In this JPMorgan Chase Institute report, we document a high-frequency relationship between stock market returns and patterns observed in consumer spending and investing behavior. The analysis draws from a core sample of approximately 12 million active Chase credit card users since 2012, and we seek to explain how the distribution of monthly credit card spending changes responds to stock market returns. The right tail of this distribution—characterized by spending increases double or triple a person’s typical level— is over two times more sensitive to the market than the center of the distribution. The relationship is more pronounced for male investors than non-investors and women. Applying the same econometric framework for stock market returns to changes in checking account-based spending and changes in labor income does not yield a statistically discernible relationship in our sample. Meanwhile, individuals’ transfers to investment accounts display a notable correlation with lagged stock returns, consistent with “returns chasing.” Such transfers roughly doubled around the onset of the COVID national emergency, alongside sharp declines in spending. Our findings imply that policies seeking to exert control over business cycles via the stock market may be successful over short time horizons. However, since stock market gains are associated with spending “splurges” on credit cards and flows into investment brokerage accounts, stimulus aimed at supporting asset prices can come with costs in the form of households’ financial vulnerability. If gains in stock prices are not followed by an improving labor market, households that over-extend themselves in terms of spending or equity market exposure would face risks.
{"title":"The Stock Market and Household Financial Behavior","authors":"Diana Farrell, George Eckerd","doi":"10.2139/ssrn.3797054","DOIUrl":"https://doi.org/10.2139/ssrn.3797054","url":null,"abstract":"In this JPMorgan Chase Institute report, we document a high-frequency relationship between stock market returns and patterns observed in consumer spending and investing behavior. The analysis draws from a core sample of approximately 12 million active Chase credit card users since 2012, and we seek to explain how the distribution of monthly credit card spending changes responds to stock market returns. The right tail of this distribution—characterized by spending increases double or triple a person’s typical level— is over two times more sensitive to the market than the center of the distribution. The relationship is more pronounced for male investors than non-investors and women. Applying the same econometric framework for stock market returns to changes in checking account-based spending and changes in labor income does not yield a statistically discernible relationship in our sample. Meanwhile, individuals’ transfers to investment accounts display a notable correlation with lagged stock returns, consistent with “returns chasing.” Such transfers roughly doubled around the onset of the COVID national emergency, alongside sharp declines in spending. Our findings imply that policies seeking to exert control over business cycles via the stock market may be successful over short time horizons. However, since stock market gains are associated with spending “splurges” on credit cards and flows into investment brokerage accounts, stimulus aimed at supporting asset prices can come with costs in the form of households’ financial vulnerability. If gains in stock prices are not followed by an improving labor market, households that over-extend themselves in terms of spending or equity market exposure would face risks.","PeriodicalId":428959,"journal":{"name":"Household Finance eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129254584","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}
Purpose This paper aims to examine whether high levels of financial inclusion is associated with greater financial risk. Design/methodology/approach The study uses regression methodology to estimate the effect of financial inclusion on financial risk. Findings The findings reveal that higher account ownership is associated with greater financial risk through high non-performing loans and high-cost inefficiency in the financial sector of developed countries, advanced countries and transition economies. Increased use of debit cards, credit cards and digital finance products reduced risk in the financial sector of advanced countries and developed countries but not for transition economies and developing countries. The findings also show that the combined use of digital finance products with increased formal account ownership improves financial sector efficiency in developing countries while the combined use of credit cards with increased formal account ownership reduces insolvency risk and improves financial sector efficiency in developing countries. Research limitations/implications The paper offers several implications for policy and financial regulation. It suggests policies that would reduce the financial risk that financial inclusion poses to the financial sector. Originality/value The recent interest in financial inclusion and the unintended consequences of policy-driven financial inclusion in some parts of the world is raising concern about the risks that financial inclusion may introduce to the formal financial sector. Little is known about the risks that financial inclusion may pose to the financial sector.
