A related paper has been added to:

#129 – Dollar Carry Trade

Authors: Shehadeh, Erdos, Li, Moore

Title: US Dollar Carry Trades in the Era of 'Cheap Money'

Link: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2765552

Abstract:

In this paper, we employ a unique dataset of actual US dollar (USD) forward positions against a number of currencies taken by so-called Commodity Trading Advisors (CTAs). We investigate to what extent these positions exhibit a pattern of USD carry trading or other patterns of currency trading over the recent period of the ultra-loose US monetary policy. Our analysis indeed shows that USD positions against emerging market currencies are characterised by a pattern of carry trading. That is, the USD, as the lower yielding currency, is associated with short positions. The payoff distributions of these positions, moreover, are found to have positive Sharpe ratios, negative skewness and high kurtosis. On the other hand, we find that USD positions against other advanced country currencies have a pattern completely opposite to carry trading which is in line with uncovered interest parity trading; that is, the lower (higher) yielding currency is associated with long (short) positions.

Notable quotations from the academic research paper:

"In the wake of the 2007-2008 financial crisis, many countries, especially developed countries including the USA, have adopted unconventional loose monetary policies with the purpose of stimulating their sluggish and unstable economies. This period is termed in the financial press as “the era of cheap money”. On the other hand, other countries, especially emerging markets, have maintained relatively high interest rates over the same period. Because of the potential impact of these effects on the trading decisions of the FX traders, it is worthwhile to consider currency trading in general and USD carry trading in particular over the sample period of the paper.

In light of this, the crux of the paper is to analyse our dataset of the USD forward positions to find out to what extent they show characteristics of USD carry trading or another trading strategy over the recent period of record-low US interest rates. In other words, we investigate whether these positions exhibit a response to the very low US interest rates by having a pattern of USD carry trading or other patterns of trading strategies can be identified across different currency markets. The distinctive feature of this study is that we have access to a dataset of daily-aggregated USD forward positions against a number of advanced and emerging currencies. It is collected from a Swedish investment specialist, Risk & Portfolio Management AB (RPM) which is a fund of hedge funds investing in Managed Futures strategies which are also known as Commodity Trading Advisors (CTAs). CTAs engage in various strategies like trend-following, short-term trading, and global macro that often employs carry trading as a sub-strategy. By exploiting and analysing our private dataset we find significant long-run equilibrium relationships which directly relate the USD forward positions to its forward premium. The relationships point to different trading strategies for emerging and advanced market currencies. For emerging currencies, we find that these relationships are consistent with carry trading. That is the lower yielding currency (the USD) is associated with short positions and vice versa. This carry trading pattern of forward shorting lower-yielding currency is induced by the expectations that the lower-yielding currency will not actually appreciate on average as much as the forward rate implies, or even it will depreciate. This in turn implies a profit on average at maturity. On the other hand, we find that the reverse holds between the USD and advanced currencies. In other words, we find a pattern of “fundamentals-based” trading consistent with the uncovered interest parity condition. That is, the lower (higher) yielding currency is associated with more long (short) positions. These anti-carry positions can reflect the unattractiveness of the advanced currencies-USD carry trading due to the increased uncertainty and narrow interest differentials for these markets over the period following the recent crisis.

Given that our data set is collected from FX traders which are mainly trend-followers, these results of the different trading strategies for emerging and advanced market currencies shed some light on the trading behaviour of this group of the FX market participants. On the one hand, the characteristics of carry trades for EM currencies which involve long high-interest currency aginst the low-interest currency reflect a trend-following strategy which is based on
the expectations that high-interest currency is going to appreciate -i.e. based on the appreciation trend of the high-interest rate currency. On the other hand, the characteristics of “fundamentals-based” trades for AM currencies which involve long low-interest currency against high-interest currency reflect a trend-following strategy which is based on the expectations that low-interest rate currency is going to appreciate –i.e. based on the appreciation trend of the low-interest rate currency. This is in line with the heterogeneous agents model developed by Spronk et al. (2013). The model demonstrates that depending on the dominant trend in the market, FX trend-followers can be in the same line of either carry traders or fundamentalists. In this sense, our results provide some insights into these features of the FX trend-following traders."

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A related paper has been added to:

#52 – Asset Growth Effect

Authors: Prombutr, Phengpis, Lam

Title: Anatomy of the Mispricing Theory: Evidence from Growth Anomalies

Link: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2746998

Abstract:

This paper investigates corporate growth anomalies in asset pricing from behavioral perspectives. Cross-sectional analyses indicate that a long-term 3-year investment growth is statistically significant in explaining subsequent stock returns, but the first 1-year growth that is closest to the formation is priced by investors the most, followed by the second and third ones, monotonically. We find that the evidence is driven by myopic mispricing in that investors tend to put more weights on recent information since the evolution of the firm’s prospects around the formation year consistently shows that the growth closest (farthest) to the formation has the most (least) severe mispricing. Further investigations show that the mispricing evolution is directly amplified by limits to arbitrage and that benchmark-adjusted returns on short positions are affected more than those on long positions. However, the farther growth is less sensitive to the limit-to-arbitrage because of the extrapolation is myopic. The asset growth anomaly also shows the same pattern as the investment growth anomaly.

