Academic, government, employer, and individual interest in personal financial literacy have mushroomed as financial decision making has become more complex, costly, and less paternalistic. Financial illiteracy in America manifests in many ways, including low levels of personal saving, high levels of personal debt, negative financial wealth, a decline in standard of living, and increased demand on social safety networks. For college students, of particular concern is the high level of public and private debt accrued while working toward a degree. It is important to understand how prepared households are for retirement planning decisions and which factors can improve their preparedness. We show that financial education is impactful in reducing financial illiteracy, and provides evidence that taking a personal risk management and insurance course helps to prepare college students to make retirement decisions. Second, we provide evidence that life stage explains differences (similarities) in how professionals self-rate the importance, familiarity, and motivation to plan and save for retirement versus their opinion on how vital the questions should be to students. Finally, additional evidence is provided showing that demographic characteristics explain differences in the importance and motivation to plan and save for retirement and in the familiarity that respondents have with retirement planning and saving products.
{"title":"A Comparative Analysis of Financial Professionals’ Perception of the Level of Graduating Business Student Retirement Planning Familiarity, Motivation, and Preparedness","authors":"M. Power, Jonathan M. Hobbs","doi":"10.1111/rmir.12044","DOIUrl":"https://doi.org/10.1111/rmir.12044","url":null,"abstract":"Academic, government, employer, and individual interest in personal financial literacy have mushroomed as financial decision making has become more complex, costly, and less paternalistic. Financial illiteracy in America manifests in many ways, including low levels of personal saving, high levels of personal debt, negative financial wealth, a decline in standard of living, and increased demand on social safety networks. For college students, of particular concern is the high level of public and private debt accrued while working toward a degree. It is important to understand how prepared households are for retirement planning decisions and which factors can improve their preparedness. We show that financial education is impactful in reducing financial illiteracy, and provides evidence that taking a personal risk management and insurance course helps to prepare college students to make retirement decisions. Second, we provide evidence that life stage explains differences (similarities) in how professionals self-rate the importance, familiarity, and motivation to plan and save for retirement versus their opinion on how vital the questions should be to students. Finally, additional evidence is provided showing that demographic characteristics explain differences in the importance and motivation to plan and save for retirement and in the familiarity that respondents have with retirement planning and saving products.","PeriodicalId":431629,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics eJournal","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132452247","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}
Behavioral bubble models typically assume that uninformed trend-chasers, presumably individual investors, cause bubbles, while informed contrarian investors such as institutions, trade against bubbles. DeLong et al. (1990a) highlight that to be considered a “bubble”, the mis-pricing must prevail in a large, diversified portfolio. To meet this criterion, we use a unique dataset of all transactions by investor type for all non-financial Korean firms, and find evidence at odds with such assumptions. Domestic individual investors systematically apply aggressive contrarian trades, while foreign and some domestic institutions are mostly trend-chasers. These findings suggest that institutional investors rather than individuals are agents of bubbles.
{"title":"Are Individual or Institutional Investors the Agents of Bubbles?","authors":"J. Choi, Haim Kedar-Levy, S. Yoo","doi":"10.2139/ssrn.2023766","DOIUrl":"https://doi.org/10.2139/ssrn.2023766","url":null,"abstract":"Behavioral bubble models typically assume that uninformed trend-chasers, presumably individual investors, cause bubbles, while informed contrarian investors such as institutions, trade against bubbles. DeLong et al. (1990a) highlight that to be considered a “bubble”, the mis-pricing must prevail in a large, diversified portfolio. To meet this criterion, we use a unique dataset of all transactions by investor type for all non-financial Korean firms, and find evidence at odds with such assumptions. Domestic individual investors systematically apply aggressive contrarian trades, while foreign and some domestic institutions are mostly trend-chasers. These findings suggest that institutional investors rather than individuals are agents of bubbles.","PeriodicalId":431629,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics eJournal","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126220899","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 present a new method to measure the intraday relationship between movements of implied volatility smiles and stock index returns. It exploits a specific characteristic of the smile profile in high-frequency data. Using transaction data for EuroStoxx 50 options from 2000 to 2011 and DAX options from 1995 to 2011 (14 million transactions), we find that the intraday evolution of volatility smiles is generally not consistent with traders' rules of thumb such as the sticky strike or sticky delta rule. On average, the impact of index return on implied volatility is 1.3 to 1.5 times stronger than the sticky strike rule predicts. The main factor driving variations of the adjustment factor is the index return. Our results have implications for option valuation, hedging and the understanding of the leverage effect.
