Academic literature on the topic 'Stocks Prices Australia'
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Journal articles on the topic "Stocks Prices Australia"
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Lee, Ming-Te, Chyi Lin Lee, Ming-Long Lee, and Chien-Ya Liao. "Price linkages between Australian housing and stock markets." International Journal of Housing Markets and Analysis 10, no. 2 (April 3, 2017): 305–23. http://dx.doi.org/10.1108/ijhma-05-2016-0037.
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Purpose The purpose of this study is to examine the linkages between Australian house prices and stock prices under the Toda and Yamamoto test framework. Specifically, it investigated whether there is a capital switching effect between house prices and stock prices. Design/methodology/approach This study examined the linkages between house prices and stock prices under the Toda and Yamamoto test framework. To accommodate the impact of the global financial crisis (GFC), a sub-period analysis was undertaken. To assess the impact of investor structure, the tests were also performed for small cap stocks and large cap stocks individually. Findings The empirical results reveal a negative lead–lag relationship between house prices and stock prices in Australia, suggesting the existence of capital switching activities between housing and stocks. The impact of the GFC on the lead–lag relationship between house prices and stock prices is also documented. Before the crisis, a causality transmission was running from house prices to stock prices, whilst stock prices appeared to lead house prices after the crisis. The capital switching activities between housing and stocks are more evident for small cap stocks. Originality/value This study is the first to examine the linkages between house prices and stock prices under the Toda and Yamamoto test framework. This is the first study to explore the impacts of the GFC on the lead–lag relationship between the two asset prices under the capital switching framework. This study is also the first to provide empirical evidence regarding the existence of capital switching activities between housing and stocks. In addition, the impact of investor structure on the interrelationship between the two asset prices is examined for the first time under the capital switching framework.
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Asem, Ebenezer, Jessica Chung, Xin Cui, and Gloria Y. Tian. "Liquidity, investor sentiment and price discount of SEOs in Australia." International Journal of Managerial Finance 12, no. 1 (February 1, 2016): 25–51. http://dx.doi.org/10.1108/ijmf-10-2013-0106.
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Purpose – The purpose of this paper is to empirically test whether stock liquidity and investor sentiment have interactive effects on seasoned equity offers (SEOs) price discounts in Australia. Design/methodology/approach – The authors focus on the implicit cost borne by firms when issuing seasoned equity capital. This cost is measured as the relative difference between the SEO offer price and the last close price prior to the announcement of the issue. The primary measure of investor sentiment is a composite index constructed similar to that in Baker and Wurgler (2007). Findings – The results show that, in periods of deteriorating investor sentiment, the increase in SEO price discounts for firms with illiquid stocks is larger than the corresponding increase for firms with liquid stocks. This suggests that, as sentiment wanes, investors become even more concerned about illiquidity, leading to even greater required compensation for holding illiquid assets. The authors find that information asymmetry is positively related to SEO price discounts but this relation is not affected by changing investor sentiment. Research limitations/implications – Collectively, the empirical results provide support for the argument that price discount of SEOs represents compensation to investors for bearing costs associated with illiquidity. The results also lend some support to the behavioural argument that pricing of equity offers is dependent upon investor sentiment, particularly for firms with illiquid stocks. Practical implications – The ability for firms to raise capital in a cost-effective manner is critical for firm growth and stability. Investors require compensation for bearing the costs of illiquidity of their investments in equity. Accordingly, firms need to be conscious of their stocks’ existing liquidity and its influence on the cost of raising additional capital which, in turn, affects their operational stability and investment opportunities. Social implications – Ultimately, the implications of this study will assist firms in capital-raising decisions, investors in making portfolio investment decisions, and investment banks in setting offer prices on equity issues. Originality/value – To the best of the authors’ knowledge, this is the first study to examine the interaction between investor sentiment and SEO price discounts in Australia.
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Fisher, Lawrence, Daniel G. Weaver, and Gwendolyn Webb. "International Real Estate Review." International Real Estate Review 15, no. 1 (April 30, 2012): 43–71. http://dx.doi.org/10.53383/100148.
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In this paper, we apply the method for removing the upward bias in returns in equally-weighted return indexes developed by Fisher, Weaver, and Webb (2010) to real estate investment trust (REIT) stocks in the US. While we find significant bias in this index, two trends are evident: first, there is less overall bias than in non-REIT stocks, and second, the bias of REIT stocks has declined over time. These trends are consistent with growing listings of REIT stocks on the New York Stock Exchange (NYSE), as well as with increasingly higher stock prices. They also support the hypothesis that there have been significant improvements in the market micro-structure environment of REIT stocks since the early 1970s. We further apply our methodology to REIT stocks listed in the two countries with the largest number of REITs outside the US: Germany and Australia. The results support the hypothesized relationship between index bias and market micro-structure environment.
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Duppati, Geeta, and Mengying Zhu. "Oil prices changes and volatility in sector stock returns: Evidence from Australia, New Zealand, China, Germany and Norway." Corporate Ownership and Control 13, no. 2 (2016): 351–70. http://dx.doi.org/10.22495/cocv13i2clp4.
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The paper examines the exposure of sectoral stock returns to oil price changes in Australia, China, Germany, New Zealand and Norway over the period 2000-2015 using weekly data drawn from DataStream. The issue of volatility has important implications for the theory of finance and as is well-known accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management (e.g. in the calculation of hedge ratios and Value-at-Risk measures), and trading strategies (David and Ruiz, 2009). This study adopts GARCH and EGARCH to understand the relationship between the returns and volatility. The findings using GARCH (EGARCH) models suggests that in the case of Germany eight (nine) out of ten sectors returns can be explained by the volatility of past oil price in Germany, while in the case of Australia, six (seven) out of ten sector returns are sensitive to the oil price changes with the exception of Industrials, Consumer Goods, Health care and Utilities. While in China and New Zealand five sectors are found sensitive to oil price changes and three sectors in Norway, namely Oil & Gas, Consumer Services and Financials. Secondly, this paper also investigated the exposure of the stock returns to oil price changes using market index data as a proxy using GARCH or EGARCH model. The results indicated that the stock returns are sensitive to the oil price changes and have leverage effects for all the five countries. Further, the findings also suggests that sector with more constituents is likely to have leverage effects and vice versa. The results have implications to market participants to make informed decisions about a better portfolio diversification for minimizing risk and adding value to the stocks.
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Beukes, Anna. "Value Investing: International Comparison." International Business & Economics Research Journal (IBER) 10, no. 5 (May 13, 2011): 1. http://dx.doi.org/10.19030/iber.v10i5.4226.
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Based on accumulated empirical evidence, the academic community has generally come to agree that value investment strategies, on average, outperform growth investment strategies (Chan and Lakonishok, 2004:71). An influential article by Fama and French (1992) tested the notion that United States stock prices might be related to the ratio of a firms book value of common equity (BV) to its market value of common equity (MV). It found that companies with high book value relative to market value of equity (BV/MV) outperform the market. This finding led to extensive testing for the value premium in developed countries around the world. Fama and French (1998a) tested it with data from twelve major European countries, as well as from Australia and the Far East. They found that between 1975 and 1995 in almost every country, value stocks delivered a higher return than growth stocks. The value premium has not been tested with the same vigor in third world or developing countries, which raises the question whether the value premium is only a first world phenomena and, if not, how third world value premiums compare to those found in developed countries. This paper compares the size of the value premium in the USA, UK, and some continental European countries with South African data.
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Keating, B. A., and P. S. Carberry. "Emerging opportunities and challenges for Australian broadacre agriculture." Crop and Pasture Science 61, no. 4 (2010): 269. http://dx.doi.org/10.1071/cp09282.
