“The strategy alternates between SPY and IEF ETFs based on full-time versus part-time employment growth, rebalancing monthly to capture economic shifts through labor market trends.”
ASSET CLASS: CFDs, ETFs, funds, futures | REGION: United States | FREQUENCY:
Monthly | MARKET: bonds, equities | KEYWORD: Timing, Employment
I. STRATEGY IN A NUTSHELL
The strategy trades SPY and IEF ETFs based on employment data. Compare the annual growth rates of full-time and part-time employment. If full-time employment growth exceeds part-time, invest in SPY (equities); otherwise, invest in IEF (bonds). The portfolio is rebalanced monthly, though the rebalancing frequency and data choice (monthly or weekly) can be adjusted based on the investor’s preference. This approach uses labor market trends to allocate between equities and bonds, aiming to capture economic momentum and adjust exposure based on workforce composition changes.
II. ECONOMIC RATIONALE
This strategy forecasts recessionary activity rather than recessions themselves, focusing on identifying reliable patterns using diverse employment data. Analyzing large datasets, normalized to the same scale and treated as separate events, produces significant, usable results, unlike relying on single indicators like the unemployment rate. Macroeconomic data, unlike market-derived indicators, cannot be traded or arbitraged away, offering stable insights. Equity prices, by contrast, are more volatile and subject to non-economic influences. Simpler models, such as differences between two variables, often outperform complex, multi-variable conditions. Since 1956, every recession named by the National Bureau of Economic Research has been identified using this approach.
III. SOURCE PAPER
Easy and Successful Macroeconomic Timing [Click to Open PDF]
- Rafter
<Abstract>
When the economy takes a turn for the worse, employment declines, right? Well, not all employment. Certainly, full-time employment declines during recessions, but concurrently part time employment rises strongly during economic downturns. An adept fiduciary can contrast the two types of employment, as well as a variety of other data, and get good broad brush investment timing decisions before an official determinationof a Recession. Using the described method enables an investor to seriously reduce drawdowns and greatly improve the reward-to-risk ratio. This works well as a stand-alone improvement to Buy & Hold or as an initial screen prior to other fundamental or technical analysis. Decisions can be made monthly, weekly or even daily for those willing to do a bit more work.
IV. BACKTEST PERFORMANCE
| Annualised Return | 11.38% |
| Volatility | 11.05% |
| Beta | 0.326 |
| Sharpe Ratio | 1.03 |
| Sortino Ratio | N/A |
| Maximum Drawdown | -16.22% |
| Win Rate | 53% |
V. FULL PYTHON CODE
import pandas as pd
class Full_Partial_Employment(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2005, 1, 1)
self.SetEndDate(2019, 7, 1)
self.SetCash(100000)
self.employment_data = pd.read_csv('https://docs.google.com/spreadsheets/d/1P0E8_ZUAm1NhqaMB1PK1Bc5uB2wL3f6W/export?format=csv', dtype={'date':str}, index_col='date') # header=None)
symbols = ['SPY', 'IEF']
for symbol in symbols:
self.AddEquity(symbol, Resolution.Daily)
self.Schedule.On(self.DateRules.MonthStart(symbols[0]), self.TimeRules.AfterMarketOpen(symbols[0]), self.Rebalance)
def Rebalance(self):
date = str(self.Time.month) + '/' + str(self.Time.year)
current_row_index = self.employment_data.index.get_loc(date)
#one month lag due to employment data reporting
this_month = self.employment_data.iloc[current_row_index-1]
last_month = self.employment_data.iloc[current_row_index-2]
full_time_diff = this_month['full_time'] - last_month['full_time']
part_time_diff = this_month['part_time'] - last_month['part_time']
if full_time_diff > part_time_diff:
if not self.Portfolio['SPY'].Invested:
self.Liquidate('IEF')
self.SetHoldings('SPY', 1)
else:
if not self.Portfolio['IEF'].Invested:
self.Liquidate('SPY')
self.SetHoldings('IEF', 1)