{"title":"Smile in Motion: An Intraday Analysis of Asymmetric Implied Volatility","authors":"Martin Wallmeier","doi":"10.2139/ssrn.2022261","DOIUrl":"https://doi.org/10.2139/ssrn.2022261","url":null,"abstract":"We present a new method to measure the intraday relationship between movements of implied volatility smiles and stock index returns. It exploits a specific characteristic of the smile profile in high-frequency data. Using transaction data for EuroStoxx 50 options from 2000 to 2011 and DAX options from 1995 to 2011 (14 million transactions), we find that the intraday evolution of volatility smiles is generally not consistent with traders' rules of thumb such as the sticky strike or sticky delta rule. On average, the impact of index return on implied volatility is 1.3 to 1.5 times stronger than the sticky strike rule predicts. The main factor driving variations of the adjustment factor is the index return. Our results have implications for option valuation, hedging and the understanding of the leverage effect.","PeriodicalId":431629,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics eJournal","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124358492","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}
Diego Amaya, Peter F. Christoffersen, Kris Jacobs, Aurelio Vasquez
We use intraday data to compute weekly realized moments for equity returns and study their time-series and cross-sectional properties. Buying stocks in the lowest realized skewness decile and selling stocks in the highest realized skewness decile generates an average return of 19 basis points the following week with a t-statistic of 3.70. This result is robust across a wide variety of implementations and is not captured by the Fama-French and Carhart factors. The relation between realized kurtosis and next week׳s stock returns is positive but not always significant. We do not find a strong relation between realized volatility and next week׳s stock returns.
{"title":"Does Realized Skewness Predict the Cross-Section of Equity Returns?","authors":"Diego Amaya, Peter F. Christoffersen, Kris Jacobs, Aurelio Vasquez","doi":"10.2139/ssrn.1898735","DOIUrl":"https://doi.org/10.2139/ssrn.1898735","url":null,"abstract":"We use intraday data to compute weekly realized moments for equity returns and study their time-series and cross-sectional properties. Buying stocks in the lowest realized skewness decile and selling stocks in the highest realized skewness decile generates an average return of 19 basis points the following week with a t-statistic of 3.70. This result is robust across a wide variety of implementations and is not captured by the Fama-French and Carhart factors. The relation between realized kurtosis and next week׳s stock returns is positive but not always significant. We do not find a strong relation between realized volatility and next week׳s stock returns.","PeriodicalId":431629,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131137082","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}
Ignoring the cost of debt in firms’ financing decisions leads to an overstatement of the effect of taxes. Separately identifying the impact of the cost of debt, I find the effect of taxes on firms’ overall debt usage to be insignificant. Rather than influencing the total debt in firms’ capital structure, taxes affect the relative composition of debt. Firms shift from private intermediated debt to public bond debt in response to increases in marginal tax rates. Firms’ debt policy is most sensitive to tax rates in high interest rate environments.
