Relevant bibliographies by topics / Business forecasting Australia

Academic literature on the topic 'Business forecasting Australia'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Business forecasting Australia"

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Mills, Anthony, David Harris, and Martin Skitmore. "The accuracy of housing forecasting in Australia." Engineering, Construction and Architectural Management 10, no. 4 (August 2003): 245–53. http://dx.doi.org/10.1108/09699980310489951.

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Panagiotelis, Anastasios, George Athanasopoulos, Rob J. Hyndman, Bin Jiang, and Farshid Vahid. "Macroeconomic forecasting for Australia using a large number of predictors." International Journal of Forecasting 35, no. 2 (April 2019): 616–33. http://dx.doi.org/10.1016/j.ijforecast.2018.12.002.

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Turner, L., N. Kulendran, and H. Fernando. "The Use of Composite National Indicators for Tourism Forecasting." Tourism Economics 3, no. 4 (December 1997): 309–17. http://dx.doi.org/10.1177/135481669700300401.

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This paper identifies the usefulness of available national indicators in forecasting tourist arrivals to Australia from the USA, Japan, the UK and New Zealand. Income, unemployment, forward exchange rate, money supply, price ratio, industrial production, imports and exports of the tourist's country of origin are potential leading indicators of the tourist arrival series. A composite indicator is developed to forecast tourist arrivals. Further, disaggregated data are analysed to determine whether there is likely to be any increase in accuracy if the travel data is divided by travel type. The main travel types analysed are ‘holiday’, ‘visiting friends and relatives’ (VFR) and ‘business’ as well as total flow. To establish the causality between the indicator and international tourist arrivals to Australia, the performance of the transfer function model incorporating the national indicator is compared with ARIMA forecasts.
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Farshadfar, Shadi, and Reza Monem. "Discretionary accruals and the predictive ability of earnings in the forecast of future cash flows: Evidence from Australia." Corporate Ownership and Control 9, no. 1 (2011): 597–608. http://dx.doi.org/10.22495/cocv9i1c6art3.

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We examine whether discretionary and non-discretionary accruals improve the predictive ability of earnings for forecasting future cash flows in an Australian context. Using both within-sample and out-of-sample forecasting tests; we demonstrate that discretionary accruals improve the predictive ability of earnings in the forecast of future cash flows. Further, discretionary and non-discretionary accruals and direct method cash flow components together are more useful than (i) aggregate earnings, (ii) aggregate cash flow from operations and total accruals, and (iii) aggregate cash flow from operations, discretionary accruals and nondiscretionary accruals.
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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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He, Li Yu (Colly), Sue Wright, and Elaine Evans. "Is fair value information relevant to investment decision-making: Evidence from the Australian agricultural sector?" Australian Journal of Management 43, no. 4 (July 11, 2018): 555–74. http://dx.doi.org/10.1177/0312896218765236.

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Despite major accounting standards boards worldwide continuing to use fair value extensively, academic evidence on the relevance of fair value accounting has focused on financial assets. This study breaks new ground to provide the first empirical evidence for the agricultural sector on the relevance of fair value accounting. It examines the forecasting power of the fair value of biological assets for future operating cash flows. Using all agribusinesses listed in Australia, where fair value accounting was first implemented in the agricultural sector, we find that fair value of biological assets does not provide incremental forecasting power for future operating cash flows, whether market-determined prices or managerially estimated value is used. The findings of this study provide empirical support for the call by Elad and Herbohn in 2011 for the International Accounting Standards Board (IASB) to revisit the implementation of fair value accounting in the agricultural sector. JEL Classification: G14, G38, M41, Q18
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Abidoye, Rotimi Boluwatife, Wei Huang, Abdul-Rasheed Amidu, and Ashad Ali Javad. "An updated survey of factors influencing property valuation accuracy in Australia." Property Management 39, no. 3 (February 15, 2021): 343–61. http://dx.doi.org/10.1108/pm-02-2020-0014.

