Enhancing Black-Litterman Portfolio via Hybrid Forecasting Model Combining Multivariate Decomposition and Noise Reduction

• URL: http://arxiv.org/abs/2505.01781v2
• Date: Sun, 25 May 2025 17:13:02 GMT
• Title: Enhancing Black-Litterman Portfolio via Hybrid Forecasting Model Combining Multivariate Decomposition and Noise Reduction
• Authors: Ziye Yang, Ke Lu, Yang Wang, Jerome Yen,
• Abstract summary: We propose a novel hybrid forecasting model SSA-MAEMD-TCN to automate and improve the view generation process.<n> Empirical tests on the Nasdaq 100 Index stocks show a significant improvement in forecasting performance compared to baseline models.<n>The optimized portfolio performs well, with annualized returns and Sharpe ratios far exceeding those of the traditional portfolio.
• Score: 13.04801847533423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern portfolio construction demands robust methods for integrating data-driven insights into asset allocation. The Black-Litterman model offers a powerful Bayesian approach to adjust equilibrium returns using investor views to form a posterior expectation along with market priors. Mainstream research mainly generates subjective views through statistical models or machine learning methods, among which hybrid models combined with decomposition algorithms perform well. However, most hybrid models do not pay enough attention to noise, and time series decomposition methods based on single variables make it difficult to fully utilize information between multiple variables. Multivariate decomposition also has problems of low efficiency and poor component quality. In this study, we propose a novel hybrid forecasting model SSA-MAEMD-TCN to automate and improve the view generation process. The proposed model combines Singular Spectrum Analysis (SSA) for denoising, Multivariate Aligned Empirical Mode Decomposition (MA-EMD) for frequency-aligned decomposition, and Temporal Convolutional Networks (TCNs) for deep sequence learning to capture complex temporal patterns across multiple financial indicators. Empirical tests on the Nasdaq 100 Index stocks show a significant improvement in forecasting performance compared to baseline models based on MAEMD and MEMD. The optimized portfolio performs well, with annualized returns and Sharpe ratios far exceeding those of the traditional portfolio over a short holding period, even after accounting for transaction costs.

