Tag Archives: database

A Really Long-Term Test Of Currency Carry Strategy

Academic research using a long time horizon (more than 100 years of data) confirmed the existence of the currency carry trade effect but found significantly lower risk-adjusted profitability than comparable empirical studies. The analysis also reveals rare occurrences of significant losses which can be worse than those in year 2008. A carry trade strategy can still provide diversification benefits to long-only equity investors as equities and currency carry trades sometimes appear to be exposed to different sources of risk. We wrote a short introduction to currency strategies in our previous article where we examined whether we can view currencies as a distinct asset class . In this article, we will continue in our investigation and elaborate more about the most popular systematic currency trading strategy – Currency Carry Trade (you can see the detailed description of this strategy and a list of related academic research papers on Quantpedia – The Encyclopedia of Quantitative Trading Strategies ). There are already several academic research papers that have examined the return-generating ability of the carry trade strategy and have found that it has significant positive excess returns or Sharpe ratio that can be twice as high as that of the equity markets. However, most of these studies cover only the period after the collapse of the Bretton-Woods system in 1973. Luckily, one important research paper by Doskow and Swinkels (2014) 1 is different from the others. Their main contribution to previous literature lies in broadening the sample period so that it covers multiple currency regimes. This also enables us to look at the risks of carry trading in the pre-Bretton-Woods period. We will attempt to interpret the findings on the risk and return characteristics of both nominal and real carry trade over a period of more than a hundred years and eventually compare these strategies to the behavior of the equity market. As a basis for their empirical work, the authors used the database from Dimson, Marsh and Staunton (2013). It offers exchange rates and treasury bill returns of 20 countries for most of the 1900-2012 period. Even though the analysis had to rely on returns on treasury bills instead of short-term deposit rates, it did not have a significant impact on the obtained results. Also, the fact that the analysis could not be made using data with higher than annual frequency was proved to be of very little importance by the authors. The methodology Doskow and Swinkels used to examine the carry trade returns was as follows: they ranked currencies based on their treasury bill returns (which serve as a proxy for ex-ante yields) every year and then invested in four currencies with the highest interest rates while shorting four with the lowest interest rates. This way they obtained the annual carry trade returns for the period of 1901-2012. In order to obtain the results for real carry trades, it was necessary to adjust the nominal returns on treasury bills by deducting the country-specific inflation and ranking the countries based on these inflation-adjusted (real) returns. What are the main findings of Doskow and Swinkels? And has there been a worse year in history for the currency carry strategy than year 2008? The historical data on the returns from nominal carry trade s revealed several interesting things: Two decades from the sample period were quite exceptional (1901-1909 and the 1950s). The Sharpe ratio in the first decade reached an outstanding value of 3.45 compared to the second best of 1.02 from the 1950s. This result was mainly attributed to the stability of returns over the decade. The steady performance lasted until the beginning of World War I. The loss of 20.2 percent from 1918 is comparable to that of the recent global financial crisis (-21.3 percent in 2008). A similar result of -21.2 percent in 1931 was achieved during the Great Recession, associated with the collapse of gold exchange standard. The most harsh period was the 1940s decade, the period of WWII. It is the only decade from the sample period that recorded a negative average return (-5.3 percent, compared to the second lowest of 1.7 percent in the previous 1930s decade) and, therefore, also a negative Sharpe ratio. The only double-digit negative returns in the post-Bretton-Woods era (besides 2008) were recorded in the middle of the 1980s (1985 and 1986), both indicating losses close to 15 percent. The cumulative performance of world equities against the carry trade strategies over the 1901-2012 (excluding 1940s) is shown in Figure 1. We can see the equity markets struggled in the pre-WWII period as opposed to carry trade strategies; after the WWII, both currency carry trade and equities showed an upward trend. (click to enlarge) The authors underline several implications: Firstly , empirical evidence suggests that the currency carry trade existed in the past even before the year 1973. But the average profitability relative to the risk is markedly lower than is usually presented in studies that use more recent data – Sharpe ratio of 0.26 over the entire period (or 0.38, excluding the 1940s) compared to the usual results often exceeding 0.6. Secondly, the large losses in the first half of the sample period could be attributed to materialized crash risk, a rare event risk, or the so-called “peso problem”, as supported by the recent literature. Analysis shows that hidden risk in currency carry trades is greater than most investors think and losses greater than the loss in the year 2008 were recorded in the past. Finally, the average Sharpe ratio of equities was close to those of carry strategies (0.31 vs. 0.38 and 0.24). However, the important result is surprisingly low correlation between nominal carry trades and equities of only 0.2, which suggests possible diversification benefits for long-only equity investors. The results obtained for the real carry trade are considerably more volatile compared to the nominal. It is again mainly due to the extreme values measured in 1940s; e.g. a positive return of 282.4(!) percent in 1948 (appreciation of long German mark) and an immediate loss of 71.7 percent in the next year. Not accounting for the 1940s provides much more similar results to those for the nominal carry trade, even though the risk-adjusted performance is still noticeably lower (average Sharpe of 0.24 compared to 0.38). Also, the real and nominal carry trade returns were weakly correlated over the entire period (0.39) due to high inflation experienced by a number of countries. However, in the period of generally low and stable inflation (after 1985), the correlation increased to 0.64. What are the conclusions for ordinary investors: Is the currency carry effect real? Apparently, yes. The really long test performed by Doskow and Swinkels (using data which hadn’t been used before) showed that currency carry is a real effect. We can reasonably expect it will probably exist also in the future. Is it a free lunch? Not at all. Analysis shows that currency carry trades had some really bad periods in the past (worse than 2008). It will have bad periods in the future too. Should we add it into a portfolio? It may be a good idea. Currency carry has a comparable long-term Sharpe ratio to equities and very low correlation. It is an alternative asset class and, therefore, not a lot of people are comfortable with a high allocation to it. But it definitely has a place in a modern diversified investment portfolio. How to invest in currency carry trade strategies? Our previous article shows that it is not a wise idea to use an active currency fund. ETFs like the PowerShares DB G10 Currency Harvest ETF (NYSEARCA: DBV ) or the iPath Optimized Currency Carry ETN (NYSEARCA: ICI ). REFERENCES 1. DOSKOW, N. – SWINKELS, L. 2014. Empirical evidence on the currency carry trade, 1900-2012 . Journal of International Money and Finance

