1. QF302 Investment & Financial Data Analysis Basic Materials Sector
Li Bingze, Chong Ern-Yuan Joel, Rachael Ramachandran, Lam Wang Kwan, Chong Yan Ting

2. Agenda General Market Outlook ETFs Chosen Criteria Used
Ranking and Performance
No boundary on the limits

3. General Market Outlook
Ten years after the most severe financial crisis since the Depression, a broad-based economic upswing is at last under way.
It seems likely that 2017, for the first time since 2010, rich-world and developing economies will put on synchronized growth spurts. The signals are strongest from the more cyclical parts of the global economy, notably manufacturing. The overall economy has been flashing green lights for seven months.

4. Basic Materials
The basic material sector is the category of stocks that accounts for companies involved with the discovery, development and processing of raw materials. The sector includes the mining and refining of metals, such as gold and silver, chemical producers and forestry products.

5. Basic Materials Sector Competitive Dynamics
Sensitive to the demand and supply fluctuations
Competitive advantages - low-cost production.
Cyclical
Operational problems
No boundary on the limits

6. Chosen ETFs Basic Materials Sector
Want to determine the most suitable model in explaining ETF return

7. 7 ETFs Chosen Basic Materials
IYM
PYZ
VAW
XLB
PSCM
RTM
FXZ
7 ETFs Chosen Basic Materials
No boundary on the limits

8. IYM
iShares U.S. Basic Materials ETF Underlying Index: Dow Jones U.S. Basic Materials Index
Source: ETF.com

9. IYM
iShares U.S. Basic Materials ETF Underlying Index: Dow Jones U.S. Basic Materials Index
Source: ETF.com

10. PYZ
PowerShares DWA Basic Materials Momentum Portfolio Underlying Index: DWA Basic Materials Technical Leaders Index
Source: ETF.com

11. PYZ
PowerShares DWA Basic Materials Momentum Portfolio Underlying Index: DWA Basic Materials Technical Leaders Index
Source: ETF.com

12. VAW
Vanguard Materials ETF Underlying Index: MSCI US Investable Market Materials 25/50 Index
Source: ETF.com

13. VAW
Vanguard Materials ETF Underlying Index: MSCI US Investable Market Materials 25/50 Index
Source: ETF.com

14. XLB
Materials Select Sector SPDR Underlying Index: S&P Materials Select Sector Index
Source: ETF.com

15. XLB
Materials Select Sector SPDR Underlying Index: S&P Materials Select Sector Index
Source: ETF.com

16. PSCM
PowerShares S&P SmallCap Materials Portfolio Underlying Index: S&P Materials Select Sector Index
Source: ETF.com

17. PSCM
PowerShares S&P SmallCap Materials Portfolio Underlying Index: S&P Materials Select Sector Index
Source: ETF.com

18. RTM
Guggenheim S&P 500 Equal Weight Materials ETF Underlying Index: S&P Equal Weights Materials Index
Source: ETF.com

19. RTM
Guggenheim S&P 500 Equal Weight Materials ETF Underlying Index: S&P Equal Weights Materials Index
Source: ETF.com

20. FXZ
First Trust Materials AlphaDex Fund Underlying Index: StrataQuant Materials Index
Source: ETF.com

21. FXZ
First Trust Materials AlphaDex Fund Underlying Index: StrataQuant Materials Index
Source: ETF.com

22. ETFs’ Return Distribution
Stocks
Z(2)-score
VR Test
Score
Skew
Excess Kurtosis
JB Stat
IYM
5.6897
Non-random
3.5107
Non-Normal
PYZ
4.3796
3.4451
XLB
4.4630
2.3877
PSCM
0.2272
1.6100
FXZ
5.3541
3.3323
VAW
5.1958
2.7742
RTM
3.6638
2.2640

23. Choosing the Correct 𝛽: Multi-Linear Regression on Fama-French Data
Want to determine the most suitable model in explaining ETF return

24. CAPM 𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝑢 𝑡
𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝑢 𝑡
Where, 𝑹 𝒊𝒕 : Return on the ETF i in period t 𝑹 𝒇 : Risk-free rate 𝑹 𝑴𝒕 : Return on the value-weight (VW) market portfolio

