Benjamin Graham introduced a very simple formula for valuing a growth stock in 1962. How does it work and why? What is a sensible way to calculate this across many stocks and provide a scoring system to compare stocks amongst each other? We are presenting a methodology here which is put into practice.
{"title":"Graham's Formula for Valuing Growth Stocks","authors":"Andreas A. Aigner, W. Schrabmair","doi":"10.2139/ssrn.3557095","DOIUrl":"https://doi.org/10.2139/ssrn.3557095","url":null,"abstract":"Benjamin Graham introduced a very simple formula for valuing a growth stock in 1962. How does it work and why? What is a sensible way to calculate this across many stocks and provide a scoring system to compare stocks amongst each other? We are presenting a methodology here which is put into practice.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115573361","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}
Winning streaks appear frequently in all financial markets including equity, commodity, foreign exchange, real estate, etc. Most stochastic process models for financial market data in the current literature focus on stylized facts such as fat-tailedness relative to normality, volatility clustering, mean reversion. However, none of existing financial models captures the pervasive feature of persistent extremes: financial indices frequently report record highs or lows in concentrated periods of time. The lack of persistent extremes in a quantitative model for asset pricing can have grave impact on the valuation and risk management of financial instruments. The new model in this paper enables us to measure and assess the impact of persistent extremes on financial derivatives and to more accurately predict option values. In addition, the model in this paper reveals a paradox that investors who bet on the growth of financial market may be worse off with pervasive winning streaks in the market. This model in this paper describes the phenomenon of market overreaction at the macro level, which complements existing behavior finance literature on this subject that explain market reactions by psychological reasoning and evidence. The paper also explores the possibility of using the model for measuring the tendency of overbought stocks and indices.
{"title":"Modeling Financial Market Movement with Winning Streaks: Sticky Maximum Process","authors":"Runhuan Feng, Pingping Jiang, H. Volkmer","doi":"10.2139/ssrn.3553389","DOIUrl":"https://doi.org/10.2139/ssrn.3553389","url":null,"abstract":"Winning streaks appear frequently in all financial markets including equity, commodity, foreign exchange, real estate, etc. Most stochastic process models for financial market data in the current literature focus on stylized facts such as fat-tailedness relative to normality, volatility clustering, mean reversion. However, none of existing financial models captures the pervasive feature of persistent extremes: financial indices frequently report record highs or lows in concentrated periods of time. The lack of persistent extremes in a quantitative model for asset pricing can have grave impact on the valuation and risk management of financial instruments. The new model in this paper enables us to measure and assess the impact of persistent extremes on financial derivatives and to more accurately predict option values. In addition, the model in this paper reveals a paradox that investors who bet on the growth of financial market may be worse off with pervasive winning streaks in the market. This model in this paper describes the phenomenon of market overreaction at the macro level, which complements existing behavior finance literature on this subject that explain market reactions by psychological reasoning and evidence. The paper also explores the possibility of using the model for measuring the tendency of overbought stocks and indices.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128526826","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 the diversification benefits of REIT preferred and common stock using a utility based framework in which investors segment based on risk aversion. Taking the view of a long run investor, we conduct our analysis using data from 1992 to 2012. We examine optimal mean-variance portfolios of investors with different levels of risk aversion given access to different classes of assets and establish three main results. First, REIT preferred and common stock provides significant diversification benefits to investors. REIT common stock helps low risk aversion investors attain portfolios with higher returns, while REIT preferred stock helps high risk aversion investors by providing a venue for risk reduction. Both asset classes receive material allocations over plausible levels of risk aversion. Second, while REIT preferred stock appears to behave somewhat like a hybrid debt/equity asset, its risk/return profile appears to not easily be replicated by those asset classes. When given the opportunity, investors will reduce allocations to REIT common stock and investment grade bonds and invest in REIT preferred stock. Finally, realistic investor constraints matter empirically. Conclusions drawn from the empirical analysis are markedly different under these constraints compared to the classical unconstrained setting.
