This paper documents a tight connection between long run consumption risks (LRRs), currency excess returns, currency risk premia and the carry trade. We adopt a novel identification strategy that estimates country level LRRs using asset market data alone. With this identification strategy in hand, we find that: (1) currencies that suffer a bad relative LRR shock appreciate on impact before depreciating over the long run, (2) the High-Minus-Low (HML) carry trade sorts currencies on the basis of global LRR exposures, (3) the dollar carry trade outperforms on impact before underperforming over the long run in response to positve US relative LRRs, (4) US relative LRRs drive global currency risk factors. We interpret these facts as evidence in favour of an international LRR model where US LRRs drive global shocks to the world economy.
{"title":"Long Run Risks in FX Markets: Are They There?","authors":"Sun Yong Kim, Konark Saxena","doi":"10.2139/ssrn.3950981","DOIUrl":"https://doi.org/10.2139/ssrn.3950981","url":null,"abstract":"This paper documents a tight connection between long run consumption risks (LRRs), currency excess returns, currency risk premia and the carry trade. We adopt a novel identification strategy that estimates country level LRRs using asset market data alone. With this identification strategy in hand, we find that: (1) currencies that suffer a bad relative LRR shock appreciate on impact before depreciating over the long run, (2) the High-Minus-Low (HML) carry trade sorts currencies on the basis of global LRR exposures, (3) the dollar carry trade outperforms on impact before underperforming over the long run in response to positve US relative LRRs, (4) US relative LRRs drive global currency risk factors. We interpret these facts as evidence in favour of an international LRR model where US LRRs drive global shocks to the world economy.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123000799","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 introduce a class of interpretable tree-based models (P-Trees) for analyzing panel data, with iterative and global (instead of recursive and local) splitting criteria to avoid overfitting and improve model performance. We apply P-Tree to generate a stochastic discount factor model and test assets for cross-sectional asset pricing. Unlike other tree algorithms, P-Trees accommodate imbalanced panels of asset returns and grow under the no-arbitrage condition. P-Trees also graphically capture nonlinearity and interaction effects and accommodate regime-switching and interactions between macroeconomic states and firm characteristics. For example, P-Tree identifies inflation as the most important macro predictor with regime-switching in U.S. equity data. Based on multiple pricing, prediction, and investment metrics, we find that (boosted or time-series) P-Trees outperform standard factor models and PCA latent factor models. An equally-weighted portfolio for five factors generated by P-Trees delivers an excess alpha of 1.09% against the Fama-French 3-factor benchmark, producing an annualized Sharpe ratio of 1.98 out-of-sample. Data-driven cutpoints in P-Trees reveal that long-run reversal, volume volatility, and industry-adjusted market equity drive cross-sectional return variations, consistent with variable importance analysis using random forests.
{"title":"Asset Pricing with Panel Trees Under Global Split Criteria","authors":"Xindi He, L. Cong, Guanhao Feng, Jingyu He","doi":"10.2139/ssrn.3949463","DOIUrl":"https://doi.org/10.2139/ssrn.3949463","url":null,"abstract":"We introduce a class of interpretable tree-based models (P-Trees) for analyzing panel data, with iterative and global (instead of recursive and local) splitting criteria to avoid overfitting and improve model performance. We apply P-Tree to generate a stochastic discount factor model and test assets for cross-sectional asset pricing. Unlike other tree algorithms, P-Trees accommodate imbalanced panels of asset returns and grow under the no-arbitrage condition. P-Trees also graphically capture nonlinearity and interaction effects and accommodate regime-switching and interactions between macroeconomic states and firm characteristics. For example, P-Tree identifies inflation as the most important macro predictor with regime-switching in U.S. equity data. Based on multiple pricing, prediction, and investment metrics, we find that (boosted or time-series) P-Trees outperform standard factor models and PCA latent factor models. An equally-weighted portfolio for five factors generated by P-Trees delivers an excess alpha of 1.09% against the Fama-French 3-factor benchmark, producing an annualized Sharpe ratio of 1.98 out-of-sample. Data-driven cutpoints in P-Trees reveal that long-run reversal, volume volatility, and industry-adjusted market equity drive cross-sectional return variations, consistent with variable importance analysis using random forests.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116460252","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}
An improved efficiency version of the LOESS algorithm is proposed that is applicable to the Monte Carlo pricing tasks common in financial engineering. A self-contained overview of the LOESS algorithm is presented followed by the suggested efficiency modifications and a discussion of strategies for variable selection that can reduce dimensionality for further improvements in efficiency as well as stability. Some numerical results are shown as a demonstration of the suggested approach.
