Modular reinforcement learning for dynamic portfolio optimization in the KOSPI market<sup>†</sup>
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(2) 214. Taeyoon Kim · Bonggyun Ko. ᄒᄋ ᆫ ᅡ ᅵᄅ ᅮᄋ ᅥᄌ ᆻᄃ ᅧ ᅡ (Guo ᄃ ᆼ, 2018; Jiang ᄃ ᅳ ᆼ, 2017; Kim ᄃ ᅳ ᆼ, 2019; Yu ᄃ ᅳ ᆼ, 2019). ᄋ ᅳ ᅵᄅ ᆫᅧ ᅥ ᆫ ᄋᄀ ᅮᄃ ᆯᄋ ᅳ ᅦᄉ ᅥᄋ ᅵᄅ ᅮ ᅥᄌ ᄋ ᆫ ᄇ ᅵ ᆼᄇ ᅡ ᆸᄃ ᅥ ᆯᄋ ᅳ ᆫᄌ ᅳ ᅮᄅ ᅩᄎ ᆼ ᄉ ᅩ ᅮᄋ ᆨᄅ ᅵ ᆯᄋ ᅲ ᅦᄄ ᅡᄅ ᅡᄀ ᆼᄒ ᅡ ᅪᄒ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋᄉ ᅮᄒ ᆼᄒ ᅢ ᅡᄂ ᆫᄋ ᅳ ᅦᄋ ᅵᄌ ᆫᄐ ᅥ ᅳᄋ ᅦ ᄇ ᅩᄉ ᆼᄋ ᅡ ᆯ ᄌ ᅳ ᆷᄋ ᅮ ᅳᄅ ᅩᄊ ᅥ ᄑ ᅵᄃ ᅳᄇ ᆨᄋ ᅢ ᅵ ᄋ ᅵ ᅮᄋ ᄅ ᅥᅵ ᄌᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅵᄃ ᅡ. ᄒ ᅡᄌ ᅵᄆ ᆫᄋ ᅡ ᅵᄋ ᆫᄀ ᅧ ᅮᄃ ᆯᄋ ᅳ ᆫᄌ ᅳ ᅮᄅ ᅩᄀ ᅥᄅ ᅢᄅ ᆼᄋ ᅣ ᅵᄆ ᆭᄋ ᅡ ᆫᄋ ᅳ ᆷᄒ ᅡ ᅩᄒ ᅪᄑ ᅨ, ᄋ ᅬᄒ ᆫ, ᄑ ᅪ ᅡᄉ ᆼᄉ ᅢ ᆼᄑ ᅡ ᆷᄃ ᅮ ᆼᄋ ᅳ ᆯᄀ ᅳ ᅥᄅ ᅢᄒ ᆷᄋ ᅡ ᅳᄅ ᅩ ᅥᄉ ᄊ ᅩᄉ ᅮᄌ ᆷᄀ ᅥ ᅥᄅ ᅢᄋ ᅴᄌ ᅦᄒ ᆫᄋ ᅡ ᆯᄇ ᅳ ᆮᄌ ᅡ ᅵᄋ ᆭᄋ ᅡ ᆻᄀ ᅡ ᅩᄌ ᆼᄆ ᅩ ᆨᄉ ᅩ ᆫᅢ ᅥ ᆨ ᄐᄋ ᅵᄌ ᅡᄋ ᅴᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄋ ᅵᄅ ᅮᄋ ᅥᄌ ᆻᄃ ᅧ ᅡ (Dempsterᄋ ᅪ Leemans, 2006; Deng ᅳ ᆼ ᄃ, 2016; Jiang ᄃ ᆼ, 2017; Kim ᄃ ᅳ ᆼ, 2019). ᄒ ᅳ ᅡᄌ ᅵᄆ ᆫᄒ ᅡ ᆫᄀ ᅡ ᆨᄌ ᅮ ᅮᄉ ᆨᄉ ᅵ ᅵᄌ ᆼᄋ ᅡ ᅦᄉ ᅥᄂ ᆫᄌ ᅳ ᆼᄆ ᅩ ᆨᄉ ᅩ ᆫᅢ ᅥ ᆨ ᄐᄋ ᅴᄆ ᆫᄌ ᅮ ᅦᄀ ᅡᄋ ᆻᄀ ᅵ ᅩ ᅩᄉ ᄉ ᅮᅥ ᆷ ᄌᄀ ᅥᄅ ᅢᄀ ᅡᄌ ᅦᄒ ᆫᄌ ᅡ ᆨᄋ ᅥ ᅵᄆ ᅧᄋ ᅵᄋ ᅦᄄ ᅡᄅ ᅡᄉ ᅢᄅ ᅩᄋ ᆫᄀ ᅮ ᆼᄒ ᅡ ᅪᄒ ᆨᄉ ᅡ ᆸᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᆯᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᆯᄑ ᅡ ᆯᄋ ᅵ ᅭᄀ ᅡᄋ ᆻᄃ ᅵ ᅡ. Deep Q-learning (Mnih ᄃ ᆼ, 2013; Mnih ᄃ ᅳ ᆼ, 2015), double deep Q-learning (Van Hasselt ᄃ ᅳ ᆼ, 2015), ᅳ dueling deep Q-leaning (Wang ᄃ ᆼ, 2016)ᄀ ᅳ ᅪᄀ ᇀᄋ ᅡ ᆫᄀ ᅳ ᅡᄎ ᅵᄀ ᅵᄇ ᆫᄇ ᅡ ᆼᄇ ᅡ ᆸᄃ ᅥ ᆯᄋ ᅳ ᆫᄋ ᅳ ᆫᄉ ᅧ ᆨᄒ ᅩ ᆼᄒ ᅧ ᆼᄃ ᅢ ᆼᅳ ᅩ ᆯ ᄋᄎ ᅱᅡ ᆯ ᄒᄉ ᅮᄋ ᆹᄀ ᅥ ᅩᄐ ᆨᄌ ᅳ ᆼ ᅥ ᆼᄐ ᅡ ᄉ ᅢᄋ ᅦᄉ ᅥᄎ ᅱᅡ ᆯ ᄒᄉ ᅮᄋ ᆻᄂ ᅵ ᆫᄀ ᅳ ᆨᄒ ᅡ ᆼᄃ ᅢ ᆼᄋ ᅩ ᅦᄃ ᅢᄒ ᆫᄀ ᅡ ᅡᄎ ᅵᄅ ᆯᄌ ᅳ ᆼᄒ ᅥ ᆨᄒ ᅪ ᅵᄎ ᅮᄌ ᆼᄒ ᅥ ᅡᄂ ᆫᄃ ᅳ ᅦᄆ ᆨᄑ ᅩ ᅭᄅ ᆯᅮ ᅳ ᆫ ᄃᄃ ᅡ. ᆫᄆ ᅡ ᄇ ᆫᅥ ᅧ ᆼ 재 ᆨ ᄎᄀ ᅵᄇ ᆫᅡ ᅡ ᆼ ᄇᄇ ᆸᄃ ᅥ ᆯᄋ ᅳ ᆫ (Schulman ᄃ ᅳ ᆼ, 2015; Schulman ᄃ ᅳ ᆼ, 2017; Silver ᄃ ᅳ ᆼ, 2014) ᄇ ᅳ ᅩᄉ ᆼᄋ ᅡ ᆯᄀ ᅳ ᆨᄃ ᅳ ᅢᄒ ᅪ ᅵᄏ ᄉ ᅵᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᆯᄆ ᅳ ᆨᄑ ᅩ ᅭᄅ ᅩᄌ ᆼᄎ ᅥ ᆨᄋ ᅢ ᆯᄎ ᅳ ᅬᄌ ᆨᄒ ᅥ ᅪᄉ ᅵᄏ ᅵᄂ ᆫᄋ ᅳ ᆯᄀ ᅡ ᅩᄅ ᅵᄌ ᆷᄋ ᅳ ᅵᄃ ᅡ. ᄌ ᆼᅢ ᅥ ᆨ ᄎᄀ ᅵᄇ ᆫᅡ ᅡ ᆼ ᄇᄇ ᆸᄋ ᅥ ᆫᄋ ᅳ ᆫᄉ ᅧ ᆨᄒ ᅩ ᆼᄋ ᅧ ᅵᄂ ᅡᄒ ᆨᄅ ᅪ ᆯᅮ ᅲ ᆫ ᄇᄑ ᅩᄒ ᆼᄋ ᅧ ᅳᄅ ᅩ ᆼᄃ ᅢ ᄒ ᆼᅳ ᅩ ᆯ ᄋᄎ ᅱᅡ ᆯ ᄒᄉ ᅮᄋ ᆻᄋ ᅵ ᅥᄅ ᅩᄇ ᆺᄌ ᅩ ᅦᄋ ᅥ, ᄇ ᅵᄃ ᅵᄋ ᅩᄀ ᅦᄋ ᆷᄃ ᅵ ᆼᄋ ᅳ ᅦᄌ ᆨᄒ ᅥ ᆸᄒ ᅡ ᅡᄃ ᅡ. ᆫᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄃ ᅳ ᅢᄉ ᆼᄌ ᅡ ᅮᄉ ᆨᄋ ᅵ ᆯᄉ ᅳ ᆫᅥ ᅥ ᆼ ᄌᄒ ᅡᄀ ᅵᄋ ᅱᄒ ᅢ 2ᄎ ᅡᄋ ᆫᄌ ᅯ ᅮᄉ ᆨᄀ ᅵ ᅥᄅ ᅢᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄏ ᅳ ᆫᄇ ᅥ ᆯᄅ ᅩ ᅮᄉ ᆫᄉ ᅧ ᆫᅧ ᅵ ᆼ ᄀᄆ ᆼᄀ ᅡ ᅪᄌ ᆼᅡ ᅡ ᆫ ᄃᄀ ᅵᄆ ᅦᄆ ᅩ ᅵ (long short term memory, LSTM)ᄅ ᄅ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅢᄀ ᅢᄇ ᆯᄌ ᅧ ᅮᄉ ᆨᄋ ᅵ ᅦᄃ ᅢᄒ ᅢᄀ ᅵᄃ ᅢᄉ ᅮᄋ ᆨᄋ ᅵ ᆯᄋ ᅳ ᅨᄎ ᆨᄒ ᅳ ᅡᄋ ᅧᄐ ᅮᄌ ᅡᄃ ᅢᄉ ᆼᄌ ᅡ ᆼᄆ ᅩ ᆨ ᅩ ᆯᄌ ᅳ ᄋ ᆼᅡ ᅥ ᄒᄂ ᆫᄀ ᅳ ᅡᄎ ᅵᄎ ᅮᄌ ᆼᄆ ᅥ ᅩᄃ ᆯ (evaluation stock module, ESM)ᄀ ᅲ ᅪᄌ ᆼᅢ ᅥ ᆨ ᄎᄀ ᅵᄇ ᆫᅡ ᅡ ᆼ ᄀᄒ ᅪᄒ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧ ESMᄋ ᆯ ᅳ ᆼᄒ ᅩ ᄐ ᅢᄉ ᆫᅥ ᅥ ᆼ ᄌᄃ ᆫᄌ ᅬ ᅮᄉ ᆨᄋ ᅵ ᅦᄃ ᅢᄒ ᅢᄌ ᅡᄉ ᆫᄋ ᅡ ᆯᄇ ᅳ ᅢᄇ ᆫᄒ ᅮ ᅡᄂ ᆫᄌ ᅳ ᅡᄉ ᆫᄇ ᅡ ᅢᄇ ᆫᄆ ᅮ ᅩᄃ ᆯ (asset allocation module, AAM)ᄋ ᅲ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄏ ᅩ ᅳᄑ ᄉ ᅵ 200ᄋ ᆯᄀ ᅳ ᅮᄉ ᆼᄒ ᅥ ᅡᄂ ᆫᄌ ᅳ ᆼᄆ ᅩ ᆨᄌ ᅩ ᆫᄎ ᅥ ᅦᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄑ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄅ ᆯᄀ ᅳ ᅮᄉ ᆼᄒ ᅥ ᆫᄃ ᅡ ᅡ.. 2. 관련 연구 ᅩᅳ ᄑ ᄐᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᅬ ᄎᄌ ᆨᄒ ᅥ ᅪᄅ ᆯᄋ ᅳ ᅱᄒ ᅢᄀ ᆼᄒ ᅡ ᅪᅡ ᆨ ᄒᄉ ᆸᄋ ᅳ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᆫᅧ ᅡ ᆫ ᄋᄀ ᅮᄌ ᆼᄀ ᅮ ᅡᄌ ᆼᄃ ᅡ ᅢᄑ ᅭᄌ ᆨᄋ ᅥ ᆫᅥ ᅵ ᆺ ᄀᄋ ᆫᄏ ᅳ ᆫᄇ ᅥ ᆯᄅ ᅩ ᅮᄉ ᆫᄉ ᅧ ᆫᅧ ᅵ ᆼ ᄀᄆ ᆼᄀ ᅡ ᅪᄉ ᆫᄒ ᅮ ᆫᄉ ᅪ ᆫ ᅵ ᄀᄆ ᆼ ᅧ ᆼᄋ ᅡ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅢ 3ᄎ ᅡᄋ ᆫᄀ ᅯ ᅡᄀ ᆨᄃ ᅧ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄂ ᆫᄃ ᅳ ᆨᄅ ᅩ ᆸᄌ ᅵ ᆨᄑ ᅥ ᆼᄀ ᅧ ᅡᄋ ᆼᅡ ᅡ ᆼ ᄉᄇ ᆯ (ensemble of identical independent ᅳ evaluators)ᄀ ᅪᄋ ᅵᄌ ᆫᄀ ᅥ ᅡᄌ ᆼᄎ ᅮ ᅵᄋ ᅦᄃ ᅢᄒ ᆫᄆ ᅡ ᅦᄆ ᅩᄅ ᅵᄅ ᆯᄌ ᅳ ᅥᄌ ᆼᄒ ᅡ ᅡᄋ ᅧᄀ ᅡᄌ ᆼᄎ ᅮ ᅵᄅ ᆯᄀ ᅳ ᅮᄒ ᆯᄄ ᅡ ᅢᄋ ᅵᄋ ᆼᄒ ᅭ ᅡᄂ ᆫᄑ ᅳ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄇ ᆨᄐ ᅦ ᅥᄆ ᅦ ᅩᄅ ᄆ ᅵ (portfolio vector memory) ᄀ ᅳᄅ ᅵᄀ ᅩᄉ ᅵᄀ ᅨᄋ ᆯᄌ ᅧ ᅡᄅ ᅭᄋ ᅦᄌ ᆨᅥ ᅥ ᆯ ᄌᄒ ᆫᄇ ᅡ ᅢᄎ ᅵᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᆯᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᅡᄀ ᅵᄋ ᅱᅡ ᆫ ᄒᄋ ᆫᄅ ᅩ ᅡᄋ ᆫᄒ ᅵ ᆨᄅ ᅪ ᆯ ᅲ ᆨᄇ ᅥ ᄌ ᅢᄎ ᅵᄒ ᆨᄉ ᅡ ᆸ (online stochastic batch learning)ᄋ ᅳ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅢᄋ ᆷᄒ ᅡ ᅩᄒ ᅪᄑ ᅨᄅ ᅩᄀ ᅮᄉ ᆼᄃ ᅥ ᆫᄑ ᅬ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄅ ᆯᄎ ᅳ ᅬᄌ ᆨᄒ ᅥ ᅪᄒ ᅡ ᆫᄋ ᅳ ᄂ ᆫᅮ ᅧ ᄀᄀ ᅡᄋ ᆻᄋ ᅵ ᆻᄋ ᅥ ᅳᄆ ᅧᄉ ᅦᄎ ᅡᄅ ᅨᄋ ᅴᄐ ᅦᄉ ᅳᄐ ᅳᄀ ᆯᄀ ᅧ ᅪᄋ ᅦᄉ ᅥᄆ ᅩᄒ ᆼᄋ ᅧ ᅴᄋ ᅮᄉ ᅮᄉ ᆼᄋ ᅥ ᅵᄋ ᆸᄌ ᅵ ᆼᄃ ᅳ ᅬᄋ ᆻᄃ ᅥ ᅡ (Jiang ᄃ ᆼ, 2017). ᅳ ᅩᄒ ᄄ ᆫᄀ ᅡ ᅡᄀ ᆨᄋ ᅧ ᅨᄎ ᆨᅳ ᅳ ᆯ ᄋᄋ ᅱᄒ ᆫᄀ ᅡ ᆼᄒ ᅡ ᅪᅡ ᆨ ᄒᄉ ᆸᄋ ᅳ ᅳᄅ ᅩᄀ ᅮᄉ ᆼᄃ ᅥ ᆫᄌ ᅬ ᅮᄋ ᆸᄉ ᅵ ᆨᄋ ᅵ ᅨᄎ ᆨᄆ ᅳ ᅩᄃ ᆯ (infused prediction module), ᄆ ᅲ ᅩᄃ ᆯᄀ ᅦ ᅵ ᆫᄀ ᅡ ᄇ ᆼᄒ ᅡ ᅪᄒ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋᄋ ᅱᄒ ᅢᄑ ᆯᄋ ᅵ ᅭᄒ ᆫᄒ ᅡ ᆨᄅ ᅪ ᆯᅮ ᅲ ᆫ ᄇᄑ ᅩᄅ ᆯᄉ ᅳ ᆼᄉ ᅢ ᆼᄌ ᅥ ᆨᄌ ᅥ ᆨᄃ ᅥ ᅢᄉ ᆫᅧ ᅵ ᆼ ᄀᄆ ᆼ (generative adversarial network)ᄋ ᅡ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅢ ᆼᅥ ᅢ ᄉ ᆼ 사 ᄒᄂ ᆫᄃ ᅳ ᅦᄋ ᅵᄐ ᅥᄌ ᆼᄀ ᅳ ᆼᄆ ᅡ ᅩᄃ ᆯ (data augmentation module), ᄐ ᅲ ᅮᄌ ᅡᄌ ᅡᄃ ᆯᄋ ᅳ ᅴᄉ ᆫᄒ ᅥ ᅩᄅ ᆯᄆ ᅳ ᅩᄇ ᆼᄒ ᅡ ᅢᄑ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄋ ᅴᄇ ᆫ ᅧ ᆼᄉ ᅩ ᄃ ᆼᄋ ᅥ ᆯᄀ ᅳ ᆷᄉ ᅡ ᅩᄉ ᅵᄏ ᅵᄀ ᅵᄋ ᅱᅡ ᆫ ᄒᄒ ᆼᄃ ᅢ ᆼᄇ ᅩ ᆨᄌ ᅩ ᅦᄆ ᅩᄃ ᆯ (behavior cloning module)ᄋ ᅲ ᆯᄃ ᅳ ᅩᄋ ᆸᄒ ᅵ ᅢᄆ ᅩᄃ ᆯᄀ ᅦ ᅵᄇ ᆫᅡ ᅡ ᆼ ᄀᄒ ᅪᄒ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋᄀ ᅮ ᆫᄒ ᅧ ᄒ ᆫᅧ ᅡ ᆫ ᄋᄀ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄋ ᅳ ᅧᄅ ᅥᄉ ᆫᅧ ᅵ ᆼ ᄀᄆ ᆼᄀ ᅡ ᅮᄌ ᅩᄅ ᆯᄇ ᅳ ᆼᄒ ᅧ ᆸᄒ ᅡ ᅢᄂ ᇁᄋ ᅩ ᆫᄉ ᅳ ᅮᄋ ᆨᄅ ᅵ ᆯᄀ ᅲ ᅪᄉ ᆼᄀ ᅥ ᅪᄌ ᅵᄑ ᅭᄅ ᆯᄃ ᅳ ᆯᄉ ᅡ ᆼᄒ ᅥ ᆯᄉ ᅡ ᅮᄋ ᆻᄂ ᅵ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅵᄒ ᆨᄋ ᅪ ᆫᄃ ᅵ ᅬᄋ ᆻ ᅥ ᅡ(Yu ᄃ ᄃ ᆼ, 2019). ᅳ ᅡᄌ ᄒ ᅵᄆ ᆫᄋ ᅡ ᅱᄋ ᆫᄀ ᅧ ᅮᄃ ᆯᄋ ᅳ ᆫᄀ ᅳ ᆼᄒ ᅡ ᅪᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅦᄉ ᅥᄋ ᅴᄋ ᆫᄉ ᅧ ᆫᄉ ᅡ ᅵᄀ ᆫᄋ ᅡ ᆯᄒ ᅳ ᅭᄋ ᆯᄌ ᅲ ᆨᄋ ᅥ ᅳᄅ ᅩᄌ ᆯᄋ ᅮ ᆯᄉ ᅵ ᅮᄋ ᆻᄂ ᅵ ᆫᄇ ᅳ ᆼᅧ ᅧ ᆯ ᄅᄒ ᅪᄋ ᅦ (Mnih ᄃ ᆼ. 2016) ᅳ ᅢᄒ ᄃ ᆫᄋ ᅡ ᆫᄀ ᅧ ᅮᄀ ᅡᄋ ᅵᄅ ᅮᄋ ᅥᄌ ᅵᄌ ᅵᄋ ᆭᄋ ᅡ ᆻᄋ ᅡ ᅳᄆ ᅧᄋ ᅵᄂ ᆫᄋ ᅳ ᆷᄒ ᅡ ᅩᄒ ᅪᄑ ᅨᄋ ᅴᄑ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄀ ᅮᄉ ᆼᄋ ᅥ ᅦᄉ ᅥᄇ ᅵᄃ ᆼᄀ ᅩ ᅵᄌ ᆨᄋ ᅥ ᅥᄃ ᅳᄇ ᆫᄐ ᅢ ᅵᄌ ᅵᄇ ᅢᄋ ᅮᄇ ᅵ ᆼᄀ ᅧ ᄑ ᅡ (asynchronous advantage actor-critic) ᄀ ᅮᄌ ᅩᄅ ᅩᄋ ᅦᄋ ᅵᄌ ᆫᄐ ᅥ ᅳᄋ ᅴᅡ ᆼ ᄀᄒ ᅪᅡ ᆨ ᄒᄉ ᆸᅳ ᅳ ᆯ ᄋᄆ ᆯᄐ ᅥ ᅵᄊ ᅳᄅ ᅦᄃ ᅳᄅ ᅩᄀ ᅮᄉ ᆼᄒ ᅥ ᅢᄒ ᆨᄉ ᅡ ᆸ ᅳ ᆨᄃ ᅩ ᄉ ᅩᄅ ᆯᄒ ᅳ ᆼᅡ ᅣ ᆼ ᄉᄉ ᅵᄏ ᅵᄀ ᅩᄋ ᅮᄉ ᅮᄒ ᆫᄉ ᅡ ᅮᄋ ᆨᅥ ᅵ ᆼ ᄉᄀ ᅪᄅ ᆯᄇ ᅳ ᅩᄋ ᅧᄌ ᆯᄉ ᅮ ᅮᄋ ᆻᄋ ᅵ ᆷᄋ ᅳ ᅵᄒ ᆨᄋ ᅪ ᆫᄃ ᅵ ᅬᄋ ᆻᄃ ᅥ ᅡ (Kim , 2019). ᆫᅧ ᅡ ᄒ ᆫ ᄑᄌ ᅮᄉ ᆨᄃ ᅵ ᅦᄋ ᅵᄐ ᅥᄋ ᅴᄌ ᆨᄋ ᅥ ᆼᄋ ᅭ ᅦᄃ ᅢᄒ ᆫᅧ ᅡ ᆫ ᄋᄀ ᅮᄋ ᅦᄂ ᆫᄆ ᅳ ᅵᄀ ᆨᄋ ᅮ ᅴᄀ ᆷᅲ ᅳ ᆼ ᄋᄌ ᅵᄉ ᅮ S&P 100 ᄃ ᅦᄋ ᅵᄐ ᅥᄋ ᅦᄉ ᅥᄋ ᆸᄌ ᅵ ᅡᄀ ᆫᄌ ᅮ ᆼᄎ ᅮ ᅬᄌ ᆨᄒ ᅥ ᅪ (paricle swarm optimization)ᄀ ᅪᄉ ᆫᄒ ᅮ ᆫᄀ ᅪ ᆼᄒ ᅡ ᅪᄒ ᆨᄉ ᅡ ᆸ (recurrent reinforcement learning)ᄋ ᅳ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄀ ᅩᄑ ᅩᄐ ᅳᄑ ᆯ ᅩ ᅵᄋ ᄅ ᅩᄀ ᆼᄒ ᅡ ᅪᅡ ᆨ ᄒᄉ ᆸᄎ ᅳ ᅬᄌ ᆨᄒ ᅥ ᅪᄋ ᅦ Calmar ᄌ ᅵᄉ ᅮᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᆷᄋ ᅡ ᅳᄅ ᅩᄊ ᅥᄒ ᅭᅪ ᄀᄌ ᆨᄋ ᅥ ᆫᄉ ᅵ ᅮᄋ ᆨᄅ ᅵ ᆯᄎ ᅲ ᅬᄌ ᆨᄒ ᅥ ᅪᄅ ᆯᄋ ᅳ ᅵᄅ ᆯᄉ ᅮ ᅮᄋ ᆻᄋ ᅵ ᆷᄋ ᅳ ᅵᄒ ᆨᄋ ᅪ ᆫᄃ ᅵ ᅬ ᆻᄃ ᅥ ᄋ ᅡ (Almahdiᄋ ᅪ Yang, 2017)..
(3) Modular reinforcement learning for dynamic portfolio optimization in the KOSPI market. 215. 3. 연구 ᅦᅵ ᄋ ᄋᄌ ᆫᄐ ᅥ ᅳᄋ ᅴᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅵᄋ ᅵᄅ ᅮᄋ ᅥᄌ ᅵᄀ ᅵᄋ ᅱᅡ ᆫ ᄒᄀ ᆼᄒ ᅡ ᅪᅡ ᆨ ᄒᄉ ᆸᄒ ᅳ ᆫᄀ ᅪ ᆼᄋ ᅧ ᆯᄌ ᅳ ᅮᄀ ᅡᄃ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄀ ᅮᄉ ᆼᄒ ᅥ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄋ ᅦᄋ ᅵᄌ ᆫᄐ ᅥ ᅳᄂ ᆫᄒ ᅳ ᅢ ᄃᄒ ᆼ ᅡ ᆫᄀ ᅪ ᆼᄋ ᅧ ᅦᄉ ᅥᄉ ᆼᄐ ᅡ ᅢᄅ ᅩᄊ ᅥᄌ ᅮᄉ ᆨᄀ ᅵ ᅡᄀ ᆨᄃ ᅧ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄇ ᅳ ᆮᄀ ᅡ ᅩᄒ ᆼᄃ ᅢ ᆼᄋ ᅩ ᅳᄅ ᅩᄌ ᅮᄉ ᆨᅥ ᅵ ᆫ 서 ᆼ ᄌ, ᄑ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄇ ᅢᄇ ᆫᅳ ᅮ ᆯ ᄋᄉ ᆯᄒ ᅵ ᆼᄒ ᅢ ᅡᄆ ᆫᄃ ᅧ ᅡᄋ ᆷ ᅳ ᅵᄌ ᄉ ᆷᅴ ᅥ ᄋᄌ ᅮᄉ ᆨᄃ ᅵ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄉ ᅳ ᆼᄐ ᅡ ᅢᄅ ᅩᄇ ᆮᄋ ᅡ ᅡᄃ ᅡᄋ ᆷᄎ ᅳ ᅬᄌ ᆨᄒ ᅥ ᆼᄃ ᅢ ᆼᅳ ᅩ ᆯ ᄋᄀ ᅨᄉ ᆫᄒ ᅡ ᅡᄀ ᅩᄉ ᅮᄋ ᆨᄋ ᅵ ᆯᄇ ᅳ ᅩᄉ ᆼᄋ ᅡ ᅳᄅ ᅩᄊ ᅥᄒ ᆨᄉ ᅡ ᆸᄒ ᅳ ᆫᄃ ᅡ ᅡ. 3.1. 가격 ᆼᅪ ᅡ ᄀ 하 ᆨ ᄒᄉ ᆸᄋ ᅳ ᅦᄋ ᅵᄌ ᆫᄐ ᅥ ᅳᄂ ᆫᄒ ᅳ ᆫᄀ ᅪ ᆼᄋ ᅧ ᅳᄅ ᅩᄇ ᅮᄐ ᅥᄉ ᆼᄐ ᅡ ᅢᄅ ᆯᄇ ᅳ ᆮᄀ ᅡ ᅩᄌ ᆼᅢ ᅥ ᆨ ᄎᄋ ᅳᄅ ᅩᄊ ᅥᄒ ᆼᄃ ᅢ ᆼᄒ ᅩ ᆫᄃ ᅡ ᅡ. ᄇ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄀ ᅮᄉ ᆼᄃ ᅥ ᆫᄒ ᅬ ᆫᄀ ᅪ ᆼᄋ ᅧ ᆫᄃ ᅳ ᅡ ᆷᄀ ᅳ ᄋ ᅪᄀ ᇀᄋ ᅡ ᆫᄉ ᅳ ᆼᄐ ᅡ ᅢᄅ ᆯᄉ ᅳ ᆼᄉ ᅢ ᆼᄒ ᅥ ᆫᄃ ᅡ ᅡ. xn ᆫᄌ ᅳ ᆼᄀ ᅩ ᅡ cn ᅩᄀ ᅡ hn ᅥᄀ ᅡ ltn , ᄀ ᅥᄅ ᅢᄅ ᆼ vtn ᄋ ᅣ ᅳᄅ ᅩᄀ ᅮᄉ ᆼᄃ ᅥ ᆫᄃ ᅬ ᅡ. tᄋ t, ᄀ t, ᄌ cn t−49 hn t−49 n xt := n lt−49 n vt−49 . ᅡᄋ ᄉ ᆼᄀ ᅭ ᅡᄂ ᆼᄒ ᅳ ᆫᄆ ᅡ ᅩᄃ ᆫᄌ ᅳ ᆼᄆ ᅩ ᆨᄋ ᅩ ᅦᄃ ᅢᄒ ᆫ ᅡ. xn ᅴ tᄋ. cn t−48 hn t−48 n lt−48 n vt−48. ··· ··· ··· ···. cn t hn t . n lt vtn. ᅦ ᄇ ᆨᄐ ᅥᄋ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄋ ᅡ ᅵᄑ ᅭᄒ ᆫᄒ ᅧ ᆫᄃ ᅡ ᅡ. ⊺ Xt := (x0t , x1t , · · · , xN t ) .. ᅢᄇ ᄀ ᆯᄌ ᅧ ᅮᄉ ᆨᄋ ᅵ ᅴᄋ ᆯᄇ ᅵ ᆯᄉ ᅧ ᅮᄋ ᆨᄅ ᅵ ᆯ ᅲ. ytn ᄀ ᅪ. (3.1). (3.2). ᆫᄎ ᅥ ᄌ ᅦᄌ ᆼᄆ ᅩ ᆨᄋ ᅩ ᅦᄃ ᅢᄒ ᆫᄇ ᅡ ᆨᄐ ᅦ ᅥ Yt ᄂ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄋ ᅡ ᅵᄑ ᅭᄒ ᆫᄒ ᅧ ᆫᄃ ᅡ ᅡ. ytn := ln. cn t+1 , cn t. Yt := (yt0 , yt1 , · · · , ytN )⊺ .. (3.3) (3.4). 3.2. 가정 ᆫᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄋ ᅦᄋ ᅵᄌ ᆫᄐ ᅥ ᅳᄀ ᅡᄉ ᆯᄒ ᅵ ᆼᄃ ᅢ ᆫᅪ ᅬ ᆫ ᄒᄀ ᆼᄋ ᅧ ᆫᄀ ᅳ ᅨᄉ ᆫᅡ ᅡ ᆼ ᄉᄋ ᅴᄑ ᆫᄋ ᅧ ᅴ, ᄌ ᅦᄒ ᆫᄃ ᅡ ᆫᄃ ᅬ ᅦᄋ ᅵᄐ ᅥ, ᄒ ᆫᄀ ᅪ ᆼᄀ ᅧ ᅮᄉ ᆼᄀ ᅥ ᅪᄆ ᅩᄒ ᆼᄋ ᅧ ᅴᅡ ᆫ ᄒᄀ ᅨᄅ ᅩᄋ ᆫ ᅵ ᅡᄋ ᄒ ᅧᅦ ᄉᄀ ᅡᄌ ᅵᄀ ᅡᄌ ᆼᄋ ᅥ ᆯᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᆫᄃ ᅡ ᅡ. ᅡᄌ ᄀ ᆼ 1. ᄀ ᅥ ᅥᄅ ᅢᄇ ᅵᄋ ᆼᄋ ᅭ ᆹᄋ ᅥ ᆷ: ᄇ ᅳ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄀ ᅳ ᅨᄉ ᆫᄋ ᅡ ᅴᄑ ᆫᄋ ᅧ ᅴᄅ ᆯᄋ ᅳ ᅱᄒ ᅢᄌ ᆼᄀ ᅳ ᆫᄀ ᅯ ᅥᄅ ᅢᄉ ᅦᅪ ᄋᄀ ᅥᄅ ᅢᄉ ᅮᄉ ᅮᄅ ᅭᄂ ᆫᄋ ᅳ ᆹᄃ ᅥ ᅡᄀ ᅩᄀ ᅡᄌ ᆼᄒ ᅥ ᆫ ᅡ ᄃ. ᅡ ᅡᄌ ᄀ ᆼ 2. ᄉ ᅥ ᅵᄌ ᆼᄎ ᅡ ᆼᄀ ᅮ ᆨᅥ ᅧ ᆹ ᄋᄋ ᆷ: ᄉ ᅳ ᅵᄌ ᆼᄋ ᅡ ᅦᄂ ᆫᄋ ᅳ ᅲᄃ ᆼᄉ ᅩ ᆼᄋ ᅥ ᅵᄎ ᆼᅮ ᅮ ᆫ ᄇᄒ ᅡᄋ ᅧᄇ ᆫᄀ ᅩ ᆼᄒ ᅡ ᅪᅡ ᆨ ᄒᄉ ᆸᄋ ᅳ ᅴᄀ ᅥᄅ ᅢᄀ ᅡᄌ ᅮᄀ ᅡᄋ ᅦᄁ ᅵᄎ ᅵᄂ ᆫᄋ ᅳ ᆼᄒ ᅧ ᆼᄋ ᅣ ᅵᄋ ᆹ ᅥ ᅡᄀ ᄃ ᅩᅡ ᄀᄌ ᆼᄒ ᅥ ᆫᄃ ᅡ ᅡ. ᅡᄌ ᄀ ᆼ 3. ᄀ ᅥ ᅥᄅ ᅢᄀ ᅡᄀ ᆨ: ᄉ ᅧ ᅵᄌ ᆼᄋ ᅡ ᅦᄂ ᆫᄋ ᅳ ᅲᄃ ᆼᄉ ᅩ ᆼᄋ ᅥ ᅵᄎ ᆼᅮ ᅮ ᆫ ᄇᄒ ᅡᄋ ᅧᄋ ᆫᄒ ᅯ ᅡᄂ ᆫᄉ ᅳ ᅵᄀ ᆫᄋ ᅡ ᅦᄉ ᅵᄌ ᆼᄀ ᅡ ᅡᄀ ᆨᄋ ᅧ ᅳᄅ ᅩᄆ ᅢᄆ ᅢᄒ ᆯᄉ ᅡ ᅮᄋ ᆻᄀ ᅵ ᅩ, ᄋ ᅦᄋ ᅵᄌ ᆫ ᅥ ᅳᄂ ᄐ ᆫᅢ ᅳ ᄆᄋ ᆯᄌ ᅵ ᆼᄀ ᅩ ᅡᄋ ᅦᄎ ᅦᄀ ᆯᄋ ᅧ ᅩᄎ ᅡᄋ ᆹᄋ ᅥ ᅵᄀ ᅥᄅ ᅢᄒ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡᄀ ᅩᄀ ᅡᄌ ᆼᄒ ᅥ ᆫᄃ ᅡ ᅡ. 3.3. 제약 ᄇᄋ ᆫ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄀ ᆼᄒ ᅡ ᅪᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅦᄋ ᅵᄌ ᆫᄐ ᅥ ᅳᄋ ᅴᄆ ᅢᄆ ᅢᄂ ᆫᄋ ᅳ ᅲᄀ ᅡᄌ ᆼᄀ ᅳ ᆫᄉ ᅯ ᅵᄌ ᆼᄋ ᅡ ᅦᄉ ᅥᄋ ᅴᄌ ᆨᄋ ᅥ ᆼᄋ ᅭ ᅦᄉ ᅥᄀ ᅢᄋ ᆫᄋ ᅵ ᅵᄀ ᅥᄅ ᅢᄇ ᆫᄃ ᅵ ᅩᄀ ᅡᄌ ᆨᄀ ᅥ ᅩᄋ ᅵᄋ ᆼ ᅭ ᅡᄀ ᄒ ᅵᄒ ᆷᄃ ᅵ ᆫᄃ ᅳ ᅮᄀ ᅡᄌ ᅵᄀ ᅥᄅ ᅢᄋ ᅲᄒ ᆼᄋ ᅧ ᆯᄌ ᅳ ᅦᄒ ᆫᄒ ᅡ ᆫᄃ ᅡ ᅡ. ᄌᄋ ᅦ ᆨ 1. ᄀ ᅣ ᆼᄆ ᅩ ᅢᄃ ᅩᄋ ᆹᄋ ᅥ ᆷ: ᄒ ᅳ ᆫᄀ ᅡ ᆨᄀ ᅮ ᆷᅲ ᅳ ᆼ ᄋᄉ ᅵᄌ ᆼᄋ ᅡ ᅦᄉ ᅥᄉ ᆫᄋ ᅵ ᆼᄀ ᅭ ᅥᄅ ᅢᄋ ᆫᄀ ᅵ ᆼᄆ ᅩ ᅢᄃ ᅩᄋ ᅴᄌ ᅮᄋ ᅭᄐ ᅮᄌ ᅡᄌ ᅡᄂ ᆫᄋ ᅳ ᅬᄀ ᆨᄋ ᅮ ᆫᄀ ᅵ ᅪᄀ ᅵᄀ ᆫᄐ ᅪ ᅮᄌ ᅡᄅ ᅩᄀ ᅢ ᆫᄋ ᅵ ᄋ ᅦᄀ ᅦᄏ ᅳᄀ ᅦᄌ ᅦᄒ ᆫᄃ ᅡ ᅬᄋ ᅥᄋ ᆻᄃ ᅵ ᅡ. ᅦᄋ ᄌ ᆨ 2. ᄉ ᅣ ᅩᄉ ᅮᄌ ᆷ ᄀ ᅥ ᅥᄅ ᅢ: ᄒ ᆫᄀ ᅡ ᆨ ᄀ ᅮ ᆷᅲ ᅳ ᆼ ᄋᄉ ᅵᄌ ᆼᄋ ᅡ ᅦᄉ ᅥ ᄒ ᆫᄀ ᅡ ᆨᄀ ᅮ ᅥᄅ ᅢᄉ ᅩᄅ ᆯ ᄐ ᅳ ᆼᄒ ᅩ ᅢ ᄋ ᅵᄅ ᅯᄌ ᅵᄂ ᆫ ᄀ ᅳ ᅥᄅ ᅢᄋ ᅴ ᄎ ᅬᄉ ᅩ ᄃ ᆫᄋ ᅡ ᅱᄂ ᆫ 1ᄌ ᅳ ᅮᄋ ᅵᄆ ᅧ 1ᄌ ᅮᅵ ᄆᄆ ᆫᅡ ᅡ ᆫ ᄃᄋ ᅱᄋ ᅴᄀ ᅥᄅ ᅢᄂ ᆫᄌ ᅳ ᆼᄋ ᅡ ᅬᄋ ᅦᄉ ᅥᄃ ᆼᄉ ᅡ ᅡᄌ ᅡᄀ ᆫᄋ ᅡ ᅴᄀ ᅨᄋ ᆨᄋ ᅣ ᅳᄅ ᅩᄋ ᅵᄅ ᅯᄌ ᆯᄉ ᅵ ᅮᄋ ᆻᄃ ᅵ ᅡ..
