PRIYA SINGH

@vit.ac.in

Assistant Professor and Department of Mathematics
Vellore Institute of Technology

PRIYA SINGH


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https://researchid.co/priya.s.parihar94

RESEARCH, TEACHING, or OTHER INTERESTS

Applied Mathematics, Finance, Multidisciplinary
9

Scopus Publications

118

Scholar Citations

4

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • Predicting Market Volatility: An Ensemble Approach for Enhanced India VIX Prediction
    Priya Singh, Akanksha Sharma, Manoj Jha, Chandan Kumar Verma
    Operations Research Forum, 2026
  • Improving Gold Price Prediction Accuracy Through Variational Mode Decomposition and Bayesian-Tuned Hybrid BiLSTM-BiGRU Model
    Priya Singh, Akanksha Sharma
    IEEE Access, 2026
    Due to the highly dynamic and unpredictable nature of the global economy, it is very difficult to make successful predictions of gold prices. Traditional approaches for forecasting price movements will frequently fail due to the complexity of the relationships among the variables driving the gold price, such as economic indicators, geopolitical events, and market sentiment. Therefore, there is a need for modern, data-driven techniques for developing accurate models that predict gold prices. In this study, we present a new hybrid model and methodology that includes multiple levels of processing to overcome many of these obstacles. The selection of input features is guided by a Granger causality analysis used as a preliminary statistical screening step to identify variables with potential predictive relationships with gold prices. Next, we utilize Variational Mode Decomposition (VMD) to denoise the temporal data. The final phase of the methodology, the hybrid Bidirectional Gated Recurrent Unit (BiGRU) and Bidirectional Long Short Term Memory (BiLSTM), was developed using a Bayesian Optimization approach to develop a model that utilizes both BiLSTMs and BiGRUs, allowing for the combined advantages of LSTMs and GRUs. Based on previously collected and recorded prices for gold, this new predictive model provides a statistically superior forecast compared to both the traditional industry benchmarks and several current internet websites that provide gold price predictions. The results of 5-fold cross-validation and an ablation study confirmed the robustness of this model. Further statistical analysis using analysis of variance (ANOVA) and the Holm-Bonferroni post-hoc procedure provides evidence that the results of this model are statistically significantly better (p < 0.05) than those of any of the benchmark models used. Additionally, the use of an Integrated Deep Learning Architecture (IDLA), advanced signal processing methodologies, and rigorous feature validation techniques has resulted in a more accurate and reliable performance in the prediction of gold price movements.
  • Enhancing Option Pricing Accuracy in the Indian Market: A CNN-BiLSTM Approach
    Akanksha Sharma, Chandan Kumar Verma, Priya Singh
    Computational Economics, 2025
  • Leveraging Deep Learning Ensembles for Stock Index Forecasting: A Nifty 50 approach
    Priya Singh, Manoj Jha
    2025 IEEE International Students Conference on Electrical Electronics and Computer Science Sceecs 2025, 2025
    A plethora of research is carried out in the field of investment decision-making but is still a non-conquered territory owing to the data complexity and uncertainty involved in the financial market. In this research, two ensemble models are developed and assessed unifying two deep learning models as base learners to form Ensemble 1: CNN+LSTM and Ensemble 2: TCN+LSTM. A stacked ensemble approach is considered using Linear regressor as meta learner with input from the base learners’ output for final prediction. The study utilizes Nifty 50 index historical data, for the empirical study. Based on benchmark comparison with single models CNN, TCN, and LSTM, the ensemble models performed better, with Ensemble 2 giving better outputs than Ensemble 1. Model evaluation is done using five performance metrics. Furthermore, models are validated through 5-fold cross-validation adding robustness to the findings and analysis.
  • Dual-Stage Feature Refinement and Wavelet Denoising for Enhanced VIX Prediction Using Residual BiLSTM
    Akanksha Sharma, Priya Singh, Chandan Kumar Verma
    IEEE Region 10 Annual International Conference Proceedings TENCON, 2025
    Derivative pricing and financial risk management rely heavily on volatility predictions. Given the dynamic and nonlinear nature of financial markets, accurately predicting volatility remains a persistent challenge. Traditional econometric models often struggle to capture the complex patterns and time-dependent behaviors present in market data. This research presents a Residual Bidirectional Long Short-Term Memory (ResBiLSTM) model-based deep learning framework for VIX price prediction. By integrating residual connections and bidirectional temporal processing, the model is able to successfully capture intricate patterns found in financial time series data. A complete array of 64 designed features, comprising technical indicators and wavelet-denoised inputs, was employed to train and assess the model. The suggested ResBiLSTM surpasses conventional models, including LSTM, GRU, CNN, BiLSTM, and Residual LSTM, across multiple criteria. The performance was additionally confirmed using 5-fold cross-validation and statistical significance assessment via paired t-tests across many experimental iterations. The findings illustrate the model's resilience, precision, and applicability for implementation in practical volatility forecasting scenarios.
  • Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 Index
    Priya Singh, Manoj Jha, Harshita Patel
    IEEE Access, 2025
    Portfolio theory underpins portfolio management, a much-researched yet uncharted field. Stock market prediction is a challenging and essential endeavour in financial research, owing to the nonlinear, volatile, and stochastic characteristics of financial time series data. Conventional statistical techniques often fall short to encapsulate complex interdependencies, resulting in diminished predictive accuracy. This research proposes an ensemble model that integrates Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Temporal Convolutional Networks (TCN) for effective stock market prediction. The Nifty 50 index dataset is utilized for the empirical evidences. Wavelet-based denoising is utilised as a preprocessing measure to mitigate the intrinsic noise in stock market data. The model’s efficacy is assessed utilising error metrics, such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>). The five-fold cross-validation is utilized to establish the robustness of the models. Furthermore, we ascertain the statistical significance of performance enhancements by parametric t-tests, including normality assessments via the Shapiro-Wilk test. Moreover, current state-of-the-art models advocates in favour of proposed study.
  • Portfolio Optimization Using Novel EW-MV Method in Conjunction with Asset Preselection
    Priya Singh, Manoj Jha
    Computational Economics, 2024
  • Elevating Stock Market Predictions: An Attention-Infused LSTM Model with Wavelet Denoising
    Priya Singh, Manoj Jha
    2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical Electronics and Computer Engineering Upcon 2024, 2024
    Financial time series data is volatile and noisy, making stock market prediction challenging. Traditional approaches sometimes fail to capture complicated, non-linear patterns in such data, resulting in poor predictions. This research suggests an Attention-driven Long Short-Term Memory model integrated with wavelet denoising using the Coif3 wavelet for predicting the NIFTY 50 index. Our model uses an additive attention technique to dynamically focus on important time steps, enhancing predictions. To find the best predictors, we used Recursive Feature Elimination (RFE) with a Random Forest Regressor on 38 historical and technical features. Wavelet transform denoising decreases noise, making data better for the LSTM model. The model is tested for RMSE, MAE, and R2, showing greater predictive power. Additionally, an ablation study critically evaluates wavelet denoising and the attention mechanism, showing that their combination improves prediction accuracy.
  • Harnessing a Hybrid CNN-LSTM Model for Portfolio Performance: A Case Study on Stock Selection and Optimization
    Priya Singh, Manoj Jha, Mohamed Sharaf, Mohammed A. El-Meligy, Thippa Reddy Gadekallu
    IEEE Access, 2023
    Portfolio theory underpins portfolio management, a much-researched yet uncharted field. This research suggests a collective framework combined with the essence of deep learning for stock selection through prediction and optimal portfolio formation through the mean-variance (MV) model. The CNN-LSTM model, proposed in Stage I blends the benefits of the convolutional neural network (CNN) and the long-short-term memory network (LSTM). The model combines feature extraction and sequential learning about temporal data fluctuations. The experiment considers thirteen input features, combining fundamental market data and technical indicators to capture the nuances of the wildly fluctuating stock market data. The input data sample of 21 stocks was collected from the National Stock Exchange (NSE) of India from January 2005 to December 2021, spanning two significant market crashes. Thus, the sample makes it possible to catch subtle market shifts for model execution. The shortlisted stocks with high potential returns are advanced to Stage II for optimal stock allocation using the MV model. The proposed hybrid CNN-LSTM outperformed the single models, i.e., CNN and LSTM, per the six-performance metrics and advocated by the 10-fold cross-validation technique. Furthermore, the statistical significance of the model is established using non-parametric tests followed by post hoc analysis. In addition, this method is validated by comparing the proposed model to four baseline strategies and relevant pieces of research, which it considerably outperforms in terms of cumulative return per year, Sharpe ratio, and average return to risk with and without transaction cost. These findings highlight the effectiveness of the hybrid CNN-LSTM approach in stock selection and portfolio optimization.

