4.3 presentation slides
- 1. 1 Practices & Updates in Financial Planning IV (Approved by the HKCAAVQ, Ref: 80/92/05) M425 Constructing High Quality MPF Portfolios 3 IFPHK CE credits 3 SFC CPT hours 3 IA CPD hours 3 MPFA non-core CPD hours Speaker: Dr. LAM Yat Fai (㨦㡴战 ◩⭺) Doctor of Business Administration (Finance) CFA CAIA FRM PRM MCSE MCNE 6:30pm to 9:30pm Friday 18th July 2014 2 Outline † Fund performance † Portfolio construction † Monte Carlo simulation † Fund risk assessment † Behavioral finance 3 03)$¶V03)SHUIRUPDQFH 4 MPFA risk classification
- 2. 5 Measures of a MPF fund (1) † Price „ Somebody will to buy at „ Somebody will to sell at P = Price = † Return Net asset value in HKD No. of units „ Successfulness of an investment during the year r = Annual return § Price one year later + · ¨ ¸ ¨ Cash received during the year ¸ ¨ ¸ ¨ ¸ © ¹ = - 1 × 100% Price one year ago 6 Measures of a MPF fund (2) † Average return „ Central tendency of annual returns r = Average return = Avg † Risk r + r + r + ... + r 1 2 3 52 52 „ Dispersion of annual returns (r - r ) + (r - r ) + (r - r ) + ... + (r - r ) ı 9RODWLOLW † Performance 2 2 2 2 1 Avg 2 Avg 3 Avg 52 Avg 52 - 1 „ Return over the deposit rate per unit risk Average return - Deposit rate Ave SR = Sharpe ratio = Volatility | rage return Volatility 7 03)$¶VIXQGULVNLQGLFDWRU † Fund risk indicator § Price this month end + · ¨ ¸ ¨ Cash received in the month ¸ ¨ ¸ ¨ ¸ © ¹ Monthly return = - 1 × 100% Price last month end ı 9RODWLOLW = Standard deviation of recent 36 monthly returns × 12 † Requires 3 years of history † Large fluctuation due to sudden cash inflows 8 Average annual return § Price 3 years later + · ¨ ¸ ¨ 3 Cash received in 3 years ¸ ¨ ¸ ¨¨ ¸¸ © ¹ - 1 × 100% Price 3 years ago † Solely determined by two end points † More stable † Less sensitive to market
- 3. 9 Remarks † Historical return is the best but not an accurate basis for estimating future return † Historical volatility is the best and a stable basis for estimating future volatility † Roughly there are 99% chance that the loss in one year will be less than 2.33 x volatility 10 Outline † Fund performance † Portfolio construction † Monte Carlo simulation † Fund risk assessment † Behavioral finance 11 12
- 4. 13 Two fund portfolio † Fund A „ NA: No. of units of fund A „ PA: Price of fund A „ rA: Annual return of fund A „ ıA: Volatility of fund B † Fund B „ NB: No. of units of fund B „ PB: Price of fund B „ rB: Annual return of fund B „ ıB: Volatility of fund B 14 Two fund portfolio † Value V = N P + N P p A A B B † Weight N P N P w = w = V V † Annual return r = w r + w r † Volatility A A B B A B p p p A A B B ı Z 2 ı 2 Z 2 ı 2 Z Z ı ı ȡ p A A B B A B A B AB 15 Correlation coefficient † Correlation coefficient „ A relational measure of co-movements between two fund returns † Between -1 and 1 „ 1 : same direction, same magnitude „ -1: opposite direction, same magnitude „ 0 : independent 52 ¦ª¬ (r A,k - r A,Avg )(r B,k - r B,Avg ) º¼ k=1 ¦ ¦ AB 52 52 2 2 A,k A,Avg B,k B,Avg K=1 k=1 ȡ (r - r ) × (r - r ) 16 Portfolio risk † When correlation = 1 ı Z ı Z ı Z Z ı ı × 1 = w ı Z ı † When correlation = 0 ı Z ı Z ı Z Z ı ı × 0 = w ı Z ı † When correlation = -1
- 5. 2 2 2 2 p A A B B A B A B A A B B 2 2 2 2 p A A B B A B A B 2 2 2 2 A A B B ı Z 2 ı 2 Z 2 ı 2 Z Z ı ı × -1 p A A B B A B A B = w ı Z ı A A B B
- 6. 17 Portfolio performance † When correlation = 1 w r + w r A A B B p A A B B † When correlation = 0 A A B B p 2 2 2 2 A A B B † When correlation = -1 A A B B p A A B B SR = w ı Z ı w r + w r SR = w ı Z ı w r + w r SR = w ı Z ı 18 Diversification effect † Portfolio risk decreases with decreasing dependency among fund returns † For the same portfolio return, portfolio risk decreases with increasing number of funds of low dependency † Marginal benefit of diversification decreases with increasing number of funds † The portfolio risk converges to a steady level when the number of funds approaches 50 19 Parking fund † Annual return „ Approaching 0 † Volatility „ Approaching 0 † Correlation with other funds „ Approaching 0 20 A portfolio and a parking fund † Portfolio + Parking fund return r = w r + w × 0 = w r q Port Port Park p p † Portfolio + Parking fund risk ı Z 2 ı 2 Z 2 2 × 0 + 2w w ı × 0 × 0 = w ı q Port Port Park Port Park Port Port Port † Portfolio + Parking fund performance w r + w × 0 r Port Port Park Port SR = = = SR w ı Z × 0 ı q q Port Port Part Port
