LOG IN⠴ݱâ

  • ȸ¿ø´ÔÀÇ ¾ÆÀ̵ð¿Í Æнº¿öµå¸¦ ÀÔ·ÂÇØ ÁÖ¼¼¿ä.
  • ȸ¿øÀÌ ¾Æ´Ï½Ã¸é ¾Æ·¡ [ȸ¿ø°¡ÀÔ]À» ´­·¯ ȸ¿ø°¡ÀÔÀ» ÇØÁֽñ⠹ٶø´Ï´Ù.

¾ÆÀ̵ð ÀúÀå

   

¾ÆÀ̵ð Áߺ¹°Ë»ç⠴ݱâ

HONGGIDONG ˼
»ç¿ë °¡´ÉÇÑ È¸¿ø ¾ÆÀ̵ð ÀÔ´Ï´Ù.

E-mail Áߺ¹È®ÀÎ⠴ݱâ

honggildong@naver.com ˼
»ç¿ë °¡´ÉÇÑ E-mail ÁÖ¼Ò ÀÔ´Ï´Ù.

¿ìÆí¹øÈ£ °Ë»ö⠴ݱâ

°Ë»ö

SEARCH⠴ݱâ

ºñ¹Ð¹øÈ£ ã±â

¾ÆÀ̵ð

¼º¸í

E-mail

ÇмúÀÚ·á °Ë»ö

´ë±Ô¸ð ÁÖ¹®ºÒ±ÕÇüÀÇ °¡°ÝÈ¿°ú¿¡ ´ëÇÑ ½ÇÁõºÐ¼®

  • °­À屸 KAIST °æ¿µ´ëÇÐ ºÎ±³¼ö
  • ¹ÚÇüÁø µ¿±¹´ëÇб³ °æ¿µ´ëÇÐ °æ¿µÇаú Á¶±³¼ö
  • ¾ÈÀçÀ² »ï¼ºÄ«µå
º» ¿¬±¸´Â ´ë±Ô¸ð ÁÖ¹®ºÒ±ÕÇüÀÇ °¡°ÝÈ¿°ú¸¦ ¸Å¸Å±¸ºÐ, ÅõÀÚÀÚ±¸ºÐ, ±â¾÷±Ô¸ð ±¸ºÐÀ» ÅëÇØ »ìÆ캸¾Ò´Ù. 2003³â 1¿ù ºÎÅÍ 2005³â 1¿ù±îÁöÀÇ 697°³ ÁֽĿ¡¼­ ÀϺ° ÁÖ¹®ºÒ±ÕÇüÀÇ Æò±ÕÀ¸·ÎºÎÅÍ ¹ÛÀÇ ÁÖ¹®ºÒ±ÕÇüÀÌ ÀÖ´ø ³¯À» ´ë±Ô¸ð ÁÖ¹®ºÒ±ÕÇü »ç°ÇÀÏ·Î Á¤ÇÏ¿´´Ù. ¸ÕÀú Àüü ÁÖ¹®ºÒ±ÕÇü°ú ¼öÀÍ·ü°£ÀÇ VAR°á°ú´Â Á¤º¸È¿°ú¿Í À¯µ¿¼ºÈ¿°ú°¡ °°ÀÌ ³ªÅ¸³µÀ¸³ª ´ë±Ô¸ð ÁÖ¹®ºÒ±ÕÇü»ç°ÇÀÇ VAR°á°ú¿¡¼­ ÀÌ·¯ÇÑ À¯µ¿¼ºÈ¿°ú°¡ °³ÀÎÅõÀÚÀÚ¿¡ ÀÇÇØ ÁÖµµµÇ¾úÀ½ÀÌ °üÂûµÇ¾ú´Ù. ÀÌ´Â ´ë±Ô¸ð ÁÖ¹®ºÒ±ÕÇüÀ» ÁÖµµÇÑ ÅõÀÚÀÚ Áý´ÜÀ¸·Î ±¸ºÐÇÑ »ç°Ç ºÐ¼®¿¡¼­ º¸´Ù ¸íÈ®ÇÏ°Ô ³ªÅ¸³µ´Ù. ±â°ü°ú ¿Ü±¹ÀÎ ÁÖµµ »ç°ÇÀÇ °æ¿ì »ç°Ç¹ß»ýÀÌÈÄ ´ë±Ô¸ð ÁÖ¹®ºÒ±ÕÇüÀÇ ¹æÇâÀ¸·Î º¯È­ÇÑ °¡°ÝÀÌ À¯ÁöµÇ¸ç ¼ø¸Å¼ö·®µµ ÁÖ¹®ºÒ±ÕÇüÀÇ Å©±â¿Í ¹æÇâÀÌ À¯»çÇÏ¿© Á¤º¸È¿°ú°¡ ³ªÅ¸³µ´Ù°í º¼ ¼ö ÀÖ¾ú´Ù. ¹Ý¸é °³ÀÎÀÇ °æ¿ì´Â ¾ç(À½)ÀÇ ´ë±Ô¸ð ÁÖ¹®ºÒ±ÕÇü ¹ß»ý½Ã »ç°Ç ¹ß»ýÀÌÀü¿¡ Áö¼ÓÀû °¡°ÝÀÇ Ç϶ô(»ó½Â)ÀÌ ÀÖ¾ú°í »ç°Ç ´çÀÏ À¯ÀÇÇÑ °¡°Ý »ó½Â(Ç϶ô)ÀÌ ÀÖ¾úÀ¸³ª ÀÌÈÄ ±Þ°ÝÈ÷ Ç϶ôÇÏ¿´´Ù. ¶ÇÇÑ »ç°Ç ´çÀÏ ¼ø¸Å¼ö°¡ ÁÖ¹®ºÒ±ÕÇüÀÇ Å©±âÀÇ 1/10¹è Á¤µµ·Î ÀÛ°Ô ³ªÅ¸³ª ÀÇ°ßÀÇ ºÒÀÏÄ¡¼ºÀÌ ³ô´Ù°í º¼ ¼ö°¡ ÀÖ¾î À¯µ¿¼ºÈ¿°ú·Î Çؼ®µÇ¾ú´Ù. ±â¾÷ÀÇ ±Ô¸ð¿¡ µû¶ó °¢ ÅõÀÚÀÚº° °Å·¡ÀÇ Á¤º¸È¿°ú¿¡ Â÷ÀÌ°¡ ³ªÅ¸³µ´Âµ¥ Àü¹ÝÀûÀ¸·Î ±â¾÷ÀÇ ±Ô¸ð°¡ Ä¿Áú¼ö·Ï »ç°Ç¹ß»ý½Ã Á¤º¸È¿°ú°¡ ¸ðµç Áý´Ü¿¡¼­ ³ô¾ÆÁ³´Ù.
ÁÖ¹®ºÒ±ÕÇü,¼ø¸Å¼ö,Á¤º¸È¿°ú,°³ÀÎÅõÀÚÀÚ,±â°üÅõÀÚÀÚ,¿Ü±¹ÀÎÅõÀÚÀÚ

An Empirical Study on the Information Effect of Abnormal Order Imbalances

  • Jangkoo Kang