{"title":"Efficient LOESS For Financial Applications","authors":"K. Haven","doi":"10.2139/ssrn.3949349","DOIUrl":"https://doi.org/10.2139/ssrn.3949349","url":null,"abstract":"An improved efficiency version of the LOESS algorithm is proposed that is applicable to the Monte Carlo pricing tasks common in financial engineering. A self-contained overview of the LOESS algorithm is presented followed by the suggested efficiency modifications and a discussion of strategies for variable selection that can reduce dimensionality for further improvements in efficiency as well as stability. Some numerical results are shown as a demonstration of the suggested approach.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122895713","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}
Kleibergen and Zhan (Robust Inference for Consumption-based Asset Pricing, Journal of Finance, 2020) propose a new approach to test consumption-based asset pricing models that is robust to the useless factor problem, i.e. concluding that a factor is priced when the factor is actually uncorrelated with the test assets. They find that recently proposed factors do not pass their test, which they attribute to a lack of factor correlation with the test assets. This conclusion is odd, as the factor correlation is significant and economically large, often 0.40 and above. Instead, I show that their testing approach lacks power in small samples. I propose simple remedies that help to achieve robust consumption-based asset pricing that comes with power.
{"title":"On Robust Inference for Consumption-based Asset Pricing","authors":"Tim A. Kroencke","doi":"10.2139/ssrn.3562169","DOIUrl":"https://doi.org/10.2139/ssrn.3562169","url":null,"abstract":"Kleibergen and Zhan (Robust Inference for Consumption-based Asset Pricing, Journal of Finance, 2020) propose a new approach to test consumption-based asset pricing models that is robust to the useless factor problem, i.e. concluding that a factor is priced when the factor is actually uncorrelated with the test assets. They find that recently proposed factors do not pass their test, which they attribute to a lack of factor correlation with the test assets. This conclusion is odd, as the factor correlation is significant and economically large, often 0.40 and above. Instead, I show that their testing approach lacks power in small samples. I propose simple remedies that help to achieve robust consumption-based asset pricing that comes with power.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132201677","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}
Blockchain technologies have enabled the creation of decentralized applications which let users own and transact scarce digital assets called nonfungible tokens or NFTs. Although still in its infancy, the industry has generated over $2.5bn in transaction volume and attracted interest from organizations such as the NBA, several football (soccer) clubs, major brands, and gaming companies to create platforms for trading digital collectibles. A major question faced by NFT platforms is how to help participants value the digital items. We introduce a novel dataset and study how traditional approaches to valuation may exhibit significant biases in this market. We find that while buyers value NFTs much like we would expect them to value physical collectibles, sellers have a tendency to price sub-optimally, which causes traditional hedonic regression approaches to generate inaccurate valuations. We develop a valuation approach based on a structural model of the selling mechanism used in a popular NFT market to highlight these biases and develop a proof-of-concept decision support tool to help participants make more informed decisions.
{"title":"Infinite but Rare: Valuation and Pricing in Marketplaces for Blockchain-Based Nonfungible Tokens","authors":"Pavel Kireyev, Ruiqi Lin","doi":"10.2139/ssrn.3737514","DOIUrl":"https://doi.org/10.2139/ssrn.3737514","url":null,"abstract":"Blockchain technologies have enabled the creation of decentralized applications which let users own and transact scarce digital assets called nonfungible tokens or NFTs. Although still in its infancy, the industry has generated over $2.5bn in transaction volume and attracted interest from organizations such as the NBA, several football (soccer) clubs, major brands, and gaming companies to create platforms for trading digital collectibles. A major question faced by NFT platforms is how to help participants value the digital items. We introduce a novel dataset and study how traditional approaches to valuation may exhibit significant biases in this market. We find that while buyers value NFTs much like we would expect them to value physical collectibles, sellers have a tendency to price sub-optimally, which causes traditional hedonic regression approaches to generate inaccurate valuations. We develop a valuation approach based on a structural model of the selling mechanism used in a popular NFT market to highlight these biases and develop a proof-of-concept decision support tool to help participants make more informed decisions.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126625858","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 appendix provides the complete list of sample firms and the robustness checks results discussed in the paper, Industrial Policy and Asset Prices: Stock Market Reactions to Made In China 2025 Policy Announcements, found here:https://ssrn.com/abstract=3521006.