- 7. 21 Three fund portfolio † A high return fund „ The highest annual return among the group „ To improve the return † A low risk fund „ The lowest volatility among the group „ To reduce the risk † A parking fund „ Similar to a local currency deposit „ To adjust the portfolio return and risk in proportion without impacting the performance † The high return fund and low risk fund have low return dependency 22 Four fund portfolio † A high return fund „ The highest annual return among the same category „ To improve the return † A low risk fund „ The lowest volatility among the same category „ To reduce the risk † A parking fund „ Similar to a local currency deposit „ To adjust the portfolio return and risk in proportion without impacting the performance † A subjectively selected fund „ Subject to qualitative factors, e.g. intuition, personal preference „ Limit to 10% of portfolio value 23 Outline † Fund performance † Portfolio construction † Monte Carlo simulation † Fund risk assessment † Behavioral finance 24 Foundation question † I know „ Average return „ Volatility † What will be the return of my MPF portfolio? „ No one knows until one year later † What is the likelihood of a return of 2.52% or higher one year later?
- 8. 25 Monte Carlo simulation † Generate two normal random nos. x and y „ x with average 5% and volatility 30% „ y with average 1.5% and volatility 16% „ x and y subject to a correlation 0.3 † Calculate the portfolio return † Repeat the above steps for 10,000 time † Form the distribution 26 Stress test † During disaster „ Average return decreases „ Volatility increases „ Correlation moves towards 1 † Use average returns, volatilities and correlations during financial tsunami 2008 to perform Monte Carlo simulation 27 Outline † Fund performance † Portfolio construction † Monte Carlo simulation † Fund risk assessment † Behavioral finance 28 Regulatory risk factors † 6)¶VNHIDFWVVWDWHPHQW „ http://www.sfc.hk/web/EN/regulatory-functions/ products/product-authorization/ products-key-facts-statements. html † +.0$¶VLPSRUWDQWIDFWVVWDWHPHQW „ http://www.hkma.gov.hk/media/eng/doc/key-information/ guidelines-and-circular/ 2011/20110418e1.pdf
- 9. 29 Fund risk assessment (1) † Market risk - dominating „ Fund price and currency rate „ Volatility † Credit risk - immaterial „ Credit rating of custodian † Operational risk - immaterial „ Complexity of fund operations † No derivatives † Derivatives are used for hedging only † Derivatives are used for profit making † Liquidity risk - immaterial „ Due to settlement delay 30 Fund risk assessment (2) † Reputation risk † Legal risk † Strategic risk † Using minimum rating „ Qualitative factors „ Industry consents „ Regulatory expectations 31 Minimum risk level † Money market fund 1 † Fixed income fund 2 † Developed market equity fund 3 † Major commodities fund 4 32 Calibration products † 1 ± US treasury fund † 2 ± Fixed income fund † 3 ± Blue chip equity fund † 4 ± Commodity fund † 5 ± 6WDWLVWLFDOOVXIILFLHQWODERYH³´
- 10. 33 Peer comparison † HSBC † Hang Seng Bank † DBS(HK) † Wing Lung Bank † Wing Hang Bank † Bank of Communications † ICBC(Asia) † ANZI(HK) 34 Point-in-time vs through-the-cycle 35 Outline † Fund performance † Portfolio construction † Monte Carlo simulation † Fund risk assessment † Behavioral finance 36 Traditional vs behavioural † Traditional finance „ 'LVPLVVHVWKHLGHDWKDWSHRSOH¶VRZQ psychology can work against them in making good investment decisions † Behavioural finance „ Argues that some financial phenomena can plausibly be understood using models in which some agents are not fully rational