25. Fama 3 Factor Model
𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝛽 2,𝑡 𝑆𝑀𝐵+ 𝛽 3,𝑡 𝐻𝑀𝐿+ 𝑢 𝑡
Where, 𝑹 𝑴𝒕 − 𝑹 𝒇 : Market Factor 𝑺𝑴𝑩 : Size Factor 𝑯𝑴𝑳: Value Factor

26. Fama 5 Factor Model
𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝛽 2,𝑡 𝑆𝑀𝐵+ 𝛽 3,𝑡 𝐻𝑀𝐿+ 𝛽 4,𝑡 𝐶𝑀𝐴+ 𝛽 5,𝑡 𝑅𝑀𝑊+ 𝑢 𝑡
Where, 𝑪𝑴𝑨 : Investment Factor 𝑹𝑴𝑾 : Profitability Factor

27. Fama French Factor
ST-Reversal
LT-Reversal
Momentum

28. Calculation of SMB 𝑆𝑀𝐵= 1 3 ( 𝑆𝑀𝐵 𝐵 𝑀 + 𝑆𝑀𝐵 𝑂𝑃 + 𝑆𝑀𝐵 (𝐼𝑁𝑉)
𝑆𝑀𝐵= 1 3 ( 𝑆𝑀𝐵 𝐵 𝑀 + 𝑆𝑀𝐵 𝑂𝑃 + 𝑆𝑀𝐵 (𝐼𝑁𝑉)
𝑆𝑀𝐵 𝐵 𝑀 = 1 3 𝑆𝑚𝑎𝑙𝑙 𝑉𝑎𝑙𝑢𝑒+𝑆𝑚𝑎𝑙𝑙 𝑁𝑒𝑢𝑡𝑟𝑎𝑙+𝑆𝑚𝑎𝑙𝑙 𝐺𝑟𝑜𝑤𝑡ℎ − 1 3 𝐵𝑖𝑔 𝑉𝑎𝑙𝑢𝑒+𝐵𝑖𝑔 𝑁𝑒𝑢𝑡𝑟𝑎𝑙+𝐵𝑖𝑔 𝐺𝑟𝑜𝑤𝑡ℎ
𝑆𝑀𝐵 𝑂𝑃 = 1 3 𝑆𝑚𝑎𝑙𝑙 𝑅𝑜𝑏𝑢𝑠𝑡+𝑆𝑚𝑎𝑙𝑙 𝑁𝑒𝑢𝑡𝑟𝑎𝑙+𝑆𝑚𝑎𝑙𝑙 𝑊𝑒𝑎𝑘 − 1 3 𝐵𝑖𝑔 𝑅𝑜𝑏𝑢𝑠𝑡+𝐵𝑖𝑔 𝑁𝑒𝑢𝑡𝑟𝑎𝑙+𝐵𝑖𝑔 𝑊𝑒𝑎𝑘
𝑆𝑀𝐵 𝐼𝑁𝑉 = 1 3 𝑆𝑚𝑎𝑙𝑙 𝐶𝑜𝑛𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑣𝑒+𝑆𝑚𝑎𝑙𝑙 𝑁𝑒𝑢𝑡𝑟𝑎𝑙+𝑆𝑚𝑎𝑙𝑙 𝐴𝑔𝑔𝑟𝑒𝑠𝑠𝑖𝑣𝑒 − 1 3 𝐵𝑖𝑔 𝐶𝑜𝑛𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑣𝑒+𝐵𝑖𝑔 𝑁𝑒𝑢𝑡𝑟𝑎𝑙+𝐵𝑖𝑔 𝐴𝑔𝑔𝑟𝑒𝑠𝑠𝑖𝑣𝑒

29. Methodology 1. Set up different multi-linear regression models
2. Determine Akaike Information Criterion (AIC)
3. Determine Bayesian Information Criterion (AIC)
4. Perform parameter stability test
No boundary on the limits

30. Why AIC & BIC?
The best model (relatively) would be one where the results of AIC and BIC coincide.
Complexity AIC tends to favour more complex models, while BIC tends to penalise complex models more heavily.
Consistency
BIC is strongly consistent but inefficient and the AIC is not consistent yet generally more efficient.

