Tag Archives: japanese

Trounce The Market With Less Risk

Over a lifetime, stocks trounce bonds more than 800-fold. Contrary to conventional thinking, LESS risk taking can lead to HIGHER returns. Active investing can significantly outperform balanced, buy-and-hold strategies. Most of us tend to think of investing in terms of the experiences of our lifetime, and in fact, of that limited span during which we were vaguely aware of economic events in the world at all. (Nope, you can’t count those teenage years…) But it is important to view things in a greater historical perspective. The chart below does that. (click to enlarge) Source: Stocks, Bonds, Treasury Bills and Inflation 1926-2010 If you zoom in on the graph, you’ll quickly grasp one salient fact: over the long run, if you can stand a bit of risk, you’ll certainly be richly rewarded for that risk. From 1926 to today, an investment in the lowest risk strategy, short term government bonds, grew your money 19-fold, but barely outpaced inflation which eroded the value of the dollar 12-fold over that same period. In stark contrast, investing in small caps grew your money 16000-fold. Yes, you read that right! Put another way, a $1000 investment grew to just over $16 million. Here are a couple of other important observations: If time is on your side, you are seriously shortchanging yourself by not investing in the stock market. A small increment in your yearly return makes a huge difference over time. Look at how a 4 percent difference between large cap stocks and long term bonds increases returns by more than 40 times over that period. Thanks to the incredible magic of compounding, the earlier you start the better off you’ll be. The more you depend on your investments for income today, the less you can (safely) earn, ironically enough. (The corresponding corollary to that in the banking sector is that the more you need a loan, the less likely you will get one. Oh well…) It may take you 20 years to recover from a market break! If you invested in the market in late 1928, you were not back to square one until 1946 !!! (If you think we have that problem solved, just talk to some Japanese investors. Or view this article on my blog.) Even government treasuries can be a poor investment. See the period from 1965 to 1970, when treasuries dropped, yet inflation was raging. Faced with the complexities of investing, sticking your head in the sand and your money under your pillow just ain’t the way to go! Just look at that inflation line. It means your $1.00 invested in 1928 buys you about 8 cents in 2015 prices. So you cannot afford to be on the sidelines. In fact, if you are not investing, you have almost a complete certainty of seeing your assets shrink. So given all of these conclusions, how should you invest your hard-earned money for the best results? Or if you’re among the fortunate few born with a silver spoon in your mouth, how should you protect your leisurely-inherited millions? The short answer is: it depends… For those of you not quite happy with that decidedly hedged answer (Ever wonder what the word hedge funds really means?), please read on. I promise to give you a more concrete response. A traditional approach would be to spread your assets widely among several groups of investments. Take a look at the following graph showing how several different categories of exchange traded funds performed in the last big stock market crash in 2008. (click to enlarge) As the graph makes clear, while the stock market was plunging, other market sectors (mortgage-backed securities, short and long term treasuries, corporate bonds and government backed securities) were rising. So by mixing your asset classes, you can significantly smooth out the volatility of your portfolio. This is particularly important for retirees, since you can choose to withdraw only from areas that have risen in value, as opposed to selling at the worst possible moment, when asset values are at all time lows. A number of mutual funds and ETF’s already subscribe to this strategy. The chart below shows the performance of the Janus Balanced Fund, plotted against the SPDR S&P 500 ETF Trust ETF (NYSEARCA: SPY ), which is a proxy for the S&P 500 index. This has averaged a 9.85% return over 20 years, with fewer big drawdowns than the S&P itself. (click to enlarge) (click to enlarge) If you pay close attention to the percentage comparison, you will note that the balanced approach actually beat the S&P 500 in overall return with less volatility along the way! Some people mistakenly assume that this “spread your marbles out evenly” strategy argues against an actively managed approach. Nothing can be further from the truth. The next two graphs show an active approach that picks the best stocks in the US stocks universe (according to our proprietary formula), times the buys according to certain technical criteria related to momentum, and rebalances the portfolio on a weekly basis. Here is a relatively low-beta (low volatility) approach, that still wallops the results of the JANUS fund shown above, as well as the S&P. (click to enlarge) And for those of you willing to sit tight through a little more volatility, how does a 16 fold return on your money over a 12 year period grab you? But don’t complain about the 50% drawdown… (click to enlarge) Source: quantopian.com Strategy back-testing based on universe of 8000 plus US stocks from 1993-2015. Graphed results are NOT based on historical performance. Real results may differ significantly from back-tested results. Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. (More…) I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article. Additional disclosure: The author currently holds positions using some of these strategies. We do not currently hold position in the Janus fund. The active strategies mentioned require margin accounts and the ability to short stocks at certain times.