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Agriculture globally and in Australia is at a critical juncture in its history with the current changes to input costs, commodity prices, consumption patterns and food stocks. Constraints are emerging in terms of land and water resources as well as imperatives to reduce greenhouse gas emissions. There is evidence that rates of increase in agricultural productivity are reducing, both in Australia and overseas. On top of all these drivers of change, agriculture is the sector probably most exposed to climate change, and Australian agriculture is as exposed as any in the world. Against this turbulent background, this paper explores some of the emerging opportunities and challenges in Australian agriculture. These include new products or services from agriculture such as biofuels, forest-based carbon storage in agricultural landscapes, bio-sequestration of carbon in agricultural soils, and environmental stewardship schemes that would reward farmers for nature conservation and related non-production services from farming land. Although there are situations where all these emerging opportunities may deliver benefits to both farmers and the wider community, an overall conclusion is that none of these, on their own, will transform the nature of Australian agriculture. Instead, the greatest emerging opportunity for Australian agriculture must be sought from productivity breakthroughs in the face of current and emerging constraints. This view is formed by looking through the lens of the global food production challenge which sees a demand for close to a doubling of food production by 2050 in the face of increasingly constrained land and water resources, soil degradation, increasing energy scarcity and limits on greenhouse gas release to the atmosphere. These same land, water, soil, energy and atmospheric constraints to agriculture apply in Australia and will shape both farming and the agricultural research agenda over coming decades. In the face of such national and global agronomic challenges, a significant threat looms with the skills challenge facing agricultural science in Australia. The demand for the integrative skills of agronomy appears strong but the sector has suffered from disinvestment in recent decades.
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Manickavasagam, Jeevananthan, and Visalakshmi S. "An investigational analysis on forecasting intraday values." Benchmarking: An International Journal 27, no. 2 (October 4, 2019): 592–605. http://dx.doi.org/10.1108/bij-11-2018-0361.
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Purpose The algorithmic trading has advanced exponentially and necessitates the evaluation of intraday stock market forecasting on the grounds that any stock market series are foreseen to follow the random walk hypothesis. The purpose of this paper is to forecast the intraday values of stock indices using data mining techniques and compare the techniques’ performance in different markets to accomplish the best results. Design/methodology/approach This study investigates the intraday values (every 60th-minute closing value) of four different markets (namely, UK, Australia, India and China) spanning from April 1, 2017 to March 31, 2018. The forecasting performance of multivariate adaptive regression spline (MARSplines), support vector regression (SVR), backpropagation neural network (BPNN) and autoregression (1) are compared using statistical measures. Robustness evaluation is done to check the performance of the models on the relative ratios of the data. Findings MARSplines produces better results than the compared models in forecasting every 60th minute of selected stocks and stock indices. Next to MARSplines, SVR outperforms neural network and autoregression (1) models. The MARSplines proved to be more robust than the other models. Practical implications Forecasting provides a substantial benchmark for companies, which entails long-run operations. Significant profit can be earned by successfully predicting the stock’s future price. The traders have to outperform the market using techniques. Policy makers need to estimate the future prices/trends in the stock market to identify the link between the financial instruments and monetary policy which gives higher insights about the mechanism of existing policy and to know the role of financial assets in many channels. Thus, this study expects that the proposed model can create significant profits for traders by more precisely forecasting the stock market. Originality/value This study contributes to the high-frequency forecasting literature using MARSplines, SVR and BPNN. Finding the most effective way of forecasting the stock market is imperative for traders and portfolio managers for investment decisions. This study reveals the changing levels of trends in investing and expectation of significant gains in a short time through intraday trading.
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Guesmi, Khaled, Frederic Teulon, and Amine Lahiani. "Australias Integration Into The ASEAN-5 Region." Journal of Applied Business Research (JABR) 29, no. 6 (October 29, 2013): 1607. http://dx.doi.org/10.19030/jabr.v29i6.8198.
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This paper attempts to evaluate the time-varying integration of Australian stock market in ASEAN-5 region (ASEAN + Australia, Korea, China, India and Japan) by using a conditional version of the international capital asset pricing model (ICAPM) allowing for dynamic changes in the degree of market integration, regional market risk price, currency risk price and domestic market risk price. Main findings are as follows: i) the prices of risk in Australia are extremely sensitive to major international economic and political events such as the different monetary and financial crises in international financial market; ii) the level of market openness and development of the stock market satisfactorily explain the time-varying degree of Australian stock integration.
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Khabbach, Abdelmajid, Mohamed Libiad, Mohamed El Haissoufi, Soumaya Bourgou, Wided Megdiche-Ksouri, Fatima Lamchouri, Zeineb Ghrabi-Gammar, et al. "Electronic commerce of the endemic plants of northern Morocco (Mediterranean coast-Rif) and Tunisia over the internet." Botanical Sciences 100, no. 1 (October 5, 2021): 139–52. http://dx.doi.org/10.17129/botsci.2850.