(4) 216. Taeyoon Kim · Bonggyun Ko. 3.4. 포트폴리오 가치 ᆼᄒ ᅡ ᄀ ᅪᅡ ᆨ ᄒᄉ ᆸᄋ ᅳ ᅴᄋ ᅦᄋ ᅵᄌ ᆫᄐ ᅥ ᅳᄂ ᆫᄌ ᅳ ᆼᅢ ᅥ ᆨ ᄎᄋ ᆯᄎ ᅳ ᅬᄌ ᆨᄒ ᅥ ᅪᄉ ᅵᄏ ᅵᄀ ᅵᄋ ᅱᄒ ᆫᄇ ᅡ ᅩᄉ ᆼᄋ ᅡ ᅳᄅ ᅩᄉ ᅮᄋ ᆨᄀ ᅵ ᅪᄑ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄀ ᅡᄎ ᅵᄒ ᆷᄉ ᅡ ᅮᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᆫ ᅡ ᄃ. ᅩ ᅡ ᄑᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄅ ᆯᄀ ᅳ ᅮᄉ ᆼᄒ ᅥ ᅡᄂ ᆫᄌ ᅳ ᆼᄆ ᅩ ᆨᄉ ᅩ ᅮᄀ ᅡ mᄋ ᆯᄄ ᅵ ᅢᄑ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄀ ᅮᄉ ᆼᄋ ᅥ ᆯᄋ ᅳ ᅱᄒ ᅢᄉ ᆫᅢ ᅥ ᆨ ᄐᄃ ᆫᄌ ᅬ ᆼᄆ ᅩ ᆨᄋ ᅩ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄋ ᅡ ᅵᄑ ᅭᄒ ᆫ ᅧ ᆫᄃ ᅡ ᄒ ᅡ. ξt := (1st selected asset, · · · , mth selected asset)⊺ . (3.5) ξn. ᅩᅳ ᄑ ᄐᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄀ ᅮᄉ ᆼᄌ ᅥ ᆼᄆ ᅩ ᆨᄋ ᅩ ᅳᄅ ᅩᄉ ᆫᅥ ᅥ ᆼ ᄌᄃ ᆫᄌ ᅬ ᅡᄉ ᆫ Xtξ ᄋ ᅡ ᆫᄀ ᅳ ᆨᄌ ᅡ ᆼᄆ ᅩ ᆨ xt t ᄋ ᅩ ᅦᄃ ᅢᄒ ᆫᄐ ᅡ ᅲᄑ ᆯᄋ ᅳ ᅵᄃ ᅡ. ξ1. ξ1. ξm. Xtξ := (xt t , xt t , · · · , xt t )⊺ . ᅦᅵ ᄋ ᄋᄌ ᆫᄐ ᅥ ᅳᄋ ᅴᄒ ᆼᄃ ᅢ ᆼᄋ ᅩ ᆫᄉ ᅳ ᅵᄌ ᆷ tᄋ ᅥ ᅦᄉ ᅥᄌ ᆼᄆ ᅩ ᆨnᄋ ᅩ ᅦᄃ ᅢᄒ ᆫᄀ ᅡ ᅡᄌ ᆼᄎ ᅮ ᅵ ᄎᄋ ᅵ ᅴᆨ ᅦᄐ ᄇ ᅥ Wt ᄂ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄋ ᅡ ᅵᄑ ᅭᄒ ᆫᄒ ᅧ ᆫᄃ ᅡ ᅡ.. wtn ᄋ ᅳᄅ ᅩ. (3.6). ᅵᄅ ᄋ ᅯᄌ ᆫᄃ ᅵ ᅡ. ᄉ ᆫᅢ ᅥ ᆨ ᄐᄃ ᆫᄌ ᅬ ᆼᄆ ᅩ ᆨ ξt ᄋ ᅩ ᅦᄃ ᅢᄒ ᆫᄀ ᅡ ᅡᄌ ᆼ ᅮ. Wt := (wt1 , · · · , wtm )⊺ .. (3.7). t=0 ᄋ ᆯᄄ ᅵ ᅢ, ᄋ ᅦᄋ ᅵᄌ ᆫᄐ ᅥ ᅳᄂ ᆫᄎ ᅳ ᅩᄀ ᅵᄌ ᅡᄉ ᆫᄀ ᅡ ᅡᄎ ᅵ P0 ᄅ ᆯᄇ ᅳ ᅮᄋ ᅧᄇ ᆮᄋ ᅡ ᅳᄆ ᅧᄋ ᅦᄋ ᅵᄌ ᆫᄐ ᅥ ᅳᄂ ᆫᄋ ᅳ ᅵᄎ ᅩᄀ ᅵᄌ ᅡᄉ ᆫᄋ ᅡ ᆯᄀ ᅳ ᅡᄌ ᆼᄎ ᅮ ᅵ W0 ᄋ ᅪ ᅮᄉ ᄌ ᆨ ᅡ ᅵ ᄀᄀ ᆨᄋ ᅧ ᅦ ᄄ ᅡᄅ ᅡ ᄇ ᆫᄇ ᅮ ᅢᄒ ᅢᄉ ᅥ ᄌ ᅮᄉ ᆨ ᄇ ᅵ ᅩᄋ ᅲ ᄀ ᅢᄉ ᅮᄅ ᆯ ᄄ ᅳ ᆺᄒ ᅳ ᅡᄂ ᆫ ᄌ ᅳ ᆼᄉ ᅥ ᅮᄀ ᆹᄋ ᅡ ᆯ ᄀ ᅳ ᅡᄌ ᅵᄂ ᆫ ᄇ ᅳ ᆨᄐ ᅦ ᅥ Ψ0 ᄅ ᆯ ᄋ ᅳ ᆮᄂ ᅥ ᆫᄃ ᅳ ᅡ. ᄋ ᅵᄒ ᅮ ᄉ ᅵᄌ ᆷ ᅥ tᄋ ᅦᄉ ᅥᅴ ᄋᄌ ᅮᄉ ᆨᄇ ᅵ ᅩᄋ ᅲᄀ ᅢᄉ ᅮ Ψt ᄂ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄃ ᅡ ᅡ. Ψt :=. h P · w1 i h P · w1 i h P · wm i⊺ t t t t t t , ,··· , . 1 1 ct ct cm t. (3.8). Φ0 ᄂ ᆫ ᄎ ᅳ ᅩᄀ ᅵ ᄌ ᅡᄉ ᆫ P0 ᄋ ᅡ ᅦᄉ ᅥ W0 ᄋ ᅦ ᄄ ᅡᄅ ᅡ ᄌ ᅡᄉ ᆫᄋ ᅡ ᆯ ᄇ ᅳ ᅢᄇ ᆫᄒ ᅮ ᅡᄀ ᅩ ᄉ ᅩᄉ ᅮᄌ ᆷ ᄀ ᅥ ᅥᄅ ᅢ ᄌ ᅦᄒ ᆫᄋ ᅡ ᅦ ᄄ ᅡᄅ ᅡ ᄂ ᆷᄋ ᅡ ᆫ ᄌ ᅳ ᆫᄋ ᅡ ᅧᄀ ᅡᄎ ᅵᄋ ᅵᄃ ᅡ. ᄋᄒ ᅵ ᅮᄉ ᅵᄌ ᆷ tᄋ ᅥ ᅦᄉ ᅥᄋ ᅴᅡ ᆫ 져 ᄋᄒ ᆫᄀ ᅧ ᆷ Φt ᄂ ᅳ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄃ ᅡ ᅡ. ᄋ ᅵᄄ ᅢ, Ctξ ᄂ ᆫ tᄉ ᅳ ᅵᄌ ᆷᄋ ᅥ ᅦᄉ ᅥᄑ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄌ ᅡᄉ ᆫᄋ ᅡ ᅴᄌ ᆼᄀ ᅩ ᅡᄋ ᅴᄇ ᆨ ᅦ ᅥᄋ ᄐ ᅵᅡ ᄃ. ξ1. ξm. ξ1. Ctξ := (ct t , ct t , · · · , ct t )⊺ , Φt :=. m X Pt · wtn ξn. n=1. .. (3.9) (3.10). Ct t. t > 0ᄋ ᆯᄄ ᅵ ᅢ, ᄃ ᅡᄋ ᆷᄉ ᅳ ᅵᄌ ᆷᄋ ᅥ ᅴᄑ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄋ ᅴᄀ ᅡᄎ ᅵ Pt+1 ᄂ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄃ ᅡ ᅡ. ξ Pt+1 = Ψt ⊙ Ct+1 + Φt ,. (3.11). ᅧᄀ ᄋ ᅵᅦ ᄋᄉ ᅥ ⊙ᄋ ᆫᄋ ᅳ ᅡᄃ ᅡᄆ ᅡᄅ ᅳᄀ ᆸ (hadamard product)ᄋ ᅩ ᅵᄃ ᅡ.. 4. 구조 ᅩᅳ ᄏ ᄉᄑ ᅵ 200 ᄌ ᆼᄆ ᅩ ᆨᅮ ᅩ ᆼ ᄌᄌ ᆨᄌ ᅥ ᆯᄒ ᅥ ᆫᄌ ᅡ ᆼᄆ ᅩ ᆨᅳ ᅩ ᆯ ᄋᄉ ᆫᅢ ᅥ ᆨ ᄐᄒ ᅡᄀ ᅩᄉ ᆫᅢ ᅥ ᆨ ᄐᄃ ᆫᄌ ᅬ ᆼᄆ ᅩ ᆨᅳ ᅩ ᆯ 우 ᆫ ᄇᄇ ᅢᄒ ᅡᄀ ᅵᄋ ᅱᄒ ᅢᄃ ᅮᄀ ᅡᄌ ᅵᄉ ᆫᅧ ᅵ ᆼ ᄀᄆ ᆼᄋ ᅡ ᅳᄅ ᅩᄀ ᅮᄉ ᆼᄃ ᅥ ᆫ ᅬ ᄃ. ᅩ ᅡ ᆼ ᄌᄆ ᆨᄋ ᅩ ᆯᄉ ᅳ ᆫᅥ ᅥ ᆼ ᄌᄒ ᅡᄀ ᅵᄋ ᅱᄒ ᅢᄌ ᆼᄆ ᅩ ᆨᄋ ᅩ ᅴᄀ ᅵᄃ ᅢᄉ ᅮᄋ ᆨᄅ ᅵ ᆯᄋ ᅲ ᆯᄋ ᅳ ᅨᄎ ᆨᄒ ᅳ ᅡᄂ ᆫ ESMᄀ ᅳ ᅪᄉ ᆫᅥ ᅥ ᆼ ᄌᄃ ᆫᄌ ᅬ ᆼᄆ ᅩ ᆨᅳ ᅩ ᆯ ᄋᄌ ᆨᄌ ᅥ ᆼᄇ ᅥ ᅵᄋ ᆯᄅ ᅲ ᅩᄇ ᆫᄇ ᅮ ᅢᄒ ᅡ ᅵᄋ ᄀ ᅱᅡ ᆫ ᄒ AAMᄋ ᅳᄅ ᅩᄋ ᅵᄅ ᅮᄋ ᅥᄌ ᆫᄃ ᅵ ᅡ. ᄌ ᆫᄎ ᅥ ᅦᄌ ᆨᆫ ᅥ ᄋ ᅵᄀ ᅪᄌ ᆼᄋ ᅥ ᆫ Figure 4.1ᄀ ᅳ ᅪᄀ ᇀᄋ ᅡ ᅵᄑ ᅭᄒ ᆫᄃ ᅧ ᆫᄃ ᅬ ᅡ. ᄒ ᅢᄃ ᆼᄆ ᅡ ᅩᄃ ᆯᄃ ᅲ ᆯᄋ ᅳ ᆫᄀ ᅳ ᆫᄅ ᅪ ᆫᄋ ᅧ ᆫ ᅧ ᅮ (Jiang ᄃ ᄀ ᆼ, 2017; Zhang ᄃ ᅳ ᆼ, 2020)ᄋ ᅳ ᅦᄉ ᅥᄉ ᅮᄒ ᆼᄃ ᅢ ᆫᄇ ᅬ ᅡᄋ ᅪᄀ ᇀᄋ ᅡ ᅵ t − 49ᄉ ᅵᄌ ᆷᄇ ᅥ ᅮᄐ ᅥ tᄉ ᅵᄌ ᆷᄁ ᅥ ᅡᄌ ᅵᄋ ᅴ 50ᄀ ᅢᄋ ᅴᄉ ᅵᄀ ᅨᄋ ᆯ ᅧ ᅦᄋ ᄃ ᅵᅥ ᄐᄅ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᆫᄃ ᅡ ᅡ..