{"title":"(How) Do Taxes Affect Capital Structure?","authors":"Andrew D. MacKinlay","doi":"10.2139/ssrn.2022518","DOIUrl":"https://doi.org/10.2139/ssrn.2022518","url":null,"abstract":"Ignoring the cost of debt in firms’ financing decisions leads to an overstatement of the effect of taxes. Separately identifying the impact of the cost of debt, I find the effect of taxes on firms’ overall debt usage to be insignificant. Rather than influencing the total debt in firms’ capital structure, taxes affect the relative composition of debt. Firms shift from private intermediated debt to public bond debt in response to increases in marginal tax rates. Firms’ debt policy is most sensitive to tax rates in high interest rate environments.","PeriodicalId":431629,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114291587","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}
Utilizing several models and regression analytics I compare factor attribution, strategies, and active management fees for 11,394 U.S. equity mutual funds and a matched sample of hedge funds from 1994 to 2010. There is modest evidence to support alpha delivery by mutual and hedge fund managers though this critically depends upon model specification. Quantile regression analysis with a robust bootstrap procedure demonstrates that typical regressions at the means do not adequately describe manager skill and factor attribution, and that these findings are not driven by the short-sample problem or backfill bias. Specifically, manager skill is demonstrably different at the 20th and 80th percentiles. Hedge funds are more actively managed than mutual funds, and thus investors pay similar fees to mutual funds and hedge funds for active management services even when taking hedge fund performance fees into consideration.
{"title":"Seeking Alpha, Getting Beta: A Comparison of Mutual and Hedge Fund Performance, Style Attribution and Active Management Fees","authors":"William R. McCumber","doi":"10.2139/ssrn.1999464","DOIUrl":"https://doi.org/10.2139/ssrn.1999464","url":null,"abstract":"Utilizing several models and regression analytics I compare factor attribution, strategies, and active management fees for 11,394 U.S. equity mutual funds and a matched sample of hedge funds from 1994 to 2010. There is modest evidence to support alpha delivery by mutual and hedge fund managers though this critically depends upon model specification. Quantile regression analysis with a robust bootstrap procedure demonstrates that typical regressions at the means do not adequately describe manager skill and factor attribution, and that these findings are not driven by the short-sample problem or backfill bias. Specifically, manager skill is demonstrably different at the 20th and 80th percentiles. Hedge funds are more actively managed than mutual funds, and thus investors pay similar fees to mutual funds and hedge funds for active management services even when taking hedge fund performance fees into consideration.","PeriodicalId":431629,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131394788","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}
Directors’ and officers’ (D&O) liability insurance is a commonly used risk management tool for corporations both in the United States and abroad. While prior research has focused on the demand for D&O insurance and its role in corporate governance, there is an absence of literature on the supply side of the D&O market. Using the newly available D&O Insurance Coverage Supplement to insurers’ statutory filings, we develop a more comprehensive understanding of the D&O insurance market and of those firms that write D&O coverage. We develop and estimate a model of the decision to write D&O insurance and the extent of market participation. Our results suggest that there are significant operational and financial differences between firms that supply D&O insurance and those that do not. Several of these differences (specifically, size, diversification, and organizational form) are consistent with the predictions of the managerial discretion hypothesis.
{"title":"The Market for Directors’ and Officers’ Insurance","authors":"Stephen G. Fier, Andre P. Liebenberg","doi":"10.1111/rmir.12023","DOIUrl":"https://doi.org/10.1111/rmir.12023","url":null,"abstract":"Directors’ and officers’ (D&O) liability insurance is a commonly used risk management tool for corporations both in the United States and abroad. While prior research has focused on the demand for D&O insurance and its role in corporate governance, there is an absence of literature on the supply side of the D&O market. Using the newly available D&O Insurance Coverage Supplement to insurers’ statutory filings, we develop a more comprehensive understanding of the D&O insurance market and of those firms that write D&O coverage. We develop and estimate a model of the decision to write D&O insurance and the extent of market participation. Our results suggest that there are significant operational and financial differences between firms that supply D&O insurance and those that do not. Several of these differences (specifically, size, diversification, and organizational form) are consistent with the predictions of the managerial discretion hypothesis.","PeriodicalId":431629,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics eJournal","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133074952","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}
Bingzheng Chen, Sharon L. Tennyson, Maoqiong Wang, H. Zhou
China's private insurance market has been developing rapidly since the 1980s. Regulation of the market has developed in tandem with its growth. This article provides a systematic overview of China's insurance regulatory system and the evolving process of insurance supervision and regulation. The nature and direction of regulatory changes are evaluated in light of theories of public reform and the special character of China among developing economies.