{"title":"Has Financial Inclusion Made the Financial Sector Riskier?","authors":"Peterson K. Ozili","doi":"10.2139/ssrn.3768963","DOIUrl":"https://doi.org/10.2139/ssrn.3768963","url":null,"abstract":"\u0000Purpose\u0000This paper aims to examine whether high levels of financial inclusion is associated with greater financial risk.\u0000\u0000\u0000Design/methodology/approach\u0000The study uses regression methodology to estimate the effect of financial inclusion on financial risk.\u0000\u0000\u0000Findings\u0000The findings reveal that higher account ownership is associated with greater financial risk through high non-performing loans and high-cost inefficiency in the financial sector of developed countries, advanced countries and transition economies. Increased use of debit cards, credit cards and digital finance products reduced risk in the financial sector of advanced countries and developed countries but not for transition economies and developing countries. The findings also show that the combined use of digital finance products with increased formal account ownership improves financial sector efficiency in developing countries while the combined use of credit cards with increased formal account ownership reduces insolvency risk and improves financial sector efficiency in developing countries.\u0000\u0000\u0000Research limitations/implications\u0000The paper offers several implications for policy and financial regulation. It suggests policies that would reduce the financial risk that financial inclusion poses to the financial sector.\u0000\u0000\u0000Originality/value\u0000The recent interest in financial inclusion and the unintended consequences of policy-driven financial inclusion in some parts of the world is raising concern about the risks that financial inclusion may introduce to the formal financial sector. Little is known about the risks that financial inclusion may pose to the financial sector.\u0000","PeriodicalId":428959,"journal":{"name":"Household Finance eJournal","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121288346","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}
Capital gains revenue estimates rely on a long history of research empirically estimating the tax elasticity of capital gains realizations. These elasticity estimates have varied from zero to well over 3 in absolute value depending on numerous factors, such as the time frame studied, the type of capital asset, and the estimation strategy employed. Often, the headline elasticity from a study of this nature is used to calculate the implied revenue-maximizing capital gains tax rate. Unfortunately, this last, policy-relevant step has received insufficient scrutiny. The standard approach fails to sufficiently acknowledge that the estimates of the revenue-maximizing rate are a product of the estimation procedure used and the context of the tax system from which the data were generated. Such an approach yields a single capital gains tax rate which applies to all realized gains and mechanically overstates the resulting revenue-maximizing rate.
{"title":"Contextualizing Elasticities for Policymaking: Capital Gains and Revenue-Maximizing Tax Rates","authors":"T. Dowd, Z. Richards","doi":"10.2139/ssrn.3767121","DOIUrl":"https://doi.org/10.2139/ssrn.3767121","url":null,"abstract":"Capital gains revenue estimates rely on a long history of research empirically estimating the tax elasticity of capital gains realizations. These elasticity estimates have varied from zero to well over 3 in absolute value depending on numerous factors, such as the time frame studied, the type of capital asset, and the estimation strategy employed. Often, the headline elasticity from a study of this nature is used to calculate the implied revenue-maximizing capital gains tax rate. Unfortunately, this last, policy-relevant step has received insufficient scrutiny. The standard approach fails to sufficiently acknowledge that the estimates of the revenue-maximizing rate are a product of the estimation procedure used and the context of the tax system from which the data were generated. Such an approach yields a single capital gains tax rate which applies to all realized gains and mechanically overstates the resulting revenue-maximizing rate.","PeriodicalId":428959,"journal":{"name":"Household Finance eJournal","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134410348","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 paper discusses the benefits and issues associated with big data and artificial intelligence (AI) for financial inclusion. The benefits of artificial intelligence and big data for financial inclusion are: improved efficiency and risk management for financial services providers; the provision of smart financial products and services to banked adults; simplification of the account opening process for unbanked adults and the creation of credit scores for unbanked adults using alternative information. Several issues associated with artificial intelligence and big data for financial inclusion that need to be addressed include: the shortage of skilled AI workers, increase in the level of unemployment in the financial ecosystem, the unconscious bias in the design of artificial intelligence systems, and other barriers caused by strict data privacy laws.
