only perpetuates the problem. At the very least, investment trustees should step up and take control of benchmarking and performance reporting. For they are the ones charged with watching the watchmen.
{"title":"Lies, Damn Lies, and Benchmarks: An Injunction for Trustees","authors":"Richard M. Ennis","doi":"10.3905/joi.2023.1.266","DOIUrl":"https://doi.org/10.3905/joi.2023.1.266","url":null,"abstract":"Most institutional investors, such as public pension funds and endowments, report their performance using biased benchmarks. The benchmarks are biased downwardly, meaning their returns tend to be less than a fair return for the market exposures and risk exhibited by the institutions’ portfolios. Significant samples of both fund types exhibit benchmark bias in the range of 1.4 to 1.7 percentage points per year. This bias enables a sizable majority of both types of funds to report outperforming their chosen benchmarks when, in fact, most underperform an appropriate passive-management benchmark by a wide margin. Benchmark bias masks serious agency problems in the management of institutional funds. For example, fund staff and consultants have strong incentives to justify complex, costly, multi-asset-class portfolios, for which they themselves are the benchmarkers. Trustees may feel they have no choice but to accept the benchmarking and reporting by staff and consultants, but this only perpetuates the problem. At the very least, investment trustees should step up and take control of benchmarking and performance reporting. For they are the ones charged with watching the watchmen.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"6 - 16"},"PeriodicalIF":0.6,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44794106","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}
Diversification is a fundamental principle for investors when forming a portfolio. When investing in mutual funds and exchange-traded funds (ETFs), purchasing a single fund comes with the benefit of indirectly holding a large number of securities; however, many funds are not as diversified as some may expect. This article documents the large concentration of portfolio holdings of many funds. The degree of concentration varies based on fund investment objective, size, and structure. High levels of concentration often are tied to managers making active bets on the portfolio weights of holdings. This concentration relates to higher idiosyncratic risk (risk originating from factors tied to a specific security or small group of assets) and, for sector and style funds, greater market risk exposure. Even though many funds make large bets on the same stocks and have overlapping holdings, a strategy of investing in multiple funds with a similar objective can often lead to much lower portfolio concentration and reduces idiosyncratic risk.
{"title":"Concentrated Holdings by Funds: Putting All Your Eggs in the Same Basket","authors":"D. Sherrill","doi":"10.3905/joi.2023.1.265","DOIUrl":"https://doi.org/10.3905/joi.2023.1.265","url":null,"abstract":"Diversification is a fundamental principle for investors when forming a portfolio. When investing in mutual funds and exchange-traded funds (ETFs), purchasing a single fund comes with the benefit of indirectly holding a large number of securities; however, many funds are not as diversified as some may expect. This article documents the large concentration of portfolio holdings of many funds. The degree of concentration varies based on fund investment objective, size, and structure. High levels of concentration often are tied to managers making active bets on the portfolio weights of holdings. This concentration relates to higher idiosyncratic risk (risk originating from factors tied to a specific security or small group of assets) and, for sector and style funds, greater market risk exposure. Even though many funds make large bets on the same stocks and have overlapping holdings, a strategy of investing in multiple funds with a similar objective can often lead to much lower portfolio concentration and reduces idiosyncratic risk.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"42 - 61"},"PeriodicalIF":0.6,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46231575","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 so-called “4% rule” is perhaps the single most commonly referenced retirement withdrawal approach. Most discussion of this rule centers on the appropriate percentage to use, 4%, 3%, or 5%. My objective is to suggest a superior retirement rule. Superior in the sense that it will deliver a higher standard of living during one’s retirement years. I start with the observation that past retirement withdrawal analysis suffers from two key deficiencies, that is, a reliance on past US stock and bond market returns (ex-post cherry picking the best performing market) and an assumption that asset class returns can be modeled with independent and identically distributed random variables, serving to mask their important time series properties. This article repairs these two deficiencies. A new retirement distribution rule is proposed. One that distributes/liquidates an ever-increasing proportion of the retiree’s then-current portfolio as they age, but subject to a minimum monthly distribution expressed in dollar terms. This approach provides simplicity, ease of execution, and transparency. It also results in a standard of living for the retiree as expressed by the median and average monthly distributions that are 101.3% and 174.2%, respectively, above those delivered by the best possible constant-dollar withdrawal rule. This pleasing result occurs for two primary reasons. First, this new rule realizes significant benefit from selling more/less shares when markets are high/low (which is the exact opposite of what transpires with all constant-dollar rules). Or to use other words, it delivers a measurably higher IRR despite utilizing an identical asset mix. Second, the new rule effectively consumes the retiree’s entire portfolio over the retirement life, leaving no unused residual balance (again, the exact opposite of all constant-dollar rules).
