1. Portfolio Management Thi-Xuan NGUYEN and Bensalem AL BAROUDI
Université d’Évry Val d’Essonne
Evry, Mars 09, 2017

2. Contents| Database Backtesting an Equal Weight Portfolio Strategy
Building Signals based on price
Maximum Diversification
Strategies for using the P/E as signals
Conclusion
Contents|

3. 1. Database Data 505 stocks of S&P 500 constituents
Source: Yahoo Finance
Period:
Frequency: Daily
Rebalancing: Monthly
Missing value Treatment
Replace NAs by the previous values
Replace NAs by 0 for the stock listed after 2001
In the first part, we intro duce some und erlying financial knowledge that is necessary for the simulation as the default mo de l, the calculating metho d for main risk ind icators (VaR, EL and ES). In the s econd part, we present ab out the functional system. We will show and analyze the functional diagram of our program. Finally, the last part is dedicated to present the way we designed the system. In the first part, we intro duce some und erlying financial knowledge that is necessary for the simulation as the default mo de l, the calculating metho d for main risk ind icators (VaR, EL and ES). In the s econd part, we present ab out the functional system. We will show and analyze the functional diagram of our program. Finally, the last part is dedicated to present the way we designed the system. In the first part, we intro duce some und erlying financial knowledge that is necessary for the simulation as the default mo de l, the calculating metho d for main risk ind icators (VaR, EL and ES). In the s econd part, we present ab out the functional system. We will show and analyze the functional diagram of our program. Finally, the last part is dedicated to present the way we designed the system

4. 2. Result 1

5. 2. Result 2 Portfolio Benchmark Cumulative Return 0.3605 0.2834
Portfolio
Benchmark
Cumulative Return
0.3605
0.2834
Annual Return
0.0195
0.0157
Annualized Sharpe Ratio
0.1027
0.0804
Annualized Volatility
0.1896
0.1959
Maximum Drawdown
Max Length Drawdown
1696
1631

6. 2. Result 3
Choice to use the quarterly rebalancing as a reference

7. 3. Construction of signal portfolio
Step 2
Step 3
Step 1
Building signal matrix
Based on: daily stock return
Rebalancing period: Monthly
Signal at end of month = average of daily signal this month
Transforming: M. signal into M. weight
Signal value < 0 => weight = 0
If not,
Transforming M. weight into D. weight
The daily weight of next month = weight at the end of previous month
Chú thích
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Thêm chữ
In the first part, we intro duce some und erlying financial knowledge that is necessary for the simulation as the default mo de l, the calculating metho d for main risk ind icators (VaR, EL and ES). In the s econd part, we present ab out the functional system. We will show and analyze the functional diagram of our program. Finally, the last part is dedicated to present the way we designed the system. In the first part, we intro duce some und erlying financial knowledge that is necessary for the simulation as the default mo de l, the calculating metho d for main risk ind icators (VaR, EL and ES). In the s econd part, we present ab out the functional system. We will show and analyze the functional diagram of our program. Finally, the last part is dedicated to present the way we designed the system. In the first part, we intro duce some und erlying financial knowledge that is necessary for the simulation as the default mo de l, the calculating metho d for main risk ind icators (VaR, EL and ES). In the s econd part, we present ab out the functional system. We will show and analyze the functional diagram of our program. Finally, the last part is dedicated to present the way we designed the system

8. 3. Backtesting the performance
Return portfolio : Diagonal resulting matrix
Signal PF outperform the EW and benchmark and has overall highest cumulative.
Peak: beginning of 2016
Downturn: 2002 and 2009 due to the global financial crisis

9. 3. Basic portfolio analytics
Signal PF:
Highest annual return, Sharpe tio but also highest volatility
Advisor:
Holding EW if risk aversion
Holding Signal PF if hope higher return but face more risky

10. 4. Maximum diversification portfolio
Goal:
Maximum the diversification ratio:
w –weights vector of portfolio
: volatility vector
: Covariance- Variance matrix
C: Correlation matrix
: weight of synthetic stock
Out put:
505 stocks reduce to invest on 64 stocks
Input:
Dmat: shringkage correlation
Amat <- cbind(rep(1,N),diag(N)
bvec <- c(1,rep(0,N)
dvec <- rep(0,N)
Solve QP

11. 4. Backtesting the performance
The MDP
Does not outperform but has the stability
Has no downturn
Lowest volatility lowest risky

12. 5. Strategies to build signals with the P/E