(5) Modular reinforcement learning for dynamic portfolio optimization in the KOSPI market. 217. Figure 4.1 Interaction between agent and environment. 4.1. 주식 평가 모듈 (evaluation stock module) ESMᄋ ᆫᄆ ᅳ ᅩᄃ ᆫᄃ ᅳ ᅢᄉ ᆼᄌ ᅡ ᆼᄆ ᅩ ᆨᄋ ᅩ ᅴᄀ ᅵᄃ ᅢᄉ ᅮᄋ ᆨᄅ ᅵ ᆯᄋ ᅲ ᆯᄋ ᅳ ᅨᄎ ᆨᄒ ᅳ ᅡᄀ ᅩᄂ ᇁᄋ ᅩ ᆫᄀ ᅳ ᅵᄃ ᅢᄉ ᅮᄋ ᆨᄅ ᅵ ᆯᅳ ᅲ ᆯ ᄋᄀ ᅡᄌ ᆫᄌ ᅵ ᆼᄆ ᅩ ᆨᄋ ᅩ ᆯᄉ ᅳ ᆫᅥ ᅥ ᆼ ᄌᄒ ᅡᄀ ᅵᄋ ᅱᄒ ᅢᄉ ᅡ ᄋᄃ ᆼ ᅭ ᆫᅡ ᅬ ᄃ. ᄎ ᅬᄀ ᆫᄋ ᅳ ᆫᄀ ᅵ ᆼᄉ ᅩ ᆫᅧ ᅵ ᆼ ᄀᄆ ᆼᄋ ᅡ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄌ ᅮᄉ ᆨᄋ ᅵ ᅴᄉ ᅮᄋ ᆨᄅ ᅵ ᆯᅳ ᅲ ᆯ ᄋᄋ ᅨᄎ ᆨᄒ ᅳ ᅡᄀ ᅵᄋ ᅱᅡ ᆫ ᄒᄋ ᆫᄀ ᅧ ᅮᄀ ᅡᄌ ᆫᄒ ᅵ ᆼᄃ ᅢ ᅬᄋ ᆻᄃ ᅥ ᅡ (Chenᄀ ᅪ He, 2018; Mehtabᄀ ᅪ Sen, 2020; Nelson ᄃ ᆼ, 2017; Selvin , 2017). ᄋ ᅳ ᆫᄀ ᅵ ᆼᄉ ᅩ ᆫᅧ ᅵ ᆼ ᄀᄆ ᆼᄀ ᅡ ᅮᄌ ᅩᄌ ᆼᄉ ᅮ ᆫᄒ ᅮ ᆫᄉ ᅪ ᆫᄀ ᅵ ᆼᄆ ᅧ ᆼᄋ ᅡ ᆫᄉ ᅳ ᅵᄀ ᅨ ᆯᄋ ᅧ ᄋ ᅨᄎ ᆨᄋ ᅳ ᅦᄌ ᆨᄒ ᅥ ᆸᄒ ᅡ ᅡᄆ ᅧᄇ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄆ ᅳ ᆫᄀ ᅥ ᅪᄀ ᅥᄇ ᅮᄐ ᅥᅬ ᄎᄀ ᆫᄁ ᅳ ᅡᄌ ᅵᄋ ᅴᄌ ᆼᄇ ᅥ ᅩᄅ ᆯᄇ ᅳ ᆫᄋ ᅡ ᆼᄒ ᅧ ᅡᄋ ᅧᄃ ᅡᄋ ᆷᄉ ᅳ ᅵᄌ ᆷᄋ ᅥ ᅴᄀ ᆹᄋ ᅡ ᆯᄋ ᅳ ᅨᄎ ᆨᄒ ᅳ ᅡ ᅵᄋ ᄀ ᅱᄒ ᅢ LSTM ᄀ ᅮᄌ ᅩᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᆫᄃ ᅡ ᅡ. ESMᄋ ᆫ (batch size, 50, 4)ᄋ ᅳ ᅴᄒ ᆼᄐ ᅧ ᅢᄅ ᆯᄀ ᅳ ᅡᄌ ᆫ 3ᄎ ᅵ ᅡᄋ ᆫᄐ ᅯ ᆫᄉ ᅦ ᅥ Xt ᄅ ᆯᄋ ᅳ ᆸᄅ ᅵ ᆨᄇ ᅧ ᆮ ᅡ ᅡᄃ ᄋ ᅮᄃ ᆫᄀ ᅡ ᅨᄋ ᅴ LSTMᄎ ᆼᄀ ᅳ ᅪᄋ ᆫᄌ ᅪ ᆫᅧ ᅥ ᆫ 여 ᆯ ᄀᄎ ᆼᄋ ᅳ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄌ ᅢᄀ ᅱᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄀ ᅵᄃ ᅢᄉ ᅮᄋ ᆨᄅ ᅵ ᆯ Ybt+1 ᄅ ᅲ ᆯᄋ ᅳ ᅨᄎ ᆨᄒ ᅳ ᆫᄃ ᅡ ᅡ. ESMᄋ ᅵ Xt ᄅ ᅩ ᅮᄐ ᄇ ᅥ Ybt+1 ᄅ ᆯᄋ ᅳ ᅨᄎ ᆨᄒ ᅳ ᅡᄂ ᆫᄀ ᅳ ᅪᄌ ᆼᄋ ᅥ ᆫ Figure 4.2ᄀ ᅳ ᅪᄀ ᇀᄋ ᅡ ᅵᄂ ᅡᄐ ᅡᄂ ᅢᄋ ᅥᄌ ᆫᄃ ᅵ ᅡ. 4.1.1. 학습 ESMᄋ ᆫᄀ ᅳ ᆨᄌ ᅡ ᆼᄆ ᅩ ᆨᄋ ᅩ ᅴᄀ ᅵᄃ ᅢᄉ ᅮᄋ ᆨᄅ ᅵ ᆯᅳ ᅲ ᆯ ᄋᄋ ᆫᄉ ᅧ ᆨᄌ ᅩ ᆨᄋ ᅥ ᆫᄀ ᅵ ᆹᄋ ᅡ ᅳᄅ ᅩᄋ ᅨᄎ ᆨᄒ ᅳ ᅡᄆ ᅧᄋ ᅨᄎ ᆨᄃ ᅳ ᆫᄀ ᅬ ᆹᄀ ᅡ ᅪᄉ ᆯᄌ ᅵ ᅦᄀ ᆹᄋ ᅡ ᅴᄋ ᅩᄎ ᅡᄌ ᅦᄀ ᆸᄒ ᅩ ᆸᄋ ᅡ ᆯᅩ ᅳ ᆫ ᄉ ᄉᄒ ᆯ ᅵ ᆷᅮ ᅡ ᄉᄅ ᅩᄒ ᅡᄋ ᅧᄉ ᆫᄉ ᅩ ᆯᄀ ᅵ ᆹᄋ ᅡ ᆯᄎ ᅳ ᅬᄉ ᅩᄅ ᅩᄒ ᅡᄀ ᅵᄋ ᅱᄒ ᅢᄀ ᅡᄌ ᆼᄎ ᅮ ᅵᄀ ᅡᄋ ᆸᄃ ᅥ ᅦᄋ ᅵᄐ ᅳᄃ ᆫᄃ ᅬ ᅡ. ᄉ ᆫᄉ ᅩ ᆯᄀ ᅵ ᆹᄋ ᅡ ᆫᄋ ᅵ ᅩᄎ ᅡᄌ ᅦᄀ ᆸᄒ ᅩ ᆸᄋ ᅡ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀ ᅡ ᅡ. ᄃ n n JE (θE ) = (yt+1 − ybt+1 )2 . (4.1) ESMᄋ ᅴᄑ ᅡᄅ ᅡᄆ ᅵᄐ ᅥ θE ᄋ ᅴᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᆫ JE ᄅ ᅳ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄃ ᅡᄋ ᆷᄀ ᅳ ᅪᅡ ᄀᄋ ᇀ ᅵᄋ ᅵᄅ ᅮᄋ ᅥᄌ ᆫᄃ ᅵ ᅡ. λᄂ ᆫᄒ ᅳ ᅡᄋ ᅵᄑ ᅥᄑ ᅡᄅ ᅡᄆ ᅵᄐ ᅥᄋ ᆫᅡ ᅵ ᄒᄉ ᆨ ᆸᄅ ᅳ ᆯᄋ ᅲ ᅵ ᅡ. ᄃ ∂JE (θE ) θE ← θE − λ . (4.2) ∂θE 4.2. 자산 분배 네트워크 (asset allocation module) ESMᄋ ᅦᄉ ᅥᄉ ᆫᅢ ᅥ ᆨ ᄐᄃ ᆫᄌ ᅬ ᆼᄆ ᅩ ᆨᄋ ᅩ ᆯᄌ ᅳ ᆨᅥ ᅥ ᆯ ᄌᄒ ᆫᄀ ᅡ ᅡᄌ ᆼᄎ ᅮ ᅵᄅ ᅩᄇ ᆫᄇ ᅮ ᅢᄒ ᅡᄀ ᅵᄋ ᅱᄒ ᅢᄉ ᅡᄋ ᆼᄃ ᅭ ᅬᄂ ᆫᄌ ᅳ ᅡᄉ ᆫᄇ ᅡ ᆫᄇ ᅮ ᅢᄂ ᅦᄐ ᅳᄋ ᅯᄏ ᅳᄂ ᆫᄏ ᅳ ᆫᄇ ᅥ ᆯᄅ ᅩ ᅮᄉ ᆫᄉ ᅧ ᆫ ᅵ ᆼᄆ ᅧ ᄀ ᆼᄋ ᅡ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᆫᅥ ᅡ ᆼ ᄌᄎ ᆨᄀ ᅢ ᅵᄇ ᆫᅡ ᅡ ᆼ ᄀᄒ ᅪᅡ ᆨ ᄒᄉ ᆸᄋ ᅳ ᅵᄃ ᅡ. ᅩᅳ ᄑ ᄐᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᅬ ᄎᄌ ᆨᄒ ᅥ ᅪᄅ ᆯᄋ ᅳ ᅱᅡ ᆫ 혀 ᆫ ᄋᄀ ᅮᄅ ᅩᄏ ᆫᄇ ᅥ ᆯᄅ ᅩ ᅮᄉ ᆫᄉ ᅧ ᆫᅧ ᅵ ᆼ ᄀᄆ ᆼᄀ ᅡ ᅮᄌ ᅩᄅ ᆯᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅢᄀ ᆯᅥ ᅧ ᆼ ᄌᄃ ᆫᄌ ᅬ ᆼᅢ ᅥ ᆨ ᄎᄋ ᆯᄑ ᅳ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄌ ᅡᄉ ᆫᄇ ᅡ ᅢ ᆫᄀ ᅮ ᄇ ᅡᄌ ᆼᄎ ᅮ ᅵᄅ ᅩᄒ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅡᄂ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅵᄀ ᆼᄒ ᅡ ᅪᄒ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋᄐ ᆼᄒ ᅩ ᆫᄌ ᅡ ᅡᄉ ᆫᄇ ᅡ ᅢᄇ ᆫᄋ ᅮ ᅦᄉ ᅥᄒ ᅭᄀ ᅪᄌ ᆨᄋ ᅥ ᆫᄀ ᅵ ᆯᄀ ᅧ ᅪᄅ ᆯᄋ ᅳ ᆮᄋ ᅥ ᆯᄉ ᅳ ᅮᄋ ᆻᄋ ᅵ ᆷᄋ ᅳ ᅵᄒ ᆨᄋ ᅪ ᆫᄃ ᅵ ᅬᄋ ᆻ ᅥ ᅡ (Jiang ᄃ ᄃ ᆼ, 2017). ᄋ ᅳ ᅵᄋ ᅦᄄ ᅡᄅ ᅡᄌ ᅡᄉ ᆫᄇ ᅡ ᆫᄇ ᅮ ᅢᄂ ᅦᄐ ᅳᄋ ᅯᄏ ᅳᄋ ᅴᄀ ᅮᄌ ᅩᄋ ᅦᄃ ᅩ 3ᄎ ᅡᄋ ᆫᄐ ᅯ ᆫᄉ ᅦ ᅥ Xtξt ᄋ ᅦᄃ ᅢᄒ ᅢᄏ ᆫᄇ ᅥ ᆯᄅ ᅩ ᅮᄉ ᆫᄑ ᅧ ᆯᄐ ᅵ ᅥ.
(6) 218. Taeyoon Kim · Bonggyun Ko. Figure 4.2 ESM for predict expectation return. ᄅᄋ ᆯ ᅳ ᅵᅭ ᆼ ᄋᄒ ᅡᄂ ᆫᄃ ᅳ ᅮᄃ ᆫᄀ ᅡ ᅨᄋ ᅴᄏ ᆫᄇ ᅥ ᆯᄅ ᅩ ᅮᄉ ᆫᄃ ᅧ ᆫᄀ ᅡ ᅨᄋ ᅪᄀ ᅨᄉ ᆫᄃ ᅡ ᆫᄑ ᅬ ᅡᄅ ᅡᄆ ᅵᄐ ᅥᄃ ᆯᄋ ᅳ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅢᄀ ᅡᄌ ᆼᄎ ᅮ ᅵᄅ ᆯᄀ ᅳ ᆯᄌ ᅧ ᆼᄒ ᅥ ᅡᄂ ᆫᄌ ᅳ ᆼᅢ ᅥ ᆨ ᄎᄀ ᆯᅥ ᅧ ᆼ ᄌᄃ ᆫᄀ ᅡ ᅨ ᅩᄋ ᄅ ᅵᅮ ᄅᄋ ᅥᄌ ᆫᄃ ᅵ ᅡ. AAMᄋ ᅴᄌ ᆫᄎ ᅥ ᅦᄌ ᆨᄋ ᅥ ᆫᄀ ᅵ ᅮᄌ ᅩᄂ ᆫ Figure 4.3ᄀ ᅳ ᅪᄀ ᇀᄋ ᅡ ᅳᄆ ᅧ m = 10 ᄋ ᆯᄄ ᅵ ᅢᄋ ᅴᄀ ᅮᄌ ᅩᄅ ᆯᄑ ᅳ ᅭᄒ ᆫᄒ ᅧ ᆫᄃ ᅡ ᅡ. AAMᄋ ᆫᄋ ᅳ ᅱᄋ ᅪᄀ ᇀᄋ ᅡ ᆫ ᅳ ξ ξ ᆫᄀ ᅵ ᄉ ᆼᄆ ᅧ ᆼᄋ ᅡ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄉ ᆫᅢ ᅥ ᆨ ᄐᄃ ᆫᄌ ᅬ ᅡᄉ ᆫᄋ ᅡ ᅴᄃ ᅦᄋ ᅵᄐ ᅥᄉ ᆼᄐ ᅡ ᅢ Xt ᄅ ᅩᄀ ᅮᄒ ᆫᄌ ᅡ ᆼᄎ ᅥ ᆨ πA (Xt )ᄋ ᅢ ᆯᄑ ᅳ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄀ ᅡᄌ ᆼᄎ ᅮ ᅵ Wt ᄅ ᅩᄉ ᅡᄋ ᆼᄒ ᅭ ᆫ ᅡ ᅡ. ᄃ 4.2.1. 학습 ξ AAMᄋ ᅴᄉ ᆼᄀ ᅥ ᅪᄂ ᆫᄉ ᅳ ᆫᄐ ᅥ ᆨᄃ ᅢ ᆫᄌ ᅬ ᆼᄆ ᅩ ᆨᄋ ᅩ ᅴ t+1ᄉ ᅵᄌ ᆷᄋ ᅥ ᅴᄉ ᅮᄋ ᆨᄅ ᅵ ᆯ Yt+1 ᅲ ᅳᄅ ᄋ ᅩᄇ ᅮᄐ ᅥᄀ ᆯᅥ ᅧ ᆼ ᄌᄃ ᆫᄃ ᅬ ᅡ. ξ1. ξm. ξ1. Ytξ := (yt t , yt t , · · · , yt t )⊺ .. (4.3). ᄇᄉ ᅩ ᆼᄋ ᅡ ᆯ Ytξ ᄆ ᅳ ᆫᄋ ᅡ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅢᄉ ᅥᄀ ᆯᅥ ᅧ ᆼ ᄌᄒ ᅡᄂ ᆫᅥ ᅳ ᄀᄋ ᆺ ᆫᄌ ᅳ ᆼᄆ ᅩ ᆨᄉ ᅩ ᆫᅢ ᅥ ᆨ ᄐᄋ ᅦᄋ ᅴᄌ ᆫᄌ ᅩ ᆨᄋ ᅥ ᅵᄆ ᅧᄌ ᅡᄉ ᆫᄇ ᅡ ᅢᄇ ᆫᄉ ᅮ ᆼᄀ ᅥ ᅪᄋ ᅦᄆ ᅮᄌ ᆨᄋ ᅡ ᅱᄉ ᆼᄋ ᅥ ᅵᄇ ᅮᄋ ᅧ ᆯᄉ ᅬ ᄃ ᅮᄋ ᆻᄋ ᅵ ᅳᄆ ᅳᄅ ᅩᄃ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄋ ᅡ ᆫᄇ ᅳ ᅩᄉ ᆼᅡ ᅡ ᆷ ᄒᄉ ᅮ JA (θA )ᄅ ᆯᄌ ᅳ ᅦᄋ ᆫᄒ ᅡ ᆫᄃ ᅡ ᅡ. ξ ξ JA (θA ) = (Yt+1 − Y¯t+1 ) ⊙ πA (Xtξ ).. (4.4). , ᅦᄉ ᄋ ᅥᄋ ᅴᄉ ᅮᄋ ᆨᄅ ᅵ ᆯᅳ ᅲ ᆯ ᄋᄌ ᅦᅬ ᄋᄒ ᅡᄀ ᅩ AAMᄋ ᆫᄋ ᅳ ᅵᄇ ᅩᄉ ᆼᅡ ᅡ ᆷ ᄒᄉ ᅮᄅ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄉ ᆫᅢ ᅥ ᆨ ᄐᄃ ᆫᄌ ᅬ ᆼᄆ ᅩ ᆨᄋ ᅩ ᅴᄀ ᆫᄋ ᅲ ᆯᄇ ᅵ ᅢᄇ ᆫ Wt = ᅮ ᅡᄉ ᄌ ᆫᅢ ᅡ ᄇᄇ ᆫᄋ ᅮ ᅦᄃ ᅢᄒ ᅢᄉ ᅥᄆ ᆫᄉ ᅡ ᆼᄀ ᅥ ᅪᄀ ᅡᄎ ᅮᄌ ᆼᄃ ᅥ ᆯᄉ ᅬ ᅮᄋ ᆻᄃ ᅵ ᅡ. AAMᄋ ᆫᄉ ᅳ ᆫᄐ ᅥ ᆨᄃ ᅢ ᆫᄌ ᅬ ᆼᄆ ᅩ ᆨᄋ ᅩ ᅦᄃ ᅢᄒ ᅢᄑ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄋ ᅴᄀ ᅡᄌ ᆼᄎ ᅮ ᅵᄅ ᆯᄀ ᅳ ᆯᅥ ᅧ ᆼ ᄌᄒ ᅡᄆ ᅧᄀ ᆼᄉ ᅧ ᅡᄉ ᆼᄉ ᅡ ᆼᄇ ᅳ ᆸᄋ ᅥ ᅦᄋ ᅴᄒ ᅢᄇ ᅩᄉ ᆼᅡ ᅡ ᆷ ᄒᄉ ᅮ JA (θA )ᄅ ᆯ ᅳ ᆨᄃ ᅳ ᄀ ᅢᅪ ᄒᄉ ᅵᄏ ᅵᄀ ᅵᄋ ᅱᅡ ᆫ ᄒᄑ ᅡᄅ ᅡᄆ ᅵᄐ ᅥ θA ᄅ ᆯᄒ ᅳ ᆨᄉ ᅡ ᆸᄒ ᅳ ᆫᄃ ᅡ ᅡ. 1 ,··· (m. θA ← θA + λ. ∂JA (θA ) . ∂θA. 1 ) m. (4.5). 5. 실험 ᄇᄋ ᆫ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄉ ᅡᄋ ᆼᄒ ᅭ ᆫᄉ ᅡ ᅩᄑ ᅳᄐ ᅳᄋ ᅰᄋ ᅥᄂ ᆫᄑ ᅳ ᅡᄋ ᅵᄊ ᆫ 3.7.6ᄋ ᅥ ᅵᄆ ᅧᄐ ᆫᄉ ᅦ ᅥᄑ ᆯᄅ ᅳ ᅩᄋ ᅮ 1.15, numpy 1.18.1, ᄋ ᆫᄃ ᅱ ᅩᄋ ᅮ 10ᄋ ᅦᄉ ᅥ ᆯᄒ ᅵ ᄉ ᆼᄃ ᅢ ᅬᄋ ᆻᄃ ᅥ ᅡ. AAMᄋ ᅴᄀ ᅮᄉ ᆼᄌ ᅥ ᅡᄉ ᆫᄀ ᅡ ᆺᄉ ᅢ ᅮ mᄋ ᆫ 8, 10, 12ᄋ ᅳ ᅦᄉ ᅥᄀ ᆨᄀ ᅡ ᆨᄎ ᅡ ᆨᄌ ᅳ ᆼᄒ ᅥ ᅡᄋ ᆻᄃ ᅧ ᅡ..
