Do retail investors’ behavioral biases in trading directly affect their consumption out of stock market wealth? We exploit a natural experiment that changed the displayed purchase prices in investors’ online portfolios. Investors are more likely to sell and consume on average 25% of “fictitious” capital gains, i.e., displayed capital gains under the new purchase prices that are capital losses under the actual purchase prices. We argue that investors are selectively inattentive: they are more responsive when fictitious gains are larger and actual losses are smaller, they notice fictitious losses, and they react even when actual purchase prices are very salient.
{"title":"Consumption out of Fictitious Capital Gains and Selective Inattention","authors":"Benjamin Loos, Steffen Meyer, Michaela Pagel","doi":"10.2139/ssrn.3576628","DOIUrl":"https://doi.org/10.2139/ssrn.3576628","url":null,"abstract":"Do retail investors’ behavioral biases in trading directly affect their consumption out of stock market wealth? We exploit a natural experiment that changed the displayed purchase prices in investors’ online portfolios. Investors are more likely to sell and consume on average 25% of “fictitious” capital gains, i.e., displayed capital gains under the new purchase prices that are capital losses under the actual purchase prices. We argue that investors are selectively inattentive: they are more responsive when fictitious gains are larger and actual losses are smaller, they notice fictitious losses, and they react even when actual purchase prices are very salient.","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"553 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77587906","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 impact of the cognitive biases of overconfidence, underconfidence and anchoring on the distribution of errors of forecasting models is analyzed using an analytical framework based on a flexible two-piece generalized distribution. The total forecasting bias, measured by the expected value of a model’s errors, is decomposed to an anchoring bias and a skewness bias. An examination of BEA’s preliminary estimates of the final GDP growth rates reveals that the underprediction present is to a large extent the result of negative skewness bias and to a lesser extent of negative anchoring bias. The latter are attributes of underconfident forecasters.
{"title":"Impact of Cognitive Biases on Forecasting Models","authors":"Panayiotis Theodossiou, Polina Ellina","doi":"10.2139/ssrn.3756478","DOIUrl":"https://doi.org/10.2139/ssrn.3756478","url":null,"abstract":"The impact of the cognitive biases of overconfidence, underconfidence and anchoring on the distribution of errors of forecasting models is analyzed using an analytical framework based on a flexible two-piece generalized distribution. The total forecasting bias, measured by the expected value of a model’s errors, is decomposed to an anchoring bias and a skewness bias. An examination of BEA’s preliminary estimates of the final GDP growth rates reveals that the underprediction present is to a large extent the result of negative skewness bias and to a lesser extent of negative anchoring bias. The latter are attributes of underconfident forecasters.","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84518649","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}
G. D'alessio, Riccardo De Bonis, A. Neri, Cristiana Rampazzi
Italian Abstract: Il lavoro analizza i risultati dell’Indagine sull’Alfabetizzazione e le Competenze Finanziarie degli Italiani (IACOFI), condotta dalla Banca d’Italia all’inizio del 2020 seguendo la metodologia OCSE-INFE che definisce l’indicatore di competenze finanziarie come la somma dei punteggi calcolati per tre aspetti: le conoscenze, i comportamenti e le attitudini. L’indagine conferma la posizione di ritardo dell’Italia nel confronto internazionale, gia rilevata nel 2017, ma mostra un miglioramento nelle conoscenze degli italiani e una sostanziale stabilita nei comportamenti e nelle attitudini. L’alfabetizzazione differisce nella popolazione a seconda del livello di istruzione – la variabile piu rilevante – del genere, dell’eta e della localizzazione geografica degli intervistati. Un esercizio econometrico focalizzato sulle conoscenze – l’indicatore piu significativo – mostra che gli italiani possono essere suddivisi in quattro gruppi, caratterizzati da livelli crescenti di conoscenze finanziarie: gli esclusi, gli incompetenti, i competenti e gli esperti. Le nostre stime consentono di approfondire, per gruppi di intervistati, le cause del miglioramento delle conoscenze registrato tra il 2017 e il 2020: le popolazioni degli esclusi e degli incompetenti sono diminuite, a fronte di un aumento dei competenti, e, in piccola misura, degli esperti. English Abstract: The paper analyses the results of the Survey on the Financial Literacy of Italian Adults, conducted by the Bank of Italy in early 2020. In line with the OECD’s methodology, the financial literacy indicator is the sum of the scores calculated for three factors: knowledge, behaviour and attitudes. The survey confirms that Italy lags behind by international standards, as already noted in the 2017 survey. Compared with 2017, the new survey shows that Italian people’s financial knowledge has improved, while their behaviour and attitudes have essentially remained stable. Financial literacy varies among the population according to the education levels – the most significant variable – gender, age and geographical location of those interviewed. An econometric analysis focused on knowledge – the most reliable component – shows that Italians can be divided into four groups, characterized by increasingly high levels of financial knowledge: excluded, incompetent, competent and expert. Between 2017 and 2020, the number of excluded and incompetent individuals in the population has decreased, whereas that of competent, and to a lesser extent, of expert individuals has increased.
