Alpha 基金“黑天鹅事件” — 思考以及原因

来源:https://uqer.io/community/share/54b39717f9f06c276f651a0d

0. 引言

2014年11月底至2014年12月初的那一周,在市场不断冲高的节奏下,alpha型对冲基金却遭遇了集体的滑铁卢,最高单周跌幅可以达到11%。这里面到底发生了什么? 本文思想以及部分数据参考自[1]

1. 风格因子

基于Fama-French经典的因子模型,这里我们考虑代表三种不同投资风格的因子:“市场”、“规模”、“价值”。

市场

市场因子反映了市场当前的趋势,是代表最广泛的变动趋势,是全市场的“动量”方向,这里我们选取了中证800指数;

规模

规模因子反映了市场对公司规模的折溢价观点。这里我们按照最初的Fama设想,买入小规模市值股票组合,卖出大规模市值股票组合。这里我们实际选取的组合依据是小盘风格指数以及大盘风格指数。

价值

价值因子反映了市场对公司估值的折溢价观点。这里我们按照最初的Fama设想,买入低估值股票组合,卖出高成长股票组合。这里我们实际选取的组合依据是价值风格指数以及成长风格指数

下图中我们可以看到这三种投资风格,2014年的整体走势。我们可以看到经过上半年的蛰伏,下半年市场因子异军突起,将规模和价值因子牢牢的甩在身后。当价值因子亦步亦趋的追赶市场的步伐的时候,规模因子在11月底12月初来了个高台跳水,丢失了上半年所有的成果。这一现象与12月后蓝筹起舞,小票低迷的市场现状是一致的。

  1. from matplotlib import pyplot as plt
  2. factorData = pd.read_excel('三因子数据.xlsx','Sheet1',index_col = 0)
  3. factorData.plot(figsize = (16,10))
  4. plt.legend(['Market', 'Size', 'Value'], loc = 'best')
  5. <matplotlib.legend.Legend at 0x594d0d0>

Alpha 基金“黑天鹅事件” — 思考以及原因 - 图1

我们也可以看到这几个因子之间收益的相关性,显著的低于一般市场指数之间的相关性,确实体现了风格上的差别

  1. factorData.pct_change()[1:].corr()
市场收益规模价值
市场收益1.000000-0.4378540.412471
规模-0.4378541.000000-0.739627
价值0.412471-0.7396271.000000

2. 风格分析

为了探究alpha基金在2014年11月末12月初这一周中“黑天鹅”事件的原因,我们选取了38只有每周净值数据的alpha型私募基金。选取的日期时间为2014年8月至2014年12月7日,在这段时间内以上基金都有数据。我们使用风格归因的方法,从这些基金的历史收益率情况猜测出他们的投资风格。

  1. alphaData = pd.read_excel('alpha基金数据.xlsx','Sheet1', index_col = 0)

这些基金的名称如下:

  1. for name in alphaData.columns.values[3:]:
  2. print name
  3. 安进1号大岩对冲
  4. 安进1号尊享K
  5. 安进1号尊享L
  6. 安进1号尊享O
  7. 安进1号尊享P
  8. 安进1号大岩对冲尊享C
  9. 安进尊享F
  10. 方正富邦基金-高程量化1
  11. 龙旗扶翼量化对冲
  12. 盈融达量化对冲1
  13. 盈融达量化对冲2
  14. 盈融达量化对冲5
  15. 盈融达量化对冲6
  16. 盈融达量化对冲7
  17. 杉杉青骓量化对冲1
  18. 朱雀漂亮阿尔法
  19. 朱雀阿尔法7
  20. 朱雀阿尔法8
  21. 朱雀投资阿尔法2
  22. 尊嘉ALPHA
  23. 尊嘉ALPHA尊享B
  24. 宁聚爬山虎1
  25. 宁聚稳进
  26. 宁聚量化对冲1
  27. 金锝2
  28. 金锝5
  29. 金锝5号尊享A
  30. 金锝5号尊享B
  31. 金锝6
  32. 金锝6号尊享A
  33. 金锝量化
  34. 通和量化对冲2
  35. 中信富享1
  36. 中信富享2
  37. 翼虎量化对冲
  38. 翼虎量化对冲2
  39. 翼虎量化对冲3
  40. 中钢投资套利优选