31. Models 𝐶𝐴𝑃𝑀: 𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝑢 𝑡
𝐶𝐴𝑃𝑀: 𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝑢 𝑡
𝐹𝑎𝑚𝑎 3−𝐹𝑎𝑐𝑡𝑜𝑟 𝑀𝑜𝑑𝑒𝑙: 𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝛽 2,𝑡 𝑆𝑀𝐵+ 𝛽 3,𝑡 𝐻𝑀𝐿+ 𝑢 𝑡
𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝛽 2,𝑡 𝑆𝑀𝐵+ 𝛽 3,𝑡 𝐶𝑀𝐴+ 𝑢 𝑡
𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝛽 2,𝑡 𝑆𝑀𝐵+ 𝛽 3,𝑡 𝑅𝑀𝑊+ 𝑢 𝑡
𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝛽 2,𝑡 𝑆𝑀𝐵+ 𝛽 3,𝑡 𝐻𝑀𝐿+ 𝛽 4,𝑡 𝐶𝑀𝐴+ 𝑢 𝑡
𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝛽 2,𝑡 𝑆𝑀𝐵+ 𝛽 3,𝑡 𝐻𝑀𝐿+ 𝛽 4,𝑡 𝑅𝑀𝑊 + 𝑢 𝑡
𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝛽 2,𝑡 𝑆𝑀𝐵+ 𝛽 3,𝑡 𝑅𝑀𝑊+ 𝛽 4,𝑡 𝐶𝑀𝐴 + 𝑢 𝑡
𝐹𝑎𝑚𝑎 5−𝐹𝑎𝑐𝑡𝑜𝑟 𝑀𝑜𝑑𝑒𝑙: 𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝛽 2,𝑡 𝑆𝑀𝐵+ 𝛽 3,𝑡 𝐻𝑀𝐿+ 𝛽 4,𝑡 𝐶𝑀𝐴+ 𝛽 5,𝑡 𝑅𝑀𝑊+ 𝑢 𝑡
𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝛽 2,𝑡 𝑆𝑀𝐵+ 𝛽 3,𝑡 𝐻𝑀𝐿+ 𝛽 4,𝑡 𝐶𝑀𝐴+ 𝛽 5,𝑡 𝑅𝑀𝑊+ 𝛽 6,𝑡 𝑆𝑇 𝑅𝑒𝑣𝑒𝑟𝑠𝑎𝑙 + 𝑢 𝑡
𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝛽 2,𝑡 𝑆𝑀𝐵+ 𝛽 3,𝑡 𝐻𝑀𝐿+ 𝛽 4,𝑡 𝐶𝑀𝐴+ 𝛽 5,𝑡 𝑅𝑀𝑊+ 𝛽 6,𝑡 𝑀𝑜𝑚𝑒𝑛𝑡𝑢𝑚+ 𝑢 𝑡
𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝛽 2,𝑡 𝑆𝑀𝐵+ 𝛽 3,𝑡 𝐻𝑀𝐿+ 𝛽 4,𝑡 𝐶𝑀𝐴+ 𝛽 5,𝑡 𝑅𝑀𝑊+ 𝛽 6,𝑡 (𝐿𝑇 𝑅𝑒𝑣𝑒𝑟𝑠𝑎𝑙)+ 𝑢 𝑡
𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝛽 2,𝑡 𝑆𝑀𝐵+ 𝛽 3,𝑡 𝐻𝑀𝐿+ 𝛽 4,𝑡 𝐶𝑀𝐴+ 𝛽 5,𝑡 𝑅𝑀𝑊+ 𝛽 6,𝑡 𝑆𝑇 𝑅𝑒𝑣𝑒𝑟𝑠𝑎𝑙 + 𝛽 7,𝑡 𝑀𝑜𝑚𝑒𝑛𝑡𝑢𝑚+ 𝑢 𝑡
𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝛽 2,𝑡 𝑆𝑀𝐵+ 𝛽 3,𝑡 𝐻𝑀𝐿+ 𝛽 4,𝑡 𝐶𝑀𝐴+ 𝛽 5,𝑡 𝑅𝑀𝑊+ 𝛽 6,𝑡 𝐿𝑇 𝑅𝑒𝑣𝑒𝑟𝑠𝑎𝑙 + 𝛽 7,𝑡 𝑀𝑜𝑚𝑒𝑛𝑡𝑢𝑚+ 𝑢 𝑡
𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝛽 2,𝑡 𝑆𝑀𝐵+ 𝛽 3,𝑡 𝐻𝑀𝐿+ 𝛽 4,𝑡 𝐶𝑀𝐴+ 𝛽 5,𝑡 𝑅𝑀𝑊+ 𝛽 6,𝑡 𝑆𝑇 𝑅𝑒𝑣𝑒𝑟𝑠𝑎𝑙 + 𝛽 7,𝑡 (𝐿𝑇 𝑅𝑒𝑣𝑒𝑟𝑠𝑎𝑙)+ 𝑢 𝑡
𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝛽 2,𝑡 𝑆𝑀𝐵+ 𝛽 3,𝑡 𝐻𝑀𝐿+ 𝛽 4,𝑡 𝐶𝑀𝐴+ 𝛽 5,𝑡 𝑅𝑀𝑊+ 𝛽 6,𝑡 𝑆𝑇 𝑅𝑒𝑣𝑒𝑟𝑠𝑎𝑙 + 𝛽 7,𝑡 (𝐿𝑇 𝑅𝑒𝑣𝑒𝑟𝑠𝑎𝑙)+ 𝛽 8,𝑡 (𝑀𝑜𝑚𝑒𝑛𝑡𝑢𝑚)+𝑢 𝑡