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Background: Internet trade popularize the ornamental interest of plants but can also threaten species’ wild populations, if this activity is performed in uncontrolled and unauthorised ways.
Questions: What endemic plants of Morocco and Tunisia are traded over the Internet by whom and at what prices?
Studied species: 94 endemic plants of northern Morocco and 83 of Tunisia.
Study site and dates: Tunisia and northern Morocco (Mediterranean coast and Rif region); internet survey between September 2018 and December 2019.
Methods: To understand the extent of this new form of trade, We recorded the type of plant material sold over the Internet for the studied taxa, their prices and suppliers using online platforms.
Results: Four northern Moroccan taxa (4.25 % of the total local endemics) were found as marketed by 18 nurseries in Europe, North America, Australia and New Zealand, while no marketing activity was detected for Tunisian endemic plants. The nurseries involved offer for sale and distribution living individuals of Abies marocana at €12.00-259.50, Rhodanthemum hosmariense at €0.35-19.5, Salvia interrupta subsp. paui at €6.23-8.90, and bulbs of Acis tangitana at €1.05-3.95. Although these taxa are classified as endangered, they are traded worldwide without permit of the Moroccan authorities. The source and origin of the plant material are not clearly indicated, and only some nurseries report that their marketed material comes from own cultivated stocks.
Conclusions: The implementation of protection laws/regulations and the monitoring of nurseries’ websites are recommended to control the illegal trade of wild plant material.
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Khabbach, Abdelmajid, Mohamed Libiad, Mohamed El Haissoufi, Soumaya Bourgou, Wided Megdiche-Ksouri, Fatima Lamchouri, Zeineb Ghrabi-Gammar, et al. "Electronic commerce of the endemic plants of northern Morocco (Mediterranean coast-Rif) and Tunisia over the internet." Botanical Sciences 100, no. 1 (October 5, 2021): 139–52. http://dx.doi.org/10.17129/botsci.2850.
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Background: Internet trade popularize the ornamental interest of plants but can also threaten species’ wild populations, if this activity is performed in uncontrolled and unauthorised ways.
Questions: What endemic plants of Morocco and Tunisia are traded over the Internet by whom and at what prices?
Studied species: 94 endemic plants of northern Morocco and 83 of Tunisia.
Study site and dates: Tunisia and northern Morocco (Mediterranean coast and Rif region); internet survey between September 2018 and December 2019.
Methods: To understand the extent of this new form of trade, We recorded the type of plant material sold over the Internet for the studied taxa, their prices and suppliers using online platforms.
Results: Four northern Moroccan taxa (4.25 % of the total local endemics) were found as marketed by 18 nurseries in Europe, North America, Australia and New Zealand, while no marketing activity was detected for Tunisian endemic plants. The nurseries involved offer for sale and distribution living individuals of Abies marocana at €12.00-259.50, Rhodanthemum hosmariense at €0.35-19.5, Salvia interrupta subsp. paui at €6.23-8.90, and bulbs of Acis tangitana at €1.05-3.95. Although these taxa are classified as endangered, they are traded worldwide without permit of the Moroccan authorities. The source and origin of the plant material are not clearly indicated, and only some nurseries report that their marketed material comes from own cultivated stocks.
Conclusions: The implementation of protection laws/regulations and the monitoring of nurseries’ websites are recommended to control the illegal trade of wild plant material.
Dissertations / Theses on the topic "Stocks Prices Australia"
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Yang, Wenling. "M-GARCH Hedge Ratios And Hedging Effectiveness In Australian Futures Markets." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2000. https://ro.ecu.edu.au/theses/1530.
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This study deals with the estimation of the optimal hedge ratios using various econometric models. Most of the recent papers have demonstrated that the conventional ordinary least squares (OLS) method of estimating constant hedge ratios is inappropriate, other more complicated models however seem to produce no more efficient hedge ratios. Using daily AOIs and SPI futures on the Australian market, optimal hedge ratios are calculated from four different models: the OLS regression model, the bivariate vector autoaggressive model (BVAR), the error-correction model (ECM) and the multivariate diagonal Vcc GARCH Model. The performance of each hedge ratio is then compared. The hedging effectiveness is measured in terms of ex-post and ex-ante risk-return traHe-off at various forcasting horizons. It is generally found that the GARCH time varying hedge ratios provide the greatest portfolio risk reduction, particularly for longer hedging horizons, but hey so not generate the highest portfolio return.