{"title":"Time to Retire: The 4% Withdrawal Rule","authors":"Brown Rob","doi":"10.3905/joi.2023.1.264","DOIUrl":"https://doi.org/10.3905/joi.2023.1.264","url":null,"abstract":"The so-called “4% rule” is perhaps the single most commonly referenced retirement withdrawal approach. Most discussion of this rule centers on the appropriate percentage to use, 4%, 3%, or 5%. My objective is to suggest a superior retirement rule. Superior in the sense that it will deliver a higher standard of living during one’s retirement years. I start with the observation that past retirement withdrawal analysis suffers from two key deficiencies, that is, a reliance on past US stock and bond market returns (ex-post cherry picking the best performing market) and an assumption that asset class returns can be modeled with independent and identically distributed random variables, serving to mask their important time series properties. This article repairs these two deficiencies. A new retirement distribution rule is proposed. One that distributes/liquidates an ever-increasing proportion of the retiree’s then-current portfolio as they age, but subject to a minimum monthly distribution expressed in dollar terms. This approach provides simplicity, ease of execution, and transparency. It also results in a standard of living for the retiree as expressed by the median and average monthly distributions that are 101.3% and 174.2%, respectively, above those delivered by the best possible constant-dollar withdrawal rule. This pleasing result occurs for two primary reasons. First, this new rule realizes significant benefit from selling more/less shares when markets are high/low (which is the exact opposite of what transpires with all constant-dollar rules). Or to use other words, it delivers a measurably higher IRR despite utilizing an identical asset mix. Second, the new rule effectively consumes the retiree’s entire portfolio over the retirement life, leaving no unused residual balance (again, the exact opposite of all constant-dollar rules).","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"91 - 111"},"PeriodicalIF":0.6,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49428421","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}
This article highlights growth and public initiatives related to digital assets, blockchain, and distributed ledger technologies (DLT) in the stablecoin sector. Stablecoins have been a rapidly growing part of the digital asset ecosystem categorized into three subsectors in the DAR Industry Taxonomy Stable & Fiat Backed sector: Fiat Collateralized Stablecoins, Crypto Collateralized Stablecoins, and Algorithmic Non-Collateralized Stablecoins.
{"title":"A Stablecoin Ecosystem Primer","authors":"Marco Manoppo","doi":"10.3905/joi.2023.1.263","DOIUrl":"https://doi.org/10.3905/joi.2023.1.263","url":null,"abstract":"This article highlights growth and public initiatives related to digital assets, blockchain, and distributed ledger technologies (DLT) in the stablecoin sector. Stablecoins have been a rapidly growing part of the digital asset ecosystem categorized into three subsectors in the DAR Industry Taxonomy Stable & Fiat Backed sector: Fiat Collateralized Stablecoins, Crypto Collateralized Stablecoins, and Algorithmic Non-Collateralized Stablecoins.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"7 - 18"},"PeriodicalIF":0.6,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41808299","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}
Taxes can be a significant drag on investor returns. As a result, minimizing the impact of taxes on total returns has become a staple in the professional investment community and an important way in which financial advisers can add value for clients. One of the most common ways in which investors can achieve tax alpha is through tax-loss harvesting (TLH), which entails selling securities at a loss to offset capital gains on other securities sold at a profit. This article starts with a high-level introduction to tax-loss harvesting and then moves on to show how the return and volatility profile of crypto assets makes them, at least historically, uniquely well-positioned to benefit from TLH strategies.
{"title":"Tax-Loss Harvesting in Crypto: A Significant and Underexplored Opportunity for Financial Advisors","authors":"Matt Hougan, David Lawant, Gayatri Choudhury","doi":"10.3905/joi.2023.1.262","DOIUrl":"https://doi.org/10.3905/joi.2023.1.262","url":null,"abstract":"Taxes can be a significant drag on investor returns. As a result, minimizing the impact of taxes on total returns has become a staple in the professional investment community and an important way in which financial advisers can add value for clients. One of the most common ways in which investors can achieve tax alpha is through tax-loss harvesting (TLH), which entails selling securities at a loss to offset capital gains on other securities sold at a profit. This article starts with a high-level introduction to tax-loss harvesting and then moves on to show how the return and volatility profile of crypto assets makes them, at least historically, uniquely well-positioned to benefit from TLH strategies.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"74 - 87"},"PeriodicalIF":0.6,"publicationDate":"2023-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42416216","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}
Investors face a million-dollar question on a daily basis, is it a right time to invest in stock market, or is the stock market under, over or fairly valued? This article answers this much-awaited question by proposing the stock market assessment indicator. It uses market level fundamental ratios like Price to Earnings (PE), Price to Book (PB), and Dividend Yield (DY) ratios to derive PE, PB, and DY scores based on their historical mean and standard deviation values. This article also derives Market Assessment Score (MAS) as an equal-weighted average of PE, PB, and DY scores. The values of all scores ranges from 0 to 100, based on that, the article classifies the current market as under-, over- or fairly-valued. The findings of this article have implications for the fund manager, portfolio manager and, at large, all investors while making a new investment or rebalancing an existing portfolio.