RECENT SCHOLAR PUBLICATIONS

  • Predicting Market Volatility: An Ensemble Approach for Enhanced India VIX Prediction
    MJCKV Priya Singh, Akanksha Sharma
    Operations Research Forum 7 (1), 2 , 2025
    2025
  • Enhancing Option Pricing Accuracy in the Indian Market: A CNN-BiLSTM Approach: A. Sharma et al.
    A Sharma, CK Verma, P Singh
    Computational Economics 65 (6), 3751-3778 , 2025
    2025
    Citations: 19
  • Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 Index
    HP Priya Singh, Manoj Jha
    IEEE Access 13, 87036 - 87047 , 2025
    2025
    Citations: 7
  • Elevating Stock Market Predictions: An Attention-Infused LSTM Model with Wavelet Denoising
    MJ Priya Singh
    2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical … , 2025
    2025
    Citations: 2
  • Leveraging Deep Learning Ensembles for Stock Index Forecasting: A Nifty 50 approach
    MJ Priya Singh
    2025 IEEE International Students' Conference on Electrical, Electronics and … , 2025
    2025
    Citations: 2
  • Portfolio optimization using novel ew-mv method in conjunction with asset preselection
    P Singh, M Jha
    Computational Economics 64 (6), 3683-3712 , 2024
    2024
    Citations: 16
  • Harnessing a hybrid CNN-LSTM model for portfolio performance: A case study on stock selection and optimization
    P Singh, M Jha, M Sharaf, MA El-Meligy, TR Gadekallu
    Ieee Access 11, 104000-104015 , 2023
    2023
    Citations: 72

MOST CITED SCHOLAR PUBLICATIONS

  • Harnessing a hybrid CNN-LSTM model for portfolio performance: A case study on stock selection and optimization
    P Singh, M Jha, M Sharaf, MA El-Meligy, TR Gadekallu
    Ieee Access 11, 104000-104015 , 2023
    2023
    Citations: 72
  • Enhancing Option Pricing Accuracy in the Indian Market: A CNN-BiLSTM Approach: A. Sharma et al.
    A Sharma, CK Verma, P Singh
    Computational Economics 65 (6), 3751-3778 , 2025
    2025
    Citations: 19
  • Portfolio optimization using novel ew-mv method in conjunction with asset preselection
    P Singh, M Jha
    Computational Economics 64 (6), 3683-3712 , 2024
    2024
    Citations: 16
  • Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 Index
    HP Priya Singh, Manoj Jha
    IEEE Access 13, 87036 - 87047 , 2025
    2025
    Citations: 7
  • Elevating Stock Market Predictions: An Attention-Infused LSTM Model with Wavelet Denoising
    MJ Priya Singh
    2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical … , 2025
    2025
    Citations: 2
  • Leveraging Deep Learning Ensembles for Stock Index Forecasting: A Nifty 50 approach
    MJ Priya Singh
    2025 IEEE International Students' Conference on Electrical, Electronics and … , 2025
    2025
    Citations: 2
  • Predicting Market Volatility: An Ensemble Approach for Enhanced India VIX Prediction
    MJCKV Priya Singh, Akanksha Sharma
    Operations Research Forum 7 (1), 2 , 2025
    2025