Original paper
Abstract
This paper outlines a tactical asset allocation (TAA) strategy that takes signals from the credit markets and applies them to the stock market. A power model is built using the Russell 2000 equity index and the Bank of America/Merrill Lynch High Yield B index. This model is then used in a tactical asset allocation strategy to judge whether equities are expensive or cheap relative to high yield bonds. Based on back-test results from 1997 to the present, the approach provides equity-like returns while lowering portfolio volatility.
Keywords: Tactical Asset Allocation, Asset Allocation, Quantitative, Capital Structure Arbitrage, Credit Markets, Market Timing
Trading rules
- Choose Russell 2000 and BofA Merrill Lynch HY/B as benchmarks for equity and credit.
- Transform the HY/B index into a default risk metric using the equation: .
- Assign T (duration until maturity) a value of 5 years, and use long-term averages for RR (recovery rate).
- Model Russell 2000 price levels with power model regression using current default probabilities.
- Daily, update the model's parameters for the upcoming trading day.
- If the actual price of Russell 2000 is beneath the predicted price, invest in equities; otherwise, otherwise stay in cash.
Python code
Backtrader
import math
import numpy as np
import backtrader as bt
class CreditSpreadStrategy(bt.Strategy):
params = (
('time_to_maturity', 5),
('recovery_rate', 0.4), # Assume long-term average recovery rate
)
def __init__(self):
self.russell2000 = self.datas[0]
self.hyb = self.datas[1]
self.default_prob = None
self.modeled_price = None
def next(self):
spread = self.hyb.close[0] - self.hyb.close[-1]
self.default_prob = 1 - math.exp(-self.params.time_to_maturity * spread * (1 / (1 - self.params.recovery_rate)))
# Power model regression with default probabilities
x = np.array(range(len(self.russell2000)))
y = np.array(self.russell2000.get(size=len(self.russell2000)))
a, b = np.polyfit(np.log(x), np.log(y), 1)
self.modeled_price = np.exp(b) * (x[-1] ** a)
# Trading logic
if self.russell2000.close[0] < self.modeled_price:
self.order_target_percent(self.russell2000, target=1.0)
else:
self.order_target_percent(self.russell2000, target=0.0)
if __name__ == '__main__':
cerebro = bt.Cerebro()
# Add data feeds for Russell 2000 and BofA Merrill Lynch HY/B
# ... (Load data feeds here)
# Add strategy
cerebro.addstrategy(CreditSpreadStrategy)
# Execute the strategy
cerebro.run()Please note that you will need to load the data feeds for Russell 2000 and BofA Merrill Lynch HY/B into the cerebro object where indicated in the code. The provided code assumes you have the required data available in the appropriate format.