Tag Archives: portfolio-strategy

Will GLD Keep Losing Its Shine?

Summary The gold market is expected to be pressured down as the U.S. dollar resumes its upward trend and the Fed moves towards raising rates. The focus is shifting towards the Fed’s normalization path. The market estimates only two to three rate hikes next year. Shares of the SPDR Gold Trust ETF (NYSEARCA: GLD ) and price of gold climbed back up last week, in part as the U.S. dollar changed course and fell following the lower than expected rate cut by the ECB. In her recent testimony to Congress, FOMC Chair Yellen signaled the U.S. economy is ready for higher rates. And the last non-farm payroll report , in which 211,000 jobs were added back in November, reaffirmed market expectations for the Fed to raise rates this month. Labor market continues to improve The recent NFP report showed a bit higher than expected growth in number of jobs. Wages rose by 0.2%, month over month and by 2.3% for the year. And while not all figures in the report were good — the real unemployment (U6) edged up to 9.9% — it was still overall good enough to pave the way for a December rate hike. Thus, this jobs report along with Yellen’s testimony should have raised the implied probabilities of a rate hike but for now the odds are at 79% — little changed from the previous week. The problem with raising rates at this stage is that the core inflation is still low. And it will be even harder for inflation to rise as the Fed’s cash rates moves up. Nonetheless, as the Fed moves towards raising rates in the next meeting, the price of GLD could resume, even if over the short term, its downward trend. And once the FOMC raises rates this month, the median outlook the Fed targeted in September will be met, as indicated in the table below. Source: Fed’s website Even though the labor market is doing well enough to prompt the Fed to raise rates this month, this week the JOLTS report will provide another perspective about the progress of the labor market. The recent depreciation of the U.S. dollar, mainly against the Euro, came after the ECB didn’t introduce stimulus as the market expected. The recent break we had from the rally of the U.S. dollar has helped pull back up GLD. And the U.S. dollar is expected to resume its rally, which will keep pressuring down GLD. Looking beyond the upcoming rate hike, and assuming the Fed moves forward and raises rates this month, the outlook for the future hikes suggest only a few rate raises in 2016. If rates were to rise at a slower pace than previously expected, this could hold the price of GLD from falling next year. (click to enlarge) Source: Fed-Watch The table above shows the implied probabilities over the next FOMC meetings 2015-2016. Based on these figures, the market expects the target rate to reach 0.84% by the end of 2016 – over 0.5 percentage point lower than the FOMC’s median outlook of 1.375%. Based on the Fed-watch outlook, this implies two rate hikes next year of 0.25% (again, assuming the Fed were to raise rates this year). If this outlook will coincide with FOMC members’ estimates, then the Fed will revise down its projections in the next meeting. And downward revisions could partly offset the expected adverse impact the rate hike will have on GLD. If rates were to remain lower than currently expected next year, the downward pressure on GLD will be less intense. Bottom line The gold market isn’t expected to shine or see rising prices anytime soon, especially as the Fed moves towards raising rates in December and U.S. dollar keeps climbing against other currencies. But following the initial rate hike, which is likely to have a short term negative impact on gold prices, it will be more important to see how the Fed plans raising rates in 2016. The current market outlook aims towards only 2 to 3 hikes next year. Lower than previously estimated rates could hold GLD from plummeting, albeit this won’t stop the general downward trend. For more please see: ” Gold and Inflation – Is there is relation? ”