{"title":"Stock Market Assessment: Invest Now or Later","authors":"Samveg A. Patel","doi":"10.3905/joi.2023.1.261","DOIUrl":"https://doi.org/10.3905/joi.2023.1.261","url":null,"abstract":"Investors face a million-dollar question on a daily basis, is it a right time to invest in stock market, or is the stock market under, over or fairly valued? This article answers this much-awaited question by proposing the stock market assessment indicator. It uses market level fundamental ratios like Price to Earnings (PE), Price to Book (PB), and Dividend Yield (DY) ratios to derive PE, PB, and DY scores based on their historical mean and standard deviation values. This article also derives Market Assessment Score (MAS) as an equal-weighted average of PE, PB, and DY scores. The values of all scores ranges from 0 to 100, based on that, the article classifies the current market as under-, over- or fairly-valued. The findings of this article have implications for the fund manager, portfolio manager and, at large, all investors while making a new investment or rebalancing an existing portfolio.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"79 - 90"},"PeriodicalIF":0.6,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43303717","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 attempt to explain cryptocurrencies, their evolution and variety, and their potential place in portfolios. For many, cryptocurrency has become synonymous with Bitcoin, but there is a great variety of cryptocurrencies, all unified by distributed ledger technology—typically blockchain—whose mechanism, theoretical strengths, and practical weaknesses we describe. We also explore where blockchain can have applications beyond cryptocurrencies. In our view, cryptocurrencies are not intended to function only as private digital currencies. We explore them as investable assets that may be fit into portfolios, and evaluate several attempts at valuing them. We also analyze the correlation of cryptos with other asset classes, concluding that their severe drawdowns make cryptos far riskier than equities, and observing that as their market capitalization increases, major cryptos are expected to behave more in line with traditional risk assets. We distinguish cryptocurrencies from the technology that enables them and highlight the potentially transformative implications of so-called atomic settlement (the simultaneous, instantaneous transfer of assets) and how it may translate to assets more generally. Cryptos’ uncertain and rapidly evolving future includes the likelihood of central bank digital currencies (CBDCs) issued by central banks as legal tender. We conclude by discussing exposure methods to cryptocurrencies and (in our view) their highly promising blockchain technology, from direct ownership to broader market approaches.
{"title":"Exploring Cryptocurrencies: The History, the Hype, and the Future","authors":"Ashley Oerth, D. Dowsett","doi":"10.3905/joi.2023.1.258","DOIUrl":"https://doi.org/10.3905/joi.2023.1.258","url":null,"abstract":"We attempt to explain cryptocurrencies, their evolution and variety, and their potential place in portfolios. For many, cryptocurrency has become synonymous with Bitcoin, but there is a great variety of cryptocurrencies, all unified by distributed ledger technology—typically blockchain—whose mechanism, theoretical strengths, and practical weaknesses we describe. We also explore where blockchain can have applications beyond cryptocurrencies. In our view, cryptocurrencies are not intended to function only as private digital currencies. We explore them as investable assets that may be fit into portfolios, and evaluate several attempts at valuing them. We also analyze the correlation of cryptos with other asset classes, concluding that their severe drawdowns make cryptos far riskier than equities, and observing that as their market capitalization increases, major cryptos are expected to behave more in line with traditional risk assets. We distinguish cryptocurrencies from the technology that enables them and highlight the potentially transformative implications of so-called atomic settlement (the simultaneous, instantaneous transfer of assets) and how it may translate to assets more generally. Cryptos’ uncertain and rapidly evolving future includes the likelihood of central bank digital currencies (CBDCs) issued by central banks as legal tender. We conclude by discussing exposure methods to cryptocurrencies and (in our view) their highly promising blockchain technology, from direct ownership to broader market approaches.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"19 - 48"},"PeriodicalIF":0.6,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42994572","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}
Samuel Gaskin, Rafay Kalim, Kelvin J. Wallace, David Islip, R. Kwon, J. Liew
This article addresses the shortcomings of the existing literature regarding cryptocurrency portfolio construction. First, we address the effectiveness of time-series models that capture stylized features. We perform a comparison study on various methods for estimating distributions for asset returns, including normal, historical, and GARCH models within a CVaR setting. The goal of this comparison is to determine the financial benefits of constructing portfolios based on estimated distributions that consider stylized features of crypto return series. Next, we create and compare various prediction models for cryptocurrencies and integrate them with mean-variance optimization to base performance on portfolio management metrics, such as Sharpe ratio and level of diversification, rather than statistical metrics like accuracy and R2 on which the literature solely focuses. We determine it is unclear which optimization approach (CVaR or Robust MVO) leads to better crypto portfolios, and so, to address this, we compare optimization procedures on out-of-sample data through a thorough cross-validation of hyperparameters for each technique. We then compare the resulting risk-optimal portfolios from each technique. The results show that a CVaR approach with a GARCH simulation and a decision tree prediction model with robust mean-variance optimization yield portfolios of similar risk. We also show that using statistical metrics to evaluate models may not always yield the best financial performance.
