ORCID Profile
0000-0002-3977-2468
Current Organisations
Bond University
,
Bangor University Bangor Business School
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Publisher: SAGE Publications
Date: 02-02-2021
Abstract: This article analyses how the financial literacy of elderly people affects their decisions on the adoption of various financial strategies. Multiple mediator models with bootstrap techniques are used to identify the mediating mechanisms of financial concerns that transmit the effects of financial literacy onto specific financial strategies. We find (1) financial concerns mediate the majority of financial literacy-strategy nexuses specifically, financially illiterate people are more likely to have financial concerns and are more likely to cut back on spending, seek job opportunities, increase debts and downsize or sell their residence as a result (2) financially literate people are more likely to seek professional financial advice, purchase a life annuity, contribute more to superannuation and invest more conservatively, regardless of their concerns. Our findings suggest professional advisors and robo-advisor developers take into account financial concerns when recommending advice. JEL Classification: D14, J14, J26, I31, G11
Publisher: Elsevier BV
Date: 04-2009
Publisher: Elsevier BV
Date: 2012
DOI: 10.2139/SSRN.2132390
Publisher: Elsevier BV
Date: 2013
DOI: 10.2139/SSRN.2312114
Publisher: Wiley
Date: 22-10-2020
DOI: 10.1111/ACFI.12708
Abstract: This study models the term structure of the European Union Emissions Trading Scheme. The one‐factor geometric Brownian motion model of Abadie and Chamorro is replicated using the data now available and then compared with a two‐factor short‐term/long‐term (STLT) stochastic model. The STLT model has the better statistical fit to the term structure of European Union Allowances (EUAs). A real options analysis of the value of the option to retrofit carbon capture and storage shows that forecasting phase four EUAs with the STLT model almost triples the estimated project net present value and lowers investment trigger prices by approximately 24 percent.
Publisher: Elsevier BV
Date: 09-2010
Publisher: Springer International Publishing
Date: 2018
Publisher: Elsevier BV
Date: 05-2022
Publisher: Elsevier BV
Date: 2009
DOI: 10.2139/SSRN.1436207
Publisher: Springer International Publishing
Date: 2020
Publisher: Wiley
Date: 29-04-2015
DOI: 10.1111/ACFI.12140
Publisher: No publisher found
Publisher: Wiley
Date: 14-04-2019
DOI: 10.1111/ACFI.12362
Publisher: Pageant Media US
Date: 31-03-2015
Publisher: IGI Global
Date: 2005
DOI: 10.4018/978-1-59140-553-5.CH031
Abstract: Soft computing represents that area of computing adapted from the physical sciences. Artificial intelligence (AI) techniques within this realm attempt to solve problems by applying physical laws and processes. This style of computing is particularly tolerant of imprecision and uncertainty, making the approach attractive to those researching within “noisy” realms, where the signal-to-noise ratio is low. Soft computing is normally accepted to include the three key areas of fuzzy logic, artificial neural networks, and probabilistic reasoning (which includes genetic algorithms, chaos theory, etc.).
Publisher: Wiley
Date: 18-03-2019
DOI: 10.1111/ACFI.12466
Publisher: Wiley
Date: 27-09-2020
DOI: 10.1111/ACFI.12543
Publisher: Elsevier BV
Date: 2010
DOI: 10.2139/SSRN.1663429
Publisher: IEEE
Date: 2014
Publisher: Wiley
Date: 30-09-2020
DOI: 10.1111/ACFI.12545
Publisher: Springer Netherlands
Date: 2009
Publisher: Wiley
Date: 21-11-2020
DOI: 10.1111/ACFI.12724
Abstract: Price momentum is a well‐documented anomaly in many of the world’s equity markets, and refers to the excess returns due to buying (selling) past winner (loser) stocks. Industry momentum refers to the excess returns due to buying (selling) stocks from past winner (loser) industries, and has been demonstrated to be more profitable than in idual stock momentum in the United States. We investigate whether industry momentum can be captured by investing with sector exchange‐traded funds (ETFs). The performance of sector ETF‐based industry momentum is very different to stock momentum, and the strong performance of an unexpected group of sector ETF momentum portfolios remains robust after controlling for risk.
Publisher: Elsevier
Date: 2015
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Elsevier BV
Date: 09-2019
DOI: 10.1016/J.IJMEDINF.2019.07.002
Abstract: Assessment of the performance of Intensive Care Units (ICU) is of vital importance for an effective healthcare system. Such assessment ensures that the limited resources of the healthcare system are allocated where they are most needed. Severity scoring systems are employed for this purpose and improving these systems is a continuing area of research which has focused on the use of more complex techniques and new variables. This paper investigates whether scoring systems could be improved through use of metrics which better summarise the high frequency data collected by automated systems for patients in the ICU. 3128 admissions to the Gold Coast University Hospital ICU are used to construct three logistic regressions based on the most widely used scoring system (APACHE III) to compare performance with and without predictors leveraging available high frequency information. Performance is assessed based on model accuracy, calibration, and discrimination. High frequency information was considered for existing pulse and mean arterial pressure physiology fields and resulting models compared against a baseline logistic regression using only APACHE III physiology variables. Model discrimination and accuracy were better for models which included high frequency predictors, with calibration remaining good in all cases. The most influential high frequency summaries were the number of turning points in a patient's mean arterial pressure or pulse in the first 24 h of ICU admission. The findings indicate that scoring systems can be improved by better accounting for high frequency data.
