Estimating stock closing indices using a GA-weighted condensed polynomial neural network

Nayak, Sarat Chandra; Misra, Bijan Bihari

doi:10.1186/s40854-018-0104-2

Cited by 39 publications

(13 citation statements)

References 38 publications

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“…Mohanty et al (2005) used FANP to select R&D projects, applying fuzzy logic to address the vagueness of preferences. In summary, while real-world economic and financial problems have been widely investigated using MCDM tools (Kou et al 2014;Zhang et al 2019), there are many other approaches for predicting the behavior of stock markets (Zhong and Enke 2019;Nayak and Misra 2018;Kaucic et al 2019). Table 2 shows a summary of prior research on portfolio selection.…”

Section: Related Workmentioning

confidence: 99%

Portfolio selection: a fuzzy-ANP approach

Galankashi¹,

Rafiei²,

Ghezelbash³

2020

Financ Innov

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This study developed specific criteria and a fuzzy analytic network process (FANP) to assess and select portfolios on the Tehran Stock Exchange (TSE). Although the portfolio selection problem has been widely investigated, most studies have focused on income and risk as the main decision-making criteria. However, there are many other important criteria that have been neglected. To fill this gap, first, a literature review was conducted to determine the main criteria for portfolio selection, and a Likert-type questionnaire was then used to finalize a list of criteria. Second, the finalized criteria were applied in an FANP to rank 10 different TSE portfolios. The results indicated that profitability, growth, market, and risk are the most important criteria for portfolio selection. Additionally, portfolios 6,

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Section: Related Workmentioning

confidence: 99%

Portfolio selection: a fuzzy-ANP approach

Galankashi¹,

Rafiei²,

Ghezelbash³

2020

Financ Innov

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“…The cost matrix for the research dataset was developed before credit scoring. The cost matrix can describe the default risk in the lending process (Nayak & Misra, 2018). If credit agencies divide non-defaulters into possibly loan defaulters, it is possible that they could lose loan interest; however, if they divide possible loan defaulters into non-defaulters, they could lose both loan interest and principal, which is usually measured as the expected loss (EL) and is calculated as shown in the following formula (Altman & Sabato, 2005;Thomas, 2010):…”

Section: Expected Lossmentioning

confidence: 99%

A Cost-Sensitive Logistic Regression Credit Scoring Model Based on Multi-Objective Optimization Approach

Shen

Wang

Shen

2019

Technological and Economic Development of Economy

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Credit scoring is an important process for peer-to-peer (P2P) lending companies as it determines whether loan applicants are likely to default. The aim of most credit scoring models is to minimize the classification error rate, which implies that all classification errors bear the same cost; however, in reality, there is a significant cost-sensitive problem in credit scoring methods. Therefore, in this paper, a new cost-sensitive logistic regression credit scoring model based on a multi-objective optimization approach is proposed that has two objectives in the cost-sensitive logistic regression process. The cost-sensitive logistic regression parameters are solved using a multiple objective particle swarm optimization (MOPSO) algorithm. In the empirical analysis, the proposed model was applied to the credit scoring of a Chinese famous P2P company, from which it was found that compared with other common credit scoring models, the proposed model was able to effectively reduce type II error rates and total classification error costs, and improve the AUC, the F1 values (reconciliation average of Recall and Precision), and the G-means. The proposed model was compared with other multi-objective optimization algorithms to further demonstrate that MOPSO is the best approach for cost-sensitive logistic regression credit scoring models.

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“…Authors applied GACPNN for five different stock indexes, namely, BSE, DJIA, NASDAQ, FTSE, and TAIEX. The model was validated using the Deibold-Mariano test and found to produce more accurate results [34].…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Statistical and Supervised Learning Techniques in Stock Data Mining

Sharma

Singh

2018

Data

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Nowadays, overwhelming stock data is available, which areonly of use if it is properly examined and mined. In this paper, the last twelve years of ICICI Bank’s stock data have been extensively examined using statistical and supervised learning techniques. This study may be of great interest for those who wish to mine or study the stock data of banks or any financial organization. Different statistical measures have been computed to explore the nature, range, distribution, and deviation of data. The different descriptive statistical measures assist in finding different valuable metrics such as mean, variance, skewness, kurtosis, p-value, a-squared, and 95% confidence mean interval level of ICICI Bank’s stock data. Moreover, daily percentage changes occurring over the last 12 years have also been recorded and examined. Additionally, the intraday stock status has been mined using ten different classifiers. The performance of different classifiers has been evaluated on the basis of various parameters such as accuracy, misclassification rate, precision, recall, specificity, and sensitivity. Based upon different parameters, the predictive results obtained using logistic regression are more acceptable than the outcomes of other classifiers, whereas naïve Bayes, C4.5, random forest, linear discriminant, and cubic support vector machine (SVM) merely act as a random guessing machine. The outstanding performance of logistic regression has been validated using TOPSIS (technique for order preference by similarity to ideal solution) and WSA (weighted sum approach).

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Estimating stock closing indices using a GA-weighted condensed polynomial neural network

Cited by 39 publications

References 38 publications

Portfolio selection: a fuzzy-ANP approach

Portfolio selection: a fuzzy-ANP approach

A Cost-Sensitive Logistic Regression Credit Scoring Model Based on Multi-Objective Optimization Approach

Performance Analysis of Statistical and Supervised Learning Techniques in Stock Data Mining

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