(7) Modular reinforcement learning for dynamic portfolio optimization in the KOSPI market. 219. Figure 4.3 AAM for asset allocation with m = 10. 5.1. 데이터 ᆫ ᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥ ᄉ ᅡᄋ ᆼᄃ ᅭ ᆫ ᄌ ᅬ ᅮᄀ ᅡᄋ ᅴ ᄃ ᅦᄋ ᅵᄐ ᅥᄂ ᆫ yahoo financeᄅ ᅳ ᅩᄇ ᅮᄐ ᅥ ᄉ ᅮᄌ ᆸᄃ ᅵ ᅬᄋ ᆻᄀ ᅥ ᅩ, 2020ᄂ ᆫ 11ᄋ ᅧ ᆯ 20ᄋ ᅯ ᆯ ᄀ ᅵ ᅵᄌ ᆫ ᅮ KOSPI 200 ᄌ ᆼᄆ ᅩ ᆨᄋ ᅩ ᅳᄅ ᅩᄉ ᆫᄌ ᅥ ᆼᄃ ᅥ ᆫᄌ ᅬ ᆼᄆ ᅩ ᆨᄋ ᅩ ᅴ 2015ᄂ ᆫ 9ᄋ ᅧ ᆯ 2ᄋ ᅯ ᆯᄇ ᅵ ᅮᄐ ᅥ 2020ᄂ ᆫ 11ᄋ ᅧ ᆯ 19ᄋ ᅯ ᆯᄁ ᅵ ᅡᄌ ᅵᄋ ᅴ 1275 ᄋ ᆼᄋ ᅧ ᆸᄋ ᅥ ᆯᄋ ᅵ ᅴᄌ ᆼ ᅩ ᅡ, ᄌ ᄀ ᅥᄀ ᅡ, ᄀ ᅩᄀ ᅡ, ᄀ ᅥᄅ ᅢᄅ ᆼᄃ ᅣ ᅦᄋ ᅵᄐ ᅥᄅ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. 5.2. 학습과 테스트 ᆨᄉ ᅡ ᄒ ᆸᄀ ᅳ ᅪ ᄐ ᅦᄉ ᅳᄐ ᅳ ᄃ ᅦᄋ ᅵᄐ ᅥ ᄇ ᆫᄒ ᅮ ᆯᄋ ᅡ ᆫ 8:2ᄅ ᅳ ᅩ ᄂ ᅡᄂ ᅮᄋ ᅥᄌ ᅧ ᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅦᄂ ᆫ 2015ᄂ ᅳ ᆫ 9ᄋ ᅧ ᆯ 2ᄋ ᅯ ᆯᄇ ᅵ ᅮᄐ ᅥ 2019ᄂ ᆫ 11ᄋ ᅧ ᆯ 7ᄋ ᅯ ᆯᄁ ᅵ ᅡᄌ ᅵ 1019ᄋ ᆼᅥ ᅧ ᆸ ᄋᄋ ᆯᄋ ᅵ ᅴᄃ ᅦᄋ ᅵᄐ ᅥᄀ ᅡᄉ ᅡᄋ ᆼᄃ ᅭ ᅬᄆ ᅧᄐ ᅦᄉ ᅳᄐ ᅳᄋ ᅦᄂ ᆫ 2019ᄂ ᅳ ᆫ 11ᄋ ᅧ ᆯ 8ᄋ ᅯ ᆯᄇ ᅵ ᅮᄐ ᅥ 2020ᄂ ᆫ 11ᄋ ᅧ ᆯ 19ᄋ ᅯ ᆯᄁ ᅵ ᅡᄌ ᅵ 255ᄋ ᆼᅥ ᅧ ᆸ ᄋᄋ ᆯ ᅵ ᅴᄃ ᄋ ᅦᅵ ᄋᄐ ᅥᄀ ᅡᄉ ᅡᄋ ᆼᄃ ᅭ ᆫᄃ ᅬ ᅡ. ᄒ ᆨᄉ ᅡ ᆸᄀ ᅳ ᅪᄌ ᆼᄋ ᅥ ᅦᄉ ᅥᄂ ᆫ AAMᄋ ᅳ ᅴᄋ ᅩᄇ ᅥᄑ ᅵᄐ ᆼᄋ ᅵ ᆯᄇ ᅳ ᆼᄌ ᅡ ᅵᄒ ᅡᄀ ᅵᄋ ᅱᄒ ᅢᄆ ᅢᄉ ᅵᄌ ᆷ tᄆ ᅥ ᅡᄃ ᅡᄑ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩ b ᆼᄆ ᅩ ᄌ ᆨ ξt ᄅ ᅩ ᆯᄆ ᅳ ᅮᄌ ᆨᄋ ᅡ ᅱᄅ ᅩᄉ ᆫᅥ ᅥ ᆼ ᄌᄒ ᆫᄃ ᅡ ᅡ. ᄀ ᅳᄅ ᅵᄀ ᅩᄐ ᅦᄉ ᅳᄐ ᅳᄋ ᅦᄉ ᅥᄂ ᆫ ESMᄋ ᅳ ᅦᄉ ᅥᄎ ᅮᄌ ᆼᄒ ᅥ ᆫ Yt+1 ᄅ ᅡ ᅩᄇ ᅮᄐ ᅥᄀ ᅵᄃ ᅢᄉ ᅮᄋ ᆨᄅ ᅵ ᆯᄋ ᅲ ᅵᄀ ᅡᄌ ᆼ ᅡ ᇁᄋ ᅩ ᄂ ᆫᄌ ᅳ ᆼᄆ ᅩ ᆨᄇ ᅩ ᅮᄐ ᅥᄑ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄀ ᅮᄉ ᆼᄌ ᅥ ᅡᄉ ᆫᄋ ᅡ ᅦᄉ ᆫᅥ ᅥ ᆼ ᄌᄒ ᆫᄃ ᅡ ᅡ. ᄇ ᆫᄉ ᅩ ᆯᄒ ᅵ ᆷᄋ ᅥ ᅦᄉ ᅥᄒ ᆨᄉ ᅡ ᆸᄀ ᅳ ᅪᄐ ᅦᄉ ᅳᄐ ᅳᄋ ᅦᄉ ᅡᄋ ᆼᄃ ᅭ ᆫᄒ ᅬ ᅡᄋ ᅵᄑ ᅥᄑ ᅡᄅ ᅡᄆ ᅵᄐ ᅥ ᆫ Table 5.1 ᄋ ᅳ ᄂ ᅦᄆ ᆼᄉ ᅧ ᅵᅬ ᄃᄋ ᅥᄋ ᆻᄃ ᅵ ᅡ. Table 5.1 Hyper-parameters hyper-parameters value description train data size 839 train data length (business days) test data size 359 test data length (business days) ESM learning steps 4,999 AAM learning steps 4,999 ESM learning rate 1 × 10−4 AAM learning rate 1 × 10−4 ESM optimization function Gradient Descent AAM optimization function ADAM AAM regularization coefficient 0.1 The L2 regularization coefficient applied to AAM training Initial asset value 1 × 108. 5.3. 결과 ᆯᅥ ᅵ ᄉ ᆷ ᄒᄋ ᅴ ᄀ ᆯᄀ ᅧ ᅪᄂ ᆫ AAMᄀ ᅳ ᅪ ESMᄋ ᆯ ᄃ ᅳ ᆼᄉ ᅩ ᅵᄋ ᅦ ᄉ ᅡᄋ ᆼᄒ ᅭ ᆻᄋ ᅢ ᆯ ᄄ ᅳ ᅢ, AAMᄀ ᅪ ESMᄋ ᆯ ᄀ ᅳ ᆨᄀ ᅡ ᆨ ᄒ ᅡ ᅡᄂ ᅡᄊ ᆨ ᄉ ᅵ ᅡᄋ ᆼᄒ ᅭ ᆻᄋ ᅢ ᆯ ᄄ ᅳ ᅢ,.
(8) 220. Taeyoon Kim · Bonggyun Ko. KOSPI 200 ᄌ ᅵᄉ ᅮ, ᄀ ᅳᄅ ᅵᄀ ᅩᄆ ᅩᄒ ᆼᄋ ᅧ ᅴᄉ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋᄑ ᆼᄀ ᅧ ᅡᄒ ᅡᄀ ᅵᄋ ᅱᄒ ᅢᄒ ᆨᄉ ᅡ ᆸᄀ ᅳ ᅵᄀ ᆫᄃ ᅡ ᆼᄋ ᅩ ᆫᄋ ᅡ ᅴᄑ ᆼᄀ ᅧ ᆫᄋ ᅲ ᆯᅧ ᅵ ᆯ ᄇᄉ ᅮᄋ ᆨᄅ ᅵ ᆯᄋ ᅲ ᅵᄀ ᅡᄌ ᆼᄂ ᅡ ᇁ ᅩ ᆫᄌ ᅳ ᄋ ᆼᄆ ᅩ ᆨᄀ ᅩ ᅪᄀ ᆫᄃ ᅲ ᆼᄀ ᅳ ᅮᄆ ᅢᄒ ᅮᄇ ᅩᄋ ᅲ (Uniform Buy And Hold, UBAH)ᄋ ᅪᄀ ᆫᄃ ᅲ ᆼᄀ ᅳ ᅩᄌ ᆼᄌ ᅥ ᅢᄇ ᅢᄇ ᆫᄑ ᅮ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩ (uniform constant rebalanced portfolio, UCRP)ᄅ ᆯᄇ ᅳ ᆫᄎ ᅦ ᅵᄆ ᅡᄏ ᅳᄉ ᅮᄋ ᆨᄅ ᅵ ᆯᄅ ᅲ ᅩᄉ ᅡᄋ ᆼᄒ ᅭ ᆫᄃ ᅡ ᅡ (Liᄀ ᅪ Hoi, 2014; Cover, 2011). 2015ᄂ ᆫ 9ᄋ ᅧ ᆯ 2ᄋ ᅯ ᆯᄇ ᅵ ᅮᄐ ᅥ 2019ᄂ ᆫ 2ᄋ ᅧ ᆯ 15ᄋ ᅯ ᆯᄁ ᅵ ᅡᄌ ᅵᄌ ᅦᄋ ᆯᄀ ᅵ ᅡᄀ ᆨᄋ ᅧ ᅵᄆ ᆭᄋ ᅡ ᅵᄉ ᆼᄉ ᅡ ᆼᄒ ᅳ ᆫᄌ ᅡ ᆼᄆ ᅩ ᆨᄋ ᅩ ᅳᄅ ᅩᄂ ᆫᄋ ᅳ ᆯᄌ ᅵ ᆫᄆ ᅵ ᅥᄐ ᅵᄅ ᅵᄋ ᆯ ᅥ ᅳᄀ ᄌ ᅡᄉ ᆫᄌ ᅥ ᆼᄃ ᅥ ᅬᄋ ᆻᄃ ᅥ ᅡ. ᄉ ᆼᄀ ᅥ ᅪᄌ ᅵᄑ ᅭᄅ ᅩᄂ ᆫᄉ ᅳ ᅮᄋ ᆨᅥ ᅵ ᆼ ᄉᄀ ᅪᄎ ᅮᄌ ᆼᄋ ᅥ ᆯᄋ ᅳ ᅱᅡ ᆫ ᄒᄂ ᅮᄌ ᆨᄑ ᅥ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄀ ᅡᄎ ᅵ (accumulate portfolio value, APV), ᄉ ᅣᄑ ᅳᄌ ᅵᄉ ᅮ (sharpe ratio, SR) ᄀ ᅳᄅ ᅵᄀ ᅩᄋ ᅱᄒ ᆷᄑ ᅥ ᆼᄀ ᅧ ᅡᄅ ᆯᄋ ᅳ ᅱᅡ ᆫ ᄒᄑ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄉ ᅮᄋ ᆨᄅ ᅵ ᆯᄋ ᅲ ᅴᄑ ᅭᄌ ᆫᄑ ᅮ ᆫᄎ ᅧ ᅡ σP , ᄎ ᅬᄃ ᅢᄒ ᅡᄅ ᆨᄑ ᅡ ᆨ (maximum drawdown)ᄋ ᅩ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᆫᄃ ᅡ ᅡ. ᆼᄀ ᅥ ᄉ ᅪᄌ ᅵᄑ ᅭ APVᄂ ᆫᄎ ᅳ ᅩᄀ ᅵᄌ ᅡᄉ ᆫᄀ ᅡ ᅡᄎ ᅵᄀ ᅡ P0 = 1ᄋ ᆯᄄ ᅵ ᅢ, ᄎ ᅬᄌ ᆼᄉ ᅩ ᅵᄌ ᆷᄋ ᅥ ᅦᄌ ᅡᄉ ᆫᄋ ᅡ ᅴᅡ ᆼ ᄉᄃ ᅢᄌ ᆨᄀ ᅥ ᅡᄎ ᅵᄋ ᅵᄃ ᅡ. AP Vt = Pt /P0 .. (5.1). ᆼᄀ ᅥ ᄉ ᅪᄎ ᆨᄌ ᅳ ᆼᄋ ᅥ ᅦᄉ ᅡᄋ ᆼᄃ ᅭ ᅬᄂ ᆫᄑ ᅳ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄉ ᅮᄋ ᆨᄅ ᅵ ᆯ RP ᄂ ᅲ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄃ ᅡ ᅡ. Pt+1 − 1. Pt σP ᄂ ᆫᄑ ᅳ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄋ ᅴᄇ ᆫᄃ ᅧ ᆼᄉ ᅩ ᆼᄋ ᅥ ᅳᄅ ᅩᄑ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄋ ᆯᅧ ᅵ ᆯ ᄇᄉ ᅮᄋ ᆨᄅ ᅵ ᆯᄋ ᅲ ᅴᄑ ᅭᄌ ᆫᄑ ᅮ ᆫᄎ ᅧ ᅡᄅ ᅩᄌ ᆼᄋ ᅥ ᅴᄒ ᆫᄃ ᅡ ᅡ. RP =. σP =. p. var(RP ).. (5.2). (5.3). APVᄋ ᅦᄂ ᆫᄑ ᅳ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄋ ᅴᄇ ᆫᄃ ᅧ ᆼᄉ ᅩ ᆼᄀ ᅥ ᅪᄉ ᅵᄌ ᆼᅡ ᅡ ᆼ ᄉᄒ ᆼᄋ ᅪ ᆯᄀ ᅳ ᅩᄅ ᅧᄒ ᅡᄌ ᅵᄋ ᆭᄂ ᅡ ᆫᄃ ᅳ ᅡᄂ ᆫᄃ ᅳ ᆫᄌ ᅡ ᆷᄋ ᅥ ᅵᄋ ᆻᄋ ᅵ ᅳᄆ ᅧᄋ ᅵᄅ ᆯᄇ ᅳ ᅩᄋ ᆫᄒ ᅪ ᅡᄀ ᅵᄋ ᅱᄒ ᅢ ᅣᄑ ᄉ ᅳᅵ ᄌᄉ ᅮᄅ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᆫᄃ ᅡ ᅡ (Sharpe, 1994). ᄉ ᅣᄑ ᅳᄌ ᅵᄉ ᅮᄂ ᆫᄋ ᅳ ᅱᄒ ᆷᄀ ᅥ ᆷᄉ ᅡ ᅮᄉ ᆼᄒ ᅥ ᆼᄋ ᅣ ᆯᄀ ᅳ ᅩᄅ ᅧᄒ ᆫᄉ ᅡ ᆼᄀ ᅥ ᅪᄅ ᆯᄎ ᅳ ᆨᄌ ᅳ ᆼᄒ ᅥ ᅡᄆ ᅧᄑ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩ ᅴᄇ ᄋ ᆫᄃ ᅧ ᆼᄉ ᅩ ᆼᄋ ᅥ ᅦᄃ ᅢᄒ ᆫᄆ ᅡ ᅮᄋ ᅱᄒ ᆷᄉ ᅥ ᅮᄋ ᆨᄅ ᅵ ᆯᅳ ᅲ ᆯ ᄋᄎ ᅩᅪ ᄀᄒ ᅡᄂ ᆫᄑ ᅳ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄋ ᅴᄉ ᅮᄋ ᆨᄋ ᅵ ᅳᄅ ᅩᄃ ᅡᄋ ᆷᄉ ᅳ ᅮᄉ ᆨᄀ ᅵ ᅪᄀ ᇀᄋ ᅡ ᅵᄀ ᅨᄉ ᆫᄃ ᅡ ᆫᄃ ᅬ ᅡ. ᄆ ᅮᄋ ᅱ ᆷᄉ ᅥ ᄒ ᅮᄋ ᆨᄅ ᅵ ᆯ RF ᄋ ᅲ ᆫᄒ ᅳ ᆫᄀ ᅡ ᆨᄋ ᅮ ᆫᄒ ᅳ ᆼᄋ ᅢ ᅦᄉ ᅥᄀ ᆼᄉ ᅩ ᅵᄒ ᅡᄂ ᆫᄀ ᅳ ᅵᄌ ᆫᄀ ᅮ ᆷᄅ ᅳ ᅵᄅ ᆯᄋ ᅳ ᆯᅧ ᅵ ᆯ ᄇᄉ ᅮᄋ ᆨᄅ ᅵ ᆯᄅ ᅲ ᅩᄒ ᆫᄉ ᅪ ᆫᅡ ᅡ ᆫ ᄒᄉ ᅮᄎ ᅵᄅ ᅩᄌ ᆼᄋ ᅥ ᅴᅡ ᆫ ᄒᄃ ᅡ. E(RP − RF ) . (5.4) σP SRᄀ ᅪ σP ᄋ ᅦᄉ ᅥᄂ ᆫᄑ ᅳ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄋ ᅴᄇ ᆫᄃ ᅧ ᆼᄉ ᅩ ᆼᄋ ᅥ ᆯᄉ ᅳ ᅮᄋ ᆨᄅ ᅵ ᆯᄋ ᅲ ᅴᄑ ᅭᄌ ᆫᄑ ᅮ ᆫᄎ ᅧ ᅡᄅ ᅩᄑ ᆼᄀ ᅧ ᅡᄒ ᅡᄌ ᅵᄆ ᆫᄋ ᅡ ᅵᄂ ᆫᄉ ᅳ ᅮᄋ ᆨᄅ ᅵ ᆯᄋ ᅲ ᅴᅡ ᆼ ᄉᄉ ᆼᄃ ᅳ ᅩᄇ ᆫᄃ ᅧ ᆼ ᅩ ᆼᄋ ᅥ ᄉ ᅴᄉ ᆼᄉ ᅡ ᆼᄋ ᅳ ᅦᄀ ᅵᄋ ᅧᄒ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡᄂ ᆫᄆ ᅳ ᆫᄌ ᅮ ᅦᄌ ᆷᄋ ᅥ ᅵᄋ ᆻᄃ ᅵ ᅡ. ᄄ ᅡᄅ ᅡᄉ ᅥᄋ ᅱᄒ ᆷᅥ ᅥ ᆼ ᄉᄑ ᆼᄀ ᅧ ᅡᄌ ᅵᄑ ᅭᄅ ᅩᄉ ᅡᄋ ᆼᄃ ᅭ ᅬᄂ ᆫ MDDᄂ ᅳ ᆫᄒ ᅳ ᅡᄅ ᆨᄋ ᅡ ᅱᄒ ᆷ ᅥ ᆯᄑ ᅳ ᄋ ᆼᅡ ᅧ ᄀᄒ ᅡᄀ ᅵᄋ ᅱᄒ ᅢᄉ ᅡᄋ ᆼᄃ ᅭ ᅬᄆ ᅧᄀ ᅨᄉ ᆫᄋ ᅡ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄋ ᅡ ᅵᄋ ᅵᄅ ᅮᄋ ᅥᄌ ᆫᄃ ᅵ ᅡ (Magdon-Ismailᄀ ᅪ Atiya, 2004). SR =. AP Vt − AP Vτ ). (5.5) AP Vt AAM ᄃ ᆫᄋ ᅡ ᆯᄀ ᅵ ᅮᄌ ᅩᅪ ᄋ UBAH, UCRPᄋ ᅴᄉ ᆼᄀ ᅥ ᅪᄎ ᆨᄌ ᅳ ᆼᄋ ᅥ ᅦᄂ ᆫᄌ ᅳ ᆼᄆ ᅩ ᆨᄉ ᅩ ᆫᅢ ᅥ ᆨ ᄐᄋ ᅵᄆ ᅢᄉ ᅵᄒ ᆼᄆ ᅢ ᅡᄃ ᅡᄆ ᅮᄌ ᆨᄋ ᅡ ᅱᄅ ᅩᄋ ᅵᄅ ᅮᄋ ᅥᄌ ᅵᄆ ᅧᄉ ᆼ ᅥ ᅪᄀ ᄀ ᅡᄆ ᅮᄌ ᆨᄋ ᅡ ᅱᄌ ᆨᄋ ᅥ ᆫᄌ ᅵ ᆼᄆ ᅩ ᆨᄉ ᅩ ᆫᅢ ᅥ ᆨ ᄐᄋ ᅦᄋ ᅴᄌ ᆫᄃ ᅩ ᅬᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᆯᄑ ᅳ ᅵᄒ ᅡᄀ ᅵᄋ ᅱᄒ ᅢ 2000ᄒ ᅬᅡ ᆫ ᄇᄇ ᆨᄎ ᅩ ᆨᄌ ᅳ ᆼᄒ ᅥ ᅡᄋ ᅧᄆ ᅢᄉ ᅵᄒ ᆼᄋ ᅢ ᅦᄉ ᅥᄋ ᅴᄀ ᆨᄉ ᅡ ᆼ ᅥ ᅪᄌ ᄀ ᅵᄑ ᅭᄇ ᆯᄀ ᅧ ᆯᄀ ᅧ ᅪᄋ ᅴᄑ ᆼᄀ ᅧ ᆫᅳ ᅲ ᆯ ᄋᄂ ᅡᄐ ᅡᄂ ᆻᄋ ᅢ ᅳᄆ ᅧᄀ ᆨᄌ ᅡ ᆫᄅ ᅥ ᆨᄋ ᅣ ᅦᄉ ᅥᄋ ᅴᄀ ᅵᄃ ᅢᄉ ᅮᄎ ᅵᄅ ᆯᄋ ᅳ ᅴᄆ ᅵᄒ ᆫᄃ ᅡ ᅡ. AAMᄆ ᆫᄉ ᅡ ᅡᄋ ᆼᄒ ᅭ ᆯᄄ ᅡ ᅢᄋ ᅴ ξt ᄂ ᆫᄆ ᅳ ᅢ ᅵᄌ ᄉ ᆷ tᄆ ᅥ ᅡᄃ ᅡᄆ ᅮᄌ ᆨᄋ ᅡ ᅱᄅ ᅩᄀ ᆯᅥ ᅧ ᆼ ᄌᄃ ᅬᄆ ᅧ UBAH, UCRPᄋ ᅦᄉ ᅥᄌ ᆼᄆ ᅩ ᆨᄉ ᅩ ᆫᅢ ᅥ ᆨ ᄐᄋ ᆫᄆ ᅳ ᅩᄃ ᆫᄉ ᅳ ᅵᄌ ᆷ tᄋ ᅥ ᅦᄃ ᅢᄒ ᅢ ξ0 = ξt ᄋ ᅵᄃ ᅡ. ᄀ ᅳᄅ ᅵ 1 1 ᅩ ESMᄋ ᄀ ᅴᅡ ᆫ ᄃᄋ ᆯᄀ ᅵ ᅮᄌ ᅩᄋ ᅦᄉ ᅥᄀ ᅡᄌ ᆼᄎ ᅮ ᅵᄂ ᆫᄆ ᅳ ᅩᄃ ᆫᄉ ᅳ ᅵᄌ ᆷ tᄋ ᅥ ᅦᄃ ᅢᄒ ᅢ Wt = ( m , · · · , m )ᄋ ᅵᄃ ᅡ. Table 5.2ᄂ ᆫ 2020ᄂ ᅳ ᆫ 11ᄋ ᅧ ᆯ 18ᄋ ᅯ ᆯᄀ ᅵ ᅵᄌ ᆫᄀ ᅮ ᆨᄌ ᅡ ᆼᄉ ᅩ ᆼᄀ ᅥ ᅪᄌ ᅵᄑ ᅭᄃ ᆯᄋ ᅳ ᅴᄀ ᆹᄋ ᅡ ᆯᄇ ᅳ ᅩᄋ ᅧᄌ ᆫᄃ ᅮ ᅡ. ᄉ ᅮᄋ ᆨᄅ ᅵ ᆯᄌ ᅲ ᅵᄑ ᅭᄋ ᆫ APV, SRᄋ ᅵ ᅦ ᅥᄂ ᄉ ᆫ m = 8, 10ᄋ ᅳ ᅦᄉ ᅥ ESMᄀ ᅪ AAMᄋ ᆯᄀ ᅳ ᇀᄋ ᅡ ᅵᄉ ᅡᄋ ᆼᄒ ᅭ ᆯᄄ ᅡ ᅢᄀ ᅡᄌ ᆼᄏ ᅡ ᆫᄉ ᅳ ᆼᄀ ᅥ ᅪᄅ ᆯᄇ ᅳ ᅩᄋ ᅧᄌ ᅮᄋ ᆻᄋ ᅥ ᅳᄆ ᅧ, AAMᄋ ᆯᄃ ᅳ ᆫᄃ ᅡ ᆨᄋ ᅩ ᅳᄅ ᅩ ᅡᄋ ᄉ ᆼᅡ ᅭ ᄒᄋ ᆻᄋ ᅧ ᆯᄄ ᅳ ᅢᄀ ᅡᄌ ᆼᄂ ᅡ ᆽᄋ ᅡ ᆫᄉ ᅳ ᆼᄀ ᅥ ᅪᄅ ᆯᄇ ᅳ ᅩᄋ ᆻᄃ ᅧ ᅡ. m = 12ᄋ ᅦᄉ ᅥᄂ ᆫ ESMᄋ ᅳ ᆯᄃ ᅳ ᆫᄃ ᅡ ᆨᄋ ᅩ ᅳᄅ ᅩᄉ ᅡᄋ ᆼᄒ ᅭ ᆯᄄ ᅡ ᅢᄀ ᅡᄌ ᆼᄏ ᅡ ᆫᄉ ᅳ ᅮᄋ ᆨᅥ ᅵ ᆼ ᄉᄋ ᆯ ᅳ ᅩᄋ ᄇ ᅧᅮ ᄌᄋ ᆻᄀ ᅥ ᅩ, ᄋ ᆯᄌ ᅵ ᆫᄆ ᅵ ᅥᄐ ᅵᄅ ᅵᄋ ᆯᄌ ᅥ ᅳᄀ ᅡᄉ ᅮᄋ ᆨᅥ ᅵ ᆼ ᄉ, ᄋ ᅱᄒ ᆷᅥ ᅥ ᆼ ᄉᄆ ᅩᄃ ᅮᄀ ᅡᄌ ᆼᄂ ᅡ ᆽᄋ ᅡ ᆫᄉ ᅳ ᆼᄀ ᅥ ᅪᄅ ᆯᄇ ᅳ ᅩᄋ ᅧᄌ ᅮᄋ ᆻᄃ ᅥ ᅡ. ᅱᄒ ᄋ ᆷᅧ ᅥ ᆼ ᄑᄀ ᅡᄌ ᅵᄑ ᅭ MDDᄋ ᅪ σP ᄋ ᅦᄉ ᅥᄂ ᆫ KOSPI 200 ᄌ ᅳ ᅵᄉ ᅮᄀ ᅡᄀ ᅡᄌ ᆼᄋ ᅡ ᅱᄒ ᆷᄋ ᅥ ᅵᄂ ᆽᄋ ᅡ ᆫᄀ ᅳ ᆯᄅ ᅥ ᅩᄇ ᅩᄋ ᅵᄆ ᅧᄎ ᅬᄀ ᅩᄀ ᅵᄃ ᅢᄉ ᅮᄋ ᆨ ᅵ ᆯᄌ ᅲ ᄅ ᆼᄆ ᅩ ᆨᄋ ᅩ ᅴᅡ ᆫ ᄃᄋ ᆯᄀ ᅵ ᅮᄉ ᆼᄋ ᅥ ᅦᄉ ᅥᄀ ᅡᄌ ᆼᄂ ᅡ ᇁᄋ ᅩ ᆫᄋ ᅳ ᅱᄒ ᆷᄋ ᅥ ᆯᄇ ᅳ ᅩᄋ ᅧᄌ ᅮᄀ ᅩᄋ ᆻᄃ ᅵ ᅡ. ᄋ ᅵᄂ ᆫᄌ ᅳ ᅮᄀ ᅡᄌ ᅵᄉ ᅮᄐ ᆨᄉ ᅳ ᆼᄉ ᅥ ᆼᄀ ᅡ ᅡᄌ ᆼᄆ ᅡ ᆭᄋ ᅡ ᆫᄌ ᅳ ᆼᄆ ᅩ ᆨᄋ ᅩ ᅳᄅ ᅩᄀ ᅮ ᆼᄋ ᅥ ᄉ ᅵᅬ ᄃᄋ ᆻᄀ ᅥ ᅵᄄ ᅢᄆ ᆫᄋ ᅮ ᅦᄂ ᇁᄋ ᅩ ᆫᄋ ᅳ ᅱᄒ ᆷᄇ ᅥ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅵᄋ ᅵᄅ ᅮᄋ ᅥᄌ ᅧᄂ ᅡᄐ ᅡᄂ ᆫᄀ ᅡ ᆯᄀ ᅧ ᅪᄅ ᅩᄉ ᆼᄀ ᅢ ᆨᄃ ᅡ ᆫᄃ ᅬ ᅡ. M DD = max( τ >t.