4 Small-Cap ETFs For A Bumpy Japan Ride

Japan has been on an uneven recovery path with wild fluctuations seen in recent quarters. This is especially true as the country lost its momentum yet again, snapping two quarters of expansion. The economy contracted 1.6% year over year in the second quarter compared to a solid 4.5% growth in the first quarter. However, this is slightly better than the market expectation of a 1.8% decline. A drop in consumer spending, weak exports and lower private consumption continued to weigh on the growth of the world’s third-largest economy. The slump in Japan’s biggest trading partner – China – added to the woes. The slowdown seems to be a major setback for Prime Minister Shinzo Abe and his reform policy – Abenomics – which is aimed at pulling the country out of two decades of deflationary pressure and returning to growth. More Stimulus in the Cards? Sluggish growth has raised speculations of additional monetary and fiscal stimulus by the central bank later in the year to boost growth. Earlier this month, Bank of Japan (BoJ) announced that it is seeking expansion in its massive asset purchase at 80 trillion yen per year if lower oil prices continue to hold back inflation at a near-zero level. The bank recently cut its annual growth outlook to 1.7% from 2% and inflation to 0.7% from 0.8% for this year. However, many economists believe that the slowdown in the second quarter was because of temporary factors, mainly the China turmoil, and that the Japanese economy will return to growth in the ongoing quarter. According to Capital Economics, Japan will return to a modest growth in the third quarter and see 1% growth for the full year. Further, as per the survey by the Japan Center for Economic Research, 40 analysts project that growth would rebound by an average 2.5% in the third quarter. This could be easily depicted in the solid manufacturing PMI data, which showed that business activity expanded in July with broad-based improvement in output, new orders, employment and exports. Notably, the PMI index climbed to 51.2 in July from 50.1 in June. Exports recovered in July on cheap yen. This is because Japan is primarily an export-oriented economy and a weaker currency makes its exports more competitive. Rise in exports and hopes of further stimulus measures would boost the stock prices in the coming months. While the rally will likely take place across various market spectrums, small caps will benefit the most, as these are less vulnerable to China’s uncertainty or any other external threat. Below, we take a look at four ETFs, which track the small-cap segment of the Japanese stock market. All of these funds offer access to pint-sized securities in the nation. These are likely to see higher volatility yet deliver better returns if the Japanese economy trends in the right direction. WisdomTree Japan SmallCap Dividend Fund (NYSEARCA: DFJ ) This fund targets the dividend-paying small-cap stocks by tracking the WisdomTree Japan SmallCap Dividend Index. Holding 598 securities in its basket, it has a spread out exposure to various components as each firm holds less than 0.9% of total assets. From a sector look, industrials and consumer discretionary take the top two spots with one-fourth share each, while materials, financials and information technology round off the next three with double-digit allocation each. The product has amassed $335 million in its asset base while trades in a lower volume of under 36,000 shares. It charges an annual fee of 58 bps and has gained about 2.4% over the past three months. The fund has a Zacks ETF Rank of 2 or ‘Buy’ rating with a Medium risk outlook. iShares MSCI Japan Small-Cap ETF (NYSEARCA: SCJ ) This fund follows the MSCI Japan Small Cap Index and holds 798 stocks in its basket. It is widely spread out across components with none holding more than 0.86% of assets. However, about one-fourth of the portfolio is allotted to industrials, closely followed by financials (18.7%) and consumer discretionary (18.2%). The fund has managed AUM of $339 million while sees lower average daily volume of around 38,000 shares. Expense ratio came in at 0.48%. The fund has added 1.7% over the past three months and has a Zacks ETF Rank of 2 with a Medium risk outlook. SPDR Russell/Nomura Small Cap Japan ETF (NYSEARCA: JSC ) This is the illiquid and unpopular ETF in the Japanese space with AUM of $66.3 million and average daily volume of just 3,000 shares per day. It tracks the Russell/Nomura Japan Small Cap Index, charging investors 40 bps in annual fees. In total, the fund holds well-diversified 692 securities in its basket with none accounting for more than 0.61% of assets. Here again, industrials make up for the top sector at 26.1%, closely followed by consumer discretionary (21.3%). The product is up 2.6% over the trailing three-month period and has a Zacks ETF Rank of 