Valuation Dashboard: Utilities – November 2015

Summary 3 key factors are reported across industries in Utilities. They give a valuation status of industries relative to their history. They give a reference for picking stocks in each industry. This article is part of a series giving a valuation dashboard by sector of companies in the S&P 500 index (NYSEARCA: SPY ). I follow up a certain number of fundamental factors for every sector, and compare them to historical averages. This article is going down at industry level in the GICS classification, and includes also mid and small cap companies. It covers Utilities. The choice of the fundamental ratios has been justified here and here . You can find in this article numbers that may be useful in a top-down approach. There is no analysis of individual stocks. A link to a list of individual stocks to consider is provided at the end. Methodology Three industry factors calculated by portfolio123 are extracted from the database: Price/Earnings (P/E), Price to sales (P/S), Return on Equity (ROE). They are compared with their own historical averages “Avg”. The difference is measured in percentage for valuation ratios and in absolute for ROE, and named “D-xxx” if xxx is the factor’s name (for example D-P/E for price/earnings). The industry factors are proprietary data from the platform. The calculation aims at eliminating extreme values and size biases, which is necessary when going out of a large cap universe. These factors are not representative of capital-weighted indices. They are useful as reference values for picking stocks in an industry, not for ETF investors. The price-to-cash-flow ratio used in my dashboards for other sectors has been eliminated here, because discontinuities and outliers make it often irrelevant in Utilities. Industry valuation table on 11/4/2015 The next table reports the 3 industry factors. For each factor, the next “Avg” column gives its average between January 1999 and October 2015, taken as an arbitrary reference of fair valuation. The next “D-xxx” column is the difference as explained above. So there are 3 columns for each ratio. P/E Avg D- P/E P/S Avg D- P/S ROE Avg D-ROE Electric Utilities 18.13 15.94 -13.74% 1.77 1.22 -45.08% 8.94 10.43 -1.49 Gas Utilities 21.8 17.24 -26.45% 1.46 0.97 -50.52% 10.34 11.49 -1.15 Multi-Utilities 19 16.59 -14.53% 1.67 0.95 -75.79% 10.22 9.48 0.74 Water Utilities 22.89 23.68 3.34% 4.7 3.94 -19.29% 3.5 7.96 -4.46 Ind.Power Prod. & Energy Traders* 34.92 34.9 -0.06% 3.33 4.16 19.95% -4.22 -5.15 0.93 * Averages since 2005 Valuation The following charts give an idea of the current status of industries relative to their historical average. In all cases, the higher the better. Price/Earnings: Price/Sales: Quality (ROE) Relative Momentum The next chart compares the price action of the SPDR Select Sector ETF (NYSEARCA: XLU ) with SPY (chart from freestockcharts.com). (click to enlarge) Conclusion Utilities have played their traditional defensive role during the correction in August, but XLU has slightly underperformed the broad market last 6 months. Looking at the valuation and quality charts above, only one industry looks attractive: Independent Power Producers and Energy Traders. Its industry P/E factor points to a fair pricing, and the 2 other factors are better than their historical averages. At the opposite, Electric and Gas Utilities look the less attractive, the 3 factors being worse than averages. However, there may be quality stocks at a reasonable price in any industry. To check them out, you can compare individual fundamental factors to the industry factors provided in the table. As an example, a list of stocks in Utilities beating their industry factors is provided on this page . If you want to stay informed of my updates, click the “Follow” tab at the top of this article. You can choose the “real-time” option if you want to be instantly notified.

