A modular Takagi-Sugeno-Kang (TSK) system based on a modified hybrid soft clustering for stock selection

Mousavi, Somayeh; Esfahanipour, Akbar; Zarandi, M. H. Fazel

doi:10.24200/sci.2019.52323.2661

Cited by 5 publications

(3 citation statements)

References 87 publications

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“…Considering the above needs, many experts and scholars have conducted in-depth analysis and discussion and finally proposed a simpler weighting method to deal with it, that is, matching a weight coefficient to all objectives separately. In this case, the problem is then transformed into a one-dimensional problem, so that the optimal solution can be obtained with the help of many single-objective optimization algorithms [15]. In addition, using this method does not need to focus on the population itself but only on the local population, which makes the computation process simpler and more efficient.…”

Section: Intelligent Scheduling Algorithmmentioning

confidence: 99%

Analysis of an Optimal Model for Liquidity Management of Financial Assets Using an Intelligent Scheduling Approach

Xia

Peng

2021

Journal of Mathematics

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This paper adopts the intelligent scheduling method to conduct an in-depth study and analysis on the optimization of financial asset liquidity management model, elaborates and analyzes the liquidity risk management theory of commercial banks, and reviews the progress of liquidity risk management research in domestic and foreign academia as the theoretical basis of this paper. After that, we analyze the liquidity risk management of Anhui Tianchang Rural Commercial Bank from both qualitative and quantitative levels and further review and analyze the problems and causes. Finally, the full research is summarized and reviewed, theoretical and practical insights are discussed and analyzed, and future liquidity risk management research priorities and directions are elaborated. Based on the analysis results, the problems of the bank in liquidity risk management are described one by one, and further deep-seated cause discovery is carried out to summarize the problems of liquidity risk management which exist in the bank’s operation process due to the lack of liquidity risk management, unbalanced asset, and liability allocation, as well as weak emergency management capability, insufficient day-to-day liquidity monitoring, and lack of professional talents. For the problems and causes of the study, effective suggestions on how to strengthen the bank’s liquidity risk management in multiple aspects are proposed. It is hoped that, by improving the bank’s liquidity risk management and reducing the chance of liquidity risk occurrence, the bank’s sustainable development can be enhanced, and it is also hoped that it can provide some reference for the liquidity risk management of similar rural small- and medium-sized financial institutions.

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Section: Intelligent Scheduling Algorithmmentioning

confidence: 99%

Analysis of an Optimal Model for Liquidity Management of Financial Assets Using an Intelligent Scheduling Approach

Xia

Peng

2021

Journal of Mathematics

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“…Takagi-Sugeno-Kang Fuzzy Inference System (TSK-FIS) [19] models the qualitative aspect of human knowledge and reasoning processes without precise quantitative analyses using fuzzy rules. Clustering in TSK-FIS applications have been reported in [19]- [22], where clusters are used to determine the number of rules and TSK-FIS predicts the corresponding outputs using heterogenous regression functions.…”

Section: Introductionmentioning

confidence: 99%

On-Line learning-based Fuzzy Clustering Modelling in Real-Time Object Tracking

2022

BJoST

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Real-time object tracking has been one of the challenging issues in autonomous navigated robotic design. Most of the solutions proposed the off-line calculation or powerful computation unit on-board, where collection of data samples is provided in prior. While a complete data collection is hard to achieve practically, this paper proposed an on-line learning-based fuzzy adaptive resonance theory (Fuzzy ART) with Takagi-Sugeno-Kang Fuzzy model aimed to cluster and predict object positions from live-stream video frames. In this study, an object is extracted from a video frame, and object' centroid position is fed for fast-learning. Fast-learning mode of Fuzzy ART is used for real-time clustering the positions. While the position obtained are considered fuzzy, the clusters formed unique patterns at each cluster and represent them as fuzzy sets. The fuzzy sets are used to predict the object position using the Takagi-Sugeno-Kang Fuzzy Inference System (TSK-FIS) model. The proposed algorithm contributes to an on-line and unsupervised fuzzy modelling for object tracking to predict the object's position in real-time. In order to validate the practicality of the proposed approach, two experiments are conducted, i.e., using simulated data samples, and a case study using real-time hand movement tracking. A bounding box for region of interest on a frame is moved according to the predicted positions, while the robustness of the tracking is measured through the box's movements. The proposed model is self-improving over time, which has great advantage for selfcorrection in autonomous applications.

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“…Financial risk is the potential to face a financial loss and the uncertainty inherent in developing a capital [3]. Investors need to look at financial risk management tools to control this risk and consider different risk-return scenarios [4]. Financial risk management consists of identifying and measuring financial risk, analyzing and evaluating it, formulating financial risk control strategies, responding and executing process, and monitoring and controlling.…”

Section: Introductionmentioning

confidence: 99%

A Quantitative Measure for Financial Resilience of Firms: Evidence from Tehran Stock Exchange

2021

Self Cite

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Recent financial crises have strained the performance of different firms, and it has challenged investors to invest in these firms' stocks. Measuring firms' resilience from a financial standpoint in terms of crises is an important indicator for investors. Logically, investing in firms with higher historical financial resilience is more attractive for investors. In the literature, resilience is defined as anticipating, preparing, responding, and adapting to incremental change and sudden disruptions to survive and prosper. In this paper, the concept of financial resilience has been studied from various dimensions, and its quantification approaches are examined. The models developed in this paper are for calculating financial resilience in terms of key indicators, Value at Risk, and Conditional Value at Risk. Then, by comparing each of these methods, it has been tried to verify the methods by applying quantitative data of four bankrupt and four non-bankrupt firms listed on the Tehran stock exchange in recent years. The results show the proper performance of the proposed measure in expressing the concept of financial resilience in critical conditions.

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A modular Takagi-Sugeno-Kang (TSK) system based on a modified hybrid soft clustering for stock selection

Cited by 5 publications

References 87 publications

Analysis of an Optimal Model for Liquidity Management of Financial Assets Using an Intelligent Scheduling Approach

Analysis of an Optimal Model for Liquidity Management of Financial Assets Using an Intelligent Scheduling Approach

On-Line learning-based Fuzzy Clustering Modelling in Real-Time Object Tracking

A Quantitative Measure for Financial Resilience of Firms: Evidence from Tehran Stock Exchange

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