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CONVERGENCE: Weighing the Future of Retail, an Investment Proposal CONVERGENCE: Weighing the Future of Retail, an Investment Proposal JESSE BERGER KRYSTEN CONNELY MUHAMMAD SAAD RAHMAN November 2015 Table of Contents Introduction 3 Objectives 3 Process 4 Macro Analysis 5 Wave Theory 5 The “New Normal” 6 Financial Markets 8 Model 1: Regression & Monte Carlo Analysis 8 Micro Analysis 10 Retail Industry Trends 10 Amazon vs. Walmart Comparison 13 Financial Analysis 13 Internal Analysis 14 Model 2: DCF & Trend Impact Analysis 17 Part I: Discounted Cash Flow Analysis 17 Part II: Trend Analysis - Weighted Comparative Impact Model 18 Part III: Final Results 20 Investment Decision, Structure & Projection 20 Combined Model Results 20 Recommended Investment Structure 21 Conclusion 22 References 23 Appendices 26 Appendix A – Raw Output for Linear Regression Model 26 Appendix B – DCF Analysis Cash Flow Inputs 27 Appendix C – Weighted Comparative Impact Model 28 AMZN VS. WMT 2 Introduction As titans of mass retail, both Amazon.com Inc. (Amazon) and Wal-Mart Stores Inc. (Walmart) have been positioning themselves to take advantage of future trends in the industry. Amazon, a pure-play online retailer with strong global brand recognition and a diversified portfolio of products and services, could be thought of as the audacious hare in this iteration of Aesop’s fabled race – a pace setter that is quick to innovate and push boundaries. Walmart, with its commitment to low prices and pervasive brick-and-mortar presence, plays the role of reliable tortoise, making small adjustments along proven pathways, ensuring soundness of footing and direction. Objectives This storied race mirrors a classic dilemma faced by all investors – choosing between growth and risk versus income and safety. The appropriate decision involves finding balance within these dichotomies based on a deliberate objective. Influencing factors typically include the identification of an investment time horizon and appetite for risk, as well as a long-term outlook on industry trends and overall market expectations. This exercise aims to evaluate the future share price performance of Amazon and Walmart over the next ten years in order to create an investment portfolio structure that minimizes risk while capturing growth potential. Drawing from the Vulpes Investment 3 Management mandate, we created a compelling opportunity that exploits the long-term forecast for both companies. Process Regarding publicly traded companies, expert analysts are hard pressed to accurately forecast precise quantitative earnings and price targets for upcoming quarters. Uncertainty around these figures only grows as the timeline extends. In this case, the challenge was to quantify a decade-long forecast that is mostly qualitative, or in other words, to predict the unpredictable. To accomplish this, a qualitative analysis of industry and academic literature was performed to evaluate the inherent range of potential future outcomes for three segments: macroeconomic environment, retail sector trends, and each company’s business strategy in relation to those trends. Each of these segments was broken down and quantified, producing two different models (Model 1 and Model 2, explained below) upon which we based a price prediction for each company in 2025. Model 1 used regression analysis to derive the coefficients of various economic indicators on share price performance, which provided a formula for calculating each share price. A Monte Carlo analysis was run using a broad range of inputs. The average outcome price represented the first ten-year price prediction. Model 2 combined a three- year Discounted Cash Flow (DCF) analysis (Part I) with a weighted impact probability score of future retail and industry trends and their impact on both companies (Part II), which accounted for the subsequent seven years worth of value. Model 2 provided the 4 second ten-year price projection. The two were then weighted and averaged to derive a final price projection. Macro Analysis Wave Theory Long-term economic wave theory, such as Fischer’s Great Waves or Kondratieff’s K-Waves, posit that price revolutions recur throughout history in a similar sequence, albeit differing in duration, magnitude, velocity, and momentum (Fischer, 1996). America has seen its power and influence wane in recent years, transitioning to BRICS nations (especially China) as the frequency and intensity of social and economic turbulence increase. To borrow from Kondratieff’s seasonal parlance, these were the falling leaves of K-wave autumn, which culminated with the speculative boom-and-bust event known as the dot-com bubble in 2000. The final and current season, winter, is characterized by diminished expectations, a widening wealth gap, repudiation of excessive debts, and possible economic depression, among other criteria (Kondratieff, 1984). Although the 2008 global financial crisis evoked a number of these traits, the full fallout was avoided. The economy did not enter a depression, debts continue to be serviced, and expectations remain elevated. 