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Chen, Gary. "Behavioural heterogeneity in ASX 200 a dissertation submitted to Auckland University of Technology in fulfilment of the requirements for the degree of Master of Business (MBus), 2009 /." Click here to access this resource online, 2009. http://hdl.handle.net/10292/758.
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Tilakaratne, Chandima University of Ballarat. "Stock market predictions based on quantified intermarket influences." University of Ballarat, 2007. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/12798.
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This research investigated the feasibility and capability of neural network-based approaches for predicting the direction of the Australian Stock market index (the target market). It includes several aspects: univariate feature selection from the historical time series of the target market, inter-market analysis for finding the most relevant influential markets, investigations of the effect of time cycles on the target market and the discovery of the optimal neural network architectures. Previous research on US stock markets and other international markets have shown that the neural network approach is one of most powerful techniques for predicting stock market behaviour. Neural networks are capable of capturing the non-linear stochastic and chaotic patterns in the stock market time series data. This study discovered that the relative return series of the Open, High, Low and Close prices of the target market, show 6-day cycles during the studied period of about 14 years. Multi-layer feedforward neural networks trained with a backpropagation algorithm were used for the experiments. Two major testing methods: testing with randomly selected test data and forward testing, were examined and compared. The best neural network developed in this study has achieved 87%, 81% 83% and 81% accuracy respectively in predicting the next-day direction of the relative return of the Open, High, Low and Close prices of the target market. The architecture of this network consists of 33 input features, one hidden layer with 3 neurons and 4 output neurons. The best input features set includes the relative returns from 1 to 6 days in the past of the Open, High, Low and Close prices of the target market, the day of the week, and the previous day’s relative return of the Close prices of the US S&P 500 Index, US Dow Jones Industrial Average Index, US Gold/Silver Index, and the US Oil Index.Doctor of Philosophy
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Tilakaratne, Chandima. "Stock market predictions based on quantified intermarket influences." University of Ballarat, 2007. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/15394.
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This research investigated the feasibility and capability of neural network-based approaches for predicting the direction of the Australian Stock market index (the target market). It includes several aspects: univariate feature selection from the historical time series of the target market, inter-market analysis for finding the most relevant influential markets, investigations of the effect of time cycles on the target market and the discovery of the optimal neural network architectures. Previous research on US stock markets and other international markets have shown that the neural network approach is one of most powerful techniques for predicting stock market behaviour. Neural networks are capable of capturing the non-linear stochastic and chaotic patterns in the stock market time series data. This study discovered that the relative return series of the Open, High, Low and Close prices of the target market, show 6-day cycles during the studied period of about 14 years. Multi-layer feedforward neural networks trained with a backpropagation algorithm were used for the experiments. Two major testing methods: testing with randomly selected test data and forward testing, were examined and compared. The best neural network developed in this study has achieved 87%, 81% 83% and 81% accuracy respectively in predicting the next-day direction of the relative return of the Open, High, Low and Close prices of the target market. The architecture of this network consists of 33 input features, one hidden layer with 3 neurons and 4 output neurons. The best input features set includes the relative returns from 1 to 6 days in the past of the Open, High, Low and Close prices of the target market, the day of the week, and the previous day’s relative return of the Close prices of the US S&P 500 Index, US Dow Jones Industrial Average Index, US Gold/Silver Index, and the US Oil Index.Doctor of Philosophy
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Tilakaratne, Chandima University of Ballarat. "A neural network approach for predicting the direction of the Australian stock market index." University of Ballarat, 2004. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/12804.
