Investment Performance Performance Versus Benchmark: Return and Sharpe Ratio
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Fact Sheet 2021
Q2 2021 GCI Select Equity TM Globescan Capital was Returns (Average Annual) Morningstar Rating (as of 3/31/21) © 2021 Morningstar founded on the principle Return Return that investing in GCI Select S&P +/- Percentile Quartile Overall Funds in high-quality companies at Equity 500TR Rank Rank Rating Category attractive prices is the best strategy to achieve long-run Year to Date 18.12 15.25 2.87 Q1 2021 Top 30% 2nd 582 risk-adjusted performance. 1-year 43.88 40.79 3.08 1 year Top 19% 1st 582 As such, our portfolio is 3-year 22.94 18.67 4.27 3 year Top 3% 1st 582 concentrated and focused solely on the long-term, Since Inception 20.94 17.82 3.12 moat-protected future free (01/01/17) cash flows of the companies we invest in. TOP 10 HOLDINGS PORTFOLIO CHARACTERISTICS Morningstar Performance MPT (6/30/2021) (6/30/2021) © 2021 Morningstar Core Principles Facebook Inc A 6.76% Number of Holdings 22 Return 3 yr 19.49 Microsoft Corp 6.10% Total Net Assets $44.22M Standard Deviation 3 yr 18.57 American Tower Corp 5.68% Total Firm Assets $119.32M Alpha 3 yr 3.63 EV/EBITDA (ex fincls/reits) 17.06x Upside Capture 3yr 105.46 Crown Castle International Corp 5.56% P/E FY1 (ex fincls/reits) 29.0x Downside Capture 3 yr 91.53 Charles Schwab Corp 5.53% Invest in businesses, EPS Growth (ex fincls/reits) 25.4% Sharpe Ratio 3 yr 1.04 don't trade stocks United Parcel Service Inc Class B 5.44% ROIC (ex fincls/reits) 14.1% Air Products & Chemicals Inc 5.34% Standard Deviation (3-year) 18.9% Booking Holding Inc 5.04% % of assets in top 5 holdings 29.6% Mastercard Inc A 4.74% % of assets in top 10 holdings 54.7% First American Financial Corp 4.50% Dividend Yield 0.70% Think long term, don't try to time markets Performance vs S&P 500 (Average Annual Returns) Be concentrated, 43.88 GCI Select Equity don't overdiversify 40.79 S&P 500 TR 22.94 20.94 18.12 18.67 17.82 15.25 Use the market, don't rely on it YTD 1 YR 3 YR Inception (01/01/2017) Disclosures Globescan Capital Inc., d/b/a GCI-Investors, is an investment advisor registered with the SEC. -
Lapis Global Top 50 Dividend Yield Index Ratios
Lapis Global Top 50 Dividend Yield Index Ratios MARKET RATIOS 2012 2013 2014 2015 2016 2017 2018 2019 2020 P/E Lapis Global Top 50 DY Index 14,45 16,07 16,71 17,83 21,06 22,51 14,81 16,96 19,08 MSCI ACWI Index (Benchmark) 15,42 16,78 17,22 19,45 20,91 20,48 14,98 19,75 31,97 P/E Estimated Lapis Global Top 50 DY Index 12,75 15,01 16,34 16,29 16,50 17,48 13,18 14,88 14,72 MSCI ACWI Index (Benchmark) 12,19 14,20 14,94 15,16 15,62 16,23 13,01 16,33 19,85 P/B Lapis Global Top 50 DY Index 2,52 2,85 2,76 2,52 2,59 2,92 2,28 2,74 2,43 MSCI ACWI Index (Benchmark) 1,74 2,02 2,08 2,05 2,06 2,35 2,02 2,43 2,80 P/S Lapis Global Top 50 DY Index 1,49 1,70 1,72 1,65 1,71 1,93 1,44 1,65 1,60 MSCI ACWI Index (Benchmark) 1,08 1,31 1,35 1,43 1,49 1,71 1,41 1,72 2,14 EV/EBITDA Lapis Global Top 50 DY Index 9,52 10,45 10,77 11,19 13,07 13,01 9,92 11,82 12,83 MSCI ACWI Index (Benchmark) 8,93 9,80 10,10 11,18 11,84 11,80 9,99 12,22 16,24 FINANCIAL RATIOS 2012 2013 2014 2015 2016 2017 2018 2019 2020 Debt/Equity Lapis Global Top 50 DY Index 89,71 93,46 91,08 95,51 96,68 100,66 97,56 112,24 127,34 MSCI ACWI Index (Benchmark) 155,55 137,23 133,62 131,08 134,68 130,33 125,65 129,79 140,13 PERFORMANCE MEASURES 2012 2013 2014 2015 2016 2017 2018 2019 2020 Sharpe Ratio Lapis Global Top 50 DY Index 1,48 2,26 1,05 -0,11 0,84 3,49 -1,19 2,35 -0,15 MSCI ACWI Index (Benchmark) 1,23 2,22 0,53 -0,15 0,62 3,78 -0,93 2,27 0,56 Jensen Alpha Lapis Global Top 50 DY Index 3,2 % 2,2 % 4,3 % 0,3 % 2,9 % 2,3 % -3,9 % 2,4 % -18,6 % Information Ratio Lapis Global Top 50 DY Index -0,24 -
