A Black Swan event-based hybrid model for Indian stock markets’ trends prediction

Unit-Specific Event-Based and Slot-Based Hybrid Model Framework with Hierarchical Structure for Short-Term Scheduling

Mathematical Problems in Engineering ◽

10.1155/2015/906280 ◽

2015 ◽

Vol 2015 ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Yue Wang ◽

Hongye Su ◽

Hanshan Shao ◽

Lei Xie

Keyword(s):

Hybrid Model ◽

Hierarchical Structure ◽

Continuous Time ◽

Production Scheduling ◽

Lower Layer ◽

Short Term ◽

Model Framework ◽

Time Model ◽

Specific Event ◽

Event Based

Unit-specific event-based continuous-time model has inaccurate calculation problems in involving resource constraints, due to the heterogeneous locations of the event points for different units. In order to address this limitation, a continuous-time unit-specific event-based and slot-based hybrid model framework with hierarchical structure is proposed in this work. A unit-specific event-based model without utility constraints is formulated in upper layer, and a slot-based model is introduced in lower layer. In the hierarchical structure, the two layers jointly address the short-term production scheduling problem of batch plants under utility consideration. The key features of this work include the following: (a) eliminating overstrict constraints on utility resources, (b) solving multiple counting problems, and (c) considering duration time of event points in calculating utility utilization level. The effectiveness and advantages of proposed model are illustrated through two benchmark examples from the literatures.

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An Analysis Method of Black Swan Event based on Impact-spread Tree

Journal of Risk Analysis and Crisis Response ◽

10.2991/jracr.k.200708.001 ◽

2020 ◽

Author(s):

Xinying 馨营 Liu 刘 ◽

Hang 航 Li 李 ◽

Xiaobing 小兵 Hu 胡

Keyword(s):

Black Swan ◽

Analysis Method ◽

Event Based

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A Hybrid Model Integrating Singular Spectrum Analysis and Backpropagation Neural Network for Stock Price Forecasting

Revue d intelligence artificielle ◽

10.18280/ria.350606 ◽

2021 ◽

Vol 35 (6) ◽

pp. 483-488

Author(s):

Asmaa Y. Fathi ◽

Ihab A. El-Khodary ◽

Muhammad Saafan

Keyword(s):

Neural Network ◽

Hybrid Model ◽

Spectrum Analysis ◽

Stock Prices ◽

Stock Markets ◽

Stock Price ◽

Singular Spectrum Analysis ◽

Predictive Performance ◽

Backpropagation Neural Network ◽

Singular Spectrum

The primary purpose of trading in stock markets is to profit from buying and selling listed stocks. However, numerous factors can influence the stock prices, such as the company's present financial situation, news, rumor, macroeconomics, psychological, economic, political, and geopolitical factors. Consequently, tremendous challenges already exist in predicting noisy stock prices. This paper proposes a hybrid model integrating the singular spectrum analysis (SSA) and the backpropagation neural network (BPNN) to forecast daily closing prices in stock markets. The model first decomposes the stock prices into several components using the SSA. Then, the extracted components are utilized for training BPNNs to forecast future prices. Compared with the BPNN, the hybrid SSA-BPNN model demonstrates a better predictive performance, indicating the SSA's ability to extract hidden information and reduce the noise effect of the original time series.

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