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This research investigated the feasibility and capability of neural network-based approaches for predicting the direction of the Australian Stock market index (the target market). It includes several aspects: univariate feature selection from the historical time series of the target market, inter-market analysis for finding the most relevant influential markets, investigations of the effect of time cycles on the target market and the discovery of the optimal neural network architectures. Previous research on US stock markets and other international markets have shown that the neural network approach is one of most powerful techniques for predicting stock market behaviour. Neural networks are capable of capturing the non-linear stochastic and chaotic patterns in the stock market time series data. This study discovered that the relative return series of the Open, High, Low and Close prices of the target market, show 6-day cycles during the studied period of about 14 years. Multi-layer feedforward neural networks trained with a backpropagation algorithm were used for the experiments. Two major testing methods: testing with randomly selected test data and forward testing, were examined and compared. The best neural network developed in this study has achieved 87%, 81% 83% and 81% accuracy respectively in predicting the next-day direction of the relative return of the Open, High, Low and Close prices of the target market. The architecture of this network consists of 33 input features, one hidden layer with 3 neurons and 4 output neurons. The best input features set includes the relative returns from 1 to 6 days in the past of the Open, High, Low and Close prices of the target market, the day of the week, and the previous day’s relative return of the Close prices of the US S&P 500 Index, US Dow Jones Industrial Average Index, US Gold/Silver Index, and the US Oil Index.Master of Information Technology by Research
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Tilakaratne, Chandima. "A neural network approach for predicting the direction of the Australian stock market index." University of Ballarat, 2004. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/15397.
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This research investigated the feasibility and capability of neural network-based approaches for predicting the direction of the Australian Stock market index (the target market). It includes several aspects: univariate feature selection from the historical time series of the target market, inter-market analysis for finding the most relevant influential markets, investigations of the effect of time cycles on the target market and the discovery of the optimal neural network architectures. Previous research on US stock markets and other international markets have shown that the neural network approach is one of most powerful techniques for predicting stock market behaviour. Neural networks are capable of capturing the non-linear stochastic and chaotic patterns in the stock market time series data. This study discovered that the relative return series of the Open, High, Low and Close prices of the target market, show 6-day cycles during the studied period of about 14 years. Multi-layer feedforward neural networks trained with a backpropagation algorithm were used for the experiments. Two major testing methods: testing with randomly selected test data and forward testing, were examined and compared. The best neural network developed in this study has achieved 87%, 81% 83% and 81% accuracy respectively in predicting the next-day direction of the relative return of the Open, High, Low and Close prices of the target market. The architecture of this network consists of 33 input features, one hidden layer with 3 neurons and 4 output neurons. The best input features set includes the relative returns from 1 to 6 days in the past of the Open, High, Low and Close prices of the target market, the day of the week, and the previous day’s relative return of the Close prices of the US S&P 500 Index, US Dow Jones Industrial Average Index, US Gold/Silver Index, and the US Oil Index.Master of Information Technology by Research
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Mikhailitchenko, Serguei, and na. "The Australian Housing Market: Price Dynamics and Capital Stock Growth." Griffith University. Department of Accounting, Finance and Economics, 2008. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20100729.074134.
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This study was motivated by the desire to contribute to the understanding of the movement of house prices and the role of the so-called economic fundamentals in the housing market, especially within an Australian context. The core objective of this thesis is to aid understanding of the economic and other mechanisms by which the Australian housing market operates. We do this by constructing an analytical framework, or model, that encompasses the most important characteristics of the housing market. This thesis examines two important aspects of the Australian housing market: movements of house prices and changes in the net capital stock of dwellings in Australia. Movements of house prices are modelled from two perspectives: firstly, using the fundamental approach, which explains the phenomena by changes in such fundamental explanatory variables as income, interest rates, population and prices of building materials, and secondly, by analysing spatial interdependence of house prices in Australian capital cities. Changes in stock of dwellings were also modelled on the basis of a fundamental approach by states and for Australia as a whole...
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Eadie, Edward Norman. "Small resource stock share price behaviour and prediction." Title page, contents and abstract only, 2002. http://web4.library.adelaide.edu.au/theses/09CM/09cme11.pdf.
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Bellamy, David Ewan. "An analysis of ex-dividend day abnormal trading volumes and share price changes in the Australian equity market /." [St. Lucia, Qld. : s.n.], 2002. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe16648.pdf.
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Lapanan, Nicha, and Stefan Anchev. "Wealth effects from asset securitization : (the case of Australia)." Thesis, Umeå universitet, Företagsekonomi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-47813.