Arbitrage Pricing Theory: Theory and Applications to Financial Data Analysis Basic Investment Equation
Risk and Portfolio Management Spring 2010 Arbitrage Pricing Theory: Theory and Applications To Financial Data Analysis Basic investment equation = Et equity in a trading account at time t (liquidation value) = + Δ Rit return on stock i from time t to time t t (includes dividend income) = Qit dollars invested in stock i at time t r = interest rate N N = + Δ + − ⎛ ⎞ Δ ()+ Δ Et+Δt Et Et r t ∑Qit Rit ⎜∑Qit ⎟r t before rebalancing, at time t t i=1 ⎝ i=1 ⎠ N N N = + Δ + − ⎛ ⎞ Δ + ε ()+ Δ Et+Δt Et Et r t ∑Qit Rit ⎜∑Qit ⎟r t ∑| Qi(t+Δt) - Qit | after rebalancing, at time t t i=1 ⎝ i=1 ⎠ i=1 ε = transaction cost (as percentage of stock price) Leverage N N = + Δ + − ⎛ ⎞ Δ Et+Δt Et Et r t ∑Qit Rit ⎜∑Qit ⎟r t i=1 ⎝ i=1 ⎠ N ∑ Qit Ratio of (gross) investments i=1 Leverage = to equity Et ≥ Qit 0 ``Long - only position'' N ≥ = = Qit 0, ∑Qit Et Leverage 1, long only position i=1 Reg - T : Leverage ≤ 2 ()margin accounts for retail investors Day traders : Leverage ≤ 4 Professionals & institutions : Risk - based leverage Portfolio Theory Introduce dimensionless quantities and view returns as random variables Q N θ = i Leverage = θ Dimensionless ``portfolio i ∑ i weights’’ Ei i=1 ΔΠ E − E − E rΔt ΔE = t+Δt t t = − rΔt Π Et E ~ All investments financed = − Δ Ri Ri r t (at known IR) ΔΠ N ~ = θ Ri Π ∑ i i=1 ΔΠ N ~ ΔΠ N ~ ~ N ⎛ ⎞ ⎛ ⎞ 2 ⎛ ⎞ ⎛ ⎞ E = θ E Ri ; σ = θ θ Cov Ri , R j = θ θ σ σ ρ ⎜ Π ⎟ ∑ i ⎜ ⎟ ⎜ Π ⎟ ∑ i j ⎜ ⎟ ∑ i j i j ij ⎝ ⎠ i=1 ⎝ ⎠ ⎝ ⎠ ij=1 ⎝ ⎠ ij=1 Sharpe Ratio ⎛ ΔΠ ⎞ N ⎛ ~ ⎞ E θ E R ⎜ Π ⎟ ∑ i ⎜ i ⎟ s = s()θ ,...,θ = ⎝ ⎠ = i=1 ⎝ ⎠ 1 N ⎛ ΔΠ ⎞ N σ ⎜ ⎟ θ θ σ σ ρ Π ∑ i j i j ij ⎝ ⎠ i=1 Sharpe ratio is homogeneous of degree zero in the portfolio weights. -
Sharpe Ratio
StatFACTS Sharpe Ratio StatMAP CAPITAL The most famous return-versus- voLATILITY BENCHMARK TAIL PRESERVATION risk measurement, the Sharpe ratio, RN TU E represents the added value over the R risk-free rate per unit of volatility risk. K S I R FF O - E SHARPE AD R RATIO T How Is it Useful? What Do the Graphs Show Me? The Sharpe ratio simplifies the options facing the The graphs below illustrate the two halves of the investor by separating investments into one of two Sharpe ratio. The upper graph shows the numerator, the choices, the risk-free rate or anything else. Thus, the excess return over the risk-free rate. The blue line is the Sharpe ratio allows investors to compare very different investment. The red line is the risk-free rate on a rolling, investments by the same criteria. Anything that isn’t three-year basis. More often than not, the investment’s the risk-free investment can be compared against any return exceeds that of the risk-free rate, leading to a other investment. The Sharpe ratio allows for apples-to- positive numerator. oranges comparisons. The lower graph shows the risk metric used in the denominator, standard deviation. Standard deviation What Is a Good Number? measures how volatile an investment’s returns have been. Sharpe ratios should be high, with the larger the number, the better. This would imply significant outperformance versus the risk-free rate and/or a low standard deviation. Rolling Three Year Return However, there is no set-in-stone breakpoint above, 40% 30% which is good, and below, which is bad. -
A Sharper Ratio: a General Measure for Correctly Ranking Non-Normal Investment Risks