我们将他们的净值数据与前节中提到的因子数据合并起来:

  1. alphaData
市场规模价值安进1号大岩对冲安进1号尊享K期安进1号尊享L期安进1号尊享O期安进1号尊享P期安进1号大岩对冲尊享C期安进尊享F期金锝6号金锝6号尊享A期金锝量化通和量化对冲2期中信富享1期中信富享2期翼虎量化对冲翼虎量化对冲2期翼虎量化对冲3期中钢投资套利优选
日期
2014-08-031.0000001.0000001.0000000.97620.96860.96860.96860.98570.97620.96861.07471.01441.26511.03951.03971.03951.07711.125100.061.0011
2014-08-101.0075881.0109570.9940050.98400.97640.97630.97630.99360.98400.97641.08881.02781.27701.04751.04771.04751.09661.142102.041.0278
2014-08-171.0243551.0209820.9912070.99040.98270.98270.98271.00010.99040.98271.09111.02991.28051.05921.05931.05921.10501.156103.531.0470
2014-08-241.0324691.0345790.9757950.99610.98840.98840.98831.00580.99610.98841.10021.03851.29591.06471.06481.06471.11501.165104.701.0712
2014-08-311.0179461.0336980.9740940.99000.98230.98230.98230.99970.99000.98231.09701.03551.29331.06131.06141.06131.11151.160103.541.0710
2014-09-071.0681161.0391190.9786130.99480.98710.98710.98701.00450.99480.98711.10131.03951.30021.07671.07681.07671.11361.160103.781.0735
2014-09-141.0707941.0544390.9669621.00380.99600.99600.99601.01361.00380.99601.12341.06041.32481.08991.09001.08991.15121.196106.891.0924
2014-09-211.0669041.0636990.9682351.00660.99880.99880.99871.01641.00660.99881.11771.05501.31951.09491.09481.09481.14671.181107.991.1005
2014-09-281.0753691.0702500.9659481.01761.00971.00971.00971.02751.01761.00971.12671.06351.32791.09561.09551.09551.14741.181108.151.1067
2014-10-051.0853201.0748790.9609481.02931.02131.02131.02131.03931.02931.02131.13651.07281.33691.10771.10761.10761.14891.182107.281.1156
2014-10-121.0945521.0736420.9600531.03321.02521.02521.02511.04331.03321.02521.13951.07561.34011.10711.10701.10691.15631.189107.851.1161
2014-10-191.0788201.0645070.9685061.02541.01741.01741.01741.03541.02541.01741.13671.07301.34461.10881.10871.10861.15191.185107.241.1146
2014-10-261.0573081.0645040.9618611.03121.02321.02321.02311.04131.03121.02321.14051.07661.35211.10971.10961.10951.15191.184107.321.1171
2014-11-021.1078301.0738740.9801161.04141.03331.03331.03331.05161.04141.03331.14461.08041.35841.12731.12721.12711.15191.184106.801.1185
2014-11-091.1053561.0797560.9811841.04701.03891.03891.03881.05721.04701.03891.15331.08861.36501.11091.11081.11071.16391.196107.851.1216
2014-11-161.1255371.0454831.0225991.03441.02641.02641.02631.04451.03441.02641.14421.08011.35841.08811.08801.08791.13701.171105.341.1017
2014-11-231.1341351.0672491.0027121.03421.02621.02621.02611.04431.03421.02621.14371.07961.35581.09001.08981.08981.14531.176106.151.1097
2014-11-301.2184641.0532911.0186191.04501.03691.03691.03681.05521.04501.03691.12141.05851.33191.07471.07451.07451.15581.196106.171.0938
2014-12-071.3227620.9901181.0820901.00400.99620.99620.99621.01381.00400.99621.05830.99901.26221.00701.00681.00681.18671.216109.110.9938
  1. 19 rows × 41 columns