  • Hyoung-jin Park
  • Jae Yul Ahn
This study empirically examines the price effect of abnormal order imbalances for Korea stocks from January 2003 to January 2005 according to classifications by trade initiators (buyer-initiated or seller-initiated), investor type(domestic individual, domestic institution, and foreigner) and firm size. First of all, in vector autoregression analysis by using all daily returns and order imbalances, both information effect and liquidity effect are shown. However, in VAR analysis with daily returns and order imbalances when large order imbalance occurs, this liquidity effect is inferred to be caused by domestic individual investors. This is supported by the results in event study; an event is defined as a day when a particular investor group forces order imbalance higher or lower than standard deviation of all daily order imbalances on a particular firm around its mean. In events by domestic institutions and foreigners, changes of prices along to the directions of large order imbalances are not reversed for 4 or 5 days after the events. Additionally, net trading volumes also are similar to order imbalances in magnitude and direction. However, for events done by domestic individual investors, around the event day, cumulative excess returns are widely reversed. Furthermore, order imbalances of domestic individual group are ten times as big as net trading volume of the investor group. This may be because of big heterogeneity in opinion about future price movement in a domestic individual group. Finally, as size of firm increases, the impact of information effect of all investor groups order imbalances seems to increase.
Order Imbalances,Net Trading Volume,Information Effect,Domestic Individual Investors,Domestic Institiutional Investors,Foreign Investors