Related papers

• WSM: Decay-Free Learning Rate Schedule via Checkpoint Merging for LLM Pre-training [64.0932926819307]
  We present Warmup-Stable and Merge (WSM), a framework that establishes a formal connection between learning rate decay and model merging.<n>WSM provides a unified theoretical foundation for emulating various decay strategies.<n>Our framework consistently outperforms the widely-adopted Warmup-Stable-Decay (WSD) approach across multiple benchmarks.
  arXiv  Detail & Related papers  (2025-07-23T16:02:06Z)
• Deep Learning Enhanced Multivariate GARCH [7.475786051454157]
  Long Short-Term Memory enhanced BEKK (LSTM-BEKK) integrates deep learning into multivariate GARCH processes.<n>Our approach is designed to better capture nonlinear, dynamic, and high-dimensional dependence structures in financial return data.<n> Empirical results across multiple equity markets confirm that the LSTM-BEKK model achieves superior performance in terms of out-of-sample portfolio risk forecast.
  arXiv  Detail & Related papers  (2025-06-03T12:22:57Z)
• An Efficient deep learning model to Predict Stock Price Movement Based on Limit Order Book [11.613073850152873]
  In high-frequency trading (HFT), leveraging limit order books (LOB) to model stock price movements is crucial for achieving profitable outcomes.<n>Even recent deep learning models often struggle to capture price movement patterns effectively.<n>We propose a novel approach in which leverages the Siamese architecture to enhance the performance of existing deep learning models.
  arXiv  Detail & Related papers  (2025-05-14T12:46:21Z)
• A Novel Hybrid Approach Using an Attention-Based Transformer + GRU Model for Predicting Cryptocurrency Prices [0.0]
  We introduce a novel deep learning hybrid model that integrates attention Transformer and Gated Recurrent Unit (GRU) architectures.<n>By combining the Transformer's strength in capturing long-range patterns with the GRU's ability to model short-term and sequential trends, the hybrid model provides a well-rounded approach to time series forecasting.<n>We evaluate the performance of our proposed model by comparing it with four other machine learning models.
  arXiv  Detail & Related papers  (2025-04-23T20:00:47Z)
• Scalable Language Models with Posterior Inference of Latent Thought Vectors [52.63299874322121]
  Latent-Thought Language Models (LTMs) incorporate explicit latent thought vectors that follow an explicit prior model in latent space.<n>LTMs possess additional scaling dimensions beyond traditional LLMs, yielding a structured design space.<n>LTMs significantly outperform conventional autoregressive models and discrete diffusion models in validation perplexity and zero-shot language modeling.
  arXiv  Detail & Related papers  (2025-02-03T17:50:34Z)
• BreakGPT: Leveraging Large Language Models for Predicting Asset Price Surges [55.2480439325792]
  This paper introduces BreakGPT, a novel large language model (LLM) architecture adapted specifically for time series forecasting and the prediction of sharp upward movements in asset prices. We showcase BreakGPT as a promising solution for financial forecasting with minimal training and as a strong competitor for capturing both local and global temporal dependencies.
  arXiv  Detail & Related papers  (2024-11-09T05:40:32Z)
• MITA: Bridging the Gap between Model and Data for Test-time Adaptation [68.62509948690698]
  Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. We propose Meet-In-The-Middle based MITA, which introduces energy-based optimization to encourage mutual adaptation of the model and data from opposing directions.
  arXiv  Detail & Related papers  (2024-10-12T07:02:33Z)
• A Dynamic Approach to Stock Price Prediction: Comparing RNN and Mixture of Experts Models Across Different Volatility Profiles [0.0]
  The MoE framework combines an RNN for volatile stocks and a linear model for stable stocks, dynamically adjusting the weight of each model through a gating network. Results indicate that the MoE approach significantly improves predictive accuracy across different volatility profiles. The MoE model's adaptability allows it to outperform each individual model, reducing errors such as Mean Squared Error (MSE) and Mean Absolute Error (MAE)
  arXiv  Detail & Related papers  (2024-10-04T14:36:21Z)
• Margin Matching Preference Optimization: Enhanced Model Alignment with Granular Feedback [64.67540769692074]
  Large language models (LLMs) fine-tuned with alignment techniques, such as reinforcement learning from human feedback, have been instrumental in developing some of the most capable AI systems to date.<n>We introduce an approach called Margin Matching Preference Optimization (MMPO), which incorporates relative quality margins into optimization, leading to improved LLM policies and reward models.<n>Experiments with both human and AI feedback data demonstrate that MMPO consistently outperforms baseline methods, often by a substantial margin, on popular benchmarks including MT-bench and RewardBench.
  arXiv  Detail & Related papers  (2024-10-04T04:56:11Z)
• GARCH-Informed Neural Networks for Volatility Prediction in Financial Markets [0.0]
  We present a new, hybrid Deep Learning model that captures and forecasting market volatility more accurately than either class of models are capable of on their own. When compared to other time series models, GINN showed superior out-of-sample prediction performance in terms of the Coefficient of Determination ($R2$), Mean Squared Error (MSE), and Mean Absolute Error (MAE)
  arXiv  Detail & Related papers  (2024-09-30T23:53:54Z)
• Predictive Churn with the Set of Good Models [61.00058053669447]
  This paper explores connections between two seemingly unrelated concepts of predictive inconsistency.<n>The first, known as predictive multiplicity, occurs when models that perform similarly produce conflicting predictions for individual samples.<n>The second concept, predictive churn, examines the differences in individual predictions before and after model updates.
  arXiv  Detail & Related papers  (2024-02-12T16:15:25Z)
• Long Short-Term Memory Neural Network for Financial Time Series [0.0]
  We present an ensemble of independent and parallel long short-term memory neural networks for the prediction of stock price movement. With a straightforward trading strategy, comparisons with a randomly chosen portfolio and a portfolio containing all the stocks in the index show that the portfolio resulting from the LSTM ensemble provides better average daily returns and higher cumulative returns over time.
  arXiv  Detail & Related papers  (2022-01-20T15:17:26Z)
• Expert Aggregation for Financial Forecasting [0.0]
  Online aggregation of experts combine the forecasts of a finite set of models in a single approach. Online mixture of experts leads to attractive portfolio performances even in environments characterised by non-stationarity. Extensions to expert and aggregation specialisations are also proposed to improve the overall mixture on a family of portfolio evaluation metrics.
  arXiv  Detail & Related papers  (2021-11-25T10:43:58Z)
• Sparse MoEs meet Efficient Ensembles [49.313497379189315]
  We study the interplay of two popular classes of such models: ensembles of neural networks and sparse mixture of experts (sparse MoEs) We present Efficient Ensemble of Experts (E$3$), a scalable and simple ensemble of sparse MoEs that takes the best of both classes of models, while using up to 45% fewer FLOPs than a deep ensemble.
  arXiv  Detail & Related papers  (2021-10-07T11:58:35Z)
• Forecasting High-Dimensional Covariance Matrices of Asset Returns with Hybrid GARCH-LSTMs [0.0]
  This paper investigates the ability of hybrid models, mixing GARCH processes and neural networks, to forecast covariance matrices of asset returns. The new model proposed is very promising as it not only outperforms the equally weighted portfolio, but also by a significant margin its econometric counterpart.
  arXiv  Detail & Related papers  (2021-08-25T23:41:43Z)
• Generative Temporal Difference Learning for Infinite-Horizon Prediction [101.59882753763888]
  We introduce the $gamma$-model, a predictive model of environment dynamics with an infinite probabilistic horizon. We discuss how its training reflects an inescapable tradeoff between training-time and testing-time compounding errors.
  arXiv  Detail & Related papers  (2020-10-27T17:54:12Z)
• Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling [54.94763543386523]
  Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the ( aggregate) posterior to encourage statistical independence of the latent factors. We present a novel multi-stage modeling approach where the disentangled factors are first learned using a penalty-based disentangled representation learning method. Then, the low-quality reconstruction is improved with another deep generative model that is trained to model the missing correlated latent variables.
  arXiv  Detail & Related papers  (2020-10-25T18:51:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.