Publisher: Informa UK Limited
Date: 27-02-2022
Publisher: Elsevier BV
Date: 2015
DOI: 10.2139/SSRN.2698743
Publisher: Springer Science and Business Media LLC
Date: 04-04-2020
Publisher: Springer Science and Business Media LLC
Date: 27-09-2022
DOI: 10.1007/S00521-022-07805-1
Abstract: This paper extends a series of deep learning models developed on US equity data to the Australian market. The model architectures are retrained, without structural modification, and tested on Australian data comparable with the original US data. Relative to the original US-based results, the retrained models are statistically less accurate at predicting next day returns. The models were also modified in the standard train/validate manner on the Australian data, and these models yielded significantly better predictive results on the holdout data. It was determined that the best-performing models were a CNN and LSTM, attaining highly significant Z-scores of 6.154 and 8.789, respectively. Due to the relative structural similarity across all models, the improvement is ascribed to regional influences within the respective training data sets. Such unique regional differences are consistent with views in the literature stating that deep learning models in computational finance that are developed and trained on a single market will always contain market-specific bias. Given this finding, future research into the development of deep learning models trained on global markets is recommended.
Publisher: Emerald
Date: 09-12-2020
Abstract: The pitching research template (PRT) is designed to help pitchers identify the core elements that form the framework of any research project. This paper aims to provide a brief commentary on an application of the PRT to pitch an environmental finance research topic with a personal reflection on the pitch exercise discussed. This paper applies the PRT developed by Faff (2015, 2019) to a research project on estimating the strength of carbon pricing signals under the European Union Emissions Trading Scheme. The PRT is found to be a valuable tool to refine broad ideas into impactful and novel research contributions. The PRT is recommended for use by all academics regardless of field and particularly PhD students to structure and communicate their research ideas. The PRT is found to be particularly well suited to pitch replication studies, as it effectively summarizes both the “idea” and proposed “twist” of a replication study. This letter is a reflection on a research teams experience with applying the PRT to pitch a replication study at the 2020 Accounting and Finance Association of Australia and New Zealand event. This event focused on replicable research and was a unique opportunity for research teams to pitch their replication research ideas.
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Wiley
Date: 12-05-2018
DOI: 10.1111/ACFI.12373
Publisher: IGI Global
Date: 2008
DOI: 10.4018/978-1-59904-941-0.CH099
Abstract: Soft computing represents that area of computing adapted from the physical sciences. Artificial intelligence (AI) techniques within this realm attempt to solve problems by applying physical laws and processes. This style of computing is particularly tolerant of imprecision and uncertainty, making the approach attractive to those researching within “noisy” realms, where the signal-to-noise ratio is low. Soft computing is normally accepted to include the three key areas of fuzzy logic, artificial neural networks, and probabilistic reasoning (which includes genetic algorithms, chaos theory, etc.).
Publisher: Elsevier BV
Date: 12-2012
Publisher: Wiley
Date: 19-02-2019
DOI: 10.1111/ACFI.12452
Publisher: Springer Science and Business Media LLC
Date: 10-2022
DOI: 10.1007/S00521-022-07792-3
Abstract: The purpose of this work is to compare predictive performance of neural networks trained using the relatively novel technique of training single hidden layer feedforward neural networks (SFNN), called Extreme Learning Machine (ELM), with commonly used backpropagation-trained recurrent neural networks (RNN) as applied to the task of financial market prediction. Evaluated on a set of large capitalisation stocks on the Australian market, specifically the components of the ASX20, ELM-trained SFNNs showed superior performance over RNNs for in idual stock price prediction. While this conclusion of efficacy holds generally, long short-term memory (LSTM) RNNs were found to outperform for a small subset of stocks. Subsequent analysis identified several areas of performance deviations which we highlight as potentially fruitful areas for further research and performance improvement.
Publisher: Springer Science and Business Media LLC
Date: 09-04-2019
Publisher: Elsevier BV
Date: 02-2022
Publisher: Georg Thieme Verlag KG
Date: 2018
Abstract: Background Various tasks within health care processes are repetitive and time-consuming, requiring personnel who could be better utilized elsewhere. The task of assigning clinical urgency categories to internal patient referrals is one such case of a time-consuming process, which may be amenable to automation through the application of text mining and natural language processing (NLP) techniques. Objective This article aims to trial and evaluate a pilot study for the first component of the task—determining reasons for referrals. Methods Text is extracted from scanned patient referrals before being processed to remove nonsensical symbols and identify key information. The processed data are compared against a list of conditions that represent possible reasons for referral. Similarity scores are used as a measure of overlap in terms used in the processed data and the condition list. Results This pilot study was successful, and results indicate that it would be valuable for future research to develop a more sophisticated classification model for determining reasons for referrals. Issues encountered in the pilot study and methods of addressing them were outlined and should be of use to researchers working on similar problems. Conclusion This pilot study successfully demonstrated that there is potential for automating the assignment of reasons for referrals and provides a foundation for further work to build on. This study also outlined a potential application of text mining and NLP to automating a manual task in hospitals to save time of human resources.
Publisher: Springer Science and Business Media LLC
Date: 27-02-2019
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Bruce Vanstone.