{"title":"Diversification Benefits of REIT Preferred and Common Stock: New Evidence from a Utility‐Based Framework","authors":"W. Boudry, J. D. de Roos, A. Ukhov","doi":"10.1111/1540-6229.12166","DOIUrl":"https://doi.org/10.1111/1540-6229.12166","url":null,"abstract":"We study the diversification benefits of REIT preferred and common stock using a utility based framework in which investors segment based on risk aversion. Taking the view of a long run investor, we conduct our analysis using data from 1992 to 2012. We examine optimal mean-variance portfolios of investors with different levels of risk aversion given access to different classes of assets and establish three main results. First, REIT preferred and common stock provides significant diversification benefits to investors. REIT common stock helps low risk aversion investors attain portfolios with higher returns, while REIT preferred stock helps high risk aversion investors by providing a venue for risk reduction. Both asset classes receive material allocations over plausible levels of risk aversion. Second, while REIT preferred stock appears to behave somewhat like a hybrid debt/equity asset, its risk/return profile appears to not easily be replicated by those asset classes. When given the opportunity, investors will reduce allocations to REIT common stock and investment grade bonds and invest in REIT preferred stock. Finally, realistic investor constraints matter empirically. Conclusions drawn from the empirical analysis are markedly different under these constraints compared to the classical unconstrained setting.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131731288","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 aims at a critical assessment of the DSGE asset pricing approach. By employing partial indirect inference, we acknowledge that parts of a model are misspecified, while others retain the claim to capture economic reality, namely the ability to price assets traded in real markets. Consequently, we use binding functions that facilitate the consistent estimation of the structural model parameters of interest (concerning investor preferences), while treating others (governing macroeconomic dynamics) as nuisance parameters that are calibrated. The results of our empirical analysis are not unfavorable for the DSGE asset pricing approach, but they also indicate that the very positive interpretation of calibration results, in particular regarding the resolution of asset pricing puzzles, should be taken with a grain of salt.
{"title":"Empirical Asset Pricing in a DSGE Framework: Reconciling Calibration and Econometrics using Partial Indirect Inference","authors":"J. Grammig, Julie Schnaitmann, Dalia Elshiaty","doi":"10.2139/ssrn.3648085","DOIUrl":"https://doi.org/10.2139/ssrn.3648085","url":null,"abstract":"This paper aims at a critical assessment of the DSGE asset pricing approach. By employing partial indirect inference, we acknowledge that parts of a model are misspecified, while others retain the claim to capture economic reality, namely the ability to price assets traded in real markets. Consequently, we use binding functions that facilitate the consistent estimation of the structural model parameters of interest (concerning investor preferences), while treating others (governing macroeconomic dynamics) as nuisance parameters that are calibrated. The results of our empirical analysis are not unfavorable for the DSGE asset pricing approach, but they also indicate that the very positive interpretation of calibration results, in particular regarding the resolution of asset pricing puzzles, should be taken with a grain of salt.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126472623","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 how exchange traded funds (ETFs) affect asset pricing, volatility and trade volume in a laboratory asset market. We consider markets with zero or negative correlations in asset returns and the presence or absence of composite ETF assets. We find that when the returns on assets are negatively correlated, the presence of an ETF asset reduces mispricing and price volatility without decreasing trading volume. In the case where returns have zero correlation, the ETF asset has no impact. Thus, our findings suggest that ETFs do not harm, and may in fact improve, price discovery and liquidity in asset markets.
{"title":"The Impact of ETFs in Secondary Asset Markets: Experimental Evidence","authors":"J. Duffy, J. Rabanal, Olga A. Rud","doi":"10.2139/ssrn.3499356","DOIUrl":"https://doi.org/10.2139/ssrn.3499356","url":null,"abstract":"We examine how exchange traded funds (ETFs) affect asset pricing, volatility and trade volume in a laboratory asset market. We consider markets with zero or negative correlations in asset returns and the presence or absence of composite ETF assets. We find that when the returns on assets are negatively correlated, the presence of an ETF asset reduces mispricing and price volatility without decreasing trading volume. In the case where returns have zero correlation, the ETF asset has no impact. Thus, our findings suggest that ETFs do not harm, and may in fact improve, price discovery and liquidity in asset markets.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128411380","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 develop an equilibrium pricing model aimed at explaining observed characteristics in equity returns, VIX futures and VIX options data. To derive our model we first specify a general framework based on affine jump-diffusive state-dynamics and representative agent endowed with Duffie-Epstein recursive utility. This allows us to derive moments of equity returns under the objective and risk-neutral measures, and subsequently semi-closed form solutions to prices of equity options, VIX futures, and VIX options. We calibrate this model to fit the salient features of the data, including moments of consumption and equity returns, variance premium, and various features of VIX derivatives data. The model matches the extremely right-skewed volatility smiles seen in VIX options, a downward-sloping term structure of implied Black'76 volatilities, large negative rates of return on VIX futures, and large VIX option risk premia. It also matches other characteristics of VIX options data, including time-variation in the shape of implied volatilities.
{"title":"The Price of Higher Order Catastrophe Insurance: The Case of VIX Options","authors":"Bjørn Eraker, Aoxiang Yang","doi":"10.2139/ssrn.3520256","DOIUrl":"https://doi.org/10.2139/ssrn.3520256","url":null,"abstract":"We develop an equilibrium pricing model aimed at explaining observed characteristics in equity returns, VIX futures and VIX options data. To derive our model we first specify a general framework based on affine jump-diffusive state-dynamics and representative agent endowed with Duffie-Epstein recursive utility. This allows us to derive moments of equity returns under the objective and risk-neutral measures, and subsequently semi-closed form solutions to prices of equity options, VIX futures, and VIX options. We calibrate this model to fit the salient features of the data, including moments of consumption and equity returns, variance premium, and various features of VIX derivatives data. The model matches the extremely right-skewed volatility smiles seen in VIX options, a downward-sloping term structure of implied Black'76 volatilities, large negative rates of return on VIX futures, and large VIX option risk premia. It also matches other characteristics of VIX options data, including time-variation in the shape of implied volatilities.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127961598","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 Hong Kong Stock Exchange (HKEx) adopted a closing call auction in 2008 but suspended its operation ten months later due to suspicion of widespread price manipulation. The Exchange relaunched the auction in 2016 with manipulation-deterrence enhancements. We exploit this unique setting by applying a triple-differences (DDD) methodology to examine the causal effect of call auction design on closing price manipulation. Our results indicate that a plain-vanilla call auction mechanism is prone to closing price manipulation. Under this mechanism overnight price reversal is more pronounced on days when derivatives expire and on days with large orders submitted just before the market close.
