Fuzzy Neural Systems for Stock Selection

Wong, Fu-Lung; Wang, P. Z.; Goh, T.H.; Quek, Boon Kiat

doi:10.2469/faj.v48.n1.47

Cited by 64 publications

(19 citation statements)

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“…dir. Steiner and Wittkemper (1997) Six Technical Analysis variables Tabrizi et al (2000) Gold coin average, USD exchange rate, volume, moving average of TSE for one and two Weeks Tan et al (1995) Closing price data Tang et al (2002) Moving average of weekly stock data Thammano (1999) Closing rates of the stock at four different lags Thawornwong and Enke (2004) Thirty one financial and economic variables Tsaih et al (1998) Ten Technical Analysis factors Walczak (1999) Variables depending on the closing value Wang (2002) Stock price values collected periodically through the day Wang and Leu (1996) Daily stock value Wikowska (1995) BRE, KAB, volume, USD exchange rate Wittkemper and Steiner (1996) Daily stock data and yearly financial data Wong et al (1992) Eleven Technical Analysis factors Wu et al (2001) Previous S& P500 values and six economical indices Yiwen et al (2000) Six economical variables Yumlu et al (2004) ISE index XU100 Yumlu et al (2005) ISE index previous data Zhang et al (2002) Opening, lowest, highest and closing index price Zhang et al (2004) Closing index price Zhongxing and Liting (1993) Raw daily data Zorin and Borisov (2002) Dow Jones Riga stock exchange Barnes et al (2000) No…”

Section: Input Variablesmentioning

confidence: 99%

Surveying stock market forecasting techniques – Part II: Soft computing methods

Atsalakis

Valavanis

2009

Expert Systems with Applications

699

322

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Section: Input Variablesmentioning

confidence: 99%

Surveying stock market forecasting techniques – Part II: Soft computing methods

Atsalakis

Valavanis

2009

Expert Systems with Applications

699

322

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“…2. Artificial neural networks have also been used in a number of other finance-related valuation situations (Hawley et al, 1990;Kryzanowski et al,1993;Trippi and Turban, 1993;Wong et al, 1992). 3.…”

mentioning

confidence: 99%

High‐tech valuation: should artificial neural networks bypass the human valuer?

Lenk

Worzala

Silva

1997

Journal of Property Valuation and Investment

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.

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“…Other papers consider more complex computing techniques like genetic algorithms (Bermúdez et al 2012), fuzzy neural systems (Wong et al 1992), or hybrid algorithms integrating different such techniques ). Most of these works are based on fuzzy variables, while fuzzy random variables are used by Hao and Liu (2009), Li and Xu (2013) etc.…”

Section: A Brief Literature Overviewmentioning

confidence: 99%

Optimal investment with a constraint on ruin for a fuzzy discrete-time insurance risk model

Vernic

2015

Fuzzy Optim Decis Making

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In this paper, we consider a mean-variance portfolio optimization problem for a fuzzy discrete-time insurance risk model. The model consists of independent, identically distributed net losses considered within successive time periods, and incorporates investment incomes from a two-asset portfolio. More precisely, in the beginning of each period, the surplus is invested in both a risk-free bond with fixed interest, and a risky stock with fuzzy return rate. Our purpose is to determine the proportion invested in the stock that maximizes the insurer's expected wealth, while reducing his risks. Therefore, for this fuzzy model, we formulate mean-variance optimization problems that also include constraints on ruin, and we present a method for determining the resulting optimal proportion to be invested in the risky stock. This method is illustrated in a numerical study in which the fuzzy return rate is considered to be an adaptive fuzzy number that generalizes the well-known trapezoidal fuzzy number.

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Fuzzy Neural Systems for Stock Selection

Cited by 64 publications

References 0 publications

Surveying stock market forecasting techniques – Part II: Soft computing methods

Surveying stock market forecasting techniques – Part II: Soft computing methods

High‐tech valuation: should artificial neural networks bypass the human valuer?

Optimal investment with a constraint on ruin for a fuzzy discrete-time insurance risk model

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