About: Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach

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About: Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach
Creator	Quek, C Tung, W
Source	Elsevier; Medline; PMC
abstract	Abstract Financial volatility refers to the intensity of the fluctuations in the expected return on an investment or the pricing of a financial asset due to market uncertainties. Hence, volatility modeling and forecasting is imperative to financial market investors, as such projections allow the investors to adjust their trading strategies in anticipation of the impending financial market movements. Following this, financial volatility trading is the capitalization of the uncertainties of the financial markets to realize investment profits in times of rising, falling and side-way market conditions. In this paper, an intelligent straddle trading system (framework) that consists of a volatility projection module (VPM) and a trade decision module (TDM) is proposed for financial volatility trading via the buying and selling of option straddles to help a human trader capitalizes on the underlying uncertainties of the Hong Kong stock market. Three different measures, namely: (1) the historical volatility (HV), (2) implied volatility (IV) and (3) model-based volatility (MV) of the Hang Seng Index (HSI) are employed to quantify the implicit volatility of the Hong Kong stock market. The TDM of the proposed straddle trading system combines the respective volatility measures with the well-established moving-averages convergence/divergence (MACD) principle to recommend trading actions to a human trader dealing in HSI straddles. However, the inherent limitation of the MACD trading rule is that it generates time-delayed trading signals due to the use of moving averages, which are essentially lagging trend indicators. This drawback is intuitively addressed in the proposed straddle trading system by applying the VPM to compute future projections of the volatility measures of the HSI prior to the activation of the TDM. The VPM is realized by a self-organising neural-fuzzy semantic network named the evolving fuzzy semantic memory (eFSM) model. As compared to existing statistical and computational intelligence based modeling techniques currently employed for financial volatility modeling and forecasting, eFSM possesses several desirable attributes such as: (1) an evolvable knowledge base to continuously address the non-stationary characteristics of the Hong Kong stock market; (2) highly formalized human-like information computations; and (3) a transparent structure that can be interpreted via a set of linguistic IF–THEN semantic fuzzy rules. These qualities provide added credence to the computed HSI volatility projections. The volatility modeling and forecasting performances of the eFSM, when benchmarked to several established modeling techniques, as well as the observed trading returns of the proposed straddle trading system, are encouraging.
has issue date	2011-05-31(xsd:dateTime)
bibo:doi	10.1016/j.eswa.2010.07.116
bibo:pmid	32288336
has license	els-covid
sha1sum (hex)	822509ad5edb0fce800bb3fec17d27b0c8accf38
schema:url	https://doi.org/10.1016/j.eswa.2010.07.116
resource representing a document's title	Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach
has PubMed Central identifier	PMC7126939
has PubMed identifier	32288336
schema:publication	Expert Systems with Applications
resource representing a document's body	covid:822509ad5edb0fce800bb3fec17d27b0c8accf38#body_text
is schema:about of	named entity 'SEMANTIC NETWORK' named entity 'ORGANISING' named entity 'SYSTEM' named entity 'TREND' named entity 'OBSERVED' named entity 'STATIONARY' named entity 'MACD' named entity 'PRINCIPLE' named entity 'HONG KONG' named entity 'ORGANISING' named entity 'TIMES' named entity 'INTENSITY' named entity 'FOLLOWING' named entity 'PERFORMANCES' named entity 'MODELING' named entity 'SEMANTIC' named entity 'EMPLOYED' named entity 'DECISION' named entity 'ACTIONS' named entity 'MODULE' named entity 'FALLING' named entity 'UNDERLYING' named entity 'UNCERTAINTIES' named entity 'BASED' named entity 'RULE' named entity 'TRANSPARENT' named entity '282' named entity 'KNOWLEDGE BASE' named entity 'POSSESSES' named entity 'NAMED' named entity 'INTELLIGENCE' named entity 'ATTRIBUTES' named entity 'PROJECTIONS' named entity 'LIMITATION' named entity 'REFERS TO' named entity 'RECOMMEND' named entity 'HUMAN' named entity 'ALLOW' named entity 'SEMANTIC MEMORY' named entity 'RULES' named entity 'QUANTIFY' named entity 'MEASURES' named entity 'FINANCIAL' named entity 'FINANCIAL MARKETS' named entity 'THESE' named entity 'THEIR' named entity 'HANG' named entity 'TRADE' named entity 'COMPUTATIONAL' named entity 'PROVIDE' named entity 'BUYING' named entity 'INTERPRETED' named entity 'INDICATORS' named entity 'TO ADJUST' named entity 'INFORMATION' named entity 'STRATEGIES' named entity 'INDEX' named entity 'EVOLVING' named entity 'EXPECTED' named entity 'ANTICIPATION' named entity 'CONDITIONS' named entity 'INTELLIGENT' named entity 'HISTORICAL' named entity 'ESTABLISHED' named entity 'STRUCTURE' named entity 'ASSET' named entity 'FLUCTUATIONS' named entity 'OPTION' named entity 'CONVERGENCE' named entity 'WAY' named entity 'IMPENDING' named entity 'DIFFERENT' named entity 'ADDED' named entity 'CURRENTLY EMPLOYED' named entity 'TDM' named entity 'FUZZY' named entity 'NEURAL' named entity 'USING' named entity 'BASED' named entity 'OPTION'

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