将价格数据转换为收益率数据:这里我们将三个因子的收益率数据做了标准化处理,这样方便比较后面的因子权重。

  1. returnData = alphaData.pct_change()
  2. returnData = returnData[1:]
  3. returnData[u'市场'] = returnData[u'市场'] / returnData[u'市场'].std() / 100.0
  4. returnData[u'规模'] = returnData[u'规模'] / returnData[u'规模'].std() / 100.0
  5. returnData[u'价值'] = returnData[u'价值'] / returnData[u'价值'].std() / 100.0
  6. returnData
市场规模价值安进1号大岩对冲安进1号尊享K期安进1号尊享L期安进1号尊享O期安进1号尊享P期安进1号大岩对冲尊享C期安进尊享F期金锝6号金锝6号尊享A期金锝量化通和量化对冲2期中信富享1期中信富享2期翼虎量化对冲翼虎量化对冲2期翼虎量化对冲3期中钢投资套利优选
日期
2014-08-100.0025750.005755-0.0029780.0079900.0080530.0079500.0079500.0080150.0079900.0080530.0131200.0132100.0094060.0076960.0076950.0076960.0181040.0151110.0197880.026671
2014-08-170.0056470.005208-0.0013980.0065040.0064520.0065550.0065550.0065420.0065040.0064520.0021120.0020430.0027410.0111690.0110720.0111690.0076600.0122590.0146020.018681
2014-08-240.0026880.006995-0.0077240.0057550.0058000.0058000.0056990.0056990.0057550.0058000.0083400.0083500.0120270.0051930.0051920.0051930.0090500.0077850.0113010.023114
2014-08-31-0.004773-0.000447-0.000866-0.006124-0.006172-0.006172-0.006071-0.006065-0.006124-0.006172-0.002909-0.002889-0.002006-0.003193-0.003193-0.003193-0.003139-0.004292-0.011079-0.000187
2014-09-070.0167240.0027540.0023040.0048480.0048860.0048860.0047850.0048010.0048480.0048860.0039200.0038630.0053350.0145110.0145090.0145110.0018890.0000000.0023180.002334
2014-09-140.0008510.007743-0.0059140.0090470.0090160.0090160.0091190.0090590.0090470.0090160.0200670.0201060.0189200.0122600.0122590.0122600.0337640.0310340.0299670.017606
2014-09-21-0.0012330.0046120.0006540.0027890.0028110.0028110.0027110.0027620.0027890.002811-0.005074-0.005092-0.0040010.0045880.0044040.004496-0.003909-0.0125420.0102910.007415
2014-09-280.0026920.003235-0.0011730.0109280.0109130.0109130.0110140.0109210.0109280.0109130.0080520.0080570.0063660.0006390.0006390.0006390.0006100.0000000.0014820.005634
2014-10-050.0031400.002271-0.0025710.0114980.0114890.0114890.0114890.0114840.0114980.0114890.0086980.0087450.0067780.0110440.0110450.0110450.0013070.000847-0.0080440.008042
2014-10-120.002887-0.000604-0.0004630.0037890.0038190.0038190.0037210.0038490.0037890.0038190.0026400.0026100.002394-0.000542-0.000542-0.0006320.0064410.0059220.0053130.000448
2014-10-19-0.004877-0.0044690.004374-0.007549-0.007608-0.007608-0.007511-0.007572-0.007549-0.007608-0.002457-0.0024170.0033580.0015360.0015360.001536-0.003805-0.003364-0.005656-0.001344
2014-10-26-0.006766-0.000002-0.0034090.0056560.0057010.0057010.0056030.0056980.0056560.0057010.0033430.0033550.0055780.0008120.0008120.0008120.000000-0.0008440.0007460.002243
2014-11-020.0162150.0046230.0094280.0098910.0098710.0098710.0099700.0098910.0098910.0098710.0035950.0035300.0046590.0158600.0158620.0158630.0000000.000000-0.0048450.001253
2014-11-09-0.0007580.0028770.0005420.0053770.0054200.0054200.0053230.0053250.0053770.0054200.0076010.0075900.004859-0.014548-0.014549-0.0145510.0104180.0101350.0098310.002772
2014-11-160.006195-0.0166710.020967-0.012034-0.012032-0.012032-0.012033-0.012013-0.012034-0.012032-0.007890-0.007808-0.004835-0.020524-0.020526-0.020528-0.023112-0.020903-0.023273-0.017743
2014-11-230.0025920.010934-0.009661-0.000193-0.000195-0.000195-0.000195-0.000191-0.000193-0.000195-0.000437-0.000463-0.0019140.0017460.0016540.0017460.0073000.0042700.0076890.007262
2014-11-300.025231-0.0068690.0078810.0104430.0104270.0104270.0104280.0104380.0104430.010427-0.019498-0.019544-0.017628-0.014037-0.014039-0.0140390.0091680.0170070.000188-0.014328
2014-12-070.029046-0.0315000.030952-0.039234-0.039252-0.039252-0.039159-0.039234-0.039234-0.039252-0.056269-0.056212-0.052331-0.062994-0.063006-0.0630060.0267350.0167220.027691-0.091424
  1. 18 rows × 41 columns

