A portfolio construction model based on sector analysis using Dempster-Shafer evidence theory and Granger causal network: An application to National stock exchange of India

Bisht, Kiran; Kumar, Arun

doi:10.1016/j.eswa.2022.119434

Cited by 16 publications

(4 citation statements)

References 50 publications

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“…Several approaches have been proposed by researchers for accurate prediction of stock prices and robust portfolio optimization. Time series decomposition and econometric approaches like ARIMA, Granger causality, and VAR are extensively used for stock price prediction and portfolio optimization [4][5][6][7][8][9][10].…”

Section: Related Workmentioning

confidence: 99%

Portfolio Optimization: A Comparative Study

Sen,

Dasgupta

2024

Artificial Intelligence

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Portfolio optimization has been an area that has attracted considerable attention from the financial research community. Designing a profitable portfolio is a challenging task involving precise forecasting of future stock returns and risks. This chapter presents a comparative study of three portfolio design approaches, the mean-variance portfolio (MVP), hierarchical risk parity (HRP)-based portfolio, and autoencoder-based portfolio. These three approaches to portfolio design are applied to the historical prices of stocks chosen from ten thematic sectors listed on the National Stock Exchange (NSE) of India. The portfolios are designed using the stock price data from January 1, 2018, to December 31, 2021, and their performances are tested on the out-of-sample data from January 1, 2022, to December 31, 2022. Extensive results are analyzed on the performance of the portfolios. It is observed that the performance of the MVP portfolio is the best on the out-of-sample data for the risk-adjusted returns. However, the autoencoder portfolios outperformed their counterparts on annual returns.

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Section: Related Workmentioning

confidence: 99%

Portfolio Optimization: A Comparative Study

Sen,

Dasgupta

2024

Artificial Intelligence

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“…b) Cross-domains: Predictions in these models are based on relationships, correlations, or interactions between different financial markets and sources. Cross-domain disentanglement is a significant topic, and several methods have been proposed for dealing with this task, including transfer learning [29], [30], cointegration [31], and sentiments such as [32], [33]. 2) Style of learning: There are different approaches for predicting time series of prices, and in this study, we categorise them into the following based on their training style: a) End-to-end methods: These models are trained to perform an entire task from raw input data to produce the desired output, and often pick up unwanted or noisy data.…”

Section: Related Workmentioning

confidence: 99%

Cross-Domain Disentanglement: A Novel Approach to Financial Market Prediction

Abdulsahib,

Ghaderi

2024

IEEE Access

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Profit maximization and risk mitigation require good financial market predictions. Financial markets have a correlated nature, which means that there are some shared patterns between them. Therefore, learning about one market might help understand the behavior of others. End-to-end training techniques have proven successful in financial markets, but they have flaws, such as picking up noise and failing to account for complicated relationships across markets. We present a promising model for predicting financial markets using the correlation between the two markets, which draws inspiration from the recent progress in disentanglement learning. This model learns to disentangle representations of features shared between markets from specific representations, and removes features that cause interference. We utilized a dilated convolutional neural network as an encoder to extract features while using self-attention and cross-attention to capture specifics and shared patterns. Our model uses Dynamic Time Warping (DTW) to minimize the similarity between specific and shared patterns, which has been used along with the Mean Square Error (MSE) to determine the optimal balance between minimizing the similarity and prediction accuracy. We conducted our experiments using datasets that included the closing prices of Apple, Samsung, Bitcoin, Ethereum, Meta platforms, and the X platform. Spearman's rank correlation coefficient was used to evaluate the disentanglement by describing the relationship between the extracted representations. The findings confirm that our model surpasses state-of-the-art approaches in prediction error, financial risk assessment, correlation evolution, and prediction net curves, thereby giving market participants more trust in their decisions.INDEX TERMS Attention mechanisms, cross domain, disentangled representation, financial market, time series prediction.

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“…Like the Bayesian theory, it is essential to develop the frame of discernment (FOD) for evidence theory [29]. This mathematical structure is used to specify all the issues to be discriminated.…”

Section: Background Knowledge Of D-s Evidence Theorymentioning

confidence: 99%

An Enhanced Pignistic Transformation-Based Fusion Scheme With Applications in Image Segmentation

Zhang

Song

et al. 2023

IEEE Access

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The traditional Pignistic transformation is limited in the context of ''betting'', which faces information loss and is inconvenient for multi-source information fusion. To tackle this challenge, an Enhanced Pignistic transformation is proposed for the first time. New divergence and information volume measures are tailor-made for the enhanced Pignistic probability, and a novel information fusion algorithm is developed. To further prove the fusion algorithm's advantages in conflict management, it is applied in a new semi-automatic image segmentation scheme. Two uncertain decision-support techniques named adaptive belief assignment and scalable information extraction are raised, and a fuzzy heuristic refinement algorithm is conducted, fulfilling the gap between evidential decision-making and segmentation refinement. Experimental analysis shows the proposed segmentation algorithm is superior on four metrics each and can enhance the robustness of foreground segmentation, indicating the effectiveness of the proposal in solving the decision inaccuracy of evidential segmentation schemes.

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A portfolio construction model based on sector analysis using Dempster-Shafer evidence theory and Granger causal network: An application to National stock exchange of India

Cited by 16 publications

References 50 publications

Portfolio Optimization: A Comparative Study

Portfolio Optimization: A Comparative Study

Cross-Domain Disentanglement: A Novel Approach to Financial Market Prediction

An Enhanced Pignistic Transformation-Based Fusion Scheme With Applications in Image Segmentation

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