{"title":"Big Data and Artificial Intelligence for Financial Inclusion: Benefits and Issues","authors":"Peterson K. Ozili","doi":"10.2139/ssrn.3766097","DOIUrl":"https://doi.org/10.2139/ssrn.3766097","url":null,"abstract":"This paper discusses the benefits and issues associated with big data and artificial intelligence (AI) for financial inclusion. The benefits of artificial intelligence and big data for financial inclusion are: improved efficiency and risk management for financial services providers; the provision of smart financial products and services to banked adults; simplification of the account opening process for unbanked adults and the creation of credit scores for unbanked adults using alternative information. Several issues associated with artificial intelligence and big data for financial inclusion that need to be addressed include: the shortage of skilled AI workers, increase in the level of unemployment in the financial ecosystem, the unconscious bias in the design of artificial intelligence systems, and other barriers caused by strict data privacy laws.","PeriodicalId":428959,"journal":{"name":"Household Finance eJournal","volume":"257 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120953968","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 study provides a new perspective to understand the rise and future potential of FinTech lending by linking it to the informational role of cashless payments. We uncover both theoretically and empirically a synergy between FinTech lending and cashless payments. FinTech lenders screen borrowers more efficiently when borrowers use more cashless payments that produce transferrable and verifiable information. Because borrowers expect lenders to rely on such payment information to screen them, a strategic consideration for a borrower to stand out of other borrowers then pushes more borrowers to adopt cashless payments. Using novel loan-level data from a large Indian FinTech lender who focuses on small-business lending, we find that a larger use of verifiable cashless payments (relative to cash) predicts a higher chance of loan approval, a lower interest rate, and lower default conditional on the interest rate obtained. These relationships are more pronounced for higher-quality firms. The uncovered synergy provides a plausible explanation for the joint rise of FinTech lending and cashless payments, and suggests an alternative banking model without a balance sheet or traditional banking relationships. Our findings also provide new policy implications on data sharing and open banking.
{"title":"FinTech Lending and Cashless Payments","authors":"Pulak Ghosh, B. Vallée, Yao Zeng","doi":"10.2139/ssrn.3766250","DOIUrl":"https://doi.org/10.2139/ssrn.3766250","url":null,"abstract":"This study provides a new perspective to understand the rise and future potential of FinTech lending by linking it to the informational role of cashless payments. We uncover both theoretically and empirically a synergy between FinTech lending and cashless payments. FinTech lenders screen borrowers more efficiently when borrowers use more cashless payments that produce transferrable and verifiable information. Because borrowers expect lenders to rely on such payment information to screen them, a strategic consideration for a borrower to stand out of other borrowers then pushes more borrowers to adopt cashless payments. Using novel loan-level data from a large Indian FinTech lender who focuses on small-business lending, we find that a larger use of verifiable cashless payments (relative to cash) predicts a higher chance of loan approval, a lower interest rate, and lower default conditional on the interest rate obtained. These relationships are more pronounced for higher-quality firms. The uncovered synergy provides a plausible explanation for the joint rise of FinTech lending and cashless payments, and suggests an alternative banking model without a balance sheet or traditional banking relationships. Our findings also provide new policy implications on data sharing and open banking.","PeriodicalId":428959,"journal":{"name":"Household Finance eJournal","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126902632","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}
I reexamine whether media articles with substantive editorial content inform the market's reaction to firms' earnings news. Using variation in earnings announcement coverage due to restructuring at The Wall Street Journal (WSJ), my analyses suggest that WSJ earnings articles improve price discovery and increase trading volume at S&P 500 earnings announcements. Additionally, textual analysis suggests media articles that differ more from the firm's earnings release increase trading volume, and that the differences speed up (slow down) price discovery when they corroborate (contradict) the tone of the firm's news. Such high difference articles are slightly longer, are more readable and specific, include more references to the industry and economy, repeat less "stale" news published in previous WSJ articles, and quote more investor and expert sources. Overall, my paper contributes to research on the role of the media in earnings news by providing evidence that journalists' editorial content helps investors understand firms' earnings, instead of simply entertaining or increasing awareness.
