Asset Allocation with Momentum quant trading strategy design
Tribute the team effort to Jinkyu Paik, Jooheon Lee, Wonjun Jung and Henry Chang
This repo is dedicated to document our designed thought process,back-testing results and the rationale behind the strategy.
Our goal is to let risk-adverse/ risk-neutral investor have a strategy that guarantee increase on return in bullish markets and be defensive in berish markets.
- Bullish Market: Take advantage of the upward trend assets by taking momentum strategy
- Bearish Market: Use defense strategy to protect our portfolio by adjusting the weight of bonds investment
- Risky Assets (Max 70%)
- iShares country EFTs excluding Asia countries (Value and Momentum Everywhere - Asness, Moskowitz and Pederson, 2012)
- SDPR industry ETFs (Do Industries Explain Momentum - Moskowitz and Grinblatt, 1999)
- Safety Assets (min 30%)
- iShares SHY
- Time Frame
- 12/2005 - 12/2018
By separating our main startegy into two components, we expect we can maximize our return in the bullish market, and we can provide a dense cusion for the portfolio in the bearish market and acquire a higher Sharpe Ratio.
- Momentum Strategy
- Select risky assets ETFs classes where the momentum effect exists
- Assign weight base on momentum score to risky assets ETFs and give the rest to bond ETFs
- Reconfirm the degree of the momentum effect of each asset classes and allocate assets depending on the scale of the momentum
- Defense Stategy
- As a base, set at least 30% weight in bond ETF, meaning at most 70% in risky assets ETFs
- Adjust weight in bond ETF base on the momentum signal from risky assets ETFs
- F-F_Research_Data_Factors.CSV Historical Fama-French factors
- etf_list.xlsx A list of all the ETFs that were
- code_summary.py