意大利摘要:扫盲工作分析调查结果和意大利的财政权力(IACOFI 2020年初),意大利银行进行的一项财政权力OCSE-INFE方法,确定了指标的基础上三个方面作为计算得分的总和:知识、态度和技能。这项调查证实了意大利在2017年的国际比较中处于领先地位，但它显示意大利人的知识有所提高，行为和态度也有了实质性的稳定。根据受教育程度(最重要的变量)、性别、年龄和地理位置，人口的识字能力各不相同。一项以知识为重点的计量经济学实践——最重要的指标——显示，意大利人可以分为四类，其特点是金融知识水平不断提高:被排斥的人、无能的人、称职的人和专家。根据我们的估计，在2017年至2020年期间，被调查者群体的知识有所提高，被排除在外和无能为力的人的数量有所下降，而有能力的人的数量有所增加，专家的数量略有增加。英国摘要:意大利银行(Bank of Italy)在2020年早些时候进行的《意大利成年人财务披露调查结果》(Financial literage Survey of Italian成年人)的纸质分析。根据经合组织的方法，财务披露指标是计算三个因素的分数的总和:知识、行为和态度。调查证实，意大利在2017年的调查中已经被国际标准认可。与2017年相比，新的调查显示，意大利人民的金融知识是在他们的行为和态度基本稳定的情况下发明的。这些采访的性别、年龄和地理位置。最可靠的成分表明，意大利人可以被分成四个集团，通过提高金融知识的高水平来发挥作用:排斥、无能、能力和专家。从2017年到2020年，人口中被排斥和缺乏能力的人的数量已经下降，技能的数量已经下降，专业人员的数量也在增加。
{"title":"L’alfabetizzazione finanziaria degli italiani: i risultati dell’indagine della Banca d’Italia del 2020 [Italian People’s Financial Literacy: The Results of the Bank of Italy’s 2020 Survey]","authors":"G. D'alessio, Riccardo De Bonis, A. Neri, Cristiana Rampazzi","doi":"10.2139/ssrn.3826435","DOIUrl":"https://doi.org/10.2139/ssrn.3826435","url":null,"abstract":"Italian Abstract: Il lavoro analizza i risultati dell’Indagine sull’Alfabetizzazione e le Competenze Finanziarie degli Italiani (IACOFI), condotta dalla Banca d’Italia all’inizio del 2020 seguendo la metodologia OCSE-INFE che definisce l’indicatore di competenze finanziarie come la somma dei punteggi calcolati per tre aspetti: le conoscenze, i comportamenti e le attitudini. L’indagine conferma la posizione di ritardo dell’Italia nel confronto internazionale, gia rilevata nel 2017, ma mostra un miglioramento nelle conoscenze degli italiani e una sostanziale stabilita nei comportamenti e nelle attitudini. L’alfabetizzazione differisce nella popolazione a seconda del livello di istruzione – la variabile piu rilevante – del genere, dell’eta e della localizzazione geografica degli intervistati. Un esercizio econometrico focalizzato sulle conoscenze – l’indicatore piu significativo – mostra che gli italiani possono essere suddivisi in quattro gruppi, caratterizzati da livelli crescenti di conoscenze finanziarie: gli esclusi, gli incompetenti, i competenti e gli esperti. Le nostre stime consentono di approfondire, per gruppi di intervistati, le cause del miglioramento delle conoscenze registrato tra il 2017 e il 2020: le popolazioni degli esclusi e degli incompetenti sono diminuite, a fronte di un aumento dei competenti, e, in piccola misura, degli esperti. \u0000 \u0000English Abstract: The paper analyses the results of the Survey on the Financial Literacy of Italian Adults, conducted by the Bank of Italy in early 2020. In line with the OECD’s methodology, the financial literacy indicator is the sum of the scores calculated for three factors: knowledge, behaviour and attitudes. The survey confirms that Italy lags behind by international standards, as already noted in the 2017 survey. Compared with 2017, the new survey shows that Italian people’s financial knowledge has improved, while their behaviour and attitudes have essentially remained stable. Financial literacy varies among the population according to the education levels – the most significant variable – gender, age and geographical location of those interviewed. An econometric analysis focused on knowledge – the most reliable component – shows that Italians can be divided into four groups, characterized by increasingly high levels of financial knowledge: excluded, incompetent, competent and expert. Between 2017 and 2020, the number of excluded and incompetent individuals in the population has decreased, whereas that of competent, and to a lesser extent, of expert individuals has increased.","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75217555","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}
Relying on gender role congruity theory, this paper investigates the relationship between the gender of the top management team of venture philanthropy (VP) firms and their risk-taking orientation. Our research also assesses if and how experience moderates this relationship. Using a combination of survey data to capture the VP firm’s risk orientation, and biographical data to identify managers’ gender and experience, we find that only gender affects the risk-taking orientation in these firms. Yet, this is in an opposite direction than what theorized, whereby teams with a higher proportion of women have a higher risk-taking profile. This suggests the existence of a gender bind dilemma in VP.