我重新审视具有实质性编辑内容的媒体文章是否会影响市场对公司盈利消息的反应。利用《华尔街日报》(The Wall Street Journal)重组导致的收益公告报道的变化，我的分析表明，《华尔街日报》的收益文章改善了价格发现，增加了标准普尔500指数(s&p;P; 500)收益公告的交易量。此外，文本分析表明，媒体文章与公司发布的收益差异较大，会增加交易量，当这些差异与公司新闻的基调相一致时，它们会加速(减慢)价格发现。这种高差异的文章篇幅略长，可读性更强，内容更具体，更多地涉及行业和经济，较少重复《华尔街日报》之前文章中的“陈腐”新闻，并引用更多投资者和专家的消息来源。总的来说，我的论文通过提供证据证明记者的编辑内容有助于投资者了解公司的收益，而不仅仅是娱乐或提高意识，从而有助于研究媒体在收益新闻中的作用。
{"title":"The Information Role of the Media in Earnings News","authors":"Nicholas Guest","doi":"10.2139/ssrn.3067387","DOIUrl":"https://doi.org/10.2139/ssrn.3067387","url":null,"abstract":"I reexamine whether media articles with substantive editorial content inform the market's reaction to firms' earnings news. Using variation in earnings announcement coverage due to restructuring at The Wall Street Journal (WSJ), my analyses suggest that WSJ earnings articles improve price discovery and increase trading volume at S&P 500 earnings announcements. Additionally, textual analysis suggests media articles that differ more from the firm's earnings release increase trading volume, and that the differences speed up (slow down) price discovery when they corroborate (contradict) the tone of the firm's news. Such high difference articles are slightly longer, are more readable and specific, include more references to the industry and economy, repeat less \"stale\" news published in previous WSJ articles, and quote more investor and expert sources. Overall, my paper contributes to research on the role of the media in earnings news by providing evidence that journalists' editorial content helps investors understand firms' earnings, instead of simply entertaining or increasing awareness.","PeriodicalId":428959,"journal":{"name":"Household Finance eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117250789","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}
Sean Foley, Bart Frijns, Alexandre Garel, Tai‐Yong Roh
We examine the relationship between national culture and a country’s Bitcoin usage. Given that Bitcoin is a high-risk currency/investment that is frequently used for illegal purposes and whose market is relatively opaque, we focus on the cultural dimension of individualism, which has been related to risk-taking behavior and overconfidence. Using unique data that includes the originating country for Bitcoin transactions, we examine the relationship between individualism and a country’s Bitcoin usage for a sample of 80 countries between 2009-2018. We find a significant and positive relationship between a country’s individualism and its use of Bitcoin consistent with cultural values affecting the demand for such high-risk currency/investments.
{"title":"Who buys Bitcoin? The Cultural Determinants of Bitcoin Usage","authors":"Sean Foley, Bart Frijns, Alexandre Garel, Tai‐Yong Roh","doi":"10.2139/ssrn.3762923","DOIUrl":"https://doi.org/10.2139/ssrn.3762923","url":null,"abstract":"We examine the relationship between national culture and a country’s Bitcoin usage. Given that Bitcoin is a high-risk currency/investment that is frequently used for illegal purposes and whose market is relatively opaque, we focus on the cultural dimension of individualism, which has been related to risk-taking behavior and overconfidence. Using unique data that includes the originating country for Bitcoin transactions, we examine the relationship between individualism and a country’s Bitcoin usage for a sample of 80 countries between 2009-2018. We find a significant and positive relationship between a country’s individualism and its use of Bitcoin consistent with cultural values affecting the demand for such high-risk currency/investments.","PeriodicalId":428959,"journal":{"name":"Household Finance eJournal","volume":"175 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127541890","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}
Ankit Kalda, Benjamin Loos, Alessandro Previtero, A. Hackethal
Using transaction-level data from two German banks, we study the effects of smartphones on investor behavior. Comparing trades by the same investor in the same month across different platforms, we find that smartphones increase purchasing of riskier and lottery-type assets and chasing past returns. After the adoption of smartphones, investors do not substitute trades across platforms and buy also riskier, lottery-type, and hot investments on other platforms. Using smartphones to trade specific assets or during specific hours contributes to explain our results. Digital nudges and the device screen size do not mechanically drive our results. Smartphone effects are not transitory.
{"title":"Smart(Phone) Investing? A within Investor-Time Analysis of New Technologies and Trading Behavior","authors":"Ankit Kalda, Benjamin Loos, Alessandro Previtero, A. Hackethal","doi":"10.2139/ssrn.3765652","DOIUrl":"https://doi.org/10.2139/ssrn.3765652","url":null,"abstract":"Using transaction-level data from two German banks, we study the effects of smartphones on investor behavior. Comparing trades by the same investor in the same month across different platforms, we find that smartphones increase purchasing of riskier and lottery-type assets and chasing past returns. After the adoption of smartphones, investors do not substitute trades across platforms and buy also riskier, lottery-type, and hot investments on other platforms. Using smartphones to trade specific assets or during specific hours contributes to explain our results. Digital nudges and the device screen size do not mechanically drive our results. Smartphone effects are not transitory.","PeriodicalId":428959,"journal":{"name":"Household Finance eJournal","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126371914","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 paper examines the effect of the increase in the Social Security Full Retirement Age (FRA) and the associated decrease in benefits for early claimants on retirement rates at ages 62 to 65. It uses information on age, sex, and labor force participation from the monthly Current Population Survey from 1976 to 2019. Critical components of the analysis include a difference/in-difference framework, comparison of three measures of retirement status, estimation of nonparametric and parametric models, and a test of the assumptions underlying the difference-indifference approach. Although our model satisfied that test, the results do not guarantee that our specification is valid.