32. ∆AIC
∆AIC is the information loss experienced if we are using the fitted model 𝑖 rather than the best model with minimal AIC. As such, it forces the ‘best’ model to have ∆AIC of 0.
∆ 𝑖 =𝐴𝐼 𝐶 𝑖 − 𝐴𝐼 𝐶 𝑚𝑖𝑛
Individual AIC values alone are not interpretable as:
Affected by sample size
Large range of AIC values
Contain arbitrary constants

33. Model Comparison

34. Model Comparison

35. Model 9
𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝛽 2,𝑡 𝑆𝑀𝐵+ 𝛽 3,𝑡 𝐻𝑀𝐿+ 𝛽 4,𝑡 𝐶𝑀𝐴+ 𝛽 5,𝑡 𝑅𝑀𝑊+ 𝛽 6,𝑡 𝑆𝑇 𝑅𝑒𝑣𝑒𝑟𝑠𝑎𝑙 + 𝑢 𝑡
Where, 𝑹 𝒊𝒕 : Return on the ETF i in period t 𝑹 𝒇 : Risk-free rate 𝑹 𝑴𝒕 is the return on the value-weight (VW) market portfolio 𝑺𝑴𝑩 : Return on a diversified portfolio of small stocks minus the return on a diversified portfolio of big stock 𝑯𝑴𝑳 : Difference between the returns on diversified portfolios of high and low Book-to-Market stocks 𝑪𝑴𝑨 : Difference between the returns on diversified portfolios of low and high investment stocks, which we call conservative and aggressive 𝑹𝑴𝑾 : Difference between the returns on diversified portfolios of stocks with robust and weak profitability 𝑺𝑻 𝑹𝒆𝒗𝒆𝒓𝒔𝒂𝒍 : Average return on the two low prior return portfolios minus the average return on the two high prior return portfolios

