한국 헤지펀드 시장의 최적의 투자전략 도입순서에 대한...

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Journal of the Korean Institute of Industrial Engineers Vol. 38, No. 4, pp. 254-257, December 2012. http://dx.doi.org/10.7232/JKIIE.2012.38.4.254 ISSN 1225-0988 | EISSN 2234-6457 © 2012 KIIE <Research Letter> 한국 헤지펀드 시장의 최적의 투자전략 도입순서에 대한 연구 권도균٠박희환٠강동훈٠김민정 KAIST 산업및시스템공학과 Optimal Introductive Sequence of Hedge Fund Baskets in the Korean Market Do-gyun Kwon٠Hee Hwan Park٠Dong Hun Kang٠Min Jeong Kim Department of Industrial and Systems Engineering, KAIST Hedge funds can be established in Korea after the deregulation about setting up private equity funds on September, 2011. Although the variety of asset allocation strategies is the strength of hedge funds, most of Korean hedge funds uses only the equity long/short strategy. Therefore, it is need to introduce other strategies into Korea hedge funds, however all strategies can not be adopted at once because of the infrastructure of Korea financial market. In this paper, we find the optimal introductive order of strategies for Korea hedge fund in view of individual or institutional investors. For this analysis, HFRI data are used for the historical return of each hedge fund strategy and three methods (network visualization, principle component analysis and efficient frontier optimization) are used for finding the optimal order. Keywords: Korean Hedge Fund, Principle Component Analysis, Optimal Asset allocation 1. 1990弱惭 畡蒱 寶漞疒草 菑瞭腭拉(Hedge Fund)悁 菱疙 孚瘉 寵產烉疒玽漉 盩璁草 稥珙畽 盾 荅庅晉 掍琙敽惑. 菑瞭腭拉悁 旵橱枙瞭杩 葉璖荅嫍 惑獾草 耙畽 痱斢由 孙溙荅珙 痵惭 瀅畢杍由 箁孙荅悁 溙梕 萂绉畅 耙畽 尝寵畡惑(Bodie et al., 2011). 嫩婝玽悁 嫭昕 寉瘉畅 槥氱晉 孚庡玽 漑瘂戅玡 孚庡檂畅 痮璖由 樈悁 菑瞭腭拉(畡荅 草孚萂 菑瞭腭拉)畅 璡璖畡 溙烑溮 歵姭悒荅班甩庅, 2011930畽櫥烉疒嫩 寵產耙畽玲玽 嫭草 檂杍烉荶昦 娉瘂猵畡 烉荶戅桡漉 草孚萂 菑瞭腭拉畅 漑枪畡 姭悒荡瘹惑. 畡玽 挝敩 孚庡畅 珙旙 瞊孹溙拑甭 2011枽歭缝 菑瞭腭拉杩 璡璖荅尝 烉畾荅珙 20126瓁 菱疙 11璡璖溙, 19娉 腭拉姭 漑瘂戅玡 畵甩桝 笊 漑瘂獎甭 7窉玢 璽玽 畡来惑. 菑瞭腭拉 烉疒 寉梕悁 獋甩晉憱 媱潺 瞊姭荅珙 2021玽悁 等潹 23, 等惭 59癝 璽玽 惙荍 婰甩晉 珵簎戉惑. 寥旙庅 菑瞭腭拉畅 盩璁 肦矂畡 惑獾草 耙畽 痱斢 孙溙畱玽憱 歵孙荅嫍 竵尝 孚庡玽 箉烉戉 菑瞭腭拉 盾 獪 80%姭 孚庡 槼 猱烉猱 瞭珚 盩烊由 耙畽 惭溮甩晉 荅悁 Long/ Short 痱斢(畡荅 Equity L/S 痱斢)由 葉璖荅悁 拞 草孚萂 菑瞭腭拉惭惑瀅姭 氱炤草 甍萂畅 耙畽 痱斢玽 籅盾荅嫍 畵惑. 畡悁 菑瞭腭拉 姱 稕檱漞由 掕玡攕枡 涽 猱悵敩 惕尝痮甩晉 孚庡 盩烊烉疒畅 檭懆漞畡 葂惭 戅悁 慝 尝珙荍 姭悒漞枵痭 癡疙荅桝, 挝敩漉 惑獾草 耙畽 痱斢嫩 耙畽 畽溝由 葉璖荅悁 草孚萂 菑瞭腭拉畅 璡璖畡 茱璁荅惑. 荅瞭枹 孚庡 略昒 槼 略英敩畅 歭癞甩晉 梕拍 耙畽 痱斢拑由 草 橵玽 憱畲荅尝玽悁 玡昑璭畡 挝杩 婰甩晉 櫡畡桝, 惕媱痮略 痱斢 憱畲畡 茱璁荅惑. 挝敩漉 櫥 珝孙玽漉悁 耙畽畽畅 畲疒玽漉 玡掍草 痱斢 憱畲 瀉漉姭 菑瞭腭拉玽 耙畽荅嫍畽 荅悁 耙畽畽拑 玽婹 姭疒 畡拊畡 戍 婰略姭 敩悁 瞵椥玽 惭草 惢由 溝玲嫢荆痮 歱漊 憱孙拑由 葉璖荅珙 玨嫍畽 草惑. 2. 데이터 歱漊由 瓱荅珙 寭晉橹 菑瞭腭拉 瞭瀅略 HFRI 瞭瀅畅 13癲畅 痱 연락저자김민정, 305-701 대전광역시 유성구 구성동 한국과학기술원 산업및시스템공학과, Tel : 042-350-3169, Fax : 042-350-3110, E-mail : [email protected] 2012118일 접수; 20121120일 수정본 접수; 20121120일 게재 확정.

