Quant Mashup - Flirting with Models Ensemble Multi-Asset Momentum [Flirting with Models]We explore a representative multi-asset momentum model that is similar to many bank-based indexes behind structured products and market-linked CDs. With a monthly rebalance cycle, we find substantial timing luck risk. Using the same basic framework, we build a simple ensemble approach, diversifying(...) Dynamic Spending in Retirement Monte Carlo [Flirting with Models]Many retirement planning analyses rely on Monte Carlo simulations with static assumptions for withdrawals. Incorporating dynamic spending rules can more closely align the simulations with how investors would likely behave during times when the plan looked like it was on a path to failure. Even a(...) Decomposing the Credit Curve [Flirting with Models]In this research note, we continue our exploration of credit. Rather than test a quantitative signal, we explore credit changes through the lens of statistical decomposition. As with the Treasury yield curve, we find that changes in the credit spread curve can be largely explained by Level, Slope,(...) Value and the Credit Spread [Flirting with Models]We continue our exploration of quantitative signals in fixed income. We use a measure of credit curve steepness as a valuation signal for timing exposure between corporate bonds and U.S. Treasuries. The value signal generates a 0.84% annualized return from 1950 to 2019 but is highly regime dependent(...) Flirting with Models - Season 2 [Flirting with Models]With a 5-star rating on iTunes, we are proud to say that Season 1 of our podcast – Flirting with Models – received a tremendously warm welcome. And so we’re happy to announce that Season 2 is now available! You can listen to the new season on: iTunes Stitcher Google Play TuneIn Android The(...) Time-Series Signals and Multi-Sector Bonds [Flirting with Models]We expand last week’s commentary to explore momentum, carry, value, and long-term reversal signals in a time-series context. Using these signals, we generate long/short portfolios for each asset class. We use a sub-sampling methodology to bootstrap and annualized return distribution. We find that(...) Quantitative Styles and Multi-Sector Bonds [Flirting with Models]In this commentary we explore the application of several quantitative signals to a broad set of fixed income exposures. Specifically, we explore value, momentum, carry, long-term reversals, and volatility signals. We find that value, 3-month momentum, carry, and 3-year reversals all create(...) Tactical Credit [Flirting with Models]In this commentary we explore tactical credit strategies that switch between high yield bonds and core fixed income exposures. We find that short-term momentum signals generate statistically significant annualized excess returns. We use a cross-section of statistically significant strategy(...) Our Systematic Value Philosophy [Flirting with Models]As a firm, Newfound Research focuses on tactical allocation strategies. However, we also spend time researching other mandates – such as systematic value – in an effort to introduce lateral thinking to our process. Three years ago, we built a systematic value portfolio that seeks to create a(...) Disproving a Signal [Flirting with Models]Last week we introduced a signal that appeared to generate statistically significant performance results for performing country rotation. This week, we walk through the steps taken to explore the robustness of the signal. We first explore out-of-sample data with sector and emerging market country(...) Country Rotation with Growth/Value Sentiment [Flirting with Models]Value investing has not only underperformed with regard to security selection, but also country selection over the last decade. In an effort to avoid country value traps, we set out to design two signals that might better confirm when a country is likely to exhibit positive re-valuation. We find(...) Tactical Portable Beta [Flirting with Models]In this commentary, we revisit the idea of portable beta: utilizing leverage to overlay traditional risk premia on existing strategic allocations. While a 1.5x levered 60/40 portfolio has historically out-performed an all equity blend with similar risk levels, it can suffer through prolonged periods(...) Style Surfing the Business Cycle [Flirting with Models]In this commentary, we ask whether we should consider rotating factor exposure based upon the business cycle. To eliminate a source of model risk, we assume perfect knowledge of future recessions, allowing us to focus only on whether prevailing wisdom about which factors work during certain economic(...) The Path-Dependent Nature of Perfect Withdrawal Rates [Flirting with Models]The Perfect Withdrawal Rate (PWR) is the rate of regular portfolio withdrawals that leads to a zero balance over a given time frame. 