Paper: arXiv 2507.01973

Authors: Junjie Guo

Abstract

Portfolio allocation via stock price prediction is inherently difficult due to the notoriously low signal-to-noise ratio of stock time series. This paper proposes a method by integrating wavelet transform convolution and channel attention with LSTM to implement stock price prediction based portfolio allocation. Stock time series data first are processed by wavelet transform convolution to reduce the noise. Processed features are then reconstructed by channel attention. LSTM is utilized to predict the stock price using the final processed features. We construct a portfolio consists of four stocks with trading signals predicted by model. Experiments are conducted by evaluating the return, Sharpe ratio and max drawdown performance. The results indicate that our method achieves robust performance even during period of post-pandemic downward market.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 6.0/10 (capped from 6.5: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper uses advanced mathematical concepts like wavelet transforms and attention mechanisms, pushing the complexity beyond basic ML. It also includes specific portfolio metrics (return, Sharpe ratio, max drawdown) and backtesting on real data, demonstrating practical implementation rigor.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Portfolio Allocation via<br>Stock Price Prediction"] --> B["Input Data<br>Four Stocks Time Series"]
  B --> C["Wavelet Transform Convolution<br>Noise Reduction"]
  C --> D["Channel Attention<br>Feature Reconstruction"]
  D --> E["LSTM Model<br>Price Prediction"]
  E --> F["Trading Signals<br>Portfolio Construction"]
  F --> G["Evaluation Metrics<br>Return, Sharpe Ratio, Max Drawdown"]
  G --> H["Key Finding<br>Robust Performance in<br>Post-Pandemic Downturn Market"]

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0 of 31 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.