Smart Beta or Unintelligent Alpha - Coursework Example

NAME: INSTITUITION: INSRTRUCTOR: INTRODUCTION A smart Beta ETF is a form of traded funds that use different index structure regulations as an alternative for the usual cap-weighted strategy, in a more transparent way. It takes into consideration factors such as volatility, size and value. Smart beta is a to a certain extent a vague term in contemporary finance in that, strategies involved in Smart beta try to deliver an improved risk and return trade-off than the conservative market limit weighted indices by through other weighting schemes based on actions like volatility or dividends Smart refers to the use of a different tactic to a certain extent instead of following an index's market-cap allocation. A smart beta asset policy is projected towards inserting value through strategic choosing, weighting and rebalancing the firms build into an index base upon purpose factors Strategic beta refers to a rising set of indexes as well as the asset products that follow the indexes. A greater part of these indexes intend to improve profits or reduce risks in relation with the traditional market that relies on capitalization and weighted benchmark. Objectives of Smart Beta include; 1) Return enrichment; it should be able to enhance the investment. 2) Risk cutback; risks should be lower when compared to the returns on investment. 3) Enhanced diversification; 4) Access to factor exposure; 5) Cost savings; should be able to save on cost 6) Income generation; Smart Beta index tackle index-level risk in a number of ways, that is; 1. Fundamental indexes weight constituents by a composite score based on companies’ economic fundamentals 2. Equal-weighted indexes weight constituents equally 3 .Equal risk contribution (ERC) indexes weight constituents’ contributions to index risk equally 4. Minimum variance indexes select stocks whose volatilities and correlations minimize index-level risk 5. Maximum diversification indexes select stocks which maximize the index’s diversification level 6. Maximum Sharpe ratio indexes select stocks which maximize the index’s Sharpe (return to risk) ratio. Smart Beta is used by investors in the following ways; To enhance investment precision Smart beta indexes pursue clear, publicly existing regulations. In addition, since smart beta indexes entrench the returns of logical investment strategies, they help shed light on the sources of return of many unrestrained active funds as well as their managers. Smart beta indexes assist investors analyze their exposure to market beta, sector, currency and factor risk and to highlight non-systematic risk which in turn helps ensure asset owners get value for the fees they pay to active managers. To advance investment outcomes Smart beta indexes can be used to address market participants’ risk concerns for instance, desire to reduce volatility or improve diversification and to target desired return outcomes through exposure to factor risk premium. Smart beta as a result increases the variety of choices for the asset owner. To help manage portfolio costs Smart beta indexes offer exposure to systematic investment strategies, many of which have traditionally been offered by active fund managers under the guise of manager skill, often at relatively high fees. Switching to smart beta can therefore help generate cost savings, benefiting members of long-term savings schemes. An investor can invest in unintelligent alpha funds because; Unintelligent alpha because they bet on factors like value and momentum, quality and size. These factors have been used in quant investment strategies forever. An investor can also invest in smart beta but it will cost them more since it is more expensive as compared to unintelligent alpha since it involves rebranding and need complex implementations for it. Investing in smart betas has their advantages and disadvantages. Advantages smart betas Smart beta strategy is be able to balance the traditional index finances in addition to offering an well-organized way of leveraging contact all the way through the asset process from design toward creation. By isolating investment decisions, investors can more precisely and efficiently capture various anomalies. This allows an investor toward improved alignment of his/her selection with their preference whether it is a rate sinking policy or risk repugnance. Disadvantages of smart betas Compared to customary index finances, a smart beta strategy is intrinsically extra expensive, hazardous and display extensive periods of losses. Crowding; Low unpredictability was a hot smart beta policy some years ago. As extra funds poured into the ETFs, their presentation gap lessened and finally they underperformed the index. False Alpha; A factor that may lead to a certain ETF to outperforming a customary index when the markets are growing may as well be the basis of a given ETF policy to have its own exceptional risk as their problem. These factors might lead the ETF to underperform by extra on the negative aspect than its positive aspect outperformance. Tracking fault; ETFs tracking indexes in general underperform the fundamental index by the total of their cost ratios. In case an investors wish to invest in smart betas, he/she would watch out on a number of issues; He/she should be aware of; risks, costs and Invest in more robust ideas, like value. Momentum isn’t robust. On that basis, my heart lies with lower-cost solutions that offer you a cheap value tilt. These are traditional cap-weighted value funds. They score highly for me because they are cheap and deliver on that factor tilt. There’s going to be periods of underperformance. At least over the very long term, you stand a chance of outperforming traditional cap-weighted indices. It is riskier to invest in smart beta as compared to unintelligent alpha. This is so because in smart betas, the