在这里开始风格归因。我们使用的是经典回归分析的方法,数据截止到2014年11月30日。关于每个基金我们得到3个风格分别的权重,即为回归方程的系数:

  1. R1×RMarket2×RSize3×RValue

例如:“安进1号大岩对冲”的三个系数为:β1=0.4682β2=0.3556β3=−0.3487

  1. from sklearn import linear_model
  2. cols = returnData.columns[3:]
  3. x = returnData[[u'市场',u'规模',u'价值']][:-1]
  4. market = []
  5. size = []
  6. value = []
  7. intercept = []
  8. for name in cols:
  9. clf = linear_model.LinearRegression()
  10. y = returnData[name][:-1]
  11. clf.fit(x,y)
  12. market.append(clf.coef_[0])
  13. size.append(clf.coef_[1])
  14. value.append(clf.coef_[2])
  15. intercept.append(clf.intercept_)
  1. regression = pd.DataFrame({'Market':market, 'Size':size, u'Value':value, u'Intercept':intercept}, index = cols)
  2. regression['Return'] = returnData[-1:].values.flatten()[3:]
  3. regression['Name'] = regression.index
  4. regression = regression.reindex(columns = ['Name', 'Return', 'Market', 'Size', 'Value', 'Intercept'])
  5. regression
NameReturnMarketSizeValueIntercept
安进1号大岩对冲安进1号大岩对冲-0.0392340.4682120.355625-0.3487030.001755
安进1号尊享K期安进1号尊享K期-0.0392520.4685600.355400-0.3499730.001757
安进1号尊享L期安进1号尊享L期-0.0392520.4687940.355543-0.3497830.001756
安进1号尊享O期安进1号尊享O期-0.0391590.4672310.360231-0.3438050.001745
安进1号尊享P期安进1号尊享P期-0.0392340.4674210.353230-0.3499060.001765
安进1号大岩对冲尊享C期安进1号大岩对冲尊享C期-0.0392340.4682120.355625-0.3487030.001755
安进尊享F期安进尊享F期-0.0392520.4685600.355400-0.3499730.001757
方正富邦基金-高程量化1号方正富邦基金-高程量化1号0.0691281.9111360.266616-0.004385-0.002195
龙旗扶翼量化对冲龙旗扶翼量化对冲-0.0368710.2963291.274064-0.5398700.001011
盈融达量化对冲1期盈融达量化对冲1期-0.0078800.1999130.341597-0.3792660.004520
盈融达量化对冲2期盈融达量化对冲2期-0.0182330.0182880.344163-0.5128960.005633
盈融达量化对冲5期盈融达量化对冲5期-0.0166430.1647440.195826-0.4188230.002362
盈融达量化对冲6期盈融达量化对冲6期-0.0157520.1626800.240728-0.4686150.003681
盈融达量化对冲7期盈融达量化对冲7期-0.0254210.0476220.667812-0.3277980.003539
杉杉青骓量化对冲1期杉杉青骓量化对冲1期-0.0000850.208083-0.472288-0.7961910.008189
朱雀漂亮阿尔法朱雀漂亮阿尔法-0.0362830.2057360.455439-0.4414300.001541
朱雀阿尔法7号朱雀阿尔法7号-0.0418880.0494950.645693-0.1926360.001548
朱雀阿尔法8号朱雀阿尔法8号-0.0517080.2700320.097040-0.8814240.002036
朱雀投资阿尔法2号朱雀投资阿尔法2号-0.0379500.1497970.388702-0.4787860.001898
尊嘉ALPHA尊嘉ALPHA-0.0776340.0457222.030097-0.015791-0.000167
尊嘉ALPHA尊享B期尊嘉ALPHA尊享B期-0.0776720.0455202.031095-0.013126-0.000167
宁聚爬山虎1期宁聚爬山虎1期-0.113581-0.3869131.870564-0.3233950.005228
宁聚稳进宁聚稳进-0.117121-0.6080062.8888060.2616540.004465
宁聚量化对冲1期宁聚量化对冲1期-0.051896-0.6003761.9732230.3875740.005642
金锝2号金锝2号-0.057131-0.4084650.9970680.4525340.002783
金锝5号金锝5号-0.062897-0.3778280.9889500.3801850.002613
金锝5号尊享A期金锝5号尊享A期-0.063031-0.3777600.9902110.3826390.002564
金锝5号尊享B期金锝5号尊享B期-0.063167-0.3786650.9946810.3856040.002540
金锝6号金锝6号-0.056269-0.3705731.0406650.2715470.002177
金锝6号尊享A期金锝6号尊享A期-0.056212-0.3736031.0370300.2707270.002197
金锝量化金锝量化-0.052331-0.3815680.8386230.2611130.003078
通和量化对冲2期通和量化对冲2期-0.0629940.0560651.6026010.487299-0.001136
中信富享1期中信富享1期-0.0630060.0575171.5967750.483519-0.001153
中信富享2期中信富享2期-0.0630060.0567531.6022130.486687-0.001149
翼虎量化对冲翼虎量化对冲0.0267350.4639620.294367-1.0577440.002474
翼虎量化对冲2期翼虎量化对冲2期0.0167220.724630-0.209773-1.3606430.001874
翼虎量化对冲3期翼虎量化对冲3期0.0276910.1601180.981666-0.5090160.001601
中钢投资套利优选中钢投资套利优选-0.091424-0.2152371.264767-0.1652200.004179