5 The “New Normal” It was not El Nino that intercepted the full brunt of winter in 2008, but the extraordinary measures undertaken by monetary policymakers. Led by the Federal Reserve, the global economy has been thrust into unchartered territory via extended zero percent interest rates and aggressive monetary base expansionary policies, effectively allowing the continuation, instead of repudiation, of excessive debts. The resulting environment is now known as the “new normal” due to its unprecedented nature and the apparent inability of policymakers to allow for reversion to the mean. Figures 1 to 3 succinctly illustrate this point. 6 7 Financial Markets As an unintended consequence, these policies fundamentally altered the relationship of economic conditions to financial markets, and by extension, the price discovery mechanism typically associated with them. The continuous meeting of buyers and sellers at the crossroad of highest bid and lowest offer still reflects the collective wisdom of market participants based on the uneven distribution of imperfect information. However, the “new normal” has skewed future expectations thanks to low borrowing costs. Companies can now maintain credibility despite operating with thinner margins for error and pushing profit projections further into the future, affecting both companies in different contexts. Based on share price valuations, it could be said that profitability still matters, but is not necessarily as valuable today as it was yesterday. Model 1: Regression & Monte Carlo Analysis To quantify the effects of the “new normal” on Amazon and Walmart, a regression analysis was run using both share prices as controls. Shares were analyzed against the following indicators over the last ten years: (1) NYSE Composite, (2) US GDP, (3) US inflation, (4) real median personal income, (5) labor force participation rate, (6) consumer debt as percentage of disposable income, (7) ecommerce retail sales as percentage of total sales, (8) US Economic Uncertainty Index, (9) China CPI, and (10) China Economic Uncertainty Index. The regression analysis yielded coefficients for these 8 indicators as they pertain to each company’s share price. Table 1 and Table 2 illustrate the formulas for projecting each company’s share price based on selected inputs for the various indicators (see Appendix A for raw output from linear regression model). To model a ten-year forecast price, a range of possible outcomes was chosen for each indicator based on historical trends and the “new normal” projected out for the next decade. A Monte Carlo analysis consisting of 40 iterations was then run to randomly generate values from within those ranges, producing 40 price outcomes. The average of those outcomes were taken as the final price projection for this first model (Table 3). 9 Micro Analysis Retail Industry Trends Future changes in mass retail are underway – a quiet race between the ‘new and the old world’ to adapt to forces shaping the global economy over the next decade. Amid lower production and declining demand in most advanced economies, increased urbanization, and growing pressure from technology innovation (McKinsey, 2015), retailers are recalibrating to continually renew growth. Today’s retailers understand that success depends on three simple factors: price, selection, and convenience. Yet, the retail game is changing fast. US retail ecommerce sales have grown consistently since 2006 (Census Bureau of the Department of Commerce, 2015). Rapidly changing consumer preferences, combined with powerful technology and digital innovation, are making way for the retailer 2.0, the omnichannel retailer that successfully weaves channels together to create a seamless, engaging, convenient, and personalized experience by converging digital technologies and brick-and-mortar. An analysis of forecasted retail trends points to a series of unstoppable shifts disrupting and redefining retail over the next decade (Frost & Sullivan, 2014; Planet Retail, 2015; Walker Sands, 2014). 10 Race to the middle: Retailers converge at the online/offline intersection. Brick-and-mortar-only or online-pure-play is ceding to the hybrid retailer that offers a seamless blend of online and offline experiences. While foot traffic in US retail stores decreased by 50% in the last five years, the last year saw occupancy rates in US malls reaching 94.2%, the highest since 1987 (ICSC & NCREIF, 2015). Meanwhile, the percentage of US consumers purchasing online and picking-up in store rose from 4% in
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