A Sharper Ratio: A General Measure for Correctly Ranking Non-Normal Investment Risks † Kent Smetters ∗ Xingtan Zhang This Version: February 3, 2014 Abstract While the Sharpe ratio is still the dominant measure for ranking risky investments, much effort has been made over the past three decades to find more robust measures that accommodate non- Normal risks (e.g., “fat tails”). But these measures have failed to map to the actual investor problem except under strong restrictions; numerous ad-hoc measures have arisen to fill the void. We derive a generalized ranking measure that correctly ranks risks relative to the original investor problem for a broad utility-and-probability space. Like the Sharpe ratio, the generalized measure maintains wealth separation for the broad HARA utility class. The generalized measure can also correctly rank risks following different probability distributions, making it a foundation for multi-asset class optimization. This paper also explores the theoretical foundations of risk ranking, including proving a key impossibility theorem: any ranking measure that is valid for non-Normal distributions cannot generically be free from investor preferences. Finally, we show that approximation measures, which have sometimes been used in the past, fail to closely approximate the generalized ratio, even if those approximations are extended to an infinite number of higher moments. Keywords: Sharpe Ratio, portfolio ranking, infinitely divisible distributions, generalized rank- ing measure, Maclaurin expansions JEL Code: G11 ∗Kent Smetters: Professor, The Wharton School at The University of Pennsylvania, Faculty Research Associate at the NBER, and affiliated faculty member of the Penn Graduate Group in Applied Mathematics and Computational Science. -
Generating Potential Income in a Low Interest Rate World
GENERATING POTENTIAL INCOME IN A LOW INTEREST Hypothetical Growth of 10K: 5/1/2013 to 1/31/2017 RATE WORLD $ 12,000.0 Write Income is solely focused on generating yield $ 11,500.0 through dividends and derivatives strategies with a focus on seeking to generate a high single-digit yield. This strategy is comprised of firms that have $ 11,000.0 sustainable business models, attractive balance sheets and strong cash flow generation with a $ 10,500.0 history of sustaining and increasing dividends over time. $ 10,000.0 $ 9,500.0 INVESTMENT OBJECTIVE 1/2014 7/2014 1/2015 7/2015 1/2016 7/2016 1/2017 Investment Horizon: Minimum of 3 Years Write Income (Gross) Write Income (Net) BarCap US Agg Bond Investment Minimum: $200,000 Past performance is not indicative of future results. See additional important disclosures on next page. Benchmark: Barclays US Agg Bond Objective: Growth with Income Write Income Performance (Annualized) Since 1 Mo 3 Mo YTD 1 Yr 3 Yr 5 Yr 10 Yr 5/1/2013 Top Holdings Write Income (Gross) 2.66-0.36-0.36 11.08 4.75 —— 4.16 Portfolio % Write Income (Net) 2.19-0.51-0.51 9.09 2.88 —— 2.30 Target Corp 5.40 BarCap US Agg Bond -2.040.200.20 1.45 2.59 —— 1.67 Cisco Systems Inc 5.38 Exxon Mobil Corp 4.39 Calendar Year Performance Waste Management Inc 4.03 YTD 2016 2015 2013 General Mills Inc 3.87 Write Income (Gross) -0.36 6.53 -2.07 4.10 Invesco Ltd 3.63 Write Income (Net) -0.51 4.63 -3.82 2.85 Eaton Corp PLC 3.62 BarCap US Agg Bond 0.20 2.65 0.55 -2.89 MetLife Inc 3.54 Wal-Mart Stores Inc 3.50 Dow Chemical Co 3.49 Risk Analysis -
In-Sample and Out-Of-Sample Sharpe Ratios of Multi-Factor Asset Pricing Models