{"title":"Portfolio Optimization Techniques for Cryptocurrencies","authors":"Samuel Gaskin, Rafay Kalim, Kelvin J. Wallace, David Islip, R. Kwon, J. Liew","doi":"10.3905/joi.2023.1.256","DOIUrl":"https://doi.org/10.3905/joi.2023.1.256","url":null,"abstract":"This article addresses the shortcomings of the existing literature regarding cryptocurrency portfolio construction. First, we address the effectiveness of time-series models that capture stylized features. We perform a comparison study on various methods for estimating distributions for asset returns, including normal, historical, and GARCH models within a CVaR setting. The goal of this comparison is to determine the financial benefits of constructing portfolios based on estimated distributions that consider stylized features of crypto return series. Next, we create and compare various prediction models for cryptocurrencies and integrate them with mean-variance optimization to base performance on portfolio management metrics, such as Sharpe ratio and level of diversification, rather than statistical metrics like accuracy and R2 on which the literature solely focuses. We determine it is unclear which optimization approach (CVaR or Robust MVO) leads to better crypto portfolios, and so, to address this, we compare optimization procedures on out-of-sample data through a thorough cross-validation of hyperparameters for each technique. We then compare the resulting risk-optimal portfolios from each technique. The results show that a CVaR approach with a GARCH simulation and a decision tree prediction model with robust mean-variance optimization yield portfolios of similar risk. We also show that using statistical metrics to evaluate models may not always yield the best financial performance.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"50 - 65"},"PeriodicalIF":0.6,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41549133","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 new financial instruments is naturally met with skepticism and apprehension. Capital markets as we know them today possess the amazing capability to package any exposure into a digestible instrument for market participants to utilize. However, prudence requires us to verify that these instruments do not suffer from inefficiencies that may prove hazardous to investors. In this article, the authors verify the efficiency of CME Bitcoin Futures Options by testing boundary arbitrage, put-call parity arbitrage, and box spread arbitrage conditions. The results strongly suggest that no reasonable arbitrage opportunities exist and that CME Bitcoin Futures Options are well-suited for institutional scale investing. Hence, we believe their use by institutions to hedge, speculate, and/or facilitate transactions between decentralized markets (“DeFi”) and traditional markets (“TradFi”) will grow significantly in the next few years.
{"title":"Checking the Box on the Efficiency of CME Bitcoin Futures Options","authors":"Eric Zhou","doi":"10.3905/joi.2023.1.257","DOIUrl":"https://doi.org/10.3905/joi.2023.1.257","url":null,"abstract":"The adoption of new financial instruments is naturally met with skepticism and apprehension. Capital markets as we know them today possess the amazing capability to package any exposure into a digestible instrument for market participants to utilize. However, prudence requires us to verify that these instruments do not suffer from inefficiencies that may prove hazardous to investors. In this article, the authors verify the efficiency of CME Bitcoin Futures Options by testing boundary arbitrage, put-call parity arbitrage, and box spread arbitrage conditions. The results strongly suggest that no reasonable arbitrage opportunities exist and that CME Bitcoin Futures Options are well-suited for institutional scale investing. Hence, we believe their use by institutions to hedge, speculate, and/or facilitate transactions between decentralized markets (“DeFi”) and traditional markets (“TradFi”) will grow significantly in the next few years.","PeriodicalId":45504,"journal":{"name":"Journal of Investing","volume":"32 1","pages":"66 - 73"},"PeriodicalIF":0.6,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48223345","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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