2 with a Medium risk outlook. WisdomTree Japan Hedged SmallCap Equity Fund (NASDAQ: DXJS ) DXJS offers exposure to the Japanese small-cap stocks while at the same time provides hedge against any fall in the Japanese yen. This is easily done by tracking the WisdomTree Japan Hedged SmallCap Equity Index. The fund has accumulated $207 million in its asset base and charges 58 bps in fees per year from investors. Volume is moderate as it exchanges 61,000 shares in hand per day on average. The product holds 619 stocks in its basket with none accounting for more than 0.94% of assets. Industrials and consumer discretionary and industrials take the top two spots with at least 24% share each, while materials, finance and information technology round off the top five. The ETF gained 6.7% in the same period. Bottom Line These small cap Japan ETFs hit a new 52-week high last week and are clearly outpacing the broad fund (NYSEARCA: EWJ ). Given the China turmoil and global growth concerns, these funds seem safer choices to play the recovering Japanese economy. Original Post

Avoiding The Big Drawdown: Is Downside Protection Helpful Or Heresy?

By Wesley R. Gray, Ph.D. Chasing the Investing Unicorn: Give me “High Returns with Limited Risk” Having your cake and eating it too is a great way to go. It’s great to have the cake, and it’s also great to eat the cake. But you can’t have it both ways. This trend continues when we speak with fellow investors: “Give me high, after-tax, net of fee returns, but with limited risk and volatility.” Now, we certainly love high returns with low risk. We also love high reward with low effort and high calories with low weight gain. Unfortunately, this brings us to our first problem with the investing unicorn: Problem #1: Unicorns don’t exist, and neither do high returns with low risk. Unless you are my youngest daughter, age 3, unicorns don’t exist. Sadly, high-return assets with low-risk profiles don’t exist either. Assets that earn high returns, such as equities (e.g., an S&P 500 index fund), come with a lot of risk (i.e., you can lose over half your wealth). The only way to earn high returns, but limit the risk, is to develop a timing methodology that identifies how to sell the high-returning asset before it decides to jump off a fiscal cliff. Which brings me to another kink in the high-reward, low-risk paradox: Problem #2: Market-timing is extremely difficult. Let’s start this conversation with a concise summary of a 55-page academic analysis of a variety of systems that claim to have perfect market-timing ability: Trying to perfectly time the market is a waste of time. There you go. You no longer need to read this classic academic paper in which Ivo Welch and Amit Goyal assess market timing variables. Our own research over several years confirms this sad reality. We’ve reviewed hundreds of different concepts, and the results are not promising. Most signals never “survive” intense empirical scrutiny, and we are generally skeptical of ANY system that purports to work all the time. Simply stated: Nothing works ALL the time . If unicorns don’t exist (high returns, low risk), is there any good news? There is a glimmer of light at the end of this investing tunnel. Specifically, academic research indicates that investors who can stomach short-term volatility, avoid benchmark comparison, and follow a model can systematically outperform over long periods of time. We find the same conclusion with what we call “downside protection.” Historically, two elements provide downside protection: Focus on Strong Absolute Performance Focus on Strong Trending Performance Of course, past performance is certainly no guarantee of future performance ; nonetheless, historically, these methodologies have worked. They haven’t eliminated short-term volatility, and one can be sure they will underperform a buy-and-hold index at some point; however, they have protected portfolios from the most extreme loss situations. Let’s explore a simple downside protection tool and what the evidence to-date can show us. Rule 1: If weak absolute performance appears, go to cash. In the illustration below, the white line represents an asset class with poor absolute performance. In general, avoid assets with poor absolute performance. (click to enlarge) For illustration purposes only. Rule 2: If weak trending performance appears, go to cash. In the illustration below, the purple line represents a long-term trend line (e.g., a moving average) and the white series represents real-time prices. The red circle highlights a point where the real-time price falls below the long-term average. In general, avoid assets with poor trending performance. For illustration purposes only. Do these simple tools work? Let’s look at the data. Moskowitz, Ooi, and Pedersen, in a formal academic paper, highlight that technical rules don’t work all the time, but