Valuation Dashboard: Financials – November 2015

Summary 4 key factors are reported across industries in the Financial sector. They give a valuation status of industries relative to their history. They give a reference for picking stocks in each industry. This article is part of a series giving a valuation dashboard by sector of companies in the S&P 500 index (NYSEARCA: SPY ). I follow up a certain number of fundamental factors for every sector, and compare them to historical averages. This article goes down to the industry level in the GICS classification. It covers Financials. The choice of the fundamental ratios has been justified here and here . You can find in this article numbers that may be useful in a top-down approach. There is no analysis of individual stocks. A link to a list of individual stocks to consider is provided at the end. Methodology Four industry factors calculated by portfolio123 are extracted from the database: Price/Earnings (P/E), Price to sales (P/S), Price to free cash flow (P/FCF), Return on Equity (ROE). They are compared with their own historical averages “Avg”. The difference is measured in percentage for valuation ratios and in absolute for ROE, and named “D-xxx” if xxx is the factor’s name (for example D-P/E for price/earnings). The industry factors are proprietary data from the platform. The calculation aims at eliminating extreme values and size biases, which is necessary when going out of a large cap universe. These factors are not representative of capital-weighted indices. They are useful as reference values for picking stocks in an industry, not for ETF investors. Industry valuation table on 11/4/2015 The next table reports the 4 industry factors. For each factor, the next “Avg” column gives its average between January 1999 and October 2015, taken as an arbitrary reference of fair valuation. The next “D-xxx” column is the difference as explained above. So there are 3 columns for each ratio. P/E Avg D- P/E P/S Avg D- P/S P/FCF Avg D- P/FCF ROE Avg D-ROE Commercial Banks 15.42 15.24 -1.18% 2.97 2.06 -44.17% 19.79 13.44 -47.25% 8.78 8.89 -0.11 Thrifts & Mortgage Finance* 18.66 20.66 9.68% 2.97 2.03 -46.31% 21.55 14.75 -46.10% 6.25 5.02 1.23 Diversified Financial Services 21.45 17.85 -20.17% 4.36 2.94 -48.30% 19.78 16.13 -22.63% 8.04 6.38 1.66 Consumer Finance* 11.58 13.15 11.94% 1.64 1.47 -11.56% 6.68 8.22 18.73% 13.36 11.83 1.53 Capital Markets* 16.39 18.07 9.30% 3.58 3.06 -16.99% 19.55 19.62 0.36% 8.96 7.89 1.07 Insurance 14.24 13.7 -3.94% 1.29 1.07 -20.56% 10.77 8.99 -19.80% 9.31 8.71 0.6 REITs** 35.85 35.42 -1.21% 5.36 4.56 -17.54% 49.26 38.74 -27.16% 5.24 4.07 1.17 Real Estate Management** 30.22 31.19 3.11% 3.79 3.06 -23.86% 24.68 25.55 3.41% 4.27 -1.33 5.6 * Averages since 2003 – ** Averages since 2006 Valuation The following charts give an idea of the current status of industries relative to their historical average. In all cases, the higher the better. Price/Earnings: Price/Sales: Price/Free Cash Flow: Quality (ROE) Relative Momentum The next chart compares the price action of the SPDR Select Sector ETF (NYSEARCA: XLF ) with SPY (chart from freestockcharts.com). (click to enlarge) Conclusion XLF and SPY have distinct ways but very similar returns in the last 6 months. From the valuation charts above, we can note that some industries look overpriced, but all of them are above or close to their historical averages in quality. Two industries in the sector look more attractive than others: Consumer Finance and Real Estate Management & Development. For both of them, 2 valuation factors out of 3 and the quality factor are better than their respective averages. Commercial Banks, Diversified Financial Services, Insurance and REITs are overpriced for the 3 valuation ratios. Commercial Banks look the weakest industry of this study, with all metrics in negative territory. However, there may be quality stocks at a reasonable price in any industry. To check them out, you can compare individual fundamental factors to the industry factors provided in the table. As an example, a list of stocks in Financials beating their industry factors is provided on this page . If you want to stay informed of my updates, click the “Follow” tab at the top of this article. You can choose the “real-time” option if you want to be instantly notified.