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ÇѱÛÁ¦¸ñ(Korean Title) |
ÇØ¿ÜÁö¼ö¿Í ÅõÀÚÀÚº° ¸Å¸Å µ¿Çâ¿¡ µû¸¥ µö·¯´× ±â¹Ý ÁÖ°¡ µî¶ô ¿¹Ãø |
¿µ¹®Á¦¸ñ(English Title) |
Deep Learning-Based Stock Fluctuation Prediction According to Overseas Indices and Trading Trend by Investors |
ÀúÀÚ(Author) |
±èŽÂ
À̼ö¿ø
Tae Seung Kim
Soowon Lee
|
¿ø¹®¼ö·Ïó(Citation) |
VOL 10 NO. 09 PP. 0367 ~ 0374 (2021. 09) |
Çѱ۳»¿ë (Korean Abstract) |
ÁÖ°¡ ¿¹ÃøÀº °æÁ¦, Åë°è, ÄÄÇ»ÅÍ °øÇÐ µî ¿©·¯ ºÐ¾ß¿¡¼ ¿¬±¸µÇ´Â ÁÖÁ¦À̸ç, ƯÈ÷ ÃÖ±Ù¿¡´Â ±âº»Àû ÁöÇ¥³ª ±â¼úÀû ÁöÇ¥ µî ´Ù¾çÇÑ ÁöÇ¥·ÎºÎÅÍ ÀΰøÁö´É ¸ðµ¨À» ÇнÀÇÏ¿© ÁÖ°¡ÀÇ º¯µ¿À» ¿¹ÃøÇÏ´Â ¿¬±¸µéÀÌ È°¹ßÇØ Áö°í ÀÖ´Ù. º» ¿¬±¸¿¡¼´Â S&P500 µîÀÇ ÇØ¿ÜÁö¼ö, °ú°Å KOSPI Áö¼ö, ±×¸®°í KOSPI ÅõÀÚÀÚº° ¸Å¸Å µ¿ÇâÀ¸·ÎºÎÅÍ KOSPIÀÇ µî¶ôÀ» ¿¹ÃøÇÏ´Â µö·¯´× ¸ðµ¨À» Á¦¾ÈÇÑ´Ù. Á¦¾È ¸ðµ¨Àº ÁÖ°¡ µî¶ô ¿¹ÃøÀ» À§ÇÏ¿© ºñÁöµµ ÇнÀ ¹æ¹ýÀÎ ÀûÃþ ¿ÀÅäÀÎÄÚ´õ¸¦ ÀÌ¿ëÇÏ¿© ÀáÀ纯¼ö¸¦ ÃßÃâÇÏ°í, ÃßÃâµÈ ÀáÀ纯¼ö·ÎºÎÅÍ ½Ã°è¿ µ¥ÀÌÅÍ ÇнÀ¿¡ ÀûÇÕÇÑ LSTM ¸ðµ¨·Î ÇнÀÇÏ¿© ´çÀÏ ½Ã°¡ ´ëºñ Á¾°¡ÀÇ µî¶ôÀ» ¿¹ÃøÇϸç, ¿¹ÃøµÈ °ªÀ» ±â¹ÝÀ¸·Î ¸Å¼ö ¶Ç´Â ¸Åµµ¸¦ °áÁ¤ÇÑ´Ù. º» ¿¬±¸¿¡¼ Á¦¾ÈÇÏ´Â ¸ðµ¨°ú ºñ±³ ¸ðµ¨µéÀÇ ¼öÀÍ·ü ¹× ¿¹Ãø Á¤È®µµ¸¦ ºñ±³ÇÑ °á°ú Á¦¾È ¸ðµ¨ÀÌ ºñ±³ ¸ðµ¨µé º¸´Ù ¿ì¼öÇÑ ¼º´ÉÀ» º¸¿´´Ù. |
¿µ¹®³»¿ë (English Abstract) |
Stock price prediction is a subject of research in various fields such as economy, statistics, computer engineering, etc. In recent years, researches on predicting the movement of stock prices by learning artificial intelligence models from various indicators such as basic indicators and technical indicators have become active. This study proposes a deep learning model that predicts the ups and downs of KOSPI from overseas indices such as S&P500, past KOSPI indices, and trading trends by KOSPI investors. The proposed model extracts a latent variable using a stacked auto-encoder to predict stock price fluctuations, and predicts the fluctuation of the closing price compared to the market price of the day by learning an LSTM suitable for learning time series data from the extracted latent variable to decide to buy or sell based on the value. As a result of comparing the returns and prediction accuracy of the proposed model and the comparative models, the proposed model showed better performance than the comparative models. |
Å°¿öµå(Keyword) |
ÁÖ°¡ µî¶ô ¿¹Ãø
µö·¯´×
ÇØ¿Ü Áö¼ö
ÅõÀÚÀÚº° ¸Å¸Å µ¿Çâ
Stock Price Fluctuation Prediction
Deep Learning
Overseas Indices
Trading Trends by Investor
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