Deep Learning Factor Investing in the Indonesian Stock Market

https://doi.org/10.22146/ijccs.112549

Fawwaz Atha Rohmatullah(1*), Farrikh Alzami(2), Ramadhan Rakhmat Sani(3), Ika Novita Dewi(4), Sri Winarno(5), Teguh Sulistyono(6)

(1) Universitas Dian Nuswantoro
(2) Universitas Dian Nuswantoro
(3) Universitas Dian Nuswantoro
(4) Universitas Dian Nuswantoro
(5) Universitas Dian Nuswantoro
(6) Universitas Dian Nuswantoro
(*) Corresponding Author

Abstract


Traditional linear factor models often fail to capture the complex, non-linear dynamics of emerging stock markets. This research designs and validates a novel Recurrence Plot (RP) matrices with β-VAE deep learning methodology to discover non-linear investment factors within the Indonesian context. We demonstrate that this framework is a systematically superior "factor factory" compared to a linear RP with PCA baseline, discovering twice as many high-quality factors (Sharpe > 0.3) and generating 7-fold more alpha on average. A key finding is the model's ability to disentangle high-frequency predictive signals (identified by SHAP) from more valuable, low-frequency profitable trends (validated by backtesting). The champion factor from this process yields a robust annualized alpha of 6.65% with a minimal max drawdown of -7.73% from 2018 to 2025. This study concludes that the RP -> β -VAE approach is a robust and resilient framework for discovering safer, non-linear sources of return unexplained by conventional models.

Keywords


factor investing; deep learning; asset pricing; Indonesian capital market; β-VAE; Recurrence Plot; non-linear dynamics; alpha generation; emerging markets

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DOI: https://doi.org/10.22146/ijccs.112549

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