Dynamic Asset Allocation

Identifying the right asset classes and proportions to diversify is difficult for an investor. The scientific methods for diversification, namely Markowitz’s Mean Variance Optimization have not been practically applicable. Investing in all asset classes evenly at all times will reduce risk but lower returns too. A diversification strategy that reduces exposure to asset classes trending down long term has historically outperformed the stock market both in terms of overall return and volatility. Diversification is widely accepted as the most important aspect in building a portfolio. For investors looking to accomplish their long term financial goals, diversification helps reduce risks and volatility as market and economy go through various expansion, contraction cycles. However the specifications on how much to diversify and in what asset classes are often vague and left to the judgment of an individual investor. There aren’t many established or prevalent public tools that would take investor characteristics as an input (for example risk tolerance, time horizon etc.) and output a recommended model portfolio. A recommended portfolio that provides a list of specific asset classes (mutual funds, ETFs or stocks) and propose percentage weights for investor to review and consider as a starting point. Further, the primary goal for diversification is looked at as risk minimization or reduced volatility in your portfolio. That comes at a cost since lower risk leads to lower return. Could diversification lead to lower risk and yet outperform the market in terms of returns? This article proposes a diversification strategy that has historically outperformed the market, with lower drawdowns and can be used by investors to build a long term asset allocation strategy. Background: Let’s start with understanding the history and state of financial theory on diversification. Harry Markowitz’s Mean Variance Optimization (MVO) method developed in 1952 forms the core backbone of financial theory on diversification. The core insight of Markowitz’s work was that by combining assets that are negatively correlated (i.e. they typically move in different directions) one can reduce the overall volatility of a portfolio without impacting the expected return. Markowitz provided a mathematical algorithm that can use this insight to generate the ideal portfolio (named as Markowitz Efficient Portfolio ) with lowest risk/volatility possible. This was a powerful algorithm and Markowitz rightfully won a Nobel Prize in 1990 for it. Unfortunately even though this was a powerful algorithm, it has not turned out to be practically applicable (Reference papers: 1 , 2 , 3 ). It entails complicated mathematics sensitive to minor changes in the input and requires accurate future forecast on potential assets. Historical returns are very poor forecasts. Variations of Markowitz’s algorithm like Black Litterman model have been proposed to overcome these limitations, however even these require sophisticated inputs (like asset market weightings, volatilities and correlations) that may not be easy to provide for by an average investor. Diversification Strategy Options: To build a model that is simple to understand, compute and specific in terms of output recommendations, we start with Markowitz’s key insight: incorporate assets that are negatively correlated in a portfolio. However correlation between two assets can change over time and rather quickly so we don’t want to assume future correlation will be same as past. Instead we incorporate asset classes that have the potential to have negative future correlation. Thus we include assets in the portfolio that are fundamentally or significantly different from each other. To illustrate this with an example, let’s start with Stocks, Gold and Bonds as three available asset classes that are fairly different from each other. Let’s pick a mutual fund or index from each of these to start with diversification in asset class itself and not be exposed to individual stock risk. I picked the Vanguard 500 Index Fund (MUTF: VFINX ), the V anguard Long Term Investment Grade Fund (MUTF: VWESX ) and the Franklin Gold and Precious Metals Fund (MUTF: FKRCX ) to represent stocks, bond and gold in this test portfolio. We could have picked ETFs like the SPDR S&P 500 Trust ETF ( SPY), the SPDR Gold Trust ETF ( GLD) and the i Shares 20+ Year Treasury Bond ETF ( TLT) but those have historical data only since 2002. Using VFINX, VWESX and FKRCX as proxies for stock, bond and gold allowed me to back test on historical data going all the way back to 1985 from Yahoo Finance. The simplest diversification without making any future assumptions on expected returns would be to allocate equal one third percentage to each asset class. How would this constant equally diversified portfolio would have worked as compared to staying 100% invested in stocks? Overall, stocks would have generated better returns but they’d have also seen larger volatility as seen in the higher drawdown in table below. The graph below shows how the two portfolios would have grown and the table shows annualized return and drawdown numbers for the duration. (click to enlarge) (click to enlarge) Looking at the above numbers, a simple strategy of equal breakdown across multiple asset classes provided a good start for reasonable growth and yet lower drawdowns. However, could we have generated better returns than being in stocks alone? We can take advantage of being in an asset class rather than an individual stock. Individual stocks can go through wild up and down swings, but asset classes do show longer bull – bear trend. For example, the graph below shows that “Gold – Precious metal equities” have been a 4 year long bear market since 2011. Similarly U.S. stocks went through 2-3 year bear market in 2000 and 2008. (click to enlarge) One improvement that we can make in our diversification strategy is to exclude any asset class that is in its longer term bear market and equally invest in all other asset classes. An asset class can be marked in bear market if its 52 week return is less than -2%. We could use any other indicator too like simple moving average or 52 week minima drop. They will all work. The important thing is to classify it as a bear and exit or reduce your sizing in that asset class. Any heuristic that improves the accuracy of classifying an asset class is in bear market will improve the strategy further. In our proposed dynamic allocation strategy we simply reduce allocation to zero on an asset class which has lost more than 2% over the last one year. All other assets are held in equal proportions to make up 100% of portfolio and balanced weekly. For simplicity we have assumed balancing weekly has zero costs, in reality transaction costs may necessitate balancing over a longer time period like 1 or 3 months. Back testing this strategy on historical data since 1984 returns an annual return of 11.87% with an average drawdown of 3.73%. The worst case drop from a 52 week high was 31.35%. So an outperformance both in terms of returns as well as lower volatility. (click to enlarge) (click to enlarge) Conclusion: Investors who manage their portfolio on their own, can use the learning above to build their own long term portfolio management strategy. They can extend the above proposed strategy to cover a comprehensive set of asset classes to include all major sectors like real estate, commodities etc. as well as international economies. Including more asset classes should help reduce risk but too many asset classes will decrease the overall return. Investors can try a variations where instead of equal allocation across all asset classes, sectors that are booming have higher weighted allocation versus sectors that are underperforming. Catching a long term bull market in an asset class and over indexing on those asset classes is likely to help improve returns. They can adjust the maximum level of weighting in a single asset class based on their risk tolerance to limit over exposure in a single asset class. Investor can thus build their own diversified portfolio, test its historical performance on returns and drawdown and thus be equipped to make smarter investing decisions for the long term. Disclaimer: The author does not have any holdings in the mutual funds (VFINX, VWESX and FKRCX) used to test described diversification strategy. These funds have been used only for illustrative purpose and the author is not making any recommendations to buy them. We use a proprietary asset allocation technique across global stocks, bonds, commodities, commodities stocks, mutual funds, ETFs and other investment options in our portfolio.