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Asset securitization is one of the most important financial innovations recently. With an impressive growth in terms of volume of issuance, from almost zero to five trillion USD, in a period of 15-20 years, it is one of the most rapidly growing markets in the financial world. Yet, little is known about this, literally invisible market. Companies engage in asset securitization for a variety of reasons and numerous advantages and disadvantages of asset securitization can be found throughout the literature. Asset securitization has an impact on a number of stakeholder groups: shareholders, managers, employees, investors, the financial markets and ultimately the overall economy and society. Asset securitization is one of the reasons for the financial crisis that started in mid 2007. Since the recent financial turmoil, it became clear the asset securitization was the primary funding source for companies in the financial industry and it was the primary supplier of credit in developed economies. Because of its importance and impact, it is very important that we study the reasons, the motivations, the consequences and the effects from this so powerful financial innovation. And it is important to study it from as many different aspects as possible. Many questions surrounding asset securitization are unanswered and it is important to answer them sooner. This study investigates the wealth effects from asset securitization on the shareholders of the securitizing companies. We study whether the announcement about a pending securitization transaction has any impact on the stock price of the securitizing company. That way we can discover whether asset securitization creates wealth, destroys wealth or has no impact on wealth at all. Not many studies have been done on this topic so far. The existing seven studies are focused mainly on the US and the EU market and report contradicting results. In this study, for the first time, data from Australia is being used. The Australian securitization market is the second, single most active securitization market in the world, after the US market. We conduct quantitative analysis on a sample of 98 securitization transactions during the period 2000-2006. With this sample, we cover almost 29% of the number of securitization transactions during that period and almost 39% in terms of volume of issuance. To analyze the data we use standard event study methodology, common for this type of studies. Our analysis reveals that investors in Australia do not perceive asset securitization favorably. Securitizing companies’ stock price decreases in the 10 days around the securitization announcement day, resulting in statistically significant wealth losses for the originating companies’ shareholders. Furthermore, the wealth losses are significant for less frequent securitizers, for securitizers that engage in small volume securitization transactions and for securitizing companies with low asset quality. With this study we make theoretical and practical contribution. We lend empirical support to the previous theories and we help managers, shareholders and investors shape their forecasts.
Books on the topic "Stocks Prices Australia"
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Price interdependence among equity markets in the Asia-Pacific region: Focus on Australia and ASEAN. Aldershot, Hampshire, Eng: Ashgate Pub., 2000.
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Allen, D. E. Excess volatility and the short run modelling of Australian stock prices. Perth, W.A: Edith Cowan University, Faculty of Business, School of Economics and Finance, 1996.
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E, Allen D. The relationship between stock prices and dividends: Evidence from the Australian stock market. Perth, W.A: Edith Cowan University, Faculty of Business, School of Economics and Finance, 1996.
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Lynch, Andrew. What price security?: Taking stock of Australia's anti-terror laws. Sydney: University of New South Wales Press, 2006.
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Ord, Terry. Investment returns from Australian mining shares: A case for prospect theory. Perth , W.A: Edith Cowan University, School of Finance and Business Economics, 1997.
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Eckhold, Kelly R. Bank asset valuation and risk in Australasia: The market's evaluation. [Wellington]: Reserve Bank of New Zealand, 1994.
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Bedford, Louise. The Secret of Candlestick Charting. Wrightbooks, 2000.
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Roca, Eduardo D. Price Interdependence among Equity Markets in the Asia-Pacific Region: Focus on Australia and ASEAN. Taylor & Francis Group, 2018.
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Roca, Eduardo D. Price Interdependence among Equity Markets in the Asia-Pacific Region: Focus on Australia and ASEAN. Taylor & Francis Group, 2018.
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Cai, Zongwu. Functional Coefficient Models for Economic and Financial Data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.6.
