This paper examines the impact of blockholders on the corporate debt maturity structure within the framework of agency theory. Using a novel and hand-collected dataset in Australia, we find support for our hypothesis that debt maturity is a concave function of block equity ownership. Our findings contribute to empirical evidence on the monitoring effects of blockholders and the use of debt maturity to control for debt-equity and manager-equity conflicts.
{"title":"The Role of Blockholders in the Corporate Debt Maturity Structure","authors":"Zheyao Pan, Kelvin Jui Keng Tan","doi":"10.2139/ssrn.1912284","DOIUrl":"https://doi.org/10.2139/ssrn.1912284","url":null,"abstract":"This paper examines the impact of blockholders on the corporate debt maturity structure within the framework of agency theory. Using a novel and hand-collected dataset in Australia, we find support for our hypothesis that debt maturity is a concave function of block equity ownership. Our findings contribute to empirical evidence on the monitoring effects of blockholders and the use of debt maturity to control for debt-equity and manager-equity conflicts.","PeriodicalId":331246,"journal":{"name":"24th Australasian Finance & Banking Conference 2011 (Archive)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129451030","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 overlapping generations model featuring stochastic birth and death rates is solved in general equilibrium. I provide sufficient conditions for the interest rate to be decreasing in the birth rate and increasing in the death rate. If preferences are recursive, demographic uncertainty is priced in financial markets, and the equity premium is higher during periods characterized by a high birth rate and low mortality than in times of a low birth and high death rate. Demographic changes explain substantial parts of the time variation in the real interest rate, equity premium and conditional stock price volatility.
{"title":"Asset Pricing Implications of Demographic Change","authors":"T. Maurer","doi":"10.2139/SSRN.1836483","DOIUrl":"https://doi.org/10.2139/SSRN.1836483","url":null,"abstract":"An overlapping generations model featuring stochastic birth and death rates is solved in general equilibrium. I provide sufficient conditions for the interest rate to be decreasing in the birth rate and increasing in the death rate. If preferences are recursive, demographic uncertainty is priced in financial markets, and the equity premium is higher during periods characterized by a high birth rate and low mortality than in times of a low birth and high death rate. Demographic changes explain substantial parts of the time variation in the real interest rate, equity premium and conditional stock price volatility.","PeriodicalId":331246,"journal":{"name":"24th Australasian Finance & Banking Conference 2011 (Archive)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124411753","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 compare statistical and economic measures of forecasting performance across a large set of stock return prediction models with time-varying mean and volatility. We find that it is very common for models to produce higher out-of-sample mean squared forecast errors than a model assuming a constant equity premium, yet simultaneously add economic value when their forecasts are used to guide portfolio decisions. While there is generally a positive correlation between a return prediction model’s out-of-sample statistical performance and its ability to add economic value, the relation tends to be weak and only explains a small part of the cross-sectional variation in different models’ economic value.
{"title":"Do Return Prediction Models Add Economic Value?","authors":"Tolga Cenesizoglu, A. Timmermann","doi":"10.2139/ssrn.1913736","DOIUrl":"https://doi.org/10.2139/ssrn.1913736","url":null,"abstract":"We compare statistical and economic measures of forecasting performance across a large set of stock return prediction models with time-varying mean and volatility. We find that it is very common for models to produce higher out-of-sample mean squared forecast errors than a model assuming a constant equity premium, yet simultaneously add economic value when their forecasts are used to guide portfolio decisions. While there is generally a positive correlation between a return prediction model’s out-of-sample statistical performance and its ability to add economic value, the relation tends to be weak and only explains a small part of the cross-sectional variation in different models’ economic value.","PeriodicalId":331246,"journal":{"name":"24th Australasian Finance & Banking Conference 2011 (Archive)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130003646","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}
Michael Francis Howard, R. Bianchi, G. Bornholt, M. Drew
Our understanding of the long-term return behavior and portfolio characteristics of public infrastructure investments is limited by a relatively short history of empirical data. We re-construct U.S. listed infrastructure index returns by mapping their monthly performance to received systematic and industry risk factors from 1927 through 2010. Our findings reveal that the infrastructure returns in recent years may understate the tail-risk that investors could experience over the long-term, however, this tail-risk is commensurate with holding a broad portfolio of U.S. stocks. For mean-variance and mean-CVaR investors, we report the benefits of holding public infrastructure assets in investment portfolios.
{"title":"Long-Term U.S. Infrastructure Returns and Portfolio Selection","authors":"Michael Francis Howard, R. Bianchi, G. Bornholt, M. Drew","doi":"10.2139/ssrn.1914055","DOIUrl":"https://doi.org/10.2139/ssrn.1914055","url":null,"abstract":"Our understanding of the long-term return behavior and portfolio characteristics of public infrastructure investments is limited by a relatively short history of empirical data. We re-construct U.S. listed infrastructure index returns by mapping their monthly performance to received systematic and industry risk factors from 1927 through 2010. Our findings reveal that the infrastructure returns in recent years may understate the tail-risk that investors could experience over the long-term, however, this tail-risk is commensurate with holding a broad portfolio of U.S. stocks. For mean-variance and mean-CVaR investors, we report the benefits of holding public infrastructure assets in investment portfolios.","PeriodicalId":331246,"journal":{"name":"24th Australasian Finance & Banking Conference 2011 (Archive)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116239641","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 employs a mixed two-stage approach to estimate and explain differences in the cross-country efficiency of ten Australian, five UK and eight Canadian banks over the period 1988 to 2008 using stochastic distance, cost and profit frontiers. The variables specified in the stochastic frontiers used to estimate efficiency include the amount and prices of labour, physical capital and deposits, along with the level of non-interest income, profits and total costs. The country and firm-specific variables specified as explanatory factors include per capita national income, capital adequacy, deposit density, the industry concentration ratio, the level of intangible assets, and ratios of provisions for loan losses-to-total loans, loans-to-deposits, debt-to-equity, loans-to-total assets and long-term debt-to-total capital, among many others. In line with the experience of the banking sector during the recent global finance crisis, the evidence indicates that Australian banks exhibit superior efficiency compared with their Canadian and UK counterparts. Key factors found to affect efficiency positively include the level of intangible assets and the loans-to-deposits and loans-to-assets ratios. In contrast, key factors found to affect efficiency negatively include bank size and the ratios of loan loss provisions-to-total loans and the debt-to-equity ratio.
