Why Regression? Binary Encoding Classification Brings Confidence to Stock Market Index Price Prediction

• URL: http://arxiv.org/abs/2506.03153v1
• Date: Sun, 18 May 2025 15:45:41 GMT
• Title: Why Regression? Binary Encoding Classification Brings Confidence to Stock Market Index Price Prediction
• Authors: Junzhe Jiang, Chang Yang, Xinrun Wang, Bo Li,
• Abstract summary: We propose a novel end-to-end framework that explicitly models the adaptive fusion of constituent stocks for index price prediction.<n>Cubic consistently outperforms state-of-the-art baselines in stock index prediction tasks.
• Score: 13.19419686734908
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Stock market indices serve as fundamental market measurement that quantify systematic market dynamics. However, accurate index price prediction remains challenging, primarily because existing approaches treat indices as isolated time series and frame the prediction as a simple regression task. These methods fail to capture indices' inherent nature as aggregations of constituent stocks with complex, time-varying interdependencies. To address these limitations, we propose Cubic, a novel end-to-end framework that explicitly models the adaptive fusion of constituent stocks for index price prediction. Our main contributions are threefold. i) Fusion in the latent space: we introduce the fusion mechanism over the latent embedding of the stocks to extract the information from the vast number of stocks. ii) Binary encoding classification: since regression tasks are challenging due to continuous value estimation, we reformulate the regression into the classification task, where the target value is converted to binary and we optimize the prediction of the value of each digit with cross-entropy loss. iii) Confidence-guided prediction and trading: we introduce the regularization loss to address market prediction uncertainty for the index prediction and design the rule-based trading policies based on the confidence. Extensive experiments across multiple stock markets and indices demonstrate that Cubic consistently outperforms state-of-the-art baselines in stock index prediction tasks, achieving superior performance on both forecasting accuracy metrics and downstream trading profitability.

Related papers

• Smooth Quadratic Prediction Markets [6.576051895863941]
  We propose a new prediction market, called the Smooth Quadratic Prediction Market.<n>Relative to the DCFMM, the Smooth Quadratic Prediction Market has a better worst-case monetary loss for AD securities.<n>Our results suggest future designs where the price update rule is separate from the fee structure, yet guarantees are preserved.
  arXiv  Detail & Related papers  (2025-05-05T18:43:58Z)
• Assessing Uncertainty in Stock Returns: A Gaussian Mixture Distribution-Based Method [19.332951287688108]
  We introduce a novel deep learning model to capture the complex, time-varying nature of asset return distributions in the Chinese stock market.<n>Our approach effectively characterizes short-term fluctuations and non-traditional features of stock returns, such as skewness and heavy tails.<n>It provides more accurate volatility forecasts and offers unique risk insights for different assets, thereby deepening the understanding of return uncertainty.
  arXiv  Detail & Related papers  (2025-03-10T05:20:56Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty [6.776900239715404]
  Trader-Company Method has high predictive power and interpretability. Uncertainty Aware Trader-Company Method captures uncertainty. Method achieves higher returns and lower risks than baselines.
  arXiv  Detail & Related papers  (2022-10-31T03:07:07Z)
• DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions [53.37679435230207]
  We propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical results suggest that the proposed deep learning-based approach effectively learns global features from high-frequency data.
  arXiv  Detail & Related papers  (2022-09-23T16:13:47Z)
• Index Tracking via Learning to Predict Market Sensitivities [1.6765420339154895]
  Index funds might replicate the index identically, which is, however, cost-ineffective and impractical. To utilize market sensitivities to replicate the index partially, they must be predicted or estimated accurately. We propose a partial-index-tracking optimization model controlling the net predicted market sensitivities of the portfolios and index to be the same.
  arXiv  Detail & Related papers  (2022-09-02T01:52:10Z)
• HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information [73.40830291141035]
  Several methods were recently proposed to mine the shared information through stock concepts extracted from the Web to improve the forecasting results. Previous work assumes the connections between stocks and concepts are stationary, and neglects the dynamic relevance between stocks and concepts. We propose a novel stock trend forecasting framework that can adequately mine the concept-oriented shared information from predefined concepts and hidden concepts.
  arXiv  Detail & Related papers  (2021-10-26T14:04:04Z)
• Learning to Predict Trustworthiness with Steep Slope Loss [69.40817968905495]
  We study the problem of predicting trustworthiness on real-world large-scale datasets. We observe that the trustworthiness predictors trained with prior-art loss functions are prone to view both correct predictions and incorrect predictions to be trustworthy. We propose a novel steep slope loss to separate the features w.r.t. correct predictions from the ones w.r.t. incorrect predictions by two slide-like curves that oppose each other.
  arXiv  Detail & Related papers  (2021-09-30T19:19:09Z)
• Stock Price Prediction Under Anomalous Circumstances [81.37657557441649]
  This paper aims to capture the movement pattern of stock prices under anomalous circumstances. We train ARIMA and LSTM models at the single-stock level, industry level, and general market level. Based on 100 companies' stock prices in the period of 2016 to 2020, the models achieve an average prediction accuracy of 98%.
  arXiv  Detail & Related papers  (2021-09-14T18:50:38Z)
• REST: Relational Event-driven Stock Trend Forecasting [76.08435590771357]
  We propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods. To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts. To address the second shortcoming, we construct a stock graph and design a new propagation layer to propagate the effect of event information from related stocks.
  arXiv  Detail & Related papers  (2021-02-15T07:22:09Z)
• A Sentiment Analysis Approach to the Prediction of Market Volatility [62.997667081978825]
  We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. The sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information.
  arXiv  Detail & Related papers  (2020-12-10T01:15:48Z)

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.