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This article discusses the use of functional coefficient models for economic and financial data analysis. It first provides an overview of recent developments in the nonparametric estimation and testing of functional coefficient models, with particular emphasis on the kernel local polynomial smoothing method, before considering misspecification testing as an important econometric question when fitting a functional (varying) coefficient model or a trending time-varying coefficient model. It then describes two major real-life applications of functional coefficient models in economics and finance: the first deals with the use of functional coefficient instrumental-variable models to investigate the empirical relation between wages and education in a random sample of young Australian female workers from the 1985 wave of the Australian Longitudinal Survey, and the second is concerned with the use of functional coefficient beta models to analyze the common stock price of Microsoft stock (MSFT) during the year 2000 using the daily closing prices.
Book chapters on the topic "Stocks Prices Australia"
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Raza, Muhammad Raheel, and Saleh Alkhamees. "Deep Learning Analysis of Australian Stock Market Price Prediction for Intelligent Service Oriented Architecture." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 173–84. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95987-6_12.
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Zhu, Liangxu, and Tiho Ancev. "Modeling the Dynamics of Implied Carbon Price and its Influence on the Stock Price Variability of Energy Companies in the Australian Electric Utility Sector." In Handbook of Energy Finance, 671–95. WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789813278387_0027.
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Woods, Rebecca J. H. "Native Colonials." In The Herds Shot Round the World. University of North Carolina Press, 2017. http://dx.doi.org/10.5149/northcarolina/9781469634661.003.0005.
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Chapter 4 turns to colonial New Zealand where questions about the relationship of type to place played out on an imperial scale. As the global price of wool plummeted in the 1860s, pastoralists in New Zealand reconfigured their predominantly merino flocks to serve a new refrigerated trade between Great Britain and her Australasian colonies. Where New Zealand breeders had predominantly focused on wool production, with the advent of refrigerated shipping in the early 1880s, they began to breed for meat as well as wool. Colonial producers throughout Australasia discovered that British diners preferred the meat of British breeds: merino mutton from the colonies did not find a ready market in London. To satisfy the contradictory demands of colonial climate and topography, which varied from Britain’s, and metropolitan demand, New Zealand breeders constructed novel colonial breeds, like the Corriedale, forged out of a cross between British longwool stock and merino sheep. They touted these types as “native” colonial breeds, thereby adding another layer of complexity to the concept, and making a rhetorical claim as settlers of a distant land only recently wrested from the indigenous Maori people.
Reports on the topic "Stocks Prices Australia"
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Dassanayake, Wajira, Xiaoming Li, and Klaus Buhr. A Revisit of Price Discovery Dynamics Across Australia and New Zealand. Unitec ePress, August 2015. http://dx.doi.org/10.34074/rsrp.039.
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This study re-investigates the price discovery dynamics of selected stocks cross-listed on the Australian Stock Exchange (ASX) and the New Zealand Stock Exchange (NZX) during a bear trading phase from January 2008 to December 2011. A differing price discovery dynamic in a bear market versus a bull market may occur because of variations in investor sentiments and disparities in the role of the stock prices. Using intraday data, we employ the vector error correction mechanism, Hasbrouck’s (1995) information share and Grammig et al.’s (2005) conditional information share methods. Consistent with previous research, we find that price discovery takes place mostly on the home market for the Australian firms and for all but one of the New Zealand firms. However, not seen in existing studies, we show that the NZX has grown in importance for both the Australian and New Zealand firms. This suggests that the NZX is deviating from being a pure satellite market.
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Dassanayake, Wajira, Xiaoming Li, and Klaus Buhr. A Revisit of Price Discovery Dynamics Across Australia and New Zealand. Unitec ePress, August 2015. http://dx.doi.org/10.34074/rsrp.039.
Full textAbstract:
This study re-investigates the price discovery dynamics of selected stocks cross-listed on the Australian Stock Exchange (ASX) and the New Zealand Stock Exchange (NZX) during a bear trading phase from January 2008 to December 2011. A differing price discovery dynamic in a bear market versus a bull market may occur because of variations in investor sentiments and disparities in the role of the stock prices. Using intraday data, we employ the vector error correction mechanism, Hasbrouck’s (1995) information share and Grammig et al.’s (2005) conditional information share methods. Consistent with previous research, we find that price discovery takes place mostly on the home market for the Australian firms and for all but one of the New Zealand firms. However, not seen in existing studies, we show that the NZX has grown in importance for both the Australian and New Zealand firms. This suggests that the NZX is deviating from being a pure satellite market.