(9) Modular reinforcement learning for dynamic portfolio optimization in the KOSPI market. 221. Table 5.2 Performance measures m Method ESM+AAM ESM KOSPI 200 UBAH UCRP Best AAM m Method ESM+AAM ESM KOSPI 200 UBAH UCRP Best AAM m Method ESM+AAM ESM KOSPI 200 UBAH UCRP Best AAM. 8 APV 1.8972 1.6173 1.2008 1.1530 1.1395 1.0922 1.0569. SR 2.2766 1.8630 0.6558 0.3713 0.3687 0.1458 0.1043. APV 2.5706 1.6654 1.2008 1.1519 1.1369 1.0922 1.0760. SR 3.1905 2.0202 0.6558 0.3840 0.3687 0.1458 0.1141. APV 1.5692 1.5724 1.2008 1.1618 1.1354 1.0922 1.1365. SR 1.6985 1.7333 0.6558 0.4231 0.3699 0.1458 0.3685. MDD 0.2533 0.3792 0.3489 0.4151 0.4209 0.5792 0.4631. σP 0.3906 0.3270 0.2940 0.3715 0.3610 0.5775 0.4244. MDD 0.4019 0.3785 0.3489 0.4127 0.4200 0.5792 0.4723. σP 0.4897 0.3254 0.2940 0.3628 0.3530 0.5775 0.4788. MDD 0.3878 0.3833 0.3489 0.4097 0.4162 0.5792 0.4192. σP 0.3304 0.3256 0.2940 0.3550 0.3470 0.5775 0.3493. 10. 12. ᆫᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄌ ᅦᄋ ᆫᄒ ᅡ ᅡᄂ ᆫᄀ ᅳ ᅮᄌ ᅩᄌ ᆼᄒ ᅮ ᅡᄂ ᅡᆫ ᄋ ᅵ AAMᄋ ᅴᅡ ᆫ ᄃᄋ ᆯᄀ ᅵ ᅮᄉ ᆼᄋ ᅥ ᆫᄉ ᅳ ᅮᄋ ᆨᄋ ᅵ ᅵᄂ ᆽᄋ ᅡ ᆯᅮ ᅳ ᆫ ᄈᄆ ᆫᄋ ᅡ ᅡᄂ ᅵᄅ ᅡᄂ ᇁᄋ ᅩ ᆫᄋ ᅳ ᅱᄒ ᆷᅥ ᅥ ᆼ ᄉᄋ ᆯ ᅳ ᄂᄐ ᅡ ᅡᅢ ᄂᄀ ᅩᄋ ᆻᄋ ᅵ ᅳᄆ ᅧᄐ ᅮᄌ ᅡᄋ ᆫᄋ ᅡ ᅳᄅ ᅩᄊ ᅥᄌ ᆨᅥ ᅥ ᆯ ᄌᄒ ᅡᄌ ᅵᄋ ᆭᄃ ᅡ ᅡᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᆯᄇ ᅳ ᅩᄋ ᅧᄌ ᆫᄃ ᅮ ᅡ. ᄒ ᅡᄌ ᅵᄆ ᆫ ESMᄀ ᅡ ᅪᄒ ᆷᄁ ᅡ ᅦᄀ ᅮᄉ ᆼᄃ ᅥ ᆯᄉ ᅬ ᅵᄉ ᅮᄋ ᆨᄅ ᅵ ᆯ ᅲ ᆨᄆ ᅳ ᄎ ᆫᅦ ᅧ ᄋᄉ ᅥᄀ ᅢᄉ ᆫᄋ ᅥ ᆯᄇ ᅳ ᅩᄋ ᅧᄌ ᅮᄀ ᅩᄋ ᆻᄋ ᅵ ᅳᄆ ᅧᄋ ᅵᄂ ᆫ AAMᄋ ᅳ ᅵᄂ ᇁᄋ ᅩ ᆫᄋ ᅳ ᅱᄒ ᆷᄀ ᅥ ᆷᄉ ᅡ ᅮᄉ ᆼᄒ ᅥ ᆼᄋ ᅣ ᆯᄀ ᅳ ᅡᄌ ᅵᄀ ᅩᄋ ᆻᄀ ᅵ ᅩᄌ ᆨᅥ ᅥ ᆯ ᄌᄒ ᆫᄌ ᅡ ᆼᄆ ᅩ ᆨᄉ ᅩ ᆫᅢ ᅥ ᆨ ᄐᄋ ᆯ ᅳ ᆼᄒ ᅩ ᄐ ᅢᄋ ᅵᄆ ᆫᄌ ᅮ ᅦᄅ ᆯᄒ ᅳ ᅭᅪ ᄀᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄋ ᆹᄋ ᅥ ᆯᄉ ᅢ ᅮᄋ ᆻᄃ ᅵ ᅡᄀ ᅩᄉ ᆼᄀ ᅢ ᆨᄃ ᅡ ᆫᄃ ᅬ ᅡ. ESMᄋ ᅴᄃ ᆫᄋ ᅡ ᆯᄀ ᅵ ᅮᄉ ᆼᄋ ᅥ ᅦᄉ ᅥᄂ ᆫᄆ ᅳ ᅩᄃ ᆫᄌ ᅳ ᅵᄑ ᅭᄋ ᅦᄉ ᅥᄃ ᅮᄇ ᆫᄍ ᅥ ᅢᄅ ᅩᄂ ᇁᄋ ᅩ ᆫᄉ ᅳ ᆼᄀ ᅥ ᅪᄅ ᆯᄇ ᅳ ᅩᄋ ᅧᄌ ᅮᄀ ᅩᄋ ᆻᄋ ᅵ ᅳᄆ ᅧᄉ ᆫᄒ ᅮ ᆫᄉ ᅪ ᆫᅧ ᅵ ᆼ ᄀᄆ ᆼᄋ ᅡ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᆫ ᅡ ᅮᄀ ᄌ ᅡᅨ ᄋᄎ ᆨᄋ ᅳ ᅵᄒ ᅭᄀ ᅪᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄋ ᅵᄅ ᅮᄋ ᅥᄌ ᆫᄀ ᅵ ᆺᄋ ᅥ ᅳᄅ ᅩᄇ ᅩᄋ ᆫᄃ ᅵ ᅡ. Figure 5.1 ᄋ ᅦᄉ ᅥᄂ ᆫᄆ ᅳ ᅩᄃ ᆫᄌ ᅳ ᆼᄆ ᅩ ᆨᄋ ᅩ ᅦᄃ ᅢᄒ ᆫᄑ ᅡ ᆼᄀ ᅧ ᆫᄋ ᅲ ᅩᄎ ᅡᄌ ᅦᄀ ᆸᄋ ᅩ ᆯ ᅳ ᅵᄀ ᄉ ᆫᅦ ᅡ ᄋᄄ ᅡᄅ ᅡᄇ ᅩᄋ ᅧᄌ ᅮᄀ ᅩᄋ ᆻᄋ ᅵ ᅳᄆ ᅧ 75 < t < 100 ᄋ ᅦᄉ ᅥᄋ ᅴᄀ ᆸᄅ ᅳ ᆨᄀ ᅡ ᅵᄀ ᆫᄋ ᅡ ᅦᄋ ᅨᄎ ᆨᄅ ᅳ ᆨᄋ ᅧ ᅵᄄ ᆯᄋ ᅥ ᅥᄌ ᅵᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅵᄒ ᆨᄋ ᅪ ᆫᄃ ᅵ ᆫᄃ ᅬ ᅡ. ESM+AAMᄀ ᅪ ESMᄋ ᅴᅡ ᆫ ᄃᄋ ᆯᄀ ᅵ ᅮᄉ ᆼᄋ ᅥ ᆫᄀ ᅳ ᆸᄅ ᅳ ᆨᄀ ᅡ ᅵᄀ ᆫᄋ ᅡ ᅦ KOSPI 200 ᄌ ᅵᄉ ᅮᄇ ᅩᄃ ᅡᄂ ᆽᄋ ᅡ ᆫᄉ ᅳ ᆼᄀ ᅥ ᅪᄅ ᆯᄇ ᅳ ᅩᄋ ᅧᄌ ᅮᄀ ᅩᄋ ᆻᄋ ᅵ ᅳᄂ ᅡ ᇁᄋ ᅩ ᄂ ᆫᅬ ᅳ ᄒᄇ ᆨᄐ ᅩ ᆫᄅ ᅡ ᆨᄉ ᅧ ᆼ (resilience)ᄋ ᅥ ᆯᄇ ᅳ ᅩᄋ ᅧᄉ ᅵᄀ ᆫᄋ ᅡ ᅵᄌ ᅵᄂ ᆷᄋ ᅡ ᅦᄄ ᅡᄅ ᅡᄌ ᅮᄀ ᅡᄌ ᅵᄉ ᅮᄉ ᆼᄀ ᅥ ᅪᄅ ᆯᄉ ᅳ ᆼᄒ ᅡ ᅬᅡ ᆷ ᄒᄋ ᆯᄇ ᅳ ᅩᄋ ᅧᄌ ᅮᄀ ᅩᄋ ᆻᄃ ᅵ ᅡ.. 6. 결론 ᆫᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄂ ᆫᄀ ᅳ ᆨᄂ ᅮ ᅢᄋ ᅴᄋ ᅲᄀ ᅡᄌ ᆼᄀ ᅳ ᆫᄉ ᅯ ᅵᄌ ᆼᄋ ᅡ ᅦᄉ ᆼᅡ ᅡ ᆼ ᄌᄃ ᆫᄌ ᅬ ᆼᄆ ᅩ ᆨᄌ ᅩ ᆼᄏ ᅮ ᅩᄉ ᅳᄑ ᅵ 200 ᄌ ᅵᄉ ᅮᄅ ᆯᄀ ᅳ ᅮᄉ ᆼᄒ ᅥ ᅡᄂ ᆫᄌ ᅳ ᆼᄆ ᅩ ᆨᄃ ᅩ ᆯᄌ ᅳ ᆼᄀ ᅮ ᅵᄃ ᅢᄉ ᅮᄋ ᆨ ᅵ ᄅᄋ ᆯ ᅲ ᅵᄂ ᇁᄋ ᅩ ᆫᄌ ᅳ ᆼᄆ ᅩ ᆨᅳ ᅩ ᆯ ᄋᄉ ᆫᅢ ᅥ ᆨ ᄐᄒ ᅡᄋ ᅧᄑ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄅ ᆯᄀ ᅳ ᅮᄉ ᆼᄒ ᅥ ᅡᄂ ᆫᄀ ᅳ ᆼᄒ ᅡ ᅪᄒ ᆨᄉ ᅡ ᆸᄀ ᅳ ᅮᄌ ᅩᄅ ᆯᄌ ᅳ ᅦᄋ ᆫᄒ ᅡ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄋ ᅲᄃ ᆼᄌ ᅩ ᆨᄋ ᅥ ᆫᄃ ᅵ ᅢᄉ ᆼᄌ ᅡ ᆼᄆ ᅩ ᆨᄋ ᅩ ᅴ ᆫᄒ ᅧ ᄇ ᅪᅦ ᄋᄃ ᅢᄎ ᅥᄒ ᅡᄀ ᅵᄋ ᅱᄒ ᅢᄀ ᅩᄋ ᆫᄃ ᅡ ᆫᄋ ᅬ ᅵᄀ ᅮᄉ ᆼᄋ ᅥ ᆫᄋ ᅳ ᅲᄀ ᅡᄌ ᆼᄀ ᅳ ᆫᄉ ᅯ ᅵᄌ ᆼᄀ ᅡ ᅪᄃ ᅢᄉ ᆼᄌ ᅡ ᆼᄆ ᅩ ᆨᄋ ᅩ ᅴᄇ ᆫᄒ ᅧ ᅪᄋ ᅦᄑ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄋ ᅴᄀ ᅮᄉ ᆼᄋ ᅥ ᆯᄋ ᅳ ᅲᄋ ᆫ ᅧ ᅡᄀ ᄒ ᅦᄇ ᆫᄒ ᅧ ᅪᄉ ᅵᄏ ᆯᄉ ᅵ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᆫᄀ ᅧ ᄋ ᅮᄋ ᅴᄒ ᆫᄀ ᅡ ᅨᄅ ᅩᄂ ᆫᄃ ᅳ ᅦᄋ ᅵᄐ ᅥᄀ ᅡᄋ ᆯᅧ ᅵ ᆯ ᄇᄅ ᅩᄀ ᅮᄉ ᆼᄃ ᅥ ᅬᄋ ᅥᄉ ᅵᄀ ᆫᄇ ᅡ ᆯᄃ ᅧ ᅦᄋ ᅵᄐ ᅥᄋ ᅦᄇ ᅵᄀ ᅭᄒ ᅢᄒ ᆫᄅ ᅮ ᆫᄃ ᅧ ᅦᄋ ᅵᄐ ᅥᄀ ᅡᄌ ᆨᄋ ᅥ ᆻᄀ ᅥ ᅩ, ᄐ ᅦᄉ ᅳᄐ ᅳᄀ ᅵ ᆫᄋ ᅡ ᄀ ᅵᄇ ᆫᄉ ᅮ ᆫᄃ ᅡ ᅬᄌ ᅵᄋ ᆭᄋ ᅡ ᅡᄒ ᆫᅥ ᅡ ᆫ ᄇᄋ ᅴᄉ ᅵᄒ ᆼᄋ ᅢ ᅳᄅ ᅩᄉ ᅮᄒ ᆼᄃ ᅢ ᅬᄋ ᆻᄃ ᅥ ᅡ. ᄉ ᅵᄀ ᅨᄋ ᆯᄋ ᅧ ᅵᄂ ᅥᄆ ᅮᄋ ᅩᄅ ᅢᄃ ᆫᄃ ᅬ ᅦᄋ ᅵᄐ ᅥᄅ ᅩᄀ ᅮᄉ ᆼᄃ ᅥ ᆫᄃ ᅬ ᅡᄂ ᆫᄆ ᅳ ᆫᄌ ᅮ ᅦᄌ ᆷ ᅥ ᅵᄋ ᄋ ᆻᄋ ᅵ ᆻᄃ ᅥ ᅡ. ᄄ ᅩᄒ ᆫᄀ ᅡ ᆼᄒ ᅡ ᅪᅡ ᆨ ᄒᄉ ᆸᄋ ᅳ ᅴᄀ ᅩᄌ ᆨᄋ ᅥ ᆫᄆ ᅵ ᆫᄌ ᅮ ᅦᄅ ᅩᄒ ᆨᄉ ᅡ ᆸᄉ ᅳ ᅵᄀ ᆫᄋ ᅡ ᅵᄋ ᅩᄅ ᅢᄀ ᆯᄅ ᅥ ᆻᄀ ᅧ ᅩ, ᄒ ᆨᄉ ᅡ ᆸᄉ ᅳ ᅵᄀ ᆫᄋ ᅡ ᆯᄒ ᅳ ᅭᅪ ᄀᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄌ ᆯᄋ ᅮ ᅵᄀ ᅵ.
(10) 222. Taeyoon Kim · Bonggyun Ko. Figure 5.1 Mean Square Error of ESM. ᅱᅡ ᄋ ᆫ ᄒᄎ ᅬᄀ ᆫᄀ ᅳ ᆼᄒ ᅡ ᅪᅡ ᆨ ᄒᄉ ᆸᄐ ᅳ ᅳᄅ ᆫᄃ ᅦ ᅳᄋ ᆫᅧ ᅵ ᆼ 벼 ᆯ ᄅᄒ ᅪᄋ ᅦᄃ ᅢᄒ ᆫᄎ ᅡ ᆼᅮ ᅮ ᆫ ᄇᄒ ᆫᅧ ᅡ ᆫ ᄋᄀ ᅮᄀ ᅡᄋ ᅵᄅ ᅮᄋ ᅥᄌ ᅵᄌ ᅵᄋ ᆭᄋ ᅡ ᆻᄃ ᅡ ᅡ. ᅩᄌ ᄄ ᆼᄀ ᅳ ᆫᄀ ᅯ ᅥᄅ ᅢᄉ ᅦ, ᄉ ᅮᄉ ᅮᄅ ᅭᄂ ᅡᄎ ᅦᄀ ᆯᄋ ᅧ ᅩᄎ ᅡ, ᄒ ᅩᄀ ᅡᄋ ᅪᄀ ᇀᄋ ᅡ ᆫᄎ ᅳ ᅬᄀ ᆫᄉ ᅳ ᅵᄉ ᅳᄐ ᆷᄐ ᅦ ᅳᄅ ᅦᄋ ᅵᄃ ᆼᄋ ᅵ ᅴᄉ ᅵᄌ ᆼᄆ ᅡ ᅵᄉ ᅵᄀ ᅮᄌ ᅩᄌ ᆨᄋ ᅥ ᅵᄉ ᅲᄅ ᆯᄇ ᅳ ᆫ ᅡ ᆼᄒ ᅧ ᄋ ᅡᄌ ᅵᄋ ᆭᄋ ᅡ ᅡᄀ ᅥᄅ ᅢᄇ ᅵᄋ ᆼᄋ ᅭ ᅵᄀ ᅥᄋ ᅴᄌ ᆫᄌ ᅩ ᅢᄒ ᅡᄌ ᅵᄋ ᆭᄂ ᅡ ᆫᄃ ᅳ ᅡᄂ ᆫᄀ ᅳ ᅡᄌ ᆼᄋ ᅥ ᅵᄋ ᆻᄋ ᅵ ᆻᄀ ᅥ ᅩ, ᄋ ᅵᄄ ᅢᄆ ᆫᄋ ᅮ ᅦᄆ ᅩᄒ ᆼᄋ ᅧ ᅴᄒ ᆫᄉ ᅧ ᆯᅥ ᅵ ᆼ ᄉᄆ ᆫᄌ ᅮ ᅦᄀ ᅡᄌ ᅵᄌ ᆨ ᅥ ᆯᄉ ᅬ ᄃ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᆫᄅ ᅮ ᄒ ᆫᄃ ᅧ ᅦᄋ ᅵᄐ ᅥᄋ ᅦᄌ ᅮᄉ ᆨᄉ ᅵ ᅵᄌ ᆼᄋ ᅡ ᅴᄏ ᆫᄒ ᅳ ᅡᄅ ᆨᅡ ᅡ ᆼ ᄌᄋ ᆯᄑ ᅳ ᅩᄒ ᆷᄃ ᅡ ᅬᄌ ᅵᄋ ᆭᄀ ᅡ ᅩᄇ ᅩᄉ ᆼᅡ ᅡ ᆷ ᄒᄉ ᅮᄋ ᅦᄑ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄋ ᅴᄋ ᅱᄒ ᆷᄉ ᅥ ᆼᄋ ᅥ ᅦᄄ ᅡᄅ ᆫᄑ ᅳ ᅢᄂ ᆯ ᅥ ᅵᄀ ᄐ ᅡᄌ ᆫᄌ ᅩ ᅢᄒ ᅡᄌ ᅵᄋ ᆭᄋ ᅡ ᅡ AAMᄋ ᅵᄂ ᇁᄋ ᅩ ᆫᄋ ᅳ ᅱᄒ ᆷᄀ ᅥ ᆷᄉ ᅡ ᅮᄉ ᆼᄒ ᅥ ᆼᄋ ᅣ ᆯᄇ ᅳ ᅩᄋ ᆻᄀ ᅧ ᅩ, ᄋ ᅵᄅ ᅩᄋ ᆫᄒ ᅵ ᅢᄏ ᅩᄅ ᅩᄂ ᅡᄇ ᅡᄋ ᅵᄅ ᅥᄉ ᅳᄀ ᆷᄋ ᅡ ᆷᄌ ᅧ ᆼ-19ᄋ ᅳ ᅴᄑ ᆫ ᅡ ᅦᄆ ᄃ ᆨᅳ ᅵ ᄋᄅ ᅩᄋ ᆫᄒ ᅵ ᆫᄌ ᅡ ᅮᄉ ᆨᄉ ᅵ ᅵᄌ ᆼᄋ ᅡ ᅴᄎ ᅦᄀ ᅨᄌ ᆨᄋ ᅥ ᅱᄒ ᆷᄋ ᅥ ᆯᄃ ᅳ ᅢᄋ ᆼᄒ ᅳ ᅡᄌ ᅵᄆ ᆺᄒ ᅩ ᅡᄂ ᆫᄀ ᅳ ᆯᄀ ᅧ ᅪᄅ ᆯᄇ ᅳ ᅩᄋ ᅧᄌ ᅮᄋ ᆻᄃ ᅥ ᅡ. ᄒ ᅡᄌ ᅵᄆ ᆫ ESMᄋ ᅡ ᅴᄌ ᆨᄌ ᅥ ᆯᄒ ᅥ ᆫ ᅡ ᆼᄆ ᅩ ᄌ ᆨᄉ ᅩ ᆫᄐ ᅥ ᆨᄋ ᅢ ᅳᄅ ᅩᄋ ᅵᄅ ᆯᄇ ᅳ ᅩᄋ ᆫᄒ ᅪ ᆯᄉ ᅡ ᅮᄋ ᆻᄂ ᅵ ᆫᄆ ᅳ ᅩᄉ ᆸᅳ ᅳ ᆯ ᄋᄇ ᅩᄋ ᅧᄌ ᅮᄋ ᆻᄀ ᅥ ᅩ, ᄒ ᆼᄒ ᅣ ᅮᄒ ᆫᄅ ᅮ ᆫᄉ ᅧ ᆺᄋ ᅦ ᅦᄉ ᅵᄌ ᆼᄋ ᅡ ᅴᄒ ᅡᄅ ᆨᅡ ᅡ ᆼ ᄌᄋ ᆯᄑ ᅳ ᅩᄒ ᆷᄒ ᅡ ᅡᄋ ᅧᄉ ᅵᄌ ᆼ ᅡ ᅴᄎ ᄋ ᅦᄀ ᅨᄌ ᆨᄋ ᅥ ᅱᄒ ᆷᄋ ᅥ ᅦᄃ ᅩᄃ ᅢᄋ ᆼᄒ ᅳ ᆯᄉ ᅡ ᅮᄋ ᆻᄋ ᅵ ᆯᄀ ᅳ ᆺᄋ ᅥ ᅳᄅ ᅩᄋ ᅨᄉ ᆼᄃ ᅡ ᆫᄃ ᅬ ᅡ. ᆼᅪ ᅡ ᄀ ᄒᄒ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋᄐ ᆼᄒ ᅩ ᆫᄑ ᅡ ᅩᄐ ᅳᄑ ᆯᄅ ᅩ ᅵᄋ ᅩᄀ ᅮᄉ ᆼᄋ ᅥ ᆫᄋ ᅳ ᆫᄀ ᅵ ᆼᄌ ᅩ ᅵᄂ ᆼᄋ ᅳ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄏ ᆫᄇ ᅳ ᅵᄌ ᆼᅳ ᅮ ᆯ ᄋᄎ ᅡᄌ ᅵᄒ ᅡᄌ ᅵᄋ ᆭᄋ ᅡ ᆻᄌ ᅡ ᅵᄆ ᆫᄋ ᅡ ᅧᄅ ᅥᄎ ᅡᄅ ᅨᄉ ᅮᄒ ᆼ ᅢ ᅬᄋ ᄃ ᅥᅪ ᆻ ᄋᄀ ᅩᄃ ᅢᄇ ᅮᄇ ᆫᄌ ᅮ ᇂᄋ ᅩ ᆫᄉ ᅳ ᆼᄀ ᅥ ᅪᄅ ᆯᄇ ᅳ ᅩᄋ ᅧᄌ ᅮᄋ ᆻᄃ ᅥ ᅡ. ᄒ ᅡᄌ ᅵᄆ ᆫᄇ ᅡ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄀ ᅳ ᆼᄒ ᅡ ᅪᄒ ᆨᄉ ᅡ ᆸ AAMᄆ ᅳ ᆫᄋ ᅡ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᆫᄉ ᅡ ᅵᄃ ᅩᄋ ᅦᄉ ᅥ ᆫᄋ ᅡ ᄃ ᆯᅵ ᅵ 셔 ᆫ ᆼ ᄀᄆ ᆼᄀ ᅡ ᅮᄉ ᆼᄋ ᅥ ᅴᅡ ᆫ ᄒᄀ ᅨᄅ ᆯᄉ ᅳ ᅵᄉ ᅡᄒ ᆻᄋ ᅢ ᅳᄆ ᅧ, ᄀ ᅳᄅ ᆷᄋ ᅥ ᅦᄃ ᅩᄇ ᆯᄀ ᅮ ᅮᄒ ᅡᄀ ᅩᄃ ᅮᄀ ᅡᄌ ᅵᄋ ᆫᄀ ᅵ ᆼᄌ ᅩ ᅵᄂ ᆼᄋ ᅳ ᅴᄒ ᆸᄅ ᅧ ᆨᄋ ᅧ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄉ ᆼᄒ ᅡ ᅩᄀ ᆫᄋ ᅡ ᅴᅡ ᆫ ᄃ ᆷᄋ ᅥ ᄌ ᆯᄇ ᅳ ᅩᄋ ᆫᄒ ᅪ ᅡᄀ ᅩᄉ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋᄀ ᅢᄉ ᆫᄒ ᅥ ᆯᄉ ᅡ ᅮᄋ ᆻᄋ ᅵ ᆷᅳ ᅳ ᆯ ᄋᄂ ᅡᄐ ᅡᄂ ᅢᄋ ᆻᄋ ᅥ ᅳᄆ ᅧᄋ ᅵᄅ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄃ ᅦᄋ ᅵᄐ ᅥᅪ 가 ᆨ ᄒᄆ ᆾᄐ ᅵ ᆼᄀ ᅩ ᅨᄒ ᆨᄋ ᅡ ᅦᄉ ᅥᄋ ᅴᄀ ᅩᄌ ᆯᄌ ᅵ ᆨᄋ ᅥ ᆫ ᅵ ᆫᄌ ᅮ ᄆ ᅦᅵ ᄋᄃ ᆫ ᅦᄋ ᅵᄐ ᅥᄑ ᆫᄒ ᅧ ᆼᄋ ᅣ ᅴᄒ ᅢᄀ ᆯᄀ ᅧ ᅡᄂ ᆼᄉ ᅳ ᆼᄋ ᅥ ᆯᄇ ᅳ ᅩᄋ ᅧᄌ ᅮᄀ ᅩᄋ ᆻᄃ ᅵ ᅡ. ᆫᄋ ᅩ ᄇ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄌ ᅦᄋ ᆫᄒ ᅡ ᅡᄂ ᆫᄆ ᅳ ᅩᄒ ᆼᄋ ᅧ ᆫᄆ ᅳ ᅩᄃ ᆫᄉ ᅳ ᆼᄒ ᅡ ᆼᄋ ᅪ ᅦᄉ ᅥᄌ ᆯᄃ ᅥ ᅢᄌ ᆨᄋ ᅥ ᆫᄋ ᅵ ᅮᄋ ᅱᄅ ᆯᄇ ᅳ ᅩᄋ ᅧᄌ ᅮᄌ ᅵᄂ ᆫᄋ ᅳ ᆭᄌ ᅡ ᅵᄆ ᆫᄋ ᅡ ᅱᄒ ᆷᄀ ᅥ ᅪᄉ ᅮᄋ ᆨᄋ ᅵ ᅴᅡ ᆼ ᄉᄎ ᆼ ᅮ ᆫᄀ ᅪ ᄀ ᅨᄅ ᆯᄀ ᅳ ᅩᄅ ᅧᄒ ᆻᄋ ᅢ ᆯᄄ ᅳ ᅢᄋ ᅴᄆ ᅵᄋ ᆻᄂ ᅵ ᆫᄐ ᅳ ᅮᄌ ᅡᄌ ᆫᄅ ᅥ ᆨᄋ ᅣ ᅵᄃ ᆯᄉ ᅬ ᅮᄋ ᆻᄋ ᅵ ᆷᅳ ᅳ ᆯ ᄋᄉ ᅵᄉ ᅡᄒ ᆫᄃ ᅡ ᅡ. ᄋ ᅵᄒ ᅮᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄒ ᅡᄋ ᅵᄑ ᅥᄑ ᅡᄅ ᅡᄆ ᅵᄐ ᅥᄋ ᅴᄌ ᆨ ᅥ ᆯᄒ ᅥ ᄌ ᆫᅩ ᅡ ᄌᄌ ᆯᄀ ᅥ ᅪᄀ ᅥᄅ ᅢᄇ ᅵᄋ ᆼ, ᄆ ᅭ ᅩᄒ ᆼᄀ ᅧ ᅮᄌ ᅩᄀ ᅢᄉ ᆫ, ᄀ ᅥ ᅳᄅ ᅵᄀ ᅩᄃ ᅦᄋ ᅵᄐ ᅥᄏ ᅳᄀ ᅵᄌ ᆼᄀ ᅳ ᅡᄅ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄋ ᆫᄀ ᅧ ᅮᄀ ᅡᄀ ᅢᄉ ᆫᄃ ᅥ ᆯᄉ ᅬ ᅮᄋ ᆻᄋ ᅵ ᆯᄀ ᅳ ᆺᄋ ᅥ ᅳᄅ ᅩ ᅧᄀ ᄋ ᅧᅵ ᄌᄆ ᅧ ESMᄀ ᅪ AAMᄋ ᅴᄒ ᆸᄅ ᅧ ᆨᄀ ᅧ ᅮᄌ ᅩᄂ ᆫᄒ ᅳ ᆼᄒ ᅣ ᅮᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄌ ᆼᄋ ᅮ ᅭᄒ ᆫᄋ ᅡ ᆨᄒ ᅧ ᆯᄋ ᅡ ᆯᄉ ᅳ ᅮᄒ ᆼᄒ ᅢ ᆯᅥ ᅡ ᆺ ᄀᄋ ᅳᄅ ᅩᄉ ᆼᄀ ᅢ ᆨᄃ ᅡ ᆫᄃ ᅬ ᅡ..
(11) Modular reinforcement learning for dynamic portfolio optimization in the KOSPI market. 223. Figure 5.2 APV over business days at m = 8 (top), 10 (middle), 12 (bottom). References Almahdi, S. and S. Y. Yang (2017). An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown. Expert Systems with Applications, 87, 267-279. Bard, N., Foerster, J. N., Chandar, S., Burch, N., Lanctot, M., Song, H. F., Parisotto, E., Dumoulin, V., Moitra, S. and Hughes, E. (2020). The hanabi challenge: A new frontier for ai research. Artificial Intelligence, 280, 103216. Bawa, V. S., Brown, S. J. and Klein, R. W. (1979). Estimation risk and optimal portfolio choice NORTHHOLLAND PUBL. CO., N. Y., 190. Blume, M. E. (1970). Portfolio theory: A step toward its practical application. The Journal of Business, 43, 152-173. Chen, S. and He, H. (2018). Stock prediction using convolutional neural network. IOP Conference Series:.