{"title":"Risk-taking in Impact Investing: The Role of Gender and Experience","authors":"Luisa Alemany, M. Scarlata, Andrew Zacharakis","doi":"10.2139/ssrn.3750274","DOIUrl":"https://doi.org/10.2139/ssrn.3750274","url":null,"abstract":"Relying on gender role congruity theory, this paper investigates the relationship between the gender of the top management team of venture philanthropy (VP) firms and their risk-taking orientation. Our research also assesses if and how experience moderates this relationship. Using a combination of survey data to capture the VP firm’s risk orientation, and biographical data to identify managers’ gender and experience, we find that only gender affects the risk-taking orientation in these firms. Yet, this is in an opposite direction than what theorized, whereby teams with a higher proportion of women have a higher risk-taking profile. This suggests the existence of a gender bind dilemma in VP.<br>","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89924150","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 show theoretically and empirically that flows into index funds raise the prices of large stocks in the index disproportionately more than the prices of small stocks. Conversely, flows predict a high future return of the small-minus-large index portfolio. This finding runs counter to the CAPM, and arises when noise traders distort prices, biasing index weights. When funds tracking value-weighted indices experience inflows, they buy mainly stocks in high noise-trader demand, exacerbating the distortion. During our sample period 2000-2019, a small-minus-large portfolio of S&P500 stocks earns ten percent per year, while no size effect exists for non-index stocks.
{"title":"Tracking Biased Weights: Asset Pricing Implications of Value-Weighted Indexing","authors":"Hao Jiang, Dimitri Vayanos, Lu Zheng","doi":"10.2139/ssrn.3749534","DOIUrl":"https://doi.org/10.2139/ssrn.3749534","url":null,"abstract":"We show theoretically and empirically that flows into index funds raise the prices of large stocks in the index disproportionately more than the prices of small stocks. Conversely, flows predict a high future return of the small-minus-large index portfolio. This finding runs counter to the CAPM, and arises when noise traders distort prices, biasing index weights. When funds tracking value-weighted indices experience inflows, they buy mainly stocks in high noise-trader demand, exacerbating the distortion. During our sample period 2000-2019, a small-minus-large portfolio of S&P500 stocks earns ten percent per year, while no size effect exists for non-index stocks.","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90438171","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}
Recent studies show evidence that investors learn about their trading abilities. This paper focuses on understanding how investors learn about their talent and proposes a unifying framework that explains many puzzling facts about individual equity investors. In my model, the investor forms subjective beliefs both about the expected return of the current stock-in-holding and about her trading talent represented by the expected return of the next replacement stock, and updates beliefs through learning with fading memory. I calibrate the memory decay parameters to individual trading records, and show that talent learning is about 7 times more sensitive to return signals than stock-in-holding learning. Consequently, the model indicates that stock switching always happens following good performance of the current stock because switching requires a sufficiently large wedge between expected returns of the replacement stock and the current stock to cover the fixed cost, which strongly predicts disposition effect in a learning perspective. This framework also accounts for the performance-contingent trading intensity and attrition, and explains why a negative shock would lead to attrition when an investor has several years of experience, which is inconsistent with the decreasing-gain updating under standard Bayesian learning.
