Investigating stockholder consumption growth is critical in asset pricing studies, as preference and risk averse of stockholders differ from that of average households. The disagreement among households about the macroeconomic uncertainty leads to their heterogeneous stock market participation decisions, and allows irrational stockholders and non-stockholders to survive in the long run. When stock market is inelastic, this paper uncovers a recursive relationship between stockholder consumption and market returns and shows theoretically that stockholder consumption growth is critical in asset pricing studies. Empirically, this paper demonstrates that the exposure to stockholder consumption risks explains over a half cross-sectional equity return variations.
{"title":"The Inelastic Market: Stockholder Wealth, Consumption Share and Belief","authors":"Xiaoyu Zong","doi":"10.2139/ssrn.3752777","DOIUrl":"https://doi.org/10.2139/ssrn.3752777","url":null,"abstract":"Investigating stockholder consumption growth is critical in asset pricing studies, as preference and risk averse of stockholders differ from that of average households. The disagreement among households about the macroeconomic uncertainty leads to their heterogeneous stock market participation decisions, and allows irrational stockholders and non-stockholders to survive in the long run. When stock market is inelastic, this paper uncovers a recursive relationship between stockholder consumption and market returns and shows theoretically that stockholder consumption growth is critical in asset pricing studies. Empirically, this paper demonstrates that the exposure to stockholder consumption risks explains over a half cross-sectional equity return variations.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130245115","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}
R. Jarrow, P. Protter, J. San Martín, Johnson School Working Paper Series
This paper provides invariance theorems that facilitate testing for the existence of an asset price bubble in a market where the price evolves as a Markov diffusion process. The test involves only the properties of the price process' quadratic variation under the statistical probability. It does not require an estimate of either the equivalent local martingale measure or the asset's drift. To augment its use, a new family of stochastic volatility price processes is also provided where the processes' strict local martingale behavior can be characterized.
{"title":"Asset Price Bubbles: Invariance Theorems*","authors":"R. Jarrow, P. Protter, J. San Martín, Johnson School Working Paper Series","doi":"10.2139/ssrn.3722111","DOIUrl":"https://doi.org/10.2139/ssrn.3722111","url":null,"abstract":"This paper provides invariance theorems that facilitate testing for the existence of an asset price bubble in a market where the price evolves as a Markov diffusion process. The test involves only the properties of the price process' quadratic variation under the statistical probability. It does not require an estimate of either the equivalent local martingale measure or the asset's drift. To augment its use, a new family of stochastic volatility price processes is also provided where the processes' strict local martingale behavior can be characterized.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124595453","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 use a decision-tree-based machine learning approach to perform relative valuation. Stocks are valued using market-to-book, enterprise-value-to-assets and enterprise-value-to-sales multiples. Our machine learning models reduce median absolute valuation errors by a minimum of 5.6 to 31.4 percentage points relative to traditional models, depending on the multiple used. The identified valuation drivers are consistent with theoretical predictions derived from a discounted cash flow approach. Accounting variables related to profitability, growth, efficiency and financial soundness are important valuation drivers. The valuations produced by machine learning models behave like fundamental values. A value-weighted strategy that buys (sells) undervalued (overvalued) stocks generates highly significant abnormal returns. When we use models trained on listed firms to value IPOs, machine learning models outperform traditional models in valuation accuracy and are better at identifying overpriced IPOs.
{"title":"Relative Valuation with Machine Learning","authors":"P. Geertsema, Helen Lu","doi":"10.2139/ssrn.3740270","DOIUrl":"https://doi.org/10.2139/ssrn.3740270","url":null,"abstract":"We use a decision-tree-based machine learning approach to perform relative valuation. Stocks are valued using market-to-book, enterprise-value-to-assets and enterprise-value-to-sales multiples. Our machine learning models reduce median absolute valuation errors by a minimum of 5.6 to 31.4 percentage points relative to traditional models, depending on the multiple used. The identified valuation drivers are consistent with theoretical predictions derived from a discounted cash flow approach. Accounting variables related to profitability, growth, efficiency and financial soundness are important valuation drivers. The valuations produced by machine learning models behave like fundamental values. A value-weighted strategy that buys (sells) undervalued (overvalued) stocks generates highly significant abnormal returns. When we use models trained on listed firms to value IPOs, machine learning models outperform traditional models in valuation accuracy and are better at identifying overpriced IPOs.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131711961","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}
Systematic mispricing primarily affects speculative stocks and tends to take the form of overpricing, predicting lower average returns. Because speculative stocks are typically deemed risky by rational models, failing to control for exposure to systematic mispricing can bias tests of risk-return tradeoffs. Controlling for the effects of systematic mispricing, we recover robust positive risk-return relations for a large number of cross-sectional risk proxies, including many low-risk and distress anomalies. We also recover robust positive illiquidity-return relations. We provide a unifying framework to explain a number of puzzles arising from the empirical failure of standard asset-pricing models and show that risk-return relations supporting rational models can be recovered from the data by accounting for the existence of time-varying common mispricing.