they have been effective at providing downside protection, historically: “We document significant ” time series momentum ” in equity index, currency, commodity, and bond futures for each of the 58 liquid instruments we consider… … A diversified portfolio of time series momentum strategies across all asset classes delivers substantial abnormal returns with little exposure to standard asset pricing factors and performs best during extreme markets.” – Moskowitz, Ooi, and Pedersen (2012) While market timing systems that work 100% of the time are impossible, we see that some systems, if followed over long periods, can work over time. It all gets back to model discipline and exploiting the behavioral biases of the market (something we love). Let’s simplify the complex analysis presented in formal academic research and focus on replicating these 2 simple rules. Let’s call our system, the “Downside Protection Model”: The Downside Protection Model ((NYSE: DPM )) follows two simple rules: Time Series Momentum Rules (TMOM) Simple Moving Average Rules (MA) Let’s review the details of our simple rules: Absolute Performance Rule: Time Series Momentum Rule (TMOM) Excess return = Total return over past 12 months less return of T-Bills If Excess return > 0, go long risky assets. Otherwise, go long alternative assets (T-Bills) Trending Performance Rule: Simple Moving Average Rule (MA) Moving Average (12) = Average 12-month prices If Current Price – Moving Average (12) > 0, go long risky assets. Otherwise, go long alternative assets (T-Bills). We need a way to combine these two principles in a simple way. We find that complexity does not add value , and simple models beat experts. We extend this belief to downside protection by keeping it simple: We create a Downside Protection Model (DPM) rule, which is 50 percent Absolute Performance (TMOM) and 50 percent Trending Performance (MA): DPM Rule: 50% TMOM, 50% MA Below is a figure that illustrates the basic trading rules we apply to provide downside protection on portfolios: (click to enlarge) The rule is simple: Trigger one rule = go to 50% cash. Trigger both rules = go to 100% cash. No rules triggered = go long. How has the Downside Protection Model performed? We provide a series of tests on the Downside Protection Model, applied to generic market indices. Our core samples includes 5 asset classes, assessed over the 1973-2014 time period: SPX = S&P 500 Total Return Index EAFE = MSCI EAFE Total Return Index LTR = The Merrill Lynch 10-year U.S. Treasury Futures Total Return Index REIT = FTSE NAREIT All Equity REITS Total Return Index GSCI = S&P GSCI Total Return CME Results are gross, no fees are included. All returns are total returns and include the reinvestment of distributions (e.g., dividends). Data sources include Bloomberg. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Comparison #1: Looking at these basic rules individually: Absolute Performance (TMOM) vs. Trending Performance (MA) Before we compare the system as a whole, let’s compare each rule against the other to see if one is particularly more effective. From January 1, 1976 through December 31st, 2014, here is what we find: TMOM wins 60% of the time, MA wins 40% of the time (Win = better Sharpe and Sortino; Loss = Sharpe and Sortino worse; Tie = a combination of some sort) TMOM triggers around 20% less than MA does (number of triggers refers to the number of times the rule breaks out of the asset class and goes to T-Bills) Bottom Line: Both rules have been effective at providing downside protection. Below are the stats. (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Comparison #2: Assess the Downside Protection Model (DPM): Absolute Performance (TMOM) plus Trending Performance (MA) Now, let’s combine the rules into our simple Downside Protection Model ( DPM ) and see if any incremental improvement occurs. Here is what we find: Downside Protection Model (DPM) wins overall (Win = better Sharpe and Sortino; Loss = Sharpe and Sortino worse; Tie = combination of some sort). Bottom Line: Combining the rules into a single Downside Protection Model (DPM) appears to work. (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Note: Additional robustness tests are available in the appendix. Are these results sustainable? The basic results above highlight that DPM significantly reduces the realized maximum drawdown on a portfolio. But perhaps the entire exercise above is an example of data mining and over-optimization. Nobody can ever prove, beyond any doubt, that a Downside Protection Model works. There is always a chance that any historical finding is driven by randomness, and thus, past performance will not reflect future performance. In the Appendix section below, we stress test this system across numerous time periods and different markets, all of which present similar conclusions. However, we believe there is