{"title":"Internet Appendix to: 'Industrial Policy and Asset Prices: Stock Market Reactions to Made In China 2025 Policy Announcements'","authors":"Xia (Summer) Liu, W. Megginson, Junjie Xia","doi":"10.2139/ssrn.3525571","DOIUrl":"https://doi.org/10.2139/ssrn.3525571","url":null,"abstract":"This appendix provides the complete list of sample firms and the robustness checks results discussed in the paper, Industrial Policy and Asset Prices: Stock Market Reactions to Made In China 2025 Policy Announcements, found here:https://ssrn.com/abstract=3521006.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132724990","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 determine the one-dimensional general solution to the semi-equilibrium prices of primitive securities in the equation for the capital asset pricing model (CAPM). Furthermore, considering the value clearing condition, we determine the analytical equilibrium solution to the CAPM market, which reveals the overall thinking in equilibrium pricing. We use a numerical example to illustrate that the CAPM equilibrium in an incomplete market does not exclude arbitrage opportunities. In addition, we show that the beta pricing formula can only be used to price marketable (within the market payoff space) assets, because the beta pricing formula is nothing more than a manifestation of the law of asset portfolio, that is, beta pricing is based on the equilibrium prices of primitive securities to compute the linear pricing of the asset portfolio in the market.
{"title":"An Analytic Solution to the CAPM Equilibrium","authors":"Pharos Abad","doi":"10.2139/ssrn.3939960","DOIUrl":"https://doi.org/10.2139/ssrn.3939960","url":null,"abstract":"We determine the one-dimensional general solution to the semi-equilibrium prices of primitive securities in the equation for the capital asset pricing model (CAPM). Furthermore, considering the value clearing condition, we determine the analytical equilibrium solution to the CAPM market, which reveals the overall thinking in equilibrium pricing. We use a numerical example to illustrate that the CAPM equilibrium in an incomplete market does not exclude arbitrage opportunities. In addition, we show that the beta pricing formula can only be used to price marketable (within the market payoff space) assets, because the beta pricing formula is nothing more than a manifestation of the law of asset portfolio, that is, beta pricing is based on the equilibrium prices of primitive securities to compute the linear pricing of the asset portfolio in the market.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127964228","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 the Capital Asset Pricing Model (CAPM), the market clearing is equivalent to the market portfolio's clearing, which is the union of the semi-clearing condition (the tangent portfolio equals the market portfolio) and the value clearing condition (the value of all investors' optimal holdings equals the market portfolio's value). We prove that the CAPM equation is equivalent to the semi-clearing condition. Only when the market portfolio's value is given, can we compute the prices of the primitive securities from the CAPM equation. Additionally, we present the analytic solution to the mimicking payoff that is equivalent to the semi-clearing condition.