36. Model 12
𝑅 𝑖𝑡 − 𝑅 𝑓 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑅 𝑀𝑡 − 𝑅 𝑓 + 𝛽 2,𝑡 𝑆𝑀𝐵+ 𝛽 3,𝑡 𝐻𝑀𝐿+ 𝛽 4,𝑡 𝐶𝑀𝐴+ 𝛽 5,𝑡 𝑅𝑀𝑊+ 𝛽 6,𝑡 𝑆𝑇 𝑅𝑒𝑣𝑒𝑟𝑠𝑎𝑙 + 𝛽 7,𝑡 𝑀𝑜𝑚𝑒𝑛𝑡𝑢𝑚+ 𝑢 𝑡
Where, 𝑹 𝒊𝒕 : Return on the ETF i in period t 𝑹 𝒇 : Risk-free rate 𝑹 𝑴𝒕 is the return on the value-weight (VW) market portfolio 𝑺𝑴𝑩 : Return on a diversified portfolio of small stocks minus the return on a diversified portfolio of big stock 𝑯𝑴𝑳 : Difference between the returns on diversified portfolios of high and low Book-to-Market stocks 𝑪𝑴𝑨 : Difference between the returns on diversified portfolios of low and high investment stocks, which we call conservative and aggressive 𝑹𝑴𝑾 : Difference between the returns on diversified portfolios of stocks with robust and weak profitability 𝑺𝑻 𝑹𝒆𝒗𝒆𝒓𝒔𝒂𝒍 : Average return on the two low prior return portfolios minus the average return on the two high prior return portfolios 𝑴𝒐𝒎𝒆𝒏𝒕𝒖𝒎 is the average return on the two high prior return portfolios minus the average return on the two low prior return portfolios

37. Parameter Stability Test

38. Beta

39. Drawbacks
There could be irrelevant factors in the models constructed since each factor must be general enough to explain the returns of all 7 ETFs.
It may not be the ‘best’ model, but it is the most representative one for the 7 ETFs in the Basic Materials Sector from 2010 onwards.

40. Monte-Carlo Simulation Determining Drift Rate
Want to determine the most suitable model in explaining ETF return

41. Drift Rate
The drift rate/mean suggested a general increase in asset prices for all the 7 ETFs, albeit being a small.

42. Results

43. Results

44. Remarks
The Monte Carlo Simulation did predict an accurate upward movement, however, the exact movement was not as accurate as we would have hoped.
If we based our investment decision solely on the Monte Carlo Simulation, we would not have obtained as desirable a return.
Nonetheless, we still acknowledge the utility of the Simulation as an indicator of future price movements/trends.

45. Other Criteria Ratios & Jensen’s Alpha
Want to determine the most suitable model in explaining ETF return

46. Information Ratio (IR)
IR measures a portfolio manager's ability to generate excess returns relative to a benchmark (index) but also attempts to identify the consistency of the investor.
The higher the IR, the more consistent a manager, with consistency being an ideal trait. Conversely, the lower the IR, the less consistency. An IR of 0.2 or 0.3 is superior (Kidd, 2011)
ETF
IYM
PYZ
XLB
PSCM
FXZ
VAW
RTM IR
0.0279

47. Sharpe Ratio
The Sharpe Ratio is a measure for calculating risk-adjusted return or how much an investor was compensated for investing in a risky asset vs a risk-free asset.
The greater a portfolio's Sharpe ratio, the better its risk-adjusted performance has been. A negative Sharpe ratio indicates that a risk-less asset would perform better than the ETF being analysed.
Sharpe Ratio cannot tell whether high standard deviation is due to large upside or downside deviations since it penalizes both equally
ETF
IYM
PYZ
XLB
PSCM
FXZ
VAW
RTM
Sharpe Ratio
0.0314
0.0006
0.0247
0.0238
0.0106
0.0152

48. Treynor Ratio
The Treynor ratio measures the performance of the ETF compared to a risk-free asset per unit of assumed market risk represented by beta, which represents the portfolio’s sensitivity to market movement.
When the value of the Treynor ratio is high, it is an indication that an investor has generated high returns on each of the market risks he has taken.
Treynor Ratio understates the relationship of return to total risk for a portfolio which contains diversifiable risk.
ETF
IYM
PYZ
XLB
PSCM
FXZ
VAW
RTM
Treynor Ratio
0.0008
0.0000
0.0006
0.0003
0.0004