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Journal of the Korean Institute of Industrial EngineersVol. 38, No. 4, pp. 254-257, December 2012. http://dx.doi.org/10.7232/JKIIE.2012.38.4.254ISSN 1225-0988 | EISSN 2234-6457 © 2012 KIIE

<Research Letter>

한국 헤지펀드 시장의 최적의 투자전략 도입순서에 대한 연구

권도균 박희환 강동훈 김민정

KAIST 산업및시스템공학과

Optimal Introductive Sequence of Hedge Fund Baskets in the Korean Market

Do-gyun Kwon Hee Hwan Park Dong Hun Kang Min Jeong Kim

Department of Industrial and Systems Engineering, KAIST

Hedge funds can be established in Korea after the deregulation about setting up private equity funds on September, 2011. Although the variety of asset allocation strategies is the strength of hedge funds, most of Korean hedge funds uses only the equity long/short strategy. Therefore, it is need to introduce other strategies into Korea hedge funds, however all strategies can not be adopted at once because of the infrastructure of Korea financial market. In this paper, we find the optimal introductive order of strategies for Korea hedge fund in view of individual or institutional investors. For this analysis, HFRI data are used for the historical return of each hedge fund strategy and three methods (network visualization, principle component analysis and efficient frontier optimization) are used for finding the optimal order.

Keywords: Korean Hedge Fund, Principle Component Analysis, Optimal Asset allocation

1. 서 론

1990 (Hedge Fund).

‘’ (Bodie et al.,

2011). ( )

, 2011 9 30 ‘’

. 20112012 6 11

, 19 7. 2021

23 , 59 .

80%

Long/ Short (Equity L/S )

.

, .

, .

.

2. 데이터

HFRI 13

연락저자 김민정, 305-701 대전광역시 유성구 구성동 한국과학기술원 산업및시스템공학과, Tel : 042-350-3169, Fax : 042-350-3110,E-mail : [email protected]

2012년 11월 8일 접수; 2012년 11월 20일 수정본 접수; 2012년 11월 20일 게재 확정.

한국 헤지펀드 시장의 최적의 투자전략 도입순서에 대한 연구 255

.

2007 6 / 5. <Table 1> .

4 . Event driven

. Equity hedge

. Global macro , , ,

relative value . HFRI Equity L/S

equity hedge equity market neutral .

Table 1. Hedge fund strategy data of HFRIStrategy Class Specific Strategy

Event Driven(ED)

1. Distressed/Restructuring2. Merger Arbitrage 3. Private Issue/Regulation D

Equity Hedge(EH)

4. Equity Market Neutral5. Quantitative Directional6. Sector-Energy/Basic Materials 7. Sector-Technology/Healthcare 8. Short Bias

Global Macro(Macro) 9. Systematic Diversified

Relative Value(RV)

10. Fixed Income-Asset Backed11. Fixed Income-Convertible Arbitrage 12. Fixed Income-Corporate 13. Yield Alternatives

3. 최적의 투자전략 도입순서

. HFRI

, HFRI (asset) .