4% is the commonly accepted lower bound for safe withdrawal rates, but this is only based on one realization of history and the actual risk investors take on by using(...) The Speed Limit of Trend [Flirting with Models]Trend following is “mechanically convex,” meaning that the convexity profile it generates is driven by the rules that govern the strategy. While the convexity can be measured analytically, the unknown nature of future price dynamics makes it difficult to say anything specific about expected(...) Revisiting The Weird Portfolio [Flirting with Models]A few years ago, we blindly applied mean-variance optimization to a set of capital market assumptions, and The Weird Portfolio was born. This portfolio is weird because it does not look like typical investor portfolios since it tilts heavily toward credit-based and alternative asset classes. Despite(...) Taxes and Trend Equity [Flirting with Models]Due to their highly active nature, trend following strategies are generally assumed to be tax inefficient. Through the lens of a simple trend equity strategy, we explore this assertion to see what the actual profile of capital gains has looked like historically. While a strategic allocation may only(...) Time Dilation [Flirting with Models]Information does not flow into the market at a constant frequency or with constant magnitude. By sampling data using a constant time horizon (e.g. “200-day simple moving average”), we may over-sample during calm market environments and under-sample in chaotic ones. As an example, we introduce a(...) Trend Following in Cash Balance Plans [Flirting with Models]Cash balance plans are retirement plans that allow participants to save higher amounts than in traditional 401(k)s and IRAs and are quickly becoming more prevalent as an attractive alternative to defined benefit retirement plans. The unique goals of these plans (specified contributions and growth(...) The Monsters of Investing: Fast and Slow Failure [Flirting with Models]Successful investing requires that investors navigate around a large number of risks throughout their lifecycle. We believe that the two most daunting risks investors face are the risk of failing fast and the risk of failing slow. Slow failure occurs when an investor does not grow their investment(...) How Much Accuracy Is Enough? [Flirting with Models]It can be difficult to disentangle the difference between luck and skill by examining performance on its own. We simulate the returns of investors with different prediction accuracy levels and find that an investor with the skill of a fair coin (i.e. 50%) would likely under-perform a simple(...) Three Applications of Trend Equity [Flirting with Models]Trend equity strategies seek to meaningfully participate with equity market growth while side-stepping significant and prolonged drawdowns. These strategies aim to achieve this goal by dynamically adjusting market exposure based upon trend-following signals. A naïve example of such a strategy would(...) G̷̖̱̓́̀litch [Flirting with Models]Trend following’s simple, systematic, and transparent approach does not make it any less frustrating to allocate to during periods of rapid market reversals. With most trend equity strategies exhibiting whipsaws in 2010, 2011, 2015-2016, and early 2018, it is tempting to ask, “is this something(...) Trend: Convexity & Premium [Flirting with Models]Trend following is unique among style premia in that it has historically exhibited a convex payoff profile with positive skew. While the historical premium is anomalous, the convexity makes sense when we use options to replicate trend following strategies. We explore reasons why frequent rebalancing(...) No Pain, No Premium [Flirting with Models]In this commentary, we discuss what we mean by the phrase, “no pain, no premium.” We re-frame the discussion of portfolio construction from one about returns to one about risk and argue that without risk, there should be no expectation of return. With a risk-based framework, we argue that(...) Tightening the Uncertain Payout of Trend-Following [Flirting with Models]Long/flat trend-following strategies have historically delivered payout profiles similar to those of call options, with positive payouts for larger positive underlying asset returns and slightly negative payouts for near-zero or negative underlying returns. However, this functional relationship(...) Drawdowns and Portfolio Longevity [Flirting with Models]While retirement planning is often performed with Monte Carlo simulations, investors only experience a single path. Large or prolonged drawdowns early in retirement can have a significant impact upon the probability of success. We explore this idea by simulation returns of a 60/40 portfolio and(...) Fragility Case Study: Dual Momentum GEM [Flirting with Models]Recent market volatility has caused many tactical models to make sudden and significant changes in their allocation profiles. Periods such as Q4 2018 highlight model specification risk: the sensitivity of a strategy’s performance to specific implementation decisions. We explore this idea with a(...) Video Digest: Process & Manager Diversification [Flirting with Models] Is Multi-Manager Diversification Worth It? [Flirting with Models]Portfolio risk is traditionally quantified by volatility. The benefits of diversification are measured in how portfolio volatility is changed with the addition or subtraction of different investments. Another measure of portfolio risk is the dispersion in terminal wealth: a measure that attempts to(...) 