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PurposeThis study updates and extends the current work on the issue of accuracy of property valuation. The paper investigates the factors that contribute to property valuation inaccuracy and examines different strategies to achieve greater accuracy in practice.Design/methodology/approachAn online questionnaire was designed and administered on the Australian Property Institute (API) registered valuers, attempting to examine their perceptions on the current state of valuation accuracy in Australia. The variables/statements from responses are ranked overall and compared for differences by the characteristics of respondents.FindingsUsing mean rating point, the survey ranked three factors; inexperience valuers, the selection, interpretation and use of comparable evidence in property valuation exercise and the complexity of the subject property in terms of design, age, material specification and state of repairs as the most significant factors currently affecting valuation inaccuracy. The results of a Chi-square test did not, however, show a significant statistical relationship between respondents' profile and the perception on the comparative importance of the factors identified. Except for valuers' age and inexperience valuers and valuers' educational qualification and inexperience valuers and the selection, interpretation and use of comparable evidence in property valuation exercise. Also, the three highly ranked strategies for reducing the level of inaccuracy are: developing a global mindset, use of advanced methodology and training valuers on market forecasting skills.Practical implicationsIn order for valuers to provide state-of-the-art service to the public and to remain relevant, there is a need to accurately and reliably estimate valuation figures. Hence, the strategies highlighted in this study could be considered in a bid to reduce property valuation inaccuracy in practice.Originality/valueThis study provides an updated overview of the issue of property valuation inaccuracy in the Australia valuation practice and examines the strategies to reduce it.
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Shen, Mei-Li, Cheng-Feng Lee, Hsiou-Hsiang Liu, Po-Yin Chang, and Cheng-Hong Yang. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates." Sustainability 13, no. 5 (March 4, 2021): 2761. http://dx.doi.org/10.3390/su13052761.

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Accurately forecasting the movement of exchange rates is of interest in a variety of fields, such as international business, financial management, and monetary policy, though this is not an easy task due to dramatic fluctuations caused by political and economic events. In this study, we develop a new forecasting approach referred to as FSPSOSVR, which is able to accurately predict exchange rates by combining particle swarm optimization (PSO), random forest feature selection, and support vector regression (SVR). PSO is used to obtain the optimal SVR parameters for predicting exchange rates. Our analysis involves the monthly exchange rates from January 1971 to December 2017 of seven countries including Australia, Canada, China, the European Union, Japan, Taiwan, and the United Kingdom. The out-of-sample forecast performance of the FSPSOSVR algorithm is compared with six competing forecasting models using the mean absolute percentage error (MAPE) and root mean square error (RMSE), including random walk, exponential smoothing, autoregressive integrated moving average (ARIMA), seasonal ARIMA, SVR, and PSOSVR. Our empirical results show that the FSPSOSVR algorithm consistently yields excellent predictive accuracy, which compares favorably with competing models for all currencies. These findings suggest that the proposed algorithm is a promising method for the empirical forecasting of exchange rates. Finally, we show the empirical relevance of exchange rate forecasts arising from FSPSOSVR by use of foreign exchange carry trades and find that the proposed trading strategies can deliver positive excess returns of more than 3% per annum for most currencies, except for AUD and NTD.
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Birt, Jacqueline L., Kala Muthusamy, and Poonam Bir. "XBRL and the qualitative characteristics of useful financial information." Accounting Research Journal 30, no. 01 (May 2, 2017): 107–26. http://dx.doi.org/10.1108/arj-11-2014-0105.