By The Numbers: ETF Investment And The Indian Market

By Utkarsh Agrawal Since the introduction of ETFs, the dynamics of investing has changed dramatically. Apart from being more transparent, with lower costs and improved tax efficiency, ETFs have helped create the opportunity for smaller investors to access asset classes previously available only to institutional investors. Emerging markets tend to be riskier than developed markets, but can also offer diversification opportunities. With emerging market ETFs, it has become possible to incorporate the objectives and constraints of investors who desire exposure to emerging markets in their portfolio construction process. Among emerging markets, India has been one of the preferred countries. The assets under management (AUM) and the number of the ETFs that provide exposure to India have increased tremendously. All of these ETFs are based on Indian equities. As of July 2015, there were 27 of them, with combined AUM of USD 12.80 billion, domiciled across seven countries (see Exhibit 1). The U.S. has been the greatest contributor in terms of both AUM and the number of ETFs, followed by France, Singapore, and other countries. Since August 2015, the combined AUM has decreased by more than USD 2.27 billion, amounting to a decline of almost 18%, and it stood at USD 10.53 billion as of September 2015. This reduction in AUM has also contributed to the volatility of the equity market and the exchange rate in India. Exhibit 1: International Equity ETFs That Provide Exposure to India Source: Morningstar. Data as of Sept. 30, 2015. Chart is provided for illustrative purposes. As opposed to the international Indian ETFs, India’s domestic ETFs are not only limited to equities. They also include commodities, fixed income investments, and money markets (see Exhibit 2). As of September 2015, the total number of domestic ETFs was 51, and the combined total AUM stood at USD 2.09 billion. The proportion of domestic equity ETFs in the combined total AUM was almost 48%, at USD 1.00 billion as of September 2015. The AUM of the domestic equity ETFs in India account for just 10% of that of the international equity ETFs that provide exposure to India. The recent rise in AUM of India’s domestic equity ETFs can be attributed to the introduction of the Central Public Sector Enterprise (CPSE) ETF, as well as the investment by the Employees’ Provident Fund Organization (EPFO). The Central Board of Trustees (CBT), the apex decision-making body of the EPFO, has recently decided to invest in India’s domestic equity ETFs within the prescribed limit of 5%-15% of the total corpus. Exhibit 2: Domestic ETFs in India Source: Morningstar, Association of Mutual Funds in India and Reserve Bank of India. Data as of Sept. 30, 2015. Chart is provided for illustrative purposes. The S&P BSE SENSEX , India’s heavily tracked bellwether index, is designed to measure the performance of the 30 largest, most-liquid, and financially sound companies across key sectors of the Indian economy. As of September 2015, it has served as the underlying index to one international equity ETF, which provides exposure to India, and five domestic Indian equity ETFs. Over the past 10 years, ending in September 2015, the S&P BSE SENSEX has yielded an annualized total return of 13.32% in Indian rupees (see Exhibit 3). Apart from domestic Indian equity ETFs based on other indices, the EPFO will also invest in the domestic S&P BSE SENSEX ETF, leading to expectations of a further boost to the AUM of this established index. Source: S&P Dow Jones Indices. Data as of Sept. 30, 2015. Chart is provided for illustrative purposes. Past performance is no guarantee of future results. Disclosure: © S&P Dow Jones Indices LLC 2015. Indexology® is a trademark of S&P Dow Jones Indices LLC (SPDJI). S&P® is a trademark of Standard & Poor’s Financial Services LLC and Dow Jones® is a trademark of Dow Jones Trademark Holdings LLC, and those marks have been licensed to S&P DJI. This material is reproduced with the prior written consent of S&P DJI. For more information on S&P DJI and to see our full disclaimer, visit www.spdji.com/terms-of-use .