3. 风格收益分析

我们用上节得到的因子权重,与2014年12月初的那一周收益率进行对比。为了更加的一目了然,我们分别按照“市场 v.s. 收益”、“规模 v.s. 收益”、“价值 v.s. 收益”三个维度进行分析。通过散点图,很清楚的显示,市场因子在这一周对于alpha基金的收益的贡献是正向反馈效应;相反的,规模以及价值因子对于alpha基金的收益是负反馈。

  1. def func(beta, alpha):
  2. def inner(x):
  3. return beta*x + alpha
  4. return inner
  1. groups = regression.groupby('Name')
  2. fig, ax = plt.subplots(figsize = (25,16))
  3. for name, group in groups:
  4. ax.plot(group.Market, group.Return, marker='o', linestyle='', ms=8, label=name)
  5. ax.grid(True)
  6. ax.legend(prop = font)
  7. ax.set_xlabel('Market Exp.', fontsize = 20)
  8. ax.set_ylabel('Return', fontsize = 20)
  9. ax.set_title('Market v.s. Return', fontsize = 25)
  10. clf = linear_model.LinearRegression()
  11. x = regression[['Market']]
  12. y = regression['Return']
  13. clf.fit(x,y)
  14. beta = clf.coef_[0]
  15. alpha = clf.intercept_
  16. applyFunc = func(beta, alpha)
  17. x = np.linspace( -0.5, 1.5, 100)
  18. y = [applyFunc(v) for v in x]
  19. plt.plot(x,y ,'k-')
  20. [<matplotlib.lines.Line2D at 0x6477550>]

Alpha 基金“黑天鹅事件” — 思考以及原因 - 图2

  1. groups = regression.groupby('Name')
  2. fig, ax = plt.subplots(figsize = (25,16))
  3. for name, group in groups:
  4. ax.plot(group.Size, group.Return, marker='o', linestyle='', ms=8, label=name)
  5. ax.grid(True)
  6. ax.legend(prop = font)
  7. ax.set_xlabel('Size Exp.', fontsize = 20)
  8. ax.set_ylabel('Return', fontsize = 20)
  9. ax.set_title('Size v.s. Return', fontsize = 25)
  10. clf = linear_model.LinearRegression()
  11. x = regression[['Size']]
  12. y = regression['Return']
  13. clf.fit(x,y)
  14. beta = clf.coef_[0]
  15. alpha = clf.intercept_
  16. applyFunc = func(beta, alpha)
  17. x = np.linspace( -0.2, 2.5, 100)
  18. y = [applyFunc(v) for v in x]
  19. plt.plot(x,y ,'k-')
  20. [<matplotlib.lines.Line2D at 0x70e9810>]