49. Jensen’s Alpha -0.0033 -0.0022 -0.0029 -0.0020 -0.0024 -0.0027 -0.0025
Jensen’s Alpha (ex post alpha) measures the ETF’s excess return with respect to the expected return given by the CAPM model.
A positive alpha signals positive abnormal return and better performance on a risk-adjusted basis.
ETF
IYM
PYZ
XLB
PSCM
FXZ
VAW
RTM
Jensen’s Alpha

50. ETFs Rank and Trading Strategy
Want to determine the most suitable model in explaining ETF return

51. Stock
IYM
PYZ
XLB
PSCM
FXZ
VAW
RTM
Beta
1.2150
1.2207
1.1487
1.1110
1.1958
1.1545
1.1328
- Model 9
1.2177
1.2232
1.1519
1.1095
1.1961
1.1566
1.1337
- Model 12
1.2122
1.2182
1.1454
1.1125
1.1955
1.1523
1.1319
Sharpe Ratio
0.0314
0.0006
0.0247
0.0238
0.0106
0.0152
Jensen Alpha
Treynor Ratio
0.0008
0.0000
0.0003
0.0004
Information Ratio
0.0279
MC Drift Rate
0.0012
0.0014
0.0013
- MC 2M: Drift Rate
0.0011
0.0002
0.0015
- MC 1Y: Drift Rate
0.0021

52. Ranking of ETFs Stock IYM PYZ XLB PSCM FXZ VAW RTM Rank 1 7 2 5 6 3 4
Score 13 35 15 28 31 21 25
Beta
Sharpe Ratio
Jensen Alpha
Treynor Ratio
Information Ratio
MC Drift Rate

53. Economic News
as of 1 Feb 2017
Private employers added 246,000 jobs in January, exceeding the 156,00 consensuses estimate handily – Automatic Data Processing
Expansion of manufacturing activity in January, the 92nd consecutive month of economic growth – Institute of Supply Management
Fed voted to stand pat on current interest rates of 0.50% to 0.75% despite improvement in labour and economic environment

54. Our Decision
Long all ETFs since our short-term view is that the momentum and outlook for the market will be good
Portfolio allocation: 50% on Beta ranking, and 10% each on other criteria ranking
IYM
PYZ
XLB
PSCM
FXZ
VAW
RTM % %
3.5714%
7.1429% % %

55. Performance on 15th Feb ETFs Our Ranking Actual Perf IYM 1 6 PYZ 7 XLB2
PSCM 5 4
FXZ 3
VAW
RTM

56. Portfolio Performance
IYM
PYZ
XLB
PSCM
FXZ
VAW
RTM
Cash
Sum
Perf
Allocation
$160,714.29
$250,000.00
$35,714.29
$71,428.57
$214,285.71
$107,142.86
$1,000,000.00
Price on 1 Feb
$87.35
$60.77
$52.06
$47.59
$37.90
$118.29
$96.23
Expense
0.44%
0.60%
0.15%
0.29%
0.40%
0.11%
0.68%
Available
$160,015.18
$248,500.00
$35,662.50
$71,221.43
$213,428.57
$160,537.50
$106,414.29
$4,454.66
$995,779.46
Units bought
1831
4089
685
1496
5631
1357
1105
Portfolio on 1 Feb
$159,937.85
$248,488.53
$35,658.49
$71,194.64
$213,414.91
$160,519.53
$106,331.39
$256.55
$995,801.89
Price on 15 Feb
$88.94
$64.20
$52.29
$48.23
$38.36
$119.91
$96.05
Portfolio on 15 Feb
$162,849.14
$262,513.79
$35,815.55
$72,152.08
$216,005.17
$162,717.88
$106,133.32
$274.77
$1,018,461.68
1.85%
Performance Rate: %

57. Sensitivity Analysis
Allocate using CAPM Beta since it is commonly used:
Allocation
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Average beta
1.6986
1.7373
1.8207
1.8462
1.8482
1.8620
1.8824
CAPM beta
1.7074
1.7272
1.7461
1.7487
1.9060
1.9262

58. Thank you