In-sample and Out-of-sample Sharpe Ratios of Multi-factor Asset Pricing Models RAYMOND KAN, XIAOLU WANG, and XINGHUA ZHENG∗ This version: November 2020 ∗Kan is from the University of Toronto, Wang is from Iowa State University, and Zheng is from Hong Kong University of Science and Technology. We thank Svetlana Bryzgalova, Peter Christoffersen, Victor DeMiguel, Andrew Detzel, Junbo Wang, Guofu Zhou, seminar participants at Chinese University of Hong Kong, London Business School, Louisiana State University, Uni- versity of Toronto, and conference participants at 2019 CFIRM Conference for helpful comments. Corresponding author: Raymond Kan, Joseph L. Rotman School of Management, University of Toronto, 105 St. George Street, Toronto, Ontario, Canada M5S 3E6; Tel: (416) 978-4291; Fax: (416) 978-5433; Email: [email protected]. In-sample and Out-of-sample Sharpe Ratios of Multi-factor Asset Pricing Models Abstract For many multi-factor asset pricing models proposed in the recent literature, their implied tangency portfolios have substantially higher sample Sharpe ratios than that of the value- weighted market portfolio. In contrast, such high sample Sharpe ratio is rarely delivered by professional fund managers. This makes it difficult for us to justify using these asset pricing models for performance evaluation. In this paper, we explore if estimation risk can explain why the high sample Sharpe ratios of asset pricing models are difficult to realize in reality. In particular, we provide finite sample and asymptotic analyses of the joint distribution of in-sample and out-of-sample Sharpe ratios of a multi-factor asset pricing model. For an investor who does not know the mean and covariance matrix of the factors in a model, the out-of-sample Sharpe ratio of an asset pricing model is substantially worse than its in-sample Sharpe ratio. -
Mersberger Financial Group, Inc. Investment Policy Statement
Mersberger Financial Group, Inc. Investment Policy Statement A Fiduciary Approach to Investing Mersberger Financial Group, Inc. Investment Policy Statement Table of Contents Firm Investment Policy Statement.............................................................1 Model 1: MFG Individual Bond Strategy....................................................9 Model 2: MFG Preferred Stock Strategy...................................................13 Model 3: MFG Tactical Equity Model.......................................................17 Model 4: MFG Passive Equity Model.......................................................23 Frequently Asked Questions....................................................................27 Firm Investment Policy Statement Executive Summary The advisors at Mersberger Financial Group, Inc. (which may be referred to as “MFG”, “Us” or “We” throughout this document) has developed an investment policy statement in order to outline the investment philosophy and the investment processes of the advisors. This document also seeks to ensure that the advisors of MFG act in a fiduciary capacity for all clients. We believe it is critical in planning for its client’s futures to form a repeatable and documentable portfolio management process. In addition, MFG believes it is important to have all financial advisors and staff members educated and cognizant of MFG’s investment strategies and philosophy. This approach allows MFG to maintain consistency in it’s investment advice, as well as to always act in the clients best interest. Purpose The purpose of this document is to outline Mersberger Financial Group’s investment philosophy, strategies and procedures. This document will attempt to create a set of standards to hold MFG accountable to, as well as outline a disciplined investment approach for the advisors to follow. We believe having formal investment processes and strategies is crucial, especially in times of market volatility when investment managers may become tempted to deviate from their core strategies. -
Picking the Right Risk-Adjusted Performance Metric
