Resource allocation neural network in portfolio selection

Ko, Po-Chang; Lin, Ping-Chen

doi:10.1016/j.eswa.2007.07.031

Cited by 46 publications

(14 citation statements)

References 18 publications

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“…As regards recommender systems in the financial domain, in [ [25,26] and multi-criteria decision making [27,28]. Moreover, Differently from most of these approaches, our framework is more oriented to financial advisors since our goal is not to automatically build a portfolio, but rather to help advisors in filtering the proposals on the basis of previous …”

Section: Accepted Manuscriptmentioning

confidence: 99%

Personalized finance advisory through case-based recommender systems and diversification strategies

Musto

Semeraro

Lops

et al. 2015

Decision Support Systems

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Section: Accepted Manuscriptmentioning

confidence: 99%

Personalized finance advisory through case-based recommender systems and diversification strategies

Musto

Semeraro

Lops

et al. 2015

Decision Support Systems

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“…Thus, a ''buy and hold'' trading strategy was adopted, as in Edirisinghe and Zhang (2007) and Ko and Lin (2008). In other words, the stocks in the final portfolio were held unchanged in each investment interval (quarter) during the study period.…”

Section: Modm Model For Capital Allocationmentioning

confidence: 99%

An integrated DEA-MODM methodology for portfolio optimization

Huang

Chiou

et al. 2014

Oper Res Int J

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Portfolio optimization generally involves maximizing the expected return of a portfolio while minimizing its risk. However, when considering these 2 conflicting objectives in decision-making contexts, an efficient approach is required to evaluate the solution in the set of efficient frontiers/Pareto optimal solutions. Selecting and managing an investment portfolio are critical for investors. This study proposes an integrated method for portfolio optimization that involves decisions on stock screening, stock selection, and capital allocation. The initial step involves examining the financial data of investment targets. The portfolio is then selected according to the firms' relative efficiency by using a data envelopment analysis (DEA) model. To determine the allocation of capital to each stock in the constructed portfolio, a multi-objective decision-making (MODM) model is developed. The integrated DEA-MODM approach is applied to Taiwan's financial market and compared with several benchmarking mutual funds for empirical testing. This comparative study shows that the proposed approach outperforms the Taiwan Excel 50 ETF and 3 other benchmarking mutual funds in numerous quarters of the testing period regarding return rates and Sharpe ratios. Therefore, the proposed integrated DEA-MODM approach can be useful to both investors and researchers.

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“…ANNs have been used to solve various problems in many domains. Examples of financial applications of ANNs include predicting corporate bankruptcy (Anandarajan et al , ; Nasir et al , ; Cho et al , ), fraud detection (Lin et al , ; Huang et al , ), forecasting foreign exchange rates (Franses and Van Griensven, ; Yao and Tan, ), derivatives pricing and hedging (Bennell and Sutcliffe, ; Tseng et al , ), credit scoring (Hu and Ansell, ), forecasting stock markets, including index movement (Hassan et al , ; Dai et al , ), stock return prediction (Desai and Bharati, ), stock price forecast (Kim and Han, ; Lu ), portfolio selection and optimization (Ko and Lin, ; Yu et al , ), to mention a few.…”

Section: Artificial Neural Network Modelling and Literature Reviewmentioning

confidence: 99%

Predicting Next Trading Day Closing Price of Qatar Exchange Index Using Technical Indicators and Artificial Neural Networks

Fadlalla

Amani

2014

Intell. Sys. Acc. Fin. Mgmt.

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Accurate prediction of stock market price is of great importance to many stakeholders. Artificial neural networks (ANNs) have shown robust capability in predicting stock price return, future stock price and the direction of stock market movement. The major aim of this study is to predict the next trading day closing price of the Qatar Exchange (QE) Index using historical data from 3 January 2010 to 31 December 2012. A multilayer perceptron ANN architecture was used as a prediction model with 10 market technical indicators as input variables. The experimental results indicate that ANNs are an effective modelling technique for predicting the QE Index with high accuracy, outperforming the well-established autoregressive integrated moving average models. To the best of our knowledge, this is the first attempt to use ANNs to predict the QE Index, and its performance results are comparable to, and sometimes better than, many stock market predictions reported in the literature. The ANN model also revealed that the weighted and simple moving averages are the most important technical indicators in predicting the QE Index, and the accumulation/distribution oscillator is the least important such indicator. The analysis results also indicated that the ANNs are resilient to stock market volatility.

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Resource allocation neural network in portfolio selection

Cited by 46 publications

References 18 publications

Personalized finance advisory through case-based recommender systems and diversification strategies

Personalized finance advisory through case-based recommender systems and diversification strategies

An integrated DEA-MODM methodology for portfolio optimization

Predicting Next Trading Day Closing Price of Qatar Exchange Index Using Technical Indicators and Artificial Neural Networks

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