(12) 224. Taeyoon Kim · Bonggyun Ko. Materials Science and Engineering. Cover, T. M. (2011). Universal portfolios. The Kelly capital growth investment criterion: Theory and practice. World Scientific, 181-209. Cumming, J., Alrajeh, D. and Dickens, L. (2015). An investigation into the use of reinforcement learning techniques within the algorithmic trading domain, Imperial College London, London. Dempster, M. A. and Leemans, V. (2006). An automated FX trading system using adaptive reinforcement learning. Expert Systems with Applications, 30, 543-552. Deng, Y., Bao, F., Kong, Y., Ren, Z. and Dai, Q. (2016). Deep direct reinforcement learning for financial signal representation and trading. IEEE Transactions on Neural Networks and Learning Systems, 28, 653-664. Elton, E. J. and Gruber, M. J. (1997). Modern portfolio theory, 1950 to date. Journal of Banking and Finance , 21, 1743-1759. Guo, Y., Fu, X., Shi, Y. and Liu, M. (2018). Robust log-optimal strategy with reinforcement learning. arXiv preprint arXiv:1805.00205. Jiang, Z., Xu, D. and Liang, J. (2017). A deep reinforcement learning framework for the financial portfolio management problem. arXiv preprint arXiv:1706.10059. Jobson, J. D. and Korkie, R. M. (1981). Putting Markowitz theory to work. The Journal of Portfolio Management, 7, 70-74. Kim, J. B., Heo, J. S., Lim, H. K., Kwon, D. H. and Han, Y. H. (2019). Blockchain based financial portfolio management using A3C. KIPS Transactions on Computer and Communication Systems, 8, 17-28. Konda, V. R. and Tsitsiklis, J. N. (2000). Actor-critic algorithms. Advances in Neural Information Processing Systems. Li, B. and Hoi, S. C. (2014). Online portfolio selection: A survey. ACM Computing Surveys (CSUR), 46, 1-36. Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D. and Wierstra, D. (2015). Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971. Magdon-Ismail, M. and Atiya, A. F. (2004). Maximum drawdown. Risk Magazine, 17, 99-102. Markowitz, H. (1959). Portfolio selection, Investment under uncertainty. Mehtab, S. and Sen, J. (2020). Stock price prediction using convolutional neural networks on a multivariate timeseries. arXiv preprint arXiv:2001.09769. Michaud, R. O. (1989). The Markowitz optimization enigma: Is ‘optimized’optimal? Financial Analysts Journal, 45, 31-42. Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D. and Kavukcuoglu, K. (2016). Asynchronous methods for deep reinforcement learning, International Conference on Machine Learning. Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D. and Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, A., Fidjeland, A. K. and Strovski, G. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529-533. Nelson, D. M., Pereira, A. C. and De Oliveira, R. A. (2017). Stock market’s price movement prediction with LSTM neural networks, 2017 International Joint Conference on Neural Networks (IJCNN), IEEE. Selvin, S., Vinayakumar, R., Gopalakrishnan, E., Menon, V. K. and Soman, K. (2017). Stock price prediction using LSTM, RNN and CNN-sliding window model, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE. Sharpe, W. F. (1994). The sharpe ratio. Journal of Portfolio Management, 21, 49-58. Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V. and Lanctot, M. (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529, 484-489. Sutton, R. S. and Barto, A. G. (2018). Reinforcement learning: An introduction, 2nd Ed., MIT press. Van Hasselt, H., Guez, A. and Silver, D. (2015). Deep reinforcement learning with double q-learning. arXiv preprint arXiv:1509.06461. Wang, Z., Schaul, T., Hessel, M., Hasselt, H., Lanctot, M. and Freitas, N. (2016). Dueling network architectures for deep reinforcement learning, International Conference on Machine Learning. Yu, P., Lee, J. S., Kulyatin, I., Shi, Z. and Dasgupta, S. (2019). Model-based deep reinforcement learning for dynamic portfolio optimization. arXiv preprint arXiv:1901.08740. Zhang, Z., Zohren, S. and Roberts, S. (2020). Deep learning for portfolio optimisation. arXiv preprint arXiv:2005.13665..
(13) Modular reinforcement learning for dynamic portfolio optimization in the KOSPI market. 225. 부록 A: 가중치 Figure A.1ᄋ ᅦᄉ ᅥᄂ ᆫ m = 10ᄋ ᅳ ᅦᄉ ᅥ t = 0, 50, 100, 150, 200, 250 ᄋ ᆯᄄ ᅵ ᅢᄉ ᆫᄐ ᅥ ᆨᄃ ᅢ ᆫᄌ ᅬ ᆼᄆ ᅩ ᆨᄃ ᅩ ᆯᄀ ᅳ ᅪᄒ ᅢᄃ ᆼᄌ ᅡ ᅡᄉ ᆫᄃ ᅡ ᆯᄋ ᅳ ᅦᄃ ᅢ ᆫᄀ ᅡ ᄒ ᅡᄌ ᆼᄎ ᅮ ᅵᄅ ᆯᄂ ᅳ ᅡᄐ ᅡᄂ ᆫᄃ ᅢ ᅡ.. Figure A.1 selected stock code and weight.
(14) Journal of the Korean Data & Information Science Society 2021, 32(1), 213–226. http://dx.doi.org/10.7465/jkdi.2021.32.1.213 ᆫᄀ ᅡ ᄒ ᆨᄃ ᅮ ᅦᄋ ᅵᄐ ᅥᄌ ᆼᄇ ᅥ ᅩᅪ ᄀᄒ ᆨᄒ ᅡ ᅬᄌ ᅵ. Modular reinforcement learning for dynamic portfolio †. optimization in the KOSPI market Taeyoon Kim1 · Bonggyun Ko2 12. Department of Mathematics and Statistics, Chonnam national University Received 1 December 2020, revised 9 January 2021, accepted 15 January 2021. Abstract In stock investment and asset management, portfolio distribution and optimization are essential parts to manage risk and maximize returns, and have been traditional problems to be solved in the financial sector. Meanwhile, a lot of research have been conducted on deep learning in recent years, and reinforcement learning is also making great progress. Accordingly, attempts have been made to apply the reinforcement learning methodology to portfolio management in recent years, but most of the research is limited to cryptocurrencies with large transactions. In this paper, we implemented a neural network that composes a portfolio through two types of an Evaluation Stock module (ESM) that selects stocks for investment and an Asset Allocation module (AAM) that allocates the selected stocks. The constituent stocks of the KOSPI 200 were considered for investment. Keywords: Deep learning, KOSPI, portfolio, reinforcement learning, time series.. †. This achievement was supported by National Research Foundation of Korea, funded by the government (Ministry of Science and ICT) (No. 2019R1G1A110070412). 1 Master course, Department of Mathematics and Statistics, 77, Yongbong-ro, Buk-gu, Gwangju 61186, Korea. 2 Corresponding author: Professor, Department of Mathematics and Statistics, 77, Yongbong-ro, Bukgu, Gwangju 61186, Korea. E-mail: [email protected].
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The dimension of a convex set is defined to be the the maximum number of its affinely independent vectors minus
• Markov property for reinforcement learning problem: When considered how a general environment might respond at time t +1 to the action at time t, in most general and
머신러닝의 작동방식은 ‘지도학습(supervised learning)’, ‘비지도학습 (unsupervised learning)’, ‘강화학습(reinforcement learning)’의 세가지로 구분 할 수
In this study, using concept mapping of a variety of learning techniques in the area of science, especially biology, has a positive effect on learning
This study aims to analyze the process and the features of corporate e-Learning as a form of informal learning. For this purpose, the concept of
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A Method for Collision Avoidance of VLCC Tankers using Velocity Obstacles and Deep Reinforcement Learning.. 지도교수
