股票 · 美国 · ETF、股票 · 每月 · 美国 Free

小盘股效应的时机选择第3版

登录后收藏

学术论文

Realized Semibetas: Signs of Things to Come

作者Realized Semibetas: Signs of Things to Come [点击查看论文]

机构

Duke University
National Bureau of Economic Research
?Duke University - Department of Economics
?Duke University - Finance
?National Bureau of Economic Research (NBER)
DEEuropean Central Bank
?European Central Bank (ECB)

论文链接https://ssrn.com/abstract=3528276

策略概要

投资范围包括小盘股和大盘股。该投资组合可以通过投资ETF（做多小盘股，做空大盘股）或直接投资小盘股和大盘股来构建。高风险月份被定义为预期市场波动率与历史波动率相比位于最高五分之一的月份。在这种月份之后，投资者做多小盘股，做空大盘股，并持有该投资组合6个月。每月检查基于月度波动率相对于历史波动率的择时指标，以相应地调整投资组合。

II. 策略合理性

股票规模溢价存在正向的风险回报关系，这归因于高风险时期，在这些时期，小盘股面临相对较高的市场波动率、违约和流动性风险。时变的SMB溢价似乎可以为评估股票市场中的跨期风险回报权衡提供有用的依据。

回测表现

波动率7.81%

夏普比率0.69

索提诺比率-0.095

胜率52%

完整 Python 代码

1import numpy as np
2from AlgorithmImports import *
3
4class TimingtheSmallCapEffect(QCAlgorithm):
5
6def Initialize(self):
7self.SetStartDate(2000, 1, 1)
8self.SetCash(100000)
9
10self.period = 21
11self.SetWarmUp(self.period, Resolution.DAILY)
12
13self.market = self.AddEquity('SPY', Resolution.Daily).Symbol
14self.data = RollingWindow[float](self.period)   # spy history
15self.historical_volatility = []
16self.min_vol_history_period = 12
17self.was_high_risk_month = False
18
19self.trade_month_count = 0
20
21data = self.AddEquity("DIA", Resolution.Daily)
22data.SetLeverage(5)
23self.large_cap = data.Symbol
24
25data = self.AddEquity("IWM", Resolution.Daily)
26data.SetLeverage(5)
27self.small_cap = data.Symbol
28
29self.Schedule.On(self.DateRules.MonthEnd(self.market), self.TimeRules.BeforeMarketClose(self.market), self.Rebalance)
30
31def OnData(self, data):
32# store market prices
33if self.market in data and data[self.market]:
34    price = data[self.market].Value
35    self.data.Add(price)
36
37def Rebalance(self):
38if self.IsWarmingUp: return
39if not self.data.IsReady: return
40
41if self.time.year == 2023 and self.time.month == 8:
42    foo=3
43
44self.trade_month_count += 1
45if self.trade_month_count == 6:
46    self.trade_month_count = 0
47    self.Liquidate()
48
49if self.was_high_risk_month:
50    self.was_high_risk_month = False
51    self.trade_month_count = 0
52
53    # One month after high risk month.
54    self.SetHoldings(self.small_cap, 1)
55    self.SetHoldings(self.large_cap, -1)            
56
57market_prices = np.array([x for x in self.data])
58market_returns = market_prices[:-1] / market_prices[1:] - 1
59market_volatility = np.std(market_returns) * np.sqrt(252)
60
61self.historical_volatility.append(market_volatility)
62if len(self.historical_volatility) >= self.min_vol_history_period:
63    top_quintile = np.percentile(self.historical_volatility[1:], 80)
64    
65    if market_volatility > top_quintile:
66        # one month lag
67        self.was_high_risk_month = True