WORKING PAPER: Picking the Right Risk-Adjusted Performance Metric HIGH LEVEL ANALYSIS QUANTIK.org Investors often rely on Risk-adjusted performance measures such as the Sharpe ratio to choose appropriate investments and understand past performances. The purpose of this paper is getting through a selection of indicators (i.e. Calmar ratio, Sortino ratio, Omega ratio, etc.) while stressing out the main weaknesses and strengths of those measures. 1. Introduction ............................................................................................................................................. 1 2. The Volatility-Based Metrics ..................................................................................................................... 2 2.1. Absolute-Risk Adjusted Metrics .................................................................................................................. 2 2.1.1. The Sharpe Ratio ............................................................................................................................................................. 2 2.1. Relative-Risk Adjusted Metrics ................................................................................................................... 2 2.1.1. The Modigliani-Modigliani Measure (“M2”) ........................................................................................................ 2 2.1.2. The Treynor Ratio ......................................................................................................................................................... -
Algorithm for Construction of Portfolio of Stocks Using Treynor's Ratio
Munich Personal RePEc Archive Algorithm for construction of portfolio of stocks using Treynor’s ratio Sinha, Pankaj and Goyal, Lavleen Faculty of Management Studies, University of Delhi 7 July 2012 Online at https://mpra.ub.uni-muenchen.de/40134/ MPRA Paper No. 40134, posted 18 Jul 2012 20:47 UTC Algorithm for construction of portfolio of stocks using Treynor’s ratio Algorithm for construction of portfolio of stocks using Treynor’s ratio Pankaj Sinha Faculty of Management Studies, University of Delhi Lavleen Goyal Indian Institute of Technology, Guwahati Abstract The aim of the paper is to implement the algorithm for selecting stocks from a pool of stocks listed in a single market index like S&P CNX 500(say) and finding the corresponding weights of the stocks in the optimized portfolio using Treynor’s ratio, on the basis of historical data of Indian stock market when the short selling is not allowed. The effectiveness of this algorithm has been demonstrated with an example. Page 1 Algorithm for construction of portfolio of stocks using Treynor’s ratio 1. Introduction Market offers several assets in various formats which are grounds for investing money and gaining returns after specific time periods. Investments are made in view of obtaining highest returns with lowest chance of losing money. The returns are however characterised by the nature of assets and the market factors that influence its pricing everyday. Since the returns cannot be foretold with certainty, the analysis of profitability in an asset becomes an objective of utmost priority in an investment procedure. A technique of judging the behaviour returns from an asset is historical data analysis of the asset with respect to market. -
MONTGOMERY COMMUNITY COLLEGE FOUNDATION 1011 Page Street ∙ Troy, NC 27371 ∙ (910) 898-9603 ∙ [email protected]