Alpha 基金“黑天鹅事件” — 思考以及原因 - 图3

  1. groups = regression.groupby('Name')
  2. fig, ax = plt.subplots(figsize = (25,16))
  3. for name, group in groups:
  4. ax.plot(group.Value, group.Return, marker='o', linestyle='', ms=8, label=name)
  5. ax.grid(True)
  6. ax.legend(prop = font)
  7. ax.set_xlabel('Value Exp.', fontsize = 20)
  8. ax.set_ylabel('Return', fontsize = 20)
  9. ax.set_title('Value v.s. Return', fontsize = 25)
  10. clf = linear_model.LinearRegression()
  11. x = regression[['Value']]
  12. y = regression['Return']
  13. clf.fit(x,y)
  14. beta = clf.coef_[0]
  15. alpha = clf.intercept_
  16. applyFunc = func(beta, alpha)
  17. x = np.linspace( -1.2, 0.2, 100)
  18. y = [applyFunc(v) for v in x]
  19. plt.plot(x,y ,'k-')
  20. [<matplotlib.lines.Line2D at 0x7d43410>]

Alpha 基金“黑天鹅事件” — 思考以及原因 - 图4

4. “黑天鹅”的原因

让我们再仔细看一下之前的各家基金的风格权重。

市场因子

我们可以看到所有的4个收益为正的基金都在市场权重最高的50%以内。并且市场因子最大的两个基金恰好都是收益为正的。

  1. regression.sort(columns = ['Market'], ascending = False)[:19]
NameReturnMarketSizeValueIntercept
方正富邦基金-高程量化1号方正富邦基金-高程量化1号0.0691281.9111360.266616-0.004385-0.002195
翼虎量化对冲2期翼虎量化对冲2期0.0167220.724630-0.209773-1.3606430.001874
安进1号尊享L期安进1号尊享L期-0.0392520.4687940.355543-0.3497830.001756
安进尊享F期安进尊享F期-0.0392520.4685600.355400-0.3499730.001757
安进1号尊享K期安进1号尊享K期-0.0392520.4685600.355400-0.3499730.001757
安进1号大岩对冲安进1号大岩对冲-0.0392340.4682120.355625-0.3487030.001755
安进1号大岩对冲尊享C期安进1号大岩对冲尊享C期-0.0392340.4682120.355625-0.3487030.001755
安进1号尊享P期安进1号尊享P期-0.0392340.4674210.353230-0.3499060.001765
安进1号尊享O期安进1号尊享O期-0.0391590.4672310.360231-0.3438050.001745
翼虎量化对冲翼虎量化对冲0.0267350.4639620.294367-1.0577440.002474
龙旗扶翼量化对冲龙旗扶翼量化对冲-0.0368710.2963291.274064-0.5398700.001011
朱雀阿尔法8号朱雀阿尔法8号-0.0517080.2700320.097040-0.8814240.002036
杉杉青骓量化对冲1期杉杉青骓量化对冲1期-0.0000850.208083-0.472288-0.7961910.008189
朱雀漂亮阿尔法朱雀漂亮阿尔法-0.0362830.2057360.455439-0.4414300.001541
盈融达量化对冲1期盈融达量化对冲1期-0.0078800.1999130.341597-0.3792660.004520
盈融达量化对冲5期盈融达量化对冲5期-0.0166430.1647440.195826-0.4188230.002362
盈融达量化对冲6期盈融达量化对冲6期-0.0157520.1626800.240728-0.4686150.003681
翼虎量化对冲3期翼虎量化对冲3期0.0276910.1601180.981666-0.5090160.001601
朱雀投资阿尔法2号朱雀投资阿尔法2号-0.0379500.1497970.388702-0.4787860.001898