{"title":"Disentangling Anomalies: Risk Versus Mispricing","authors":"Justin Birru, Hannes Mohrschladt, T. Young","doi":"10.2139/ssrn.3739944","DOIUrl":"https://doi.org/10.2139/ssrn.3739944","url":null,"abstract":"Systematic mispricing primarily affects speculative stocks and tends to take the form of overpricing, predicting lower average returns. Because speculative stocks are typically deemed risky by rational models, failing to control for exposure to systematic mispricing can bias tests of risk-return tradeoffs. Controlling for the effects of systematic mispricing, we recover robust positive risk-return relations for a large number of cross-sectional risk proxies, including many low-risk and distress anomalies. We also recover robust positive illiquidity-return relations. We provide a unifying framework to explain a number of puzzles arising from the empirical failure of standard asset-pricing models and show that risk-return relations supporting rational models can be recovered from the data by accounting for the existence of time-varying common mispricing.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114729982","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 examine the impact of pandemics on equilibrium in an integrated epidemic-economy model with production. Two types of technologies are considered: a neo-classical technology and one capturing the notion of time-to-produce. The impact of a shelter-in-place policy with and without layoffs is studied. The paper documents adjustments in interest rate, market price of risk, stock market and real wage as the epidemic propagates. It shows the qualitative effects of a shelter-in-place policy in the model are consistent with the patterns displayed by the stock market and real wage during the COVID-19 outbreak. Puzzles emerging from the analysis are outlined.
{"title":"Asset Prices and Pandemics: The Effects of Lockdowns","authors":"J. Detemple","doi":"10.2139/ssrn.3826647","DOIUrl":"https://doi.org/10.2139/ssrn.3826647","url":null,"abstract":"We examine the impact of pandemics on equilibrium in an integrated epidemic-economy model with production. Two types of technologies are considered: a neo-classical technology and one capturing the notion of time-to-produce. The impact of a shelter-in-place policy with and without layoffs is studied. The paper documents adjustments in interest rate, market price of risk, stock market and real wage as the epidemic propagates. It shows the qualitative effects of a shelter-in-place policy in the model are consistent with the patterns displayed by the stock market and real wage during the COVID-19 outbreak. Puzzles emerging from the analysis are outlined.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127022177","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}
Using a machine learning model known as a Long-Short Term Memory model to overcome high dimensionality obstacles, I jointly predict the conditional second moments of eight international indices and test the conditional Capital Asset Pricing Model (CAPM). My results indicate that the world price of covariance risk is equal across eight world equity markets according to the conditional CAPM. Strengths and weaknesses of the estimation process are studied. All results are assessed and reported using out-of-sample tests.
{"title":"The Efficacy of the Conditional CAPM: Improved Tests in an International Context","authors":"Stephen R. Owen, Jr.","doi":"10.2139/ssrn.3716370","DOIUrl":"https://doi.org/10.2139/ssrn.3716370","url":null,"abstract":"Using a machine learning model known as a Long-Short Term Memory model to overcome high dimensionality obstacles, I jointly predict the conditional second moments of eight international indices and test the conditional Capital Asset Pricing Model (CAPM). My results indicate that the world price of covariance risk is equal across eight world equity markets according to the conditional CAPM. Strengths and weaknesses of the estimation process are studied. All results are assessed and reported using out-of-sample tests.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121216717","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 analyses the volume-return relationships across top 30 most traded cryptocurrencies from the April 2013 to June 2019 using a high-frequency intraday data. We use a novel approach for the classification of cryptocurrencies with respect to multiple qualitative factors, such as geographical location of headquarters, founder and founder’s origin, platform on which cryptocurrency built on, consensus algorithm, to name but a few. We identified significant bidirectional causalities between trading volume and returns at high-frequency intervals, however, those linkages are disappearing with increased frequencies of data. The findings confirm the leading position of the Bitcoin trading volume in the cryptocurrency price formation. This evidence will help investors to design effective trading strategies in cryptocurrency market providing useful insights from cryptocurrency categorisation.