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Purpose eXtensible Business Reporting Language (XBRL) is an internet-based interactive form of reporting language that is expected to enhance the usefulness of financial reporting (Yuan and Wang, 2009). In the UK and the USA, XBRL is mandatory, and in Australia, it is voluntarily adopted. It has been reported that in the not too distant future, XBRL will be the standard format for the preparation and exchange of business reports (Gettler, 2015). Using an experimental approach, this study assesses the usefulness of financial reports with XBRL tagged information compared to PDF format information for non-professional investors. The authors investigate participants’ perceptions of usefulness in relation to the qualitative characteristics of relevance, understandability and comparability. Design/methodology/approach This paper uses an experimental approach featuring a profit-forecasting task to determine if participants perceive XBRL-tagged information to be more useful compared to PDF-formatted information. Findings Results reveal that financial information presented with XBRL tagging is significantly more relevant, understandable and comparable to non-professional investors. Originality/value The authors address a gap in the literature by examining XBRL usefulness in Australia where XBRL adoption will be mandated within the not too distant future. Currently, the voluntary adoption of XBRL by preparers and users is low, possibly, because of a lack of awareness about XBRL and its potential benefits. This study yields significant implications for the accounting regulators in creating more awareness on the benefits of using XBRL and to create an impetus for XBRL adoption.
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Matvieieva, Yuliia. "Modelling and Forecasting Energy Efficiency Impact on the Human Health." Health Economics and Management Review 3, no. 2 (2022): 78–85. http://dx.doi.org/10.21272/hem.2022.2-09.

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Nowadays, one of the main pollutant factors is the inefficient use and breakdown of energy technologies. Improving access to modern energy, which emits less pollution, both at home and in the community could benefit the health of many people. Besides, it could contribute to the long-term goals of sustainable development. Health is a universal indicator of progress towards sustainable energy. Given the issue’s relevance, this article examines the impact of energy on public health. The study’s purpose is to substantiate the prospects for achieving sustainable development and human well-being, which depends on the quality of the environment and could be provided by a carbon-free economy. The methodological basis of the work is general scientific research methods, such as empirical and theoretical methods, as well as systemic and functional methods. This study applied VOSviewer tools, Web of Science and Scopus analysis tools, and Google Trends to conduct a bibliometric analysis of the impact of energy factors on public health. Based on Scopus data, the findings confirmed the hypothesis concerning a growing trend of publications examining the impact of energy factors on human health. In the study framework, the VOSviewer 1.6.18 tools allowed the detection of six clusters of research streams: renewable resources, sustainable development, public, energy policy, energy efficiency, and solar energy. The authors noted that different countries research the impact of energy on public health. These issues are most actively studied in China, the USA, and India. A separate dynamics of the publications were studied for 10 countries leading in the publication activity on the subject. The Google Trends tool has identified public interest in the topic. The interest of business and industry is considered separately. The findings showed that in the first case, the interest is more in the health factor. In turn, businesses and industries pay more attention to developing renewable energy sources. Google Trends analysis of the popularity of the search query «renewable energy» identified Korea, Turkey, Nigeria, Bangladesh, and Germany as leaders in the number of queries. However, the keywords healthy leaders are New Zealand, USA, Canada, Poland, and Australia.
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Dissertations / Theses on the topic "Business forecasting Australia"

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Hii, Teck K. "Reducing the redundancy of financial ratios and assessing the stability of financial patterns." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2000. https://ro.ecu.edu.au/theses/1343.

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The purpose of this study was to examine the financial patterns of Australian industrial firms along the lines of Pinches et al.’s (1973; 1975) study. The financial ratios used in prior Australian corporate failure studies were used to derive a reduced set of factors that was predictive of corporate failure (e.g., Castagna & Matolesy, 1981: Booth, 1983). These factors were examined for the short – and long-term stability of these factors. The set of firms used was selected from FINSELECT database, which covered the period from 1989 to 1997. A random list of 199 Australian industrial firms that survived between 1989 and 1997 was selected. A total of thirty-one unique financial ratios were calculated based on the models derived in prior Australian failure prediction studies. These financial rations were factor analysed. The financial factors that were predictive of corporate failure were Return on Investment, Short-Term Liquidity (I and II), Financial Leverage (I and II) and Decomposition Measure.
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Hossari, Ghassan, and hossari7@bigpond net au. "A ratio-based multi-level modelling approach for signalling corporate collapse a study of Australian corporations." Swinburne University of Technology. Australian Graduate School of Entrepreneurship, 2006. http://adt.lib.swin.edu.au./public/adt-VSWT20060320.114422.