, .

. ,

, network visualization .

principle component analysis ,

. ,

Sharpe ratio .

3.1 Network Visualization

2

. <Figure 1>feasible set .

Equity L/S

.

.

. Kolar et al.(2010)

covariance selection(partial correlation matrix)

, .

Figure 1. Feasible region varied by correlation coefficient

<Figure 2> <Figure 3> network visualization/ 5

. , .

, .

, Macro .

security , commodity

(Frank, 2009). / ,

.

256 Do-gyun Kwon Hee Hwan Park Dong Hun Kang Min Jeong Kim

Figure 2. Simplified correlation network : before crisis

Figure 3. Simplified correlation network : after crisis

3.2 Principle component analysis

3.1 Network visualization

(principle component analysis : PCA). PCA

(principle component : PC) , . PCA

PC factor , PC.

Figure 4. Principal component coefficients : using 3 PCs

<Figure 4> 3 factor PCA , 2.

. , 8 short bias

4 equity market neutral.

80% 4 PCequity market neutral

, <Table 2> . <Table 2>

Equity L/Sfeasible set .

Table 2. Optimal order from PCA : using 4 PCsOptimal Order Specific Strategy

- Equity Market Neutral1 Sector-Energy/Basic Materials2 Merger arbitrage3 Systematic diversified4 Short bias5 Fixed Income-Asset Backed6 Private issue/regulation D7 Fixed Income-Corporate8 Sector-Technology/Healthcare9 Yield Alternatives

10 Fixed Income-Convertible Arbitrage11 Distressed/restructuring12 Quantitative directional

3.3 Efficient frontier optimization (EFO)

PCA .

. Sharpe ratio

.

Figure 5. Sharpe ratio

Sharpe ratio.

Optimal Introductive Sequence of Hedge Fund Baskets in the Korean Market 257

<Figure 5> Sharpe ratio mean-variance

, Sharpe ratio. equity market neutral

Sharpe ratio.

Sharpe ratio.

n, , j 1 t .

⋯ .

= kth optimal strategy (k = 1 : equity market neutral) = = weight of strategies ⋯ if strategy i is

kth optimal strategy = geometric mean return of strategies ⋯ if

strategy i is kth optimal strategy = covariance of strategies ⋯ if strategy i

is kth optimal strategy

where ∈ ⋯ ⋯ , ∈ ⋯

Figure 6. Efficient frontier of 13 strategies

<Figure 6> , efficient

frontier mean-variance . efficient frontier , ,

efficient frontier .

Table 3. Optimal order and Sharpe ratio from EFOOptimal

Order Specific Strategy Sharperatio

- Equity Market Neutral 3.50941 Distressed/Restructuring 3.91512 Short Bias 4.03143 Fixed Income-Corporate 4.10264 Sector-Energy/Basic Materials 4.16865 Yield Alternatives 4.22156 Fixed Income-Asset Backed 4.23487 Systematic Diversified 4.23488 Private Issue/Regulation D 4.23489 Fixed Income-Convertible Arbitrage 4.2348

10 Quantitative Directional 4.234811 Sector-Technology/Healthcare 4.234812 Merger Arbitrage 4.2348

<Table 3> Sharpe ratioSharpe ratio .

Sharpe ratio. 3

Sharpe ratio 98% . .

4. 결 론

. network visualization

. , PCASharpe ratio

.

.

.

참고문헌

Bodie, Z., Kane, A., and Marcus, A. J. (2011), Investments and Portfolio Management, McGraw-Hill.

Frank., N. (2009), Linkages between Asset Classes during the Financial Crisis, Accounting for Market Microstructure Noise and Non- Synchronous Trading, Oxford University Working Paper, Oxford- Man Institute of Finance and Department of Economics.

Kolar, M., Parikh, A., and Xing, E. (2010), On Sparse Nonparametric Conditional Covariance Selection, International Conference on Machine Learning.