2018 Highlights – The Top 20 Posts You Might Have Missed [Flirting with Models]As 2018 comes to a close, we are thankful for all those who have read, commented upon, and shared the research that we have published this year. This year, we wrote 53 new research commentaries, averaging north of 3,000 words per piece. And we hope our approach of accessible and thoughtful(...) Dart-Throwing Monkeys and Process Diversification [Flirting with Models]This week’s commentary is a short addendum to last week’s piece, attempting to serve as a (very) brief and simplified summary of process diversification. Volatility is only one way of measuring risk; dispersion in terminal wealth is another. Using simulations of dart-throwing monkeys, we plot(...) What do portfolios and teacups have in common? [Flirting with Models]Portfolio risk is often measured as the variance of returns over time. Another form of risk is the variance of terminal wealth that can arise from small variations in strategy inputs or asset returns. Strategies or portfolios that are more sensitive to small changes in inputs are inherently(...) The Risk in the Risk-Free Rate [Flirting with Models]The risk-free rate is an important concept in financial theory, but the risk-free rate accessible to most investors can vary significantly in level. The variation in risk-free rate not only has an important impact on the theoretically optimal portfolio, but it can have a very real impact upon(...) Maximizing Diversification [Flirting with Models]Diversification within a portfolio can be quantified using the diversification ratio, which measures how much the volatility is reduced relative to a scenario where all assets are perfectly correlated. By maximizing the diversification ratio, we can construct the most diversified portfolio for a(...) Directionally Right and Precisely Wrong [Flirting with Models]Portfolio construction decisions tell us about more than just our objective: they tell us about our beliefs. In practice, our beliefs extend beyond views of returns, volatilities, and correlations; we also hold views about our ability to measure these concepts and our confidence in those measures.(...) The Yield is Gravity [Flirting with Models]Rolling 12-month returns for the Newfound Multi-Asset Income strategy are currently ranked 47th of 49 since strategy inception in September 2013. We reflect upon research performed over the last several years that continually points back to one critical idea: yield matters. We rebuild this(...) Measuring the Benefit of Diversification [Flirting with Models]The benefits of diversification are often touted, but many investors feel disappointed in diversified portfolios because of the dispersion in performance of the individual holdings. In the context of three different unconstrained sleeves, we look at a way to measure and visualize the benefit (or(...) Video Digest: When Simplicity Met Fragility [Flirting with Models] When Simplicity Met Fragility [Flirting with Models]Research suggests that simple heuristics are often far more robust than more complicated, theoretically optimal solutions. Taken too far, we believe simplicity can actually introduce significant fragility into an investment process. Using trend equity as an example, we demonstrate how using only a(...) Attack of the Clone: Lessons from Replicating Long/Short Equity [Flirting with Models]In this commentary we attempt to identify the sources of performance in long/short equity strategies. Using Kalman Filtering, we attempt to replicate the Credit Suisse Long/Short Liquid Index with a set of common factors designed to capture equity beta, regional, and style tilts. We find that as a(...) A Carry-Trend-Hedge Approach to Duration Timing [Flirting with Models]In this paper we discuss simple rules for timing exposure to 10-year U.S. Treasuries. We explore signals based upon the slope of the yield curve (“carry”), prior returns (“trend”), and prior equity returns (“hedge”). We implement long/short implementations of each strategy covering the(...) Managing Equity Risk When Rates Rise [Flirting with Models]Last week was a good reminder that there is no ironclad law that rates and equities can’t sell-off at the same time. Strategic diversification with bonds is akin to an uncertain insurance policy whose price and ultimate payoff in the event of a market crash is highly dependent on the level and(...) Measuring Risk Tolerance [Flirting with Models]Risk tolerance, capacity, and need all factor into determining whether a portfolio is appropriate for an investor. Capacity and need are generally straightforward to quantify and map to an appropriate portfolio, but risk tolerance is more difficult, with many questionnaires potentially(...) Decomposing Trend Equity [Flirting with Models]We introduce the simple arithmetic of portfolio construction where a strategy can be broken into a strategic allocation and a self-financing trading strategy. For long/flat trend equity strategies, we introduce two potential decompositions. The first implementation is similar to equity exposure with(...) Video Digest: A Trend Equity Primer [Flirting with Models] A Trend Equity Primer [Flirting with Models]Trend-following strategies exploit the fact that investors exhibit behavioral biases that cause trends to persist. While many investment strategies have a concave payoff profile that reaps small rewards at the risk of large losses, trend-following strategies exhibit a convex payoff profile, one that(...) The Misleading Lessons of History [Flirting with Models]Constructing an asset allocation that never lost money over given rolling periods leads to unsettling allocations: large positions in small-caps, long-term U.S. Treasuries, and precious metals. In many investment analyses, past results may be a downright misleading guide to the future because one(...) Timing Equity Returns Using Monetary Policy [Flirting with Models]Can the monetary policy environment be used to predict global equity market returns? Should we overweight/buy countries with expansionary monetary policy regimes and underweight/sell countries with contractionary monetary policy regimes? In twelve of the fourteen countries studied, both nominal and(...) Video Digest: Trade Optimization [Flirting with Models]