a behavioral story underlying the success of our simple downside protection rules. Consider the concept of dynamic risk aversion, which is the idea that human beings don’t stick to a set risk/reward behavior – their appetite for risk can change depending on their recent experience. For example, imagine we are making a decision to build a new house in California along the San Andreas Fault. If we just lived through an earthquake, taking on the risk of building a new house on the San Andreas Fault is probably scarier, even though the probability of another earthquake may not have changed. In contrast, when there hasn’t been an earthquake in fifty years, building a new house along a fault is not a big deal. As this example shows, our perception of risk is not constant, and can change based upon recent experience (if you doubt this example, kindly look at a picture of San Francisco’s skyline). In terms of market crashes, we will likely overreact to extreme times and underreact to peaceful times, despite the statistical probability to the contrary. Another assumption economists sometimes make is that risk, often measured in terms of standard deviation, or “volatility,” is relatively constant. These assumptions are challenged when extreme stock market drawdowns occur. Let’s look at another example: a 50% market correction, when fundamentals imply a 20% correction is sufficient. As market prices drop below the twenty percent threshold, an economist assumes that the new price is a bargain. Expected returns have gone up after prices have moved down, while volatility and risk aversion are assumed to be relatively constant. Implicitly, investors should swoop in to buy these cheap shares and bring the market to equilibrium (which, in our example, is their so-called fundamental value). But this doesn’t happen. Stocks can – and have – gone down over fifty percent, and these movements are much more volatile than the underlying dividends and cash flows of the stocks they represent! Remember 2008/2009? How many investors swooped in to buy value versus threw the baby out with the bathwater and kept selling? One approach to understanding this puzzle is by challenging the assumption that investors maintain a constant aversion to risk. Consider the possibility that investors change their view on risk after a steep drawdown (i.e., they just lived through an earthquake). Even though expected returns go up dramatically, risk aversion shoots up dramatically as well. This change means prices have to go down a lot further to justify an investment in these “cheap” stocks. This heightened aversion to risk – following a steep price drop – leads to more selling, and more selling leads to even more hate for risk, which leads to more selling, and so forth. What you end up with is a stampede for the exit and an intense sell-off in the marketplace – below fundamental value, and well beyond what a traditional economist would consider “rational.” The discussion above is a simplified story of investor psychology in the context of a stock market drawdown. For exposition purposes, we are leaving out many potentially important details. However, if one believes that investors rethink their tolerance for risk during a market debacle and tend to sell shares at any price, this might help explain why long-term trend-following rules, which systematically get an investor out of a cliff-diving bear market before everyone has jumped ship, have worked over time. Of course, technical rules will only work if the massive bear market doesn’t happen in a short time period before the long-term trend rules can signal an exit. Technical rules will not save an investor from a 1987-type “flash” crash, but they can save an investor from a 1929- or a 2008-type crash, which can take a few months to develop. In the end, if one believes in a price dynamic that involves steep and relatively sharp declines, followed by slow grinding uphill climbs, simple technical rules will, by design, improve risk-adjusted performance. Conclusion Simple timing rules, focused on absolute and trending asset class performance, seem to be useful in a downside protection context. Our analysis of the downside protection model (DPM), applied on various market indices, indicates there is a possibility of lowering maximum drawdown risk, while also offering a chance to participate in the upside associated with a given asset class. Important to note, applying the DPM to a portfolio will not eliminate volatility, and the portfolio will deviate (perhaps wildly) from standard benchmarks. For many investors, these are risky propositions and should be considered when using a DPM construct. Note: We will be implementing a version of our downside protection model with our new automated advisor offering, Alpha Architect Advisor . Appendix Robustness test of the DPM model across time periods and markets Subperiod: 