more an investor invests, the higher the return accompanied by higher risks involved. There are various strategies used in smart beta. The one that dominated in 2016 is the wisdom tree eight for eight as shown in the table below. From the table, if someone owned each of the major size segments of the U.S. equity market dividend-weighted, one would have generated excess returns in relation to owning the entire U.S. market capitalization-weighted. https://www.wisdomtree.com/blog/2017-01-24/which-smart-beta-strategies-worked-in-2016 Since the Wisdom Tree Indexes rebalances yearly back to a gauge of absolute value, once a year they trade away from the price-driven movement of cap-weighted benchmarks, which the gives Wisdom Tree a opportunity to strike into return premiums like value and quality. From the given data, we can establish the relationship between the ETFs and their contribution to the return on investment as discussed below; The table below shows the relationship between PRF(Y) and SPYG(X). SUMMARY OUTPUT Regression Statistics Multiple R 0.914082 R Square 0.835546 Adjusted R Square 0.833128 Standard Error 0.014183 Observations 70 ANOVA   df SS MS F Significance F Regression 1 0.069499 0.069499 345.4907 2.33E-28 Residual 68 0.013679 0.000201 Total 69 0.083178         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -0.00038 0.001785 -0.21062 0.833813 -0.00394 0.003185 -0.00394 0.003185 X Variable 1 0.957879 0.051534 18.58738 2.33E-28 0.855045 1.060714 0.855045 1.060714 Y= Mx + C Y= 0.9579x – 0.00038 A change in PRF will lead to 0.9579 changes in SPYG. SUMMARY OUTPUT Regression Statistics Multiple R 0.883183 R Square 0.780012 Adjusted R Square 0.776777 Standard Error 0.016404 Observations 70 ANOVA   df SS MS F Significance F Regression 1 0.06488 0.06488 241.1084 4.76E-24 Residual 68 0.018298 0.000269 Total 69 0.083178         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 0.004264 0.001995 2.137178 0.036183 0.000283 0.008245 0.000283 0.008245 X Variable 1 0.61863 0.039841 15.52767 4.76E-24 0.53913 0.698131 0.53913 0.698131 This table shows the relationship between PRF and VTWG Y= Mx+ c Y= 0.6186x+0.00426 A change in PRF will cause a 0.6186 change in VTWG. SUMMARY OUTPUT Regression Statistics Multiple R 0.897887 R Square 0.8062 Adjusted R Square 0.80335 Standard Error 0.015397 Observations 70 ANOVA   df SS MS F Significance F Regression 1 0.067058 0.067058 282.8779 6.3E-26 Residual 68 0.01612 0.000237 Total 69 0.083178         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 0.00336 0.001882 1.785147 0.078697 -0.0004 0.007115 -0.0004 0.007115 X Variable 1 0.615149 0.036575 16.81898 6.3E-26 0.542165 0.688132 0.542165 0.688132 this is the relationship between PRF and PXSV since the equation of a line takes the form Y=Mx+c Y=0.6151m+0.00336 0.6151 or 61.51% changes in PXSV are contributed by a single change in PRF. SUMMARY OUTPUT Regression Statistics Multiple R 0.924065 R Square 0.853897 Adjusted R Square 0.851748 Standard Error 0.013368 Observations 70 ANOVA   df SS MS F Significance F Regression 1 0.071025 0.071025 397.4251 4.13E-30 Residual 68 0.012153 0.000179 Total 69 0.083178         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 0.005324 0.001615 3.296538 0.001558 0.002101 0.008546 0.002101 0.008546 X Variable 1 0.520707 0.02612 19.93552 4.13E-30 0.468586 0.572827 0.468586 0.572827 This sheet shows the relationship between PRF and SPHQ SPHB Y=Mx+c Y=0.520707X+ 0.0053 A change in Y is contributed by a 0.5207 change in X Y rep PRF and X rep SPHQ Conclusion Smart beta covers a range of systematic, index-based investment strategies across asset classes, including two principal categories, alternatively-weighted and factor indexes. Alternatively-weighted indexes address concentration risks in traditional, capitalization-weighted indexes, while factor indexes target specific factor return premium in a transparent, rules-based and investable format. A lot of are common with major investors, which seem to be where the main interest lies; nevertheless, the number of minor investors is rising. Financial advisors may make use of these products frankly or by using the ETF strategists. In both cases, they ought to appreciate how the smart beta strategy operates and why, if they will give supplementary value and better returns or lower risk for their clients. The 2016 FTSE Russell survey revealed rapidly rising interest in smart beta amongst institutional investors, with Europe leading the way in smart beta adoption. Investors said they were using smart beta for multiple purposes, including return enhancement, risk reduction, improved diversification, and access to factor exposure, cost savings and income generation. It’s clear that smart beta strategies are being increasingly employed to directly meet defined investor objectives and desired outcomes. Smart beta indexation has matured to the point where it has become and will continue to be a varied and flexible tool within the investor’s toolkit. REFERENCES Back, K. 2010. Asset Pricing and Portfolio Choice Theory. New York: Oxford University Press. Ben Dor, A., and Z. Xu. 2011. “Fallen Angels: Characteristics, Performance, and Implications for Investors.” Journal of Fixed Income, vol. 20, no. 4 (Spring): 33–58. Black, F., and R. Litterman. 1990. “Asset Allocation: Combining Investor Views with Market Equilibrium.” Goldman Sachs Fixed Income Research (September) Black, F., and R. Litterman. 1990. “Asset Allocation: Combining Investor Views with Market Equilibrium.” Goldman Sachs Fixed Income Research (September). Ilmanen, A. 2011. Expected Returns: An Investor’s Guide to Harvesting Market Rewards. Hoboken, NJ: John Wiley & Sons Mehra, R. 2008. “The Equity Premium Puzzle: A Review.” Foundations and Trends in Finance, vol. 2, no. 1: 1–81. https://www.wisdomtree.com/blog/2017-01-24/which-smart-beta-strategies-worked-in-2016 Read More