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The objective of this study is to introduce a more refined methodological approach for signalling corporate collapse. The proposed methodological approach provides informed stakeholders in a corporation with a tool that would help them signal impending collapse with a higher degree of accuracy than the existing mainstream methodology. By doing so, the proposed methodological approach helps stakeholders take appropriate measures, if possible, to save their company from collapse. The motivation behind this study emanates from a need in the literature in relation to coming up with a new methodological approach that is superior to what is available. For example, Jones and Hensher (2004), one of the most recent studies in the field, stated that over the past three decades there has been a conspicuous absence of modelling innovation in the literature on financial distress prediction, as well as a failure to keep abreast of important methodological developments emerging in other fields of the social sciences. Specifically, this study introduces a new ratio-based multivariate methodological approach for signalling corporate collapse, called Multi-Level Modelling (MLM). Moreover, this study demonstrated that MLM provides informed stakeholders in a corporation with a tool that would help them signal impending collapse with a higher degree of accuracy than Multiple Discriminant Analysis (MDA), which is the mainstream benchmark methodological approach. By doing so, MLM helps stakeholders take appropriate measures, if possible, to save their company from collapse. The empirical results depicted the superiority of MLM over MDA. MLM generated better overall predictive power and dramatically reduced the occurrence of Type I error (classifying a collapsed company as non-collapsed). Moreover, MLM achieved those results while at the same time capturing variations in industry sectors among the data sample of companies. This is something that MDA was not capable of.
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Del, Rio Victor. "High-profile crisis management in Australian and New Zealand Organisations /." Connect to thesis, 2007. http://repository.unimelb.edu.au/10187/2272.

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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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Theodossiou, Konstantinos P. "Forecasting outbound tourism from Australia to Greece." Thesis, 1992. https://vuir.vu.edu.au/15712/.

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With the view of stressing the importance of forecasting and discovering underlying patterns in Tourism data series -for the purpose of assisting management and policy makers in the Tourism Industry - a multiple regression model and one classical decomposition model were constructed in order to investigate Outbound Tourism from Australia to Greece.
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Hatangala, Chinthana. "Identifying the Future Directions of Australian Excess Stock Returns and Their Determinants Using Binary Models." Thesis, 2016. https://vuir.vu.edu.au/32888/.

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The predictability of excess stock returns has been debated by researchers over time, with many studies proving that stock returns can be predicted to some extent. To enable an effective investment strategy, it is vital for investors to identify the future directions of stock returns and the factors causing directional changes. This study sought to determine whether Australian monthly excess stock return signs are predictable, and identify the key determinants of Australian monthly excess stock return directions. Three different binary models were considered to predict stock directions: discriminant, logistic and probit models. The predictive powers of benchmark static logistic and probit models were also compared with dynamic, autoregressive and dynamic autoregressive models. In order to identify the key determinants, this study considered various economic, international and financial factors, as well as past volatility measures of explanatory variables. It also tested a United States (US) binary recession indicator and Organisation for Economic Co-operation and Development (OECD) composite leading indicator as explanatory variables in the predictive models.
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Mason, Robert J. "Critical factors in the development and performance of food and wine trails in Australia." Thesis, 2010. https://vuir.vu.edu.au/16039/.