规模

我们可以看到3个收益为正的基金在规模权重最低的50%以内。而且这3个基金的规模权重都在最低的前10名以内。

  1. regression.sort(columns = ['Size'], ascending = True)[:19]
NameReturnMarketSizeValueIntercept
杉杉青骓量化对冲1期杉杉青骓量化对冲1期-0.0000850.208083-0.472288-0.7961910.008189
翼虎量化对冲2期翼虎量化对冲2期0.0167220.724630-0.209773-1.3606430.001874
朱雀阿尔法8号朱雀阿尔法8号-0.0517080.2700320.097040-0.8814240.002036
盈融达量化对冲5期盈融达量化对冲5期-0.0166430.1647440.195826-0.4188230.002362
盈融达量化对冲6期盈融达量化对冲6期-0.0157520.1626800.240728-0.4686150.003681
方正富邦基金-高程量化1号方正富邦基金-高程量化1号0.0691281.9111360.266616-0.004385-0.002195
翼虎量化对冲翼虎量化对冲0.0267350.4639620.294367-1.0577440.002474
盈融达量化对冲1期盈融达量化对冲1期-0.0078800.1999130.341597-0.3792660.004520
盈融达量化对冲2期盈融达量化对冲2期-0.0182330.0182880.344163-0.5128960.005633
安进1号尊享P期安进1号尊享P期-0.0392340.4674210.353230-0.3499060.001765
安进1号尊享K期安进1号尊享K期-0.0392520.4685600.355400-0.3499730.001757
安进尊享F期安进尊享F期-0.0392520.4685600.355400-0.3499730.001757
安进1号尊享L期安进1号尊享L期-0.0392520.4687940.355543-0.3497830.001756
安进1号大岩对冲安进1号大岩对冲-0.0392340.4682120.355625-0.3487030.001755
安进1号大岩对冲尊享C期安进1号大岩对冲尊享C期-0.0392340.4682120.355625-0.3487030.001755
安进1号尊享O期安进1号尊享O期-0.0391590.4672310.360231-0.3438050.001745
朱雀投资阿尔法2号朱雀投资阿尔法2号-0.0379500.1497970.388702-0.4787860.001898
朱雀漂亮阿尔法朱雀漂亮阿尔法-0.0362830.2057360.455439-0.4414300.001541
朱雀阿尔法7号朱雀阿尔法7号-0.0418880.0494950.645693-0.1926360.001548

价值

我们可以看到3个收益为正的基金在价值权重最低的50%以内。而且这3个基金的价值权重都在最低的前10名以内。特别的,价值权重最低的两个基金恰好都为正收益。

  1. regression.sort(columns = ['Value'], ascending = True)[:19]
NameReturnMarketSizeValueIntercept
翼虎量化对冲2期翼虎量化对冲2期0.0167220.724630-0.209773-1.3606430.001874
翼虎量化对冲翼虎量化对冲0.0267350.4639620.294367-1.0577440.002474
朱雀阿尔法8号朱雀阿尔法8号-0.0517080.2700320.097040-0.8814240.002036
杉杉青骓量化对冲1期杉杉青骓量化对冲1期-0.0000850.208083-0.472288-0.7961910.008189
龙旗扶翼量化对冲龙旗扶翼量化对冲-0.0368710.2963291.274064-0.5398700.001011
盈融达量化对冲2期盈融达量化对冲2期-0.0182330.0182880.344163-0.5128960.005633
翼虎量化对冲3期翼虎量化对冲3期0.0276910.1601180.981666-0.5090160.001601
朱雀投资阿尔法2号朱雀投资阿尔法2号-0.0379500.1497970.388702-0.4787860.001898
盈融达量化对冲6期盈融达量化对冲6期-0.0157520.1626800.240728-0.4686150.003681
朱雀漂亮阿尔法朱雀漂亮阿尔法-0.0362830.2057360.455439-0.4414300.001541
盈融达量化对冲5期盈融达量化对冲5期-0.0166430.1647440.195826-0.4188230.002362
盈融达量化对冲1期盈融达量化对冲1期-0.0078800.1999130.341597-0.3792660.004520
安进1号尊享K期安进1号尊享K期-0.0392520.4685600.355400-0.3499730.001757
安进尊享F期安进尊享F期-0.0392520.4685600.355400-0.3499730.001757
安进1号尊享P期安进1号尊享P期-0.0392340.4674210.353230-0.3499060.001765
安进1号尊享L期安进1号尊享L期-0.0392520.4687940.355543-0.3497830.001756
安进1号大岩对冲安进1号大岩对冲-0.0392340.4682120.355625-0.3487030.001755
安进1号大岩对冲尊享C期安进1号大岩对冲尊享C期-0.0392340.4682120.355625-0.3487030.001755
安进1号尊享O期安进1号尊享O期-0.0391590.4672310.360231-0.3438050.001745