{"title":"Call Auction Mechanism and Closing Price Manipulation: Evidence from the Hong Kong Stock Exchange","authors":"S. Park, Wing Suen, K. Wan","doi":"10.2139/ssrn.3482351","DOIUrl":"https://doi.org/10.2139/ssrn.3482351","url":null,"abstract":"The Hong Kong Stock Exchange (HKEx) adopted a closing call auction in 2008 but suspended its operation ten months later due to suspicion of widespread price manipulation. The Exchange relaunched the auction in 2016 with manipulation-deterrence enhancements. We exploit this unique setting by applying a triple-differences (DDD) methodology to examine the causal effect of call auction design on closing price manipulation. Our results indicate that a plain-vanilla call auction mechanism is prone to closing price manipulation. Under this mechanism overnight price reversal is more pronounced on days when derivatives expire and on days with large orders submitted just before the market close.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115357954","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 jointly explain the equity and value premium variations in a model with both short-run (SRR) and long-run (LRR) consumption risk. In our empirical analysis, we find that SRR varies with the business cycle, and it has a substantial predictive power for market excess returns and the value premium—both in-sample and out-of-sample. The LRR component also differs significantly from zero, and value stocks have a larger exposure to both LRR and SRR than growth stocks. To explain these patterns in asset returns, we propose an extended LRR model. The model can be solved using log-linear approximations with economically small errors.
{"title":"Short-run Risk, Business Cycle, and the Value Premium","authors":"Yunhao He, Markus Leippold","doi":"10.2139/ssrn.3519985","DOIUrl":"https://doi.org/10.2139/ssrn.3519985","url":null,"abstract":"We jointly explain the equity and value premium variations in a model with both short-run (SRR) and long-run (LRR) consumption risk. In our empirical analysis, we find that SRR varies with the business cycle, and it has a substantial predictive power for market excess returns and the value premium—both in-sample and out-of-sample. The LRR component also differs significantly from zero, and value stocks have a larger exposure to both LRR and SRR than growth stocks. To explain these patterns in asset returns, we propose an extended LRR model. The model can be solved using log-linear approximations with economically small errors.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126644961","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 is the supplemental material to the paper titled "Measuring 'Dark Matter' in Asset Pricing Models." It includes detailed derivations, as well as additional empirical and theoretical results.
{"title":"Online Appendix for 'Measuring the 'Dark Matter' in Asset Pricing Models'","authors":"Hui Chen, W. Dou, L. Kogan","doi":"10.2139/ssrn.3461503","DOIUrl":"https://doi.org/10.2139/ssrn.3461503","url":null,"abstract":"This is the supplemental material to the paper titled \"Measuring 'Dark Matter' in Asset Pricing Models.\" It includes detailed derivations, as well as additional empirical and theoretical results.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131684194","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 propose a novel way to study asset prices based on price distortions rather than abnormal returns. We derive the correct identity linking current mispricing to subsequent returns, generating a price-level analogue to the fundamental asset pricing equation, E[MR^e]=0, used to study returns. Our empirical test reveals that the CAPM describes the cross-section of prices better than it describes expected short-horizon returns. Despite the improvement, significant mispricing remains. An interaction of book-to-market and quality provides a parsimonious model of CAPM mispricing that both long-term buy-and-hold investors and researchers disciplining models from the price perspective should prioritize.
{"title":"Putting the Price in Asset Pricing","authors":"Thummim Cho, Christopher Polk","doi":"10.2139/ssrn.3499681","DOIUrl":"https://doi.org/10.2139/ssrn.3499681","url":null,"abstract":"We propose a novel way to study asset prices based on price distortions rather than abnormal returns. We derive the correct identity linking current mispricing to subsequent returns, generating a price-level analogue to the fundamental asset pricing equation, E[MR^e]=0, used to study returns. Our empirical test reveals that the CAPM describes the cross-section of prices better than it describes expected short-horizon returns. Despite the improvement, significant mispricing remains. An interaction of book-to-market and quality provides a parsimonious model of CAPM mispricing that both long-term buy-and-hold investors and researchers disciplining models from the price perspective should prioritize.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125249705","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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