01/01/1976-12/31/1995 DPM is 50% invested in a TMOM strategy and 50% invested in an MA strategy. Strategies invest in T-bills when a trading rule triggers. DPM wins 3/5, B&H wins 1/5, DPM ~ B&H 1/5 (Win = Sharpe & Sortino; Loss = Sharpe & Sortino; Tie = other) Bottomline: TMOM and MA provide downside protection. (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Subperiod: 01/01/1996-12/31/2014 Bottom Line: DPM holds and provides better protection. (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Out of Sample Test #1-> U.S. Market (01/01/1928-12/31/1975) Our core sample includes 1 asset class, assessed over the 1928-1975 time period: SPX = S&P 500 Total Return Index Results are gross, no fees are included. All returns are total returns and include the reinvestment of distributions (e.g., dividends). Data sources include Bloomberg. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Both TMOM and MA work well for downside protection, significantly lowering total drawdowns. Strategies invest in T-bills when a trading rule triggers. Bottomline: TMOM and MA provide downside protection and have similar results to the Downside Protection Model. (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Drawdown Comparison Both TMOM and MA significantly lower downside risk when the top drawdowns of the buy-and-hold benchmark occurs. MA and TMOM provide similar drawdown protection during buy-and-hold drawdowns. TMOM and MA protect capital at different times (see bold text below). Bottomline: Downside Protection Model diversifies risk management by combining the rules. (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Out of Sample Test #2 -> Japanese and German Stock Markets Our robustness samples include 2 global markets (Japan and Germany): NKY = Nikkei 225 Index (1971 to 2014) DAX = Deutsche Boerse AG German Stock Index (1961 to 2014) Results are gross, no fees are included. All returns are price returns and DO NOT include the reinvestment of distributions (e.g., dividends). Data sources include Bloomberg. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. We use zero as the alternative asset return when a trading rule is triggered. Nikkei Summary Results (1971-2014): Both TMOM and MA work well on drawdown protection. TMOM works slightly better overall. TMOM has the highest return during this period. DPM lowers the sum of total drawdowns by a material amount. NKY_DPM (TMOM and MA): Equal weight on NKY_TMOM and NKY_MA; portfolio earns zero returns when flat. NKY_TMOM: Times series momentum applied on NKY with 12-month formation window, and earns zero returns when flat. NKY_MA: 1-month and 12-month MA rule applied on NKY and earns zero returns when flat. NKY_B&H: Buy and hold on Nikkei 225 price-only series. (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Drawdown Comparison (Nikkei) Both TMOM and MA significantly lower downside risk during the top drawdowns of the buy-and-hold benchmark. MA and TMOM provide similar drawdown protection during buy-and-hold drawdowns. TMOM and MA protect capital at different times (see bold). The Downside Protection Model is diversifying risk management. (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. DAX Summary Results (1961-2014) Both TMOM and MA work well on drawdown protection. TMOM has higher CAGR and lower drawdown. The Downside Protection Model is roughly equivalent to TMOM with lower Max Drawdown. DAX_50,50 (TMOM and MA): Equal weight on DAX_TMOM and DAX_MA; portfolio earns zero returns when flat. DAX_TMOM: Times series momentum applied on DAX with 12-month formation window and earns zero returns when flat. DAX_MA: 1-month and 12 month MA rule applied on DAX and earns zero returns when flat. DAX_B&H: Buy and hold on DAX 40 price-only series. (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Drawdown Comparison (NASDAQ: DAX ) Both TMOM and MA significantly lower downside risk during the top drawdowns of the buy-and-hold benchmark. MA and TMOM provide similar drawdown protection during buy-and-hold drawdowns. TMOM and MA protect capital at different times (see bold). The Downside Protection Model is diversifying risk management. (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Statistics Definitions CAGR: Compound Annual Growth Rate Standard Deviation: Sample standard deviation Downside Deviation: Sample standard deviation, but only monthly observations below 41.67 bps (5%/12) are included in the calculation. Sharpe Ratio (annualized): Average monthly return minus Treasury bills divided by standard deviation Sortino Ratio (annualized): Average monthly return minus Treasury bills divided by downside deviation Worst Drawdown: Worst peak-to-trough performance (measured based on monthly returns) Mathematical Relationship Between TMOM and MA (click to enlarge) The results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request. Original Post