{"title":"Subjective Learning of Trading Talent: Theory and Evidence from Individual Investors in the U.S.","authors":"Xindi He","doi":"10.2139/ssrn.3732447","DOIUrl":"https://doi.org/10.2139/ssrn.3732447","url":null,"abstract":"Recent studies show evidence that investors learn about their trading abilities. This paper focuses on understanding how investors learn about their talent and proposes a unifying framework that explains many puzzling facts about individual equity investors. In my model, the investor forms subjective beliefs both about the expected return of the current stock-in-holding and about her trading talent represented by the expected return of the next replacement stock, and updates beliefs through learning with fading memory. I calibrate the memory decay parameters to individual trading records, and show that talent learning is about 7 times more sensitive to return signals than stock-in-holding learning. Consequently, the model indicates that stock switching always happens following good performance of the current stock because switching requires a sufficiently large wedge between expected returns of the replacement stock and the current stock to cover the fixed cost, which strongly predicts disposition effect in a learning perspective. This framework also accounts for the performance-contingent trading intensity and attrition, and explains why a negative shock would lead to attrition when an investor has several years of experience, which is inconsistent with the decreasing-gain updating under standard Bayesian learning.","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85446687","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 disposition effect has been widely studied in academia, while the reverse disposition effect observed in mutual funds has gained relatively little attention. This study examines the reverse disposition effect in detail by using policy-level data from a Finnish life insurer with a considerable sample size. The results show that the Finnish savings policies with a positive return have a surrender rate that is over 30 percent lower than that of policies with a negative return. Tax incentives and expected future returns do not seem to cause this reverse disposition effect directly. Salient information strengthens the reverse disposition effect, and higher policyholder age and surrender fees weaken it. These empirical findings deepen the understanding of the reverse disposition effect.
{"title":"How Return Affects the Decision to Surrender a Savings Insurance Policy: Detailed Observations on the Reverse Disposition Effect","authors":"Jukka Johansson","doi":"10.2139/ssrn.3733876","DOIUrl":"https://doi.org/10.2139/ssrn.3733876","url":null,"abstract":"The disposition effect has been widely studied in academia, while the reverse disposition effect observed in mutual funds has gained relatively little attention. This study examines the reverse disposition effect in detail by using policy-level data from a Finnish life insurer with a considerable sample size. The results show that the Finnish savings policies with a positive return have a surrender rate that is over 30 percent lower than that of policies with a negative return. Tax incentives and expected future returns do not seem to cause this reverse disposition effect directly. Salient information strengthens the reverse disposition effect, and higher policyholder age and surrender fees weaken it. These empirical findings deepen the understanding of the reverse disposition effect.","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84182251","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 author analyzes the statistics of words and phrases related to financial market trading practices in millions of volumes from Google's book collection and available at Google Ngram Viewer. In recent almost 30 years, as the analyzed data shows, the scholars and practitioners' interest in the specific market strategies and technique shifted toward those more automotive, aggressive, speculative, but less dependent on fundamental analysis, information and data processing, and investors' reasoning and research. This shift may indicate the increasing share of unsophisticated investors trying to cover the lack of experience and professional knowledge through extensive use of technology-supported strategies. In a long-run perspective, this may generate the growth of market instability, risks, and uncertainty.