{"title":"A Comparative Technical, Cost and Profit Efficiency Analysis of Australian, Canadian and UK Banks: Feasible Efficiency Improvements in the Context of Controllable and Uncontrollable Factors","authors":"D. Xiang, Abul Shamsuddin, A. Worthington","doi":"10.2139/ssrn.1914094","DOIUrl":"https://doi.org/10.2139/ssrn.1914094","url":null,"abstract":"This paper employs a mixed two-stage approach to estimate and explain differences in the cross-country efficiency of ten Australian, five UK and eight Canadian banks over the period 1988 to 2008 using stochastic distance, cost and profit frontiers. The variables specified in the stochastic frontiers used to estimate efficiency include the amount and prices of labour, physical capital and deposits, along with the level of non-interest income, profits and total costs. The country and firm-specific variables specified as explanatory factors include per capita national income, capital adequacy, deposit density, the industry concentration ratio, the level of intangible assets, and ratios of provisions for loan losses-to-total loans, loans-to-deposits, debt-to-equity, loans-to-total assets and long-term debt-to-total capital, among many others. In line with the experience of the banking sector during the recent global finance crisis, the evidence indicates that Australian banks exhibit superior efficiency compared with their Canadian and UK counterparts. Key factors found to affect efficiency positively include the level of intangible assets and the loans-to-deposits and loans-to-assets ratios. In contrast, key factors found to affect efficiency negatively include bank size and the ratios of loan loss provisions-to-total loans and the debt-to-equity ratio.","PeriodicalId":331246,"journal":{"name":"24th Australasian Finance & Banking Conference 2011 (Archive)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116660381","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 study examines the impact of natural disasters on market returns and on several industries that are likely to be affected by the disasters. We find that different natural disasters have different impacts on the returns of the market and on those of industries. Our evidence suggests that while earthquake, hurricane and tornado could negatively affect market returns several weeks after the events, other disasters such as flood, tsunami and volcanic eruption may have limited impact on market returns. We also find that construction and materials industry is positively affected by natural disasters but nonlife and travel industries are likely to suffer when a natural disaster strikes.
{"title":"Natural Disasters - Blessings in Disguise?","authors":"Hardjo Koerniadi, C. Krishnamurti, A. Tourani-Rad","doi":"10.2139/ssrn.1913664","DOIUrl":"https://doi.org/10.2139/ssrn.1913664","url":null,"abstract":"This study examines the impact of natural disasters on market returns and on several industries that are likely to be affected by the disasters. We find that different natural disasters have different impacts on the returns of the market and on those of industries. Our evidence suggests that while earthquake, hurricane and tornado could negatively affect market returns several weeks after the events, other disasters such as flood, tsunami and volcanic eruption may have limited impact on market returns. We also find that construction and materials industry is positively affected by natural disasters but nonlife and travel industries are likely to suffer when a natural disaster strikes.","PeriodicalId":331246,"journal":{"name":"24th Australasian Finance & Banking Conference 2011 (Archive)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124404959","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 models the macro drivers of Australian housing affordability using aggregate quarterly data over the period September 1985 to June 2010 and an autoregressive distributed lag (ARDL) approach. We employ two alternative measures of relative housing affordability: the Housing Industry Association’s Housing Affordability Index and the housing price-earnings multiplier. Six sets of variables are then used to proxy the economic, demographic, financial, social and other factors that influence housing affordability, including conditions relating to housing finance, housing construction activity and costs, economic growth, population, alternative investments and taxation. In the long run, the results indicate that the primary drivers of affordability are housing finance, dwelling approvals and financial assets. Interestingly, economic and population growth only have an influence on affordability in the short run, while taxation related to housing has only a limited impact on affordability in the long run. The findings also indicate the high-speed adjustment following a shock to the short-run equilibrium of deteriorating housing affordability in Australia.
{"title":"Macro Drivers of Australian Housing Affordability, 1985–2010: An Autoregressive Distributed Lag Approach","authors":"A. Worthington, H. Higgs","doi":"10.2139/ssrn.1913972","DOIUrl":"https://doi.org/10.2139/ssrn.1913972","url":null,"abstract":"This paper models the macro drivers of Australian housing affordability using aggregate quarterly data over the period September 1985 to June 2010 and an autoregressive distributed lag (ARDL) approach. We employ two alternative measures of relative housing affordability: the Housing Industry Association’s Housing Affordability Index and the housing price-earnings multiplier. Six sets of variables are then used to proxy the economic, demographic, financial, social and other factors that influence housing affordability, including conditions relating to housing finance, housing construction activity and costs, economic growth, population, alternative investments and taxation. In the long run, the results indicate that the primary drivers of affordability are housing finance, dwelling approvals and financial assets. Interestingly, economic and population growth only have an influence on affordability in the short run, while taxation related to housing has only a limited impact on affordability in the long run. The findings also indicate the high-speed adjustment following a shock to the short-run equilibrium of deteriorating housing affordability in Australia.","PeriodicalId":331246,"journal":{"name":"24th Australasian Finance & Banking Conference 2011 (Archive)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125781562","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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