MONTGOMERY COMMUNITY COLLEGE FOUNDATION 1011 Page Street ∙ Troy, NC 27371 ∙ (910) 898-9603 ∙ [email protected] The regular meeting of the Foundation Board of Directors of Montgomery Community College will be held on Wednesday, August 12, 2020 at 1:00 p.m. via zoom. Call to Order – Jean Abbott, Foundation President Approval of the Agenda – Jean Abbott, Foundation President – Action Welcome – Jean Abbott Minutes – Jean Abbott ∗ February 12, 2020 Foundation Board Minutes – Appendix A– Action ∗ July 8, 2020 Called Foundation Board Minutes – Appendix B – Action Finance Committee Report – Gary McRae, Finance Committee Chair ∗ 4th Quarter Investment Report – Appendix C ∗ Wells Fargo Presentation – Jay Jacob & Brian Green Nominating Committee Report – Claudia Bulthuis, Nominating Committee Chair ∗ Board Member Recommendations – Appendix D – Action ∗ Election of Officers - Action Treasurer Report – Jeanette McBride, Foundation Treasurer ∗ Fund Statements – Appendix E – Action ∗ Career and College Promise Book Program Update – Appendix F Foundation Reports – Korrie Ervin, Director of Resource Development ∗ 2019-2020 Budget Review – Appendix G ∗ 2020-2021 Budget – Appendix H ∗ 2019-2020 Occupational Scholarships – Appendix I ∗ Grant Updates – Appendix J ∗ 2020 Golf Tournament Update – Appendix J.1 ∗ Non- Event Fall Fundraiser – Appendix K ∗ 3rd Annual Shooting Clay Tournament – Discussion ∗ Veteran’s Day Event ∗ Calendar of Events – Appendix L ∗ 2020 Annual Fund Drive Cumulative Donations – Appendix M President’s Report New Business Adjourn Next Meeting: November 11, 2020 www.montgomery.edu Montgomery Community College Foundation is a 501(c)(3) corporation Appendix A.1 Montgomery Community College Foundation Board Meeting February 12, 2020 The regular meeting of the Foundation Board of Directors of Montgomery Community College was held on Wednesday, February 12, 2020 at noon in the College Boardroom in Capel Hall. -
Chapter 4 Sharpe Ratio, CAPM, Jensen's Alpha, Treynor Measure
Excerpt from Prof. C. Droussiotis Text Book: An Analytical Approach to Investments, Finance and Credit Chapter 4 Sharpe Ratio, CAPM, Jensen’s Alpha, Treynor Measure & M Squared This chapter will continue to emphasize the risk and return relationship. In the previous chapters, the risk and return characteristics of a given portfolio were measured at first versus other asset classes and then second, measured to market benchmarks. This chapter will re-emphasize these comparisons by introducing other ways to compare via performance measurements ratios such the Share Ratio, Jensen’s Alpha, M Squared, Treynor Measure and other ratios that are used extensively on wall street. Learning Objectives After reading this chapter, students will be able to: Calculate various methods for evaluating investment performance Determine which performance ratios measure is appropriate in a variety of investment situations Apply various analytical tools to set up portfolio strategy and measure expectation. Understand to differentiate the between the dependent and independent variables in a linear regression to set return expectation Determine how to allocate various assets classes within the portfolio to achieve portfolio optimization. [Insert boxed text here AUTHOR’S NOTES: In the spring of 2006, just 2 years before the worse financial crisis the U.S. has ever faced since the 1930’s, I visited few European countries to promote a new investment opportunity for these managers who only invested in stocks, bonds and real estate. The new investment opportunity, already established in the U.S., was to invest equity in various U.S. Collateral Loan Obligations (CLO). The most difficult task for me is to convince these managers to accept an average 10-12% return when their portfolio consists of stocks, bonds and real estate holdings enjoyed returns in excess of 30% per year for the last 3-4 years.