{"title":"The Development and Regulation of China's Insurance Market: History and Perspectives","authors":"Bingzheng Chen, Sharon L. Tennyson, Maoqiong Wang, H. Zhou","doi":"10.1111/rmir.12012","DOIUrl":"https://doi.org/10.1111/rmir.12012","url":null,"abstract":"China's private insurance market has been developing rapidly since the 1980s. Regulation of the market has developed in tandem with its growth. This article provides a systematic overview of China's insurance regulatory system and the evolving process of insurance supervision and regulation. The nature and direction of regulatory changes are evaluated in light of theories of public reform and the special character of China among developing economies.","PeriodicalId":431629,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics eJournal","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122433067","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 this paper we extend the methodology of our earlier work (Papailias and Thomakos, 2011) on a modified moving average technical trading rule by allowing short sales. We show how short sales change the trading rule which now acts as a dynamic trailing "stop-and-reverse", instead of a dynamic trailing stop as in the context of "long-only" trades. Then we empirically examine the performance of our modification in the context of a "long/short" trading approach and discuss the differentiation and implications in strategy performance. We compare both the modified version of our trading rule with the standard moving average approach and also compare the long/short approach with the long-only approach of the earlier paper.
{"title":"An Improved Moving Average Technical Trading Rule II: Can We Obtain Performance Improvements with Short Sales?","authors":"Fotis Papailias, D. Thomakos","doi":"10.2139/ssrn.1958906","DOIUrl":"https://doi.org/10.2139/ssrn.1958906","url":null,"abstract":"In this paper we extend the methodology of our earlier work (Papailias and Thomakos, 2011) on a modified moving average technical trading rule by allowing short sales. We show how short sales change the trading rule which now acts as a dynamic trailing \"stop-and-reverse\", instead of a dynamic trailing stop as in the context of \"long-only\" trades. Then we empirically examine the performance of our modification in the context of a \"long/short\" trading approach and discuss the differentiation and implications in strategy performance. We compare both the modified version of our trading rule with the standard moving average approach and also compare the long/short approach with the long-only approach of the earlier paper.","PeriodicalId":431629,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics eJournal","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117259094","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}
Institutional ownership affects the sensitivity of stock returns to changes in market liquidity (liquidity risk). Overall, institutional ownership lowers the liquidity risk of stocks. However, different types of institutions affect liquidity risk in opposite ways. Stocks held by hedge funds, especially levered hedge funds, as marginal investors are more sensitive to changes in market liquidity than comparable stocks held by other types of institutions or by individuals. In contrast, stocks held by banks are less sensitive to changes in aggregate liquidity. These findings are robust to alternative specifications that control for institutional preferences for different stock characteristics and risk.
{"title":"Liquidity Risk and Institutional Ownership","authors":"C. Cao, Lubomir Petrasek","doi":"10.2139/ssrn.1960625","DOIUrl":"https://doi.org/10.2139/ssrn.1960625","url":null,"abstract":"Institutional ownership affects the sensitivity of stock returns to changes in market liquidity (liquidity risk). Overall, institutional ownership lowers the liquidity risk of stocks. However, different types of institutions affect liquidity risk in opposite ways. Stocks held by hedge funds, especially levered hedge funds, as marginal investors are more sensitive to changes in market liquidity than comparable stocks held by other types of institutions or by individuals. In contrast, stocks held by banks are less sensitive to changes in aggregate liquidity. These findings are robust to alternative specifications that control for institutional preferences for different stock characteristics and risk.","PeriodicalId":431629,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics eJournal","volume":"430 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116819370","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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