- 11. 37 Heuristic learning process † Representativeness „ Investors base expectations upon past experience, applying stereotypes † Overconfidence „ People placing too much confidence in the ability to predict † Anchoring „ The inability to fully incorporate the impact of new information on projections † Aversion to ambiguity „ Fear of the unknown „ If they perceive the odds are in their favour, individuals are more likely to take the bet † Snake bite and house money 38 Frame dependence † Frame dependence „ Judging information within the framework it is received rather than on its own merits † Loss aversion „ 7KHLQGLYLGXDO¶VUHOXFWDQFHWRDFFHSWDORVVRUKROG on to losers too long „ Can lead to risk seeking behaviour † Self-control „ When investors use a self imposed control mechanism to achieve investment needs and goals † Money illusion „ The way individuals react to inflation and its impact on investment performance 39 Regret, self-attribution bias and financial advisors † Mental accounting „ Separating assets into different accounts to meet specific goals † Regret „ Felling (in hindsight) associated with making a bad decision † Financial advisors „ Credit himself for successful investments „ Blame financial advisors for failed investments 40 Portfolio structure † 1/N diversification „ Investors allocate equal amount to N funds † Familiarity „ Individuals investing in fund with which they are familiar
- 12. 41 Retirement plan † Status quo bias „ ,QYHVWRUV¶WHQGHQFWRPDNHDQRULJLQDO allocation and not change it † Myopic loss aversion „ ,QYHUVLRQV¶IRFXVRQVKRUW-term performance and their aversion to losses † Endorsement effect „ The misconception that by providing a list of funds, the MPF service company is implicitly endorsing them as good investments 42 Alpha hunters and beta grazers † Acute market inefficiencies „ Transient in mature, relatively easily identified „ They can be exploited using an arbitrage strategy, and any uncertainty can usually be hedged away † Chronic market inefficiencies „ Less easily identified, longer term in nature „ They are resistant to investor strategies that focus on identifying mis-pricings and their subsequent corrections † Process versus outcome „ Investors overemphasize their recent performance „ Let recent performance drive investment decisions 43 Alpha hunters and beta grazers † Herding (Convoy) behavior „ Investors follow what other investors are doing † Rigid views (Bayesian rigidity) „ Investors holding on to their old views despite the presence of new information † Price target revisions „ Due to overconfidence and not fundamental analysis, an investor sets a new higher target price for a fund † Ebullience cycle „ In an up market investors exhibit exuberance and become too active in their investing „ In a down market investors ignore their portfolios 44 Rebalancing † Holders „ Tend not to adjust their portfolio allocations with changes in equity values † Rebalancers „ Have rigid portfolio allocations „ Any deviation from the target allocation results rebalancing back to the original weights † Valuators „ Base their rebalancing decisions on whether the market is cheap or rich † Shifters „ Rebalance their portfolios in response some non-market value related event
- 13. 45 Investors † Cautious investors „ Prefer safe investments and do not like making their investment decisions † Methodical investors „ Research markets, industries and firms to gather investment information „ Investments tend to be conservative † Individualistic investors „ Do their own research „ Are confident in their ability to make investment decisions „ Are less risk avers than methodical investors † Spontaneous investors „ Constantly adjust their portfolios in response to changing market conditions „ Lack investment expertise „ React to changing investment trends 46 Q A 47 Thank You 48 Upcoming IFPHK Continuing Education Programs: http://www.ifphk.org/CEP/ce-calendar Institute of Financial Planners of Hong Kong 13/F, Causeway Bay Plaza 2, 463 - 483 Lockhart Road, Hong Kong Tel: 2982 7888 Fax: 2982 7777 Email: education@ifphk.org Website: www.ifphk.org