{"title":"Intraday Volume-Return Nexus in Cryptocurrency Markets: A Novel Evidence From Cryptocurrency Classification","authors":"L. Yarovaya, D. Zięba","doi":"10.2139/ssrn.3711667","DOIUrl":"https://doi.org/10.2139/ssrn.3711667","url":null,"abstract":"This paper analyses the volume-return relationships across top 30 most traded cryptocurrencies from the April 2013 to June 2019 using a high-frequency intraday data. We use a novel approach for the classification of cryptocurrencies with respect to multiple qualitative factors, such as geographical location of headquarters, founder and founder’s origin, platform on which cryptocurrency built on, consensus algorithm, to name but a few. We identified significant bidirectional causalities between trading volume and returns at high-frequency intervals, however, those linkages are disappearing with increased frequencies of data. The findings confirm the leading position of the Bitcoin trading volume in the cryptocurrency price formation. This evidence will help investors to design effective trading strategies in cryptocurrency market providing useful insights from cryptocurrency categorisation.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133187085","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}
On this purpose, this work is focused on a non-conventional profitability measure, at least in terms of assets pricing models, where dividends or profits are widely used. The attention is focused on a proxy measure of Operating Cash Flows: the “Ebitda after Capex”. The relationship returns – cash flows’ volatility has been examined through an empirical analysis conducted on the stocks of the S&P500 Index combining the main quantitative and statistical approach with a qualitative overview respect the macroeconomic background. Starting from a correlation rolling window approach, three different regressions techniques have been implemented; the simple Ordinary Least Squares regressions (OLS), the linear Quantile (LQR) regression and the Multiple regression model (MLR), all performed at different levels in terms of stocks (QoQ and YoY) and sectors (MoM, QoQ, YoY).
The cross-sectional and time-series results support the effects of cash flow’ volatility on the stocks’ performance and highlighted its sensitivity respect not only the different short-term and long-term horizons, but also in terms of sector’ exposure.
{"title":"Stock Returns and Cash Flows: A New Asset Pricing Approach","authors":"Sonia Di Tomaso, D. M. Montagna, Antonio Amendola","doi":"10.2139/ssrn.3709525","DOIUrl":"https://doi.org/10.2139/ssrn.3709525","url":null,"abstract":"On this purpose, this work is focused on a non-conventional profitability measure, at least in terms of assets pricing models, where dividends or profits are widely used. The attention is focused on a proxy measure of Operating Cash Flows: the “Ebitda after Capex”. The relationship returns – cash flows’ volatility has been examined through an empirical analysis conducted on the stocks of the S&P500 Index combining the main quantitative and statistical approach with a qualitative overview respect the macroeconomic background. Starting from a correlation rolling window approach, three different regressions techniques have been implemented; the simple Ordinary Least Squares regressions (OLS), the linear Quantile (LQR) regression and the Multiple regression model (MLR), all performed at different levels in terms of stocks (QoQ and YoY) and sectors (MoM, QoQ, YoY).<br><br>The cross-sectional and time-series results support the effects of cash flow’ volatility on the stocks’ performance and highlighted its sensitivity respect not only the different short-term and long-term horizons, but also in terms of sector’ exposure.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116055016","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}
Despite half a century of research, we still do not know the best way to model skewness of financial returns. We address this question by comparing the predictive ability and associated portfolio performance of several prominent skewness models in a sample of ten international equity market indices. Models that employ information from the option markets provide the best outcomes overall. We develop an option-based model that accounts for the skewness risk premium. The new model produces the most informative forecasts of future skewness, the lowest prediction errors and the best portfolio performance in most of our tests.
{"title":"Modeling Skewness in Portfolio Choice","authors":"Trung H. Le, A. Kourtis, Raphael N. Markellos","doi":"10.2139/ssrn.3708200","DOIUrl":"https://doi.org/10.2139/ssrn.3708200","url":null,"abstract":"Despite half a century of research, we still do not know the best way to model skewness of financial returns. We address this question by comparing the predictive ability and associated portfolio performance of several prominent skewness models in a sample of ten international equity market indices. Models that employ information from the option markets provide the best outcomes overall. We develop an option-based model that accounts for the skewness risk premium. The new model produces the most informative forecasts of future skewness, the lowest prediction errors and the best portfolio performance in most of our tests.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126610538","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 study drift and cyclical components in Treasury bonds. We find that bond yields are drifting because they reflect the drift in monetary policy rates. Empirically, modeling the monetary policy drift using demographics and productivity trends, plus long-term inflation expectations, leads to stationary cyclical deviations of bond prices from their drift that predict U.S. bond excess returns in- and out-of-sample. These bond cycles can originate from either term premia or temporary deviations from rational expectations in a behavioral framework. Through the lens of our model, we detect a significant role of the latter in determining the cyclical properties of yields.
{"title":"Monetary Policy and Bond Prices with Drifting Equilibrium Rates and Diagnostic Expectations","authors":"Carlo A. Favero, Alessandro Melone, A. Tamoni","doi":"10.2139/ssrn.3704241","DOIUrl":"https://doi.org/10.2139/ssrn.3704241","url":null,"abstract":"We study drift and cyclical components in Treasury bonds. \u0000We find that bond yields are drifting because they reflect the drift in monetary policy rates. \u0000Empirically, modeling the monetary policy drift using demographics and productivity trends, plus long-term inflation expectations, leads to stationary cyclical deviations of bond prices from their drift that predict U.S. bond excess returns in- and out-of-sample. \u0000These bond cycles can originate from either term premia or temporary deviations from rational expectations in a behavioral framework. \u0000Through the lens of our model, we detect a significant role of the latter in determining the cyclical properties of yields.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124051826","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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