Name
Abstract | ||
|---|---|---|
Standard financial techniques neglect extreme situations and regards large market shifts as too unlikely to matter. This approach may account for what occurs most of the time in the market, but the picture it presents does not reflect the reality, as major events happen in the residual time and investors are ‘surprised’ by ‘unexpected’ market movements. An alternative fuzzy approach permits fluctuations well beyond the probability type of uncertainty and allows one to make fewer assumptions about the data distribution and market behaviour. We suggest a fuzzy criterion, and subsequently derive a measure of the risk associated with each investment opportunity and an estimate of the projects’ robustness towards market uncertainty. The procedure is applied to 35 UK companies traded on the London Stock Exchange. Finally, neural networks’ capabilities of approximating the fuzzy appraisal function are investigated, as an initial step towards building a soft investment classifier based on the developed alternative ranking technique. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/S0165-0114(03)00166-0 | Fuzzy Sets and Systems |
Keywords | Field | DocType |
Finance,Risk analysis,Fuzzy intervals | Financial risk,Econometrics,Stock exchange,Fuzzy set,Probability distribution,Artificial intelligence,Fuzzy control system,Valuation (finance),Ranking,Fuzzy logic,Operations research,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
142 | 3 | 0165-0114 |
Citations | PageRank | References |
9 | 0.61 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antoaneta Serguieva | 1 | 23 | 5.05 |
| John Hunter | 2 | 9 | 0.61 |