{"title":"Investors Behavior Under Growing Financial Market Uncertainty.","authors":"V. Milovidov","doi":"10.2139/ssrn.3733825","DOIUrl":"https://doi.org/10.2139/ssrn.3733825","url":null,"abstract":"The author analyzes the statistics of words and phrases related to financial market trading practices in millions of volumes from Google's book collection and available at Google Ngram Viewer. In recent almost 30 years, as the analyzed data shows, the scholars and practitioners' interest in the specific market strategies and technique shifted toward those more automotive, aggressive, speculative, but less dependent on fundamental analysis, information and data processing, and investors' reasoning and research. This shift may indicate the increasing share of unsophisticated investors trying to cover the lack of experience and professional knowledge through extensive use of technology-supported strategies. In a long-run perspective, this may generate the growth of market instability, risks, and uncertainty.<br>","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"85 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83883959","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 collects results on the repeated risk-taking of skewed risks. An extensive body of theoretical and experimental literature has shown that, in one-time decision situations, humans are skewness-seeking and dislike risks that feature unlikely but large losses (i.e., negatively skewed risks). We show that, contrary to intuition, the often-observed phenomenon of penny-picking—repeatedly taking negatively skewed risks—is not at odds with skewness-seeking, but, to the contrary, may even be caused by it. The skewness of the distribution that results from repeatedly taking a skewed risk depends in non-trivial ways on the risk-taking strategy and may even differ in sign from that of the individual risk. With sufficient time available, every risk—no matter how negatively skewed—can be gambled in such a way that, in total, skewness is positive. Because recent work has shown that skewness is decisive whether risk is taken, this result may be important for economics and finance on a fundamental level.
{"title":"On Taking a Skewed Risk More Than Once","authors":"S. Ebert","doi":"10.2139/ssrn.3731565","DOIUrl":"https://doi.org/10.2139/ssrn.3731565","url":null,"abstract":"This paper collects results on the repeated risk-taking of skewed risks. An extensive body of theoretical and experimental literature has shown that, in one-time decision situations, humans are skewness-seeking and dislike risks that feature unlikely but large losses (i.e., negatively skewed risks). We show that, contrary to intuition, the often-observed phenomenon of penny-picking—repeatedly taking negatively skewed risks—is not at odds with skewness-seeking, but, to the contrary, may even be caused by it. The skewness of the distribution that results from repeatedly taking a skewed risk depends in non-trivial ways on the risk-taking strategy and may even differ in sign from that of the individual risk. With sufficient time available, every risk—no matter how negatively skewed—can be gambled in such a way that, in total, skewness is positive. Because recent work has shown that skewness is decisive whether risk is taken, this result may be important for economics and finance on a fundamental level.","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89591709","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 “Disagreement, Tastes, and Asset Prices,” Fama and French argue that the assumptions of standard asset pricing models, such as the Capital Asset Pricing Model (CAPM), are unrealistic and that both ‘disagreement’ and ‘tastes’ can affect asset pricing. The Popularity Asset Pricing Model (PAPM) builds on the familiar CAPM but relaxes these two key unrealistic CAPM assumptions. In the PAPM, investors have heterogeneous expectations (disagreement) about expected security returns, and can have risk and non-risk preferences / tastes. By allowing for diverse investor forecasts and incorporating multiple investor preferences / tastes, the PAPM takes two major steps towards asset pricing in the real world. The PAPM is nevertheless simple and intuitive, serving as a general umbrella model encompassing not only the CAPM as a special case, but also many other classical and behavioral asset pricing models.
{"title":"The Popularity Asset Pricing Model","authors":"Thomas M. Idzorek, P. Kaplan, R. Ibbotson","doi":"10.2139/ssrn.3451554","DOIUrl":"https://doi.org/10.2139/ssrn.3451554","url":null,"abstract":"In “Disagreement, Tastes, and Asset Prices,” Fama and French argue that the assumptions of standard asset pricing models, such as the Capital Asset Pricing Model (CAPM), are unrealistic and that both ‘disagreement’ and ‘tastes’ can affect asset pricing. The Popularity Asset Pricing Model (PAPM) builds on the familiar CAPM but relaxes these two key unrealistic CAPM assumptions. In the PAPM, investors have heterogeneous expectations (disagreement) about expected security returns, and can have risk and non-risk preferences / tastes. By allowing for diverse investor forecasts and incorporating multiple investor preferences / tastes, the PAPM takes two major steps towards asset pricing in the real world. The PAPM is nevertheless simple and intuitive, serving as a general umbrella model encompassing not only the CAPM as a special case, but also many other classical and behavioral asset pricing models.","PeriodicalId":8731,"journal":{"name":"Behavioral & Experimental Finance eJournal","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79935208","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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