Notable quotations from the academic research paper:

"With respect to the extrapolative mispricing theory, we hypothesize that the growth effect cannot happen without mispricing. The growth premium coincides with the disappointment from extrapolating the firm’s prospects into the future. Along with the extrapolative mispricing theory, we investigate two additional behavioral theories: limits to arbitrage and arbitrage asymmetry. We realize that rational and behavioral theories cannot be differentiated (See Lin and Zhang, (2013)) because the characteristic-based factor models are linear approximations of investment returns. Hence, if the three behavioral theories (extrapolative mispricing, limits to arbitrage and arbitrage asymmetry) are connected and contemporaneously supported by the data, our paper will strengthen the validity of behavioral explanations without disapproving the rational q-theory.

Importantly, in this paper, we expand an investigation into the growth anomaly by decomposing the growth measures. We decompose a long-term 3-year growth measure into three consecutive short-term 1-year growth measures based on the concept of term structure. With this decomposition, we can do several anatomy tests on the corporate growth anomaly. The anatomy tests along with the evolution of mispricing and returns should be able to show more clearly if predictions of the above three behavioral theories are consistent with empirical evidence. Prior studies have not decomposed long-term growth measures into short-term components or investigated the three behavioral theories concurrently.

We find, based on cross-sectional regressions, that the explanatory power of the 3-year long-term growth (IG13) on stock returns is statistically significant and actually comes from its most recent 1-year growth component (IG1, one year closest to the formation period). The farther 1-year growth components (IG2 and IG3, two and three years away from the formation period, respectively) show monotonically diminishing explanatory power. These results indicate short memory of investors which are consistent with the myopic theory from the behavioral perspective. Cognitive biases such as representativeness state that investors tend to put too much weight on recent information (Kahneman and Tversky, 1974). Combining the myopic theory with the mispricing theory that investors extrapolate too much into the future about firms’ prospects and valuations for high and low growth firms, more severe extrapolative mispricing should exist closer to the portfolio formation year than farther distant years.

We empirically find that (1) long-term growth (IG13) demonstrates less extrapolative mispricing around the formation year than short-term growth that is measured right before the formation year (IG1), (2) compared to the IG1, the farther 1-year growth measures (IG2 and IG3, two and three years away from the formation period) show monotonic decreases in mispricing around the formation year, and (3) return performances associated with these growth measures show patterns that are consistent with the degrees of mispricing described in (1) and (2). Combined with the above cross-sectional regression findings, these results strongly suggest that the investment growth anomaly can be explained by the extrapolative myopic mispricing theory.

All of the above tests are redone using asset growth measures instead of investment growth measures. The results are similar and in fact even more pronounced. In sum, we conclude that the investment or asset growth effect is associated with mispricing. The mispricing is short-lived, so a long-term 3-year growth has the most severe mispricing near the formation year. Additionally, limits to arbitrage lead to more severe mispricing and a short position is affected more than a long position. However, the mispricing evolution tends to be less pronounced when the farther growth measures are used since investors are myopic."

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A new academic paper related to our previous blog-post (Benchmarking Commodity CTAs):

Authors: Blocher, Cooper, Molyboga

Title: Performance (and) Persistence in Commodity Funds

Link: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2658153

Abstract:

This study documents persistent, net-of-fees, alpha-generating commodity trading advisor funds focused on commodity investment (“Commodity Funds”). The baseline for performance measurement is a new benchmark model that includes factors established in the literature. A nonparametric bootstrap test establishes the existence of alpha that cannot be explained by luck. Performance persists 12 months out of sample and subsequently disappears. Such performance, without a reversal, indicates that persistent alpha is based in information about fundamentals, not fund flows or sentiment. These results are robust to data biases established in the literature.

Notable quotations from the academic research paper:

"To evaluate fund manager performance, we first implement and test a five-factor asset pricing model as a benchmark for commodity manager performance measurement. The model includes a market factor, a time series momentum factor, a spot basis factor, and high and low term premia factors. These factors are drawn from the extant literature, based in commodity fundamentals, and each has been shown separately to capture a risk premium embedded in commodity futures (e.g., Szymanowska et al. 2014, Bakshi, Gao Bakshi, and Rossi 2014, Moskowitz, Ooi, and Pedersen 2012). This factor model for commodity futures parallels Fama and French’s now ubiquitous model for publicly traded equities (now also with five factors, see Fama and French 2015).

We then use this five-factor model benchmark to identify commodity fund manager performance and persistence. First, we conduct a bootstrap analysis of the distribution of alpha t-statistics and find both top and bottom performers that cannot be explained by luck. Second, we find that both good and bad performance persists for approximately 12 months. Annualized alpha of the top performing quintile is 2.53%, while the same for the bottom performing  quintile is -1.94%. This performance persistence disappears after 12 months, but does not reverse. This nonreversal indicates that commodity fund manager performance is based on information and/or skill, rather than sentiment or other non-fundamental factor, which is often the case in mutual funds (Blocher 2015, Lou 2012).

