Expert System Portfolios of Australian and UK Securitised Property Investments

Ellis, Craig; Wilson, Patrick

doi:10.1080/14445921.2006.11104201

Cited by 4 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other hand, rule-based ES is a computer program that is capable to use information in a knowledge base, using a set of inference procedures, to solve problems that are difficult enough to require significant human expertise for their solution [124]. The set of inference procedures are provided by a human expert in the particular area of interest, while the knowledge base is an accumulation of relevant data, facts, judgments and outcomes [125]. ES consists of three main components including the knowledge base, the inference engine and the user interface.…”

Section: Expert System Applications In Financial Domainmentioning

confidence: 99%

“…This system helps investment decision maker and project analysts to choose a project for their investing portfolio. In another study, [125] designed an ES for portfolios of Australian and UK securitized property investments. They concluded that their proposed expert system outperforms general property market and randomly selected portfolios in select cases, although the outperformance was not statistically significant.…”

Section: Portfolio Managementmentioning

confidence: 99%

See 1 more Smart Citation

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee

2010

Neural Comput & Applic

442

192

View full text Add to dashboard Cite

Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore, the need to solve highly nonlinear, time variant problems has been growing rapidly. These problems along with other problems of traditional models caused growing interest in artificial intelligent techniques. In this paper, comparative research review of three famous artificial intelligence techniques, i.e., artificial neural networks, expert systems and hybrid intelligence systems, in financial market has been done. A financial market also has been categorized on three domains: credit evaluation, portfolio management and financial prediction and planning. For each technique, most famous and especially recent researches have been discussed in comparative aspect. Results show that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns. However, this outperformance is not absolute.

show abstract

Section: Expert System Applications In Financial Domainmentioning

confidence: 99%

Section: Portfolio Managementmentioning

confidence: 99%

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee

2010

Neural Comput & Applic

442

192

View full text Add to dashboard Cite

show abstract

“…artificial intelligent algorithms and rough sets). Recently, artificial intel- ligent methods (specially ANN) are the most popular tool used for credit scoring and has been reported that its accuracy is superior to that of traditional statistical methods in dealing with credit scoring problems, especially in regards to nonlinear patterns [29,31,35,60]. In this paper we used two famous artificial intelligence algorithms i.e.…”

Section: Resultsmentioning

confidence: 99%

“…In other hand, rulebased expert system is a computer program that is capable of using information contained in a knowledge base, along with a set of inference procedures, to solve problems that are difficult enough to require significant human expertise for their solution [34]. The set of inference procedures are provided by a human expert in the particular area of interest, while the knowledge base is an accumulation of relevant data, facts, judgments and outcomes [35]. As illustrated in Fig.…”

Section: Expert Systemmentioning

confidence: 99%

Hybrid credit ranking intelligent system using expert system and artificial neural networks

et al. 2009

View full text Add to dashboard Cite

The main goal of all commercial banks is to collect the savings of legal and real persons and allocate them as credit to industrial, services and production companies. Non repayment of such credits cause many problems to the banks such as incapability to repay the central bank's loans, increasing the amount of credit allocations comparing to credit repayment and incapability to allocate more credits to customers. The importance of credit allocation in banking industry and it's important role in economic growth and employment creation leads the development of many models to evaluate the credit risk of applicants. But many of these models are classic and are incapable to do credit evaluation completely and efficiently. Therefore the demand to use artificial intelligence in this field has grown up. In this paper after providing appropriate credit ranking model and collecting expert's knowledge, we design a hybrid intelligent system for credit ranking using reasoning-transformational models. Expert system as symbolic module and artificial neural network as non-symbolic module are components of this hybrid system. Such models provide the unique features of each components, the reasoning and explanation of expert system and the generalization and adaptability of artificial neural networks. The results of this system demonstrate hybrid intelligence system is more accurate and powerful in credit ranking comparing to expert systems and traditional banking models.

show abstract

“…In that research, Ellis and Wilson find neural network constructed domestic real estate portfolios outperformed a general securitized property market benchmark captured by both the ASX 300 property market index and a property market index constructed by Datastream International. While Ellis and Wilson (2006) use a rule based expert system to examine international diversification from an Australian investor's perspective, the present article appears to be the first to use a neural network model to examine the benefits of international diversification of real estate assets.…”

Section: Introductionmentioning

confidence: 99%

Real Estate ‘Value’ Stocks and International Diversification

Ellis

Wilson

Zurbruegg

2007

Journal of Property Research

Self Cite

View full text Add to dashboard Cite

In recent years there has been an increased interest in the extent to which managers can improve their property portfolio position through international diversification. Much of this interest has centred on the use of various statistical/econometric tests of time-varying correlations and longrun equilibrium positions using whole of country property indices. In this paper, a short-run tactical asset allocation approach to securitized property is adopted. Using neural network methodology, a neural network model that 'learns' well-established rules of portfolio investment is built. The model uses a set of individual property companies across three of the most highly securitized property markets in the world viz. the US, the UK and Australia. The standpoint of a UK investor is adopted and the model is asked to compare portfolios constructed purely from domestic assets with portfolios constructed from internationally held assets allowing for foreign exchange adjustments. When the foreign exchange risk is actively managed, the outcomes from the analysis suggest that the gains from hedging are conditional on both the return to the unhedged position and the volatility of the underlying currency being hedged.

show abstract

Expert System Portfolios of Australian and UK Securitised Property Investments

Abstract: ABSTRACT

Cited by 4 publications

References 8 publications

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Hybrid credit ranking intelligent system using expert system and artificial neural networks

Real Estate ‘Value’ Stocks and International Diversification

Contact Info

Product

Resources

About