本論文探討使用卷積神經網路(CNN)應用於由股價和成交量生 成的影像，來預測股票報酬率的可能性。我們採用多類別分類模型預 測股票報酬，其表現優於二元分類模型。此外，我們的研究還考慮了 不同股票型態的可預測性，發現小市值股票通常較大市值股票具有較 高的年度夏普比率和更顯著的月超額報酬。我們的研究成果強調在股 票報酬預測中，考慮橫截斷效應和股票型態異質性的重要性，為投資 者和研究人員提供了新的見解。

This paper investigates the predictability of stock returns using Convolutional Neural Networks (CNNs) apply to images generated from stock prices and volumes. We employ multi-class classification models to predict stock returns that outperform binary classification models. Additionally, our study examines the predictability of different stock styles, revealing that small-capital stocks generally exhibit higher annual Sharpe ratios and more pronounced monthly excess returns than large-capital stocks. Our findings underscore the importance of considering cross-sectional effects and stock style heterogeneity in stock return predictions, providing valuable insights for investors and researchers alike.

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