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A Few Notes on DIY Financial Advisor

| | Posted in: Big Ideas, Strategic Allocation

Wesley Gray, Jack Vogel and David Foulke preface their 2015 book, DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth, by stating that: “This book is a synopsis of our research findings developed while serving as a consultant and asset manager for large family offices. …Our book is meant to be an educational journey that slowly builds confidence in one’s own ability to manage a portfolio. In our book, we explore a potential solution that can be applicable to a wide variety of investors, from the ultra-high-net-worth to middle-class individual, all of whom are focused on similar goals  of preserving and growing their capital over time.” Based on their research, they conclude that:

From Chapter 1, “Are Experts Trying Too Hard?” (Pages 11, 13-14): “We recommend focusing on those experts who have long-term goals, are transparent about their investment strategy, and have an ability to explain their approach in one sentence. …qualitative information, more information, and experience/intuition do not lead to more accurate or reliable forecasts, but instead lead to poorer decision-making.”

From Chapter 2, “Simple Models Typically Beat the Experts” (Page 29): “A surprisingly robust, but neglected branch of academic literature, has studied, for more than 60 years, the assumption that experts make unbiased decisions. The evidence tells a decidedly one-sided story: Systematic decision-making, through use of simple quantitative models with limited inputs, outperforms discretionary decisions made by experts.”

From Chapter 3, “Experts are Biased and Overconfident” (Page 33): “Humans are not wired to engage in detailed cost/benefit analysis decision-making all of the time. It is too cognitively demanding. Natural selection has blessed–and cursed–us with efficient decision-making shortcuts known as heuristics. …they can lead to flawed decision-making in the context of financial markets.”

From Chapter 4, “Experts Tell Us Stories, Not Facts” (Pages 54, 60): “…one’s investment process should not be based on a story, but rather, on an evidence-based process that demonstrates robustness over time. …we need to appreciate our natural preference for coherent stories over evidence that conflicts with those stories.”

From Chapter 5, “A Framework for Investment Decisions” (Pages 69, 76): “In order to beat the experts what we need is an evidence-based systematic decision-making process. …the vast majority of taxable family offices and high-net-worth individuals should focus on strategies with lower costs, higher accessibility and liquidity, easily understood investment processes, higher tax-efficiency, and limited due diligence requirements.”

From Chapter 6, “A Simple Asset Allocation Model That Works” (Page 104): “The summary effect of this portfolio is a 40 percent allocation to equities (20 percent domestic and 20 percent foreign), a 40 percent allocation to real assets (20 percent real estate and 20 percent commodities), and a 20 percent allocation to bonds. …keep…costs down, and rebalance periodically…”

From Chapter 7, “A Simple Risk Management Model That Works” (Pages 108-109): “…simple technical rules–the simplest trading rules out there [simple moving averages and time series momentum]–are, ironically, the most robust and show the most promise for protecting against significant drawdowns. Granted, these rules are not meant to ‘beat the market’ but rather to allow a do-it-yourself investor to manage declines in the market easily and at low cost.”

From Chapter 8, “Simple Security Selection Models That Work” (Pages 140, 146, 149): “…we think that sorting stocks on EBIT/TEV [earnings before interest and taxes divided by total enterprise value] is a simple and effect way to capture the value premium. …human behavioral biases cause a systematic momentum premium. …The way in which we choose to implement a simple momentum strategy is as follows: Calculate the cumulative returns to stocks over the past 12 months, ignoring the last month. …sort stocks based on [these] returns, and buy an equal-weighted basket of stocks from the top decile. We repeat this process each month. …the combination of 50 percent value and 50 percent momentum…generates even higher Sharpe and Sortino ratios…”

From Chapter 9, “The Do-It-Yourself (DIY) Solution” (Page 163): “…the Ivy 5 allocation concept coupled with a moving average rule is a reasonable solution. …we can improve on this model by focusing on our portfolio mission, introducing security selection, and enhance the risk-management system. …replace generic passive allocations to domestic and international equity with high-conviction tax-efficient value and momentum alternatives. …calculate moving average and time-series momentum risk-management calculations on each ‘line’ of the system… …[implement] tax management techniques…”

From Chapter 10, “Some Practical Advice” (Page 185): “We want to warn potential DIY investors to stop worrying about being a hero. Simply stick to the model, and let other people talk about their ‘heroic’ individual stock picks.”

In summary, investors will likely find DIY Financial Advisor a well-reasoned, accessible and useful translation of financial markets research into simple personal practice.

Cautions regarding conclusions include:

  • Use of indexes in backtests implies that real funds can accurately and cheaply track them. The costs of tracking some indexes may be material, such that actual funds meaningfully underperform respective indexes and respond differently to risk management methods.
  • As noted in the book, backtests generally report gross performance. Trading frictions associated with portfolio maintenance would reduce performance. Also, exploration of value and momentum premiums over long sample periods (Chapter 8) involves trading frictions that are sometimes very large (much larger than current levels), such that past net profitability is difficult to estimate. See “Trading Frictions Over the Long Run”. The relatively low current trading frictions may affect investor behavior in a way that suppresses gross premiums.
  • As the DIY solution accrues some complexity (Chapter 9), the risk of impounding inherited and direct data snooping bias in its design grows.

Disclosure: The authors’ company is a sponsor of CXOadvisory.com. We don’t think this relationship affects the above summary.

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