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Food and wine trails are a new and exciting part of culinary tourism in Australia. Such attractions are now an integral part of tourism in all the main food and wine production states. While trails join multiple food and wine venues into linked destinations for tourists and have grown in number, they are not widely understood. There has been very little research on them in Australia or overseas. This qualitative research was undertaken in the three states of South Australia, New South Wales and Victoria. The research objectives incorporated: • establishing the principal factors involved in the development and performance of food and wine trails in Australia. • understanding the economic importance of food and wine trails, • comprehending and elaborating upon critical components, • developing an awareness of the demography and expectation of tourist-consumers on food and wine trails. Finally, a principal aim was to develop a theoretical framework through which food and wine trails can be understood.
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Hossari, Ghassan. "A ratio-based multi-level modelling approach for signalling corporate collapse a study of Australian corporations /." 2006. http://adt.lib.swin.edu.au./public/adt-VSWT20060320.114422.

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Thesis (PhD) - Australian Graduate School of Entrepreneurship, Swinburne University of Technology, 2006.
A thesis submitted to the fulfillment of the requirements for the degree Doctor of Philosophy, Australian Graduate School of Entrepreneurship, Swinburne University of Technology - 2006. Typescript. Includes bibliographical references (p. 307-341).
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Books on the topic "Business forecasting Australia"

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2020 Vision: How global business leaders see Australia's future. Sydney: Allen & Unwin, 2003.

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Christoff, Peter. Four Degrees of Global Warming: Australia in a Hot World. Taylor & Francis Group, 2013.

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Christoff, Peter. Four Degrees of Global Warming: Australia in a Hot World. Taylor & Francis Group, 2013.

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Christoff, Peter. Four Degrees of Global Warming: Australia in a Hot World. Taylor & Francis Group, 2013.

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Bedford, Louise. The Secret of Candlestick Charting. Wrightbooks, 2000.

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1954-, Mills Stephen, ed. The Australian financial review Asian business insight. Sydney: Financial Review Library, 1994.

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Committee for Economic Development of Australia. and SACS Consulting Group, eds. Business intelligence report: Analysis and forecasts on key issues shaping the Australian business environment. 4th ed. Melbourne: CEDA, 1994.

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Four Degrees Of Global Warming. Taylor & Francis Ltd, 2013.

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2020 Vision: How Global Business Leaders See Australia's Future. Allen & Unwin Academic, 2003.

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Book chapters on the topic "Business forecasting Australia"

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Hooke, Angus, and Lauren A. Alati. "The Economies of Oceania." In Emerging Business and Trade Opportunities Between Oceania and Asia, 1–16. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4126-5.ch001.

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This chapter provides an overview of the economic performance and current situation of the economies in the Oceanic region. Oceania comprises the economically developed countries of Australia and New Zealand plus the more than 20 island countries and protectorates of Melanesia, Micronesia, and Polynesia. In 2018, Australia dominated the region in terms of population (71% of the total for Oceania) and gross domestic product (86%). Melanesia, which includes Papua New Guinea, Fiji, and New Caledonia, contained 26% of the region's population and generated 4% of its gross regional product. According to the authors' forecasting model, Oceania's share of both world population and gross world product will rise strongly during the coming three decades, admittedly from a low base. Reflecting the high degree of complementarity of the Oceanic economies with those of Asia, this strong growth in the Oceanic region should support increasingly attractive opportunities for trade and investment with the economies of Asia.
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Yao, Jing Tao, and Chew Lim Tan. "Neural Networks for Technical Forecasting of Foreign Exchange Rates." In Neural Networks in Business, 189–204. IGI Global, 2002. http://dx.doi.org/10.4018/978-1-930708-31-0.ch012.

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This chapter describes the application of neural networks in foreign exchange rate forecasting between American dollar and five other major currencies: Japanese yen, Deutsch mark, British pound, Swiss franc and Australian dollar. Technical indicators and time series data are fed to neural networks to mine, or discover, the underlying “rules” of the movement in currency exchange rates. The results presented in this chapter show that without the use of extensive market data or knowledge, useful prediction can be made and significant paper profit can be achieved for out-of-sample data with simple technical indicators. The neural-network-based forecasting is also shown to compare favorably with the traditional statistical approach.
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