The paper found that:
• The increase in the FRA decreased the retirement rate at age 62 by about 5 percentage points (or 30 percent) for men and by about 2 percentage points (or about 20 percent) for women. The retirement rate at age 63 to 65 was not affected.
• Estimates of the parametric model show that a 1 percent increase in the early claiming reduction decreases the retirement rate by between 0.7 and 0.9 percentage points for men and between 0.2 and 0.4 percentage points for women. The policy implications of the findings are:
• Our findings can inform evaluations of policy proposals to further increase the Social Security FRA.
• Our findings contribute to the understanding of the effects that the availability and generosity of pension benefits have on retirement decisions.
{"title":"The Effect of Early Claiming Benefit Reduction on Retirement Rates","authors":"Damir Cosic, C. E. Steuerle","doi":"10.2139/ssrn.3786236","DOIUrl":"https://doi.org/10.2139/ssrn.3786236","url":null,"abstract":"This paper examines the effect of the increase in the Social Security Full Retirement Age (FRA) and the associated decrease in benefits for early claimants on retirement rates at ages 62 to 65. It uses information on age, sex, and labor force participation from the monthly Current Population Survey from 1976 to 2019. Critical components of the analysis include a difference/in-difference framework, comparison of three measures of retirement status, estimation of nonparametric and parametric models, and a test of the assumptions underlying the difference-indifference approach. Although our model satisfied that test, the results do not guarantee that our specification is valid.<br><br>The paper found that:<br><br>• The increase in the FRA decreased the retirement rate at age 62 by about 5 percentage points (or 30 percent) for men and by about 2 percentage points (or about 20 percent) for women. The retirement rate at age 63 to 65 was not affected. <br><br>• Estimates of the parametric model show that a 1 percent increase in the early claiming reduction decreases the retirement rate by between 0.7 and 0.9 percentage points for men and between 0.2 and 0.4 percentage points for women. The policy implications of the findings are: <br><br>• Our findings can inform evaluations of policy proposals to further increase the Social Security FRA. <br><br>• Our findings contribute to the understanding of the effects that the availability and generosity of pension benefits have on retirement decisions.","PeriodicalId":428959,"journal":{"name":"Household Finance eJournal","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122143807","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}
I provide a novel framework for machine learning models to ingest quantified soft information during the life of a loan, using cutting-edge natural language processing techniques on salient unstructured text. This soft information, from servicer call transcripts, is not restricted to mere positive/negative sentiments and provides efficiency and alleviates the information asymmetry between the lender (and/or issuer) and the borrower. Proprietary servicer comments are hardly accessible and offer the soft in-formation for real-time delinquency status of the mortgages. I investigate whether the special servicer invoked by the investor can utilize the valuable comments from the master servicer. The time-varying soft information about the borrower’s financial condition, health of the loan and the property condition from these master servicer comments renders the predictive power and has asset pricing implications. Given this valuable information, the special servicer may choose to use this information, as I anecdotally see with several private equity investors. The well-known unresolved conflict of interest between the master and special servicers can be resolved, thereby reducing moral hazard and increasing efficiency and transparency.
{"title":"Quantifying Soft Information, Mortgage Market Efficiency & Asset Pricing Implications","authors":"A. Bandyopadhyay","doi":"10.2139/ssrn.3755698","DOIUrl":"https://doi.org/10.2139/ssrn.3755698","url":null,"abstract":"I provide a novel framework for machine learning models to ingest quantified soft information during the life of a loan, using cutting-edge natural language processing techniques on salient unstructured text. This soft information, from servicer call transcripts, is not restricted to mere positive/negative sentiments and provides efficiency and alleviates the information asymmetry between the lender (and/or issuer) and the borrower. Proprietary servicer comments are hardly accessible and offer the soft in-formation for real-time delinquency status of the mortgages. I investigate whether the special servicer invoked by the investor can utilize the valuable comments from the master servicer. The time-varying soft information about the borrower’s financial condition, health of the loan and the property condition from these master servicer comments renders the predictive power and has asset pricing implications. Given this valuable information, the special servicer may choose to use this information, as I anecdotally see with several private equity investors. The well-known unresolved conflict of interest between the master and special servicers can be resolved, thereby reducing moral hazard and increasing efficiency and transparency.","PeriodicalId":428959,"journal":{"name":"Household Finance eJournal","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128009634","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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