{"title":"Market Clearing Conditions and the CAPM Equation","authors":"Pharos Abad","doi":"10.2139/ssrn.3939959","DOIUrl":"https://doi.org/10.2139/ssrn.3939959","url":null,"abstract":"In the Capital Asset Pricing Model (CAPM), the market clearing is equivalent to the market portfolio's clearing, which is the union of the semi-clearing condition (the tangent portfolio equals the market portfolio) and the value clearing condition (the value of all investors' optimal holdings equals the market portfolio's value). We prove that the CAPM equation is equivalent to the semi-clearing condition. Only when the market portfolio's value is given, can we compute the prices of the primitive securities from the CAPM equation. Additionally, we present the analytic solution to the mimicking payoff that is equivalent to the semi-clearing condition.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130183707","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}
Fabian Hollstein, Marcel Prokopczuk, Victoria Voigts
We comprehensively investigate the robustness of well-known factor models to altered factor-formation breakpoints. Deviating from the standard 30th and 70th percentile selection, we use an extensive set of anomaly test portfolios to uncover two main findings: First, there is a trade-off between specification versus diversification. More centered breakpoints tend to result in less (idiosyncratic) risk. More extreme sorts create stronger exposures to the underlying anomalies and, thus, higher average returns. Second, the models are robust to different degrees. The Hou, Xue, and Zhang (2015) model is much more sensitive to changes in breakpoints than the Fama-French models.
我们全面研究了众所周知的因子模型对改变因子形成断点的鲁棒性。偏离标准的30和70百分位选择，我们使用一组广泛的异常测试组合来揭示两个主要发现:首先，在规范与多样化之间存在权衡。更集中的断点往往导致更少的(特殊的)风险。更极端的投资类型会对潜在的异常现象产生更大的风险敞口，从而产生更高的平均回报。其次，模型具有不同程度的鲁棒性。Hou, Xue, and Zhang(2015)模型比Fama-French模型对断点的变化更敏感。
{"title":"How Robust are Empirical Factor Models to the Choice of Breakpoints?","authors":"Fabian Hollstein, Marcel Prokopczuk, Victoria Voigts","doi":"10.2139/ssrn.3924821","DOIUrl":"https://doi.org/10.2139/ssrn.3924821","url":null,"abstract":"We comprehensively investigate the robustness of well-known factor models to altered factor-formation breakpoints. Deviating from the standard 30th and 70th percentile selection, we use an extensive set of anomaly test portfolios to uncover two main findings: First, there is a trade-off between specification versus diversification. More centered breakpoints tend to result in less (idiosyncratic) risk. More extreme sorts create stronger exposures to the underlying anomalies and, thus, higher average returns. Second, the models are robust to different degrees. The Hou, Xue, and Zhang (2015) model is much more sensitive to changes in breakpoints than the Fama-French models.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132787761","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}
Standard financial portfolio theory recommends increasing the equity share of the portfolio as the equity premium rises. On the other hand, purely mechanically high stock prices imply low expected returns. Motivated by these opposite predictions I use data from 16 developed economies between 1873 to 2015 to study the composition of aggregate household wealth portfolio and document that in most countries the equity share has moved in very low-frequency cycles, which take decades to mean revert. I document a negative relationship between equity share and subsequent stock market returns with equity share having significant predictive power over future returns while outperforming the historical mean and traditional predictors in- and out-of-sample. I derive two new decompositions based on present value identities that help to understand these results in a framework of multiple assets classes. Furthermore, a high level of equity share is associated with a higher probability of a financial crisis. Standard asset pricing models have difficulty explaining the presented facts but a behavioral model where households have extrapolative expectations driven by unobserved market sentiment can offer a solution.
{"title":"The Equity Share Cycle","authors":"Paul Rintamäki","doi":"10.2139/ssrn.3924180","DOIUrl":"https://doi.org/10.2139/ssrn.3924180","url":null,"abstract":"Standard financial portfolio theory recommends increasing the equity share of the portfolio as the equity premium rises. On the other hand, purely mechanically high stock prices imply low expected returns. Motivated by these opposite predictions I use data from 16 developed economies between 1873 to 2015 to study the composition of aggregate household wealth portfolio and document that in most countries the equity share has moved in very low-frequency cycles, which take decades to mean revert. I document a negative relationship between equity share and subsequent stock market returns with equity share having significant predictive power over future returns while outperforming the historical mean and traditional predictors in- and out-of-sample. I derive two new decompositions based on present value identities that help to understand these results in a framework of multiple assets classes. Furthermore, a high level of equity share is associated with a higher probability of a financial crisis. Standard asset pricing models have difficulty explaining the presented facts but a behavioral model where households have extrapolative expectations driven by unobserved market sentiment can offer a solution.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126571197","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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