"

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A related paper has been added to:

#21 – Momentum Effect in Commodities
#22 – Term Structure Effect in Commodities
#118 – Time Series Momentum Effect

Authors: Blocher, Cooper, Molyboga

Title: Benchmarking Commodity Investments

Link: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2744766

Abstract:

While much is known about the financialization of commodities, less is known about how to profitably invest in commodities. Existing studies of Commodity Trading Advisors (CTAs) do not adequately address this question because only 19% of CTAs invest solely in commodities, despite their name. We compare a novel four-factor asset pricing model to existing benchmarks used to evaluate CTAs. Only our four-factor model prices both commodity spot and term risk premia. Overall, our four-factor model prices commodity risk premia better than the Fama-French three-factor model prices equity risk premia, and thus is an appropriate benchmark to evaluate commodity investment vehicles.

Notable quotations from the academic research paper:

"The four factors in our model include a market factor, a time series momentum factor, and separate high and low term premia factors, sorted on commodity basis. These factors are drawn from the extant literature and based in commodity fundamentals, and each has been shown separately to capture a risk premium embedded in commodity futures, though never together in the form we propose.

We consider factors for each premium in turn, starting with the spot premium. We first include a market factor (MKT), which is an equally weighted average of all commodities’ one period spot return. Next, we include a momentum factor. We choose a time series momentum factor (TSMOM) as in Moskowitz, Ooi, and Pedersen (2012), which is the difference in return between an equally weighted portfolio of commodities with a positive return over the previous twelve months and one with a negative return over the previous twelve months. We next consider the term premium. To price the term premium, we choose two factors. First, we construct a high-term premium factor (Hterm) consisting of the average of the 2-month, 4-month, and 6-month realized term premia for the 10 commodities with above-median basis (as previously defined in the HML factor). We also construct a low-term premium factor (Lterm), computed the same way as Hterm, except using the 10 commodities with below-median basis.

Until now, benchmarking commodity investments has been inhibited by a lack of understanding of the drivers of risk premia. Recently, however, the literature has coalesced around a few key drivers of commodity risk premia, represented by the four factors in our model. Simultaneously, increased interest in commodity investment in the past decade combined with the poor performance of passive market indexes means sophisticated investors are more interested in evaluating the performance of active commodity fund managers. Financial advisors have even suggested that individuals include commodities in their personal asset allocation.5 Yet, to our knowledge, there is not a thoroughly tested and established benchmark to evaluate commodity fund managers or commodity ETFs.

While commodity investment often is included as a subset of the hedge fund/CTA literature, there are more similarities between commodity markets and equity markets than between commodities and hedge funds. Both commodities and equities are publicly traded with public closing prices, providing clear, end-of-day portfolio values. Both have a clearly identified regulatory body (the CFTC and SEC). Both represent a defined investment set within which a manager (Commodity Fund or Mutual Fund) must choose either long or short positions. Given these similarities, our paper can be seen as establishing a factor model benchmark for Commodity Funds in the same way that Fama and French (1992, 2015) have established a benchmark for Mutual Funds."

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An academic paper related to multiple strategies:

Authors: Docherty

Title: How Smart is Smart Beta Investing? Evidence from Australia

Link: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2706246

Abstract:

"Smart beta" investing is an alternative to the traditional active and passive approaches to funds management, whereby investors adopt a systematic method that provides exposure to factors that are argued to be related with expected returns at low cost. Therefore, the question of how smart is smart beta investing can be empirically examined by testing the performance of those factors that underlie smart beta portfolios. We use a long time-series of data and show that the value, momentum, low volatility and quality factors all generate positive abnormal returns in the Australian equity market. Rather than ranking these factors based on relative performance, we argue that the optimal approach to smart beta investing is to diversify across these factors, given the low correlations between factor returns. Our results provide important implications for the Australian funds management industry. First, while this study does not examine the specific strategies applied by smart beta fund managers, the evidence presented provides a justification for the application of smart beta as a low cost alternative to active investment. Second, given evidence that multiple factors explain equity returns, multi-factor models should be used to measure active portfolio manager performance in order to distinguish pure alpha from abnormal returns generated due to smart beta exposure.

Notable quotations from the academic research paper:

"Given most of the academic evidence regarding the performance of smart beta factors has focused on the United States equity market, this study provides a summary of the key literature and updated empirical findings regarding the five key systematic risk factors in the Australian equity market: size, value, momentum, low volatility and quality. We use a long time-series of 25 years of historical data to show that there is statistically significant evidence to support the existence of the value, momentum, low volatility and quality factors in the Australian equity market. However given the low correlation between factor returns, diversifying across factors is superior to constructing a smart beta portfolio that only provides exposure to a single factor.

Given the evidence presented above that several factors have historically explained returns in the Australian equity market, a natural extension is to ask which of these factors is best. However, we argue that such a debate is moot. Given the justification for smart beta investing is that it provides exposure to additional factors that are related to changes in investor wealth beyond the market risk premium, the returns across these factors should vary across different states. The smart beta factors are only moderately correlated with the market risk premium, and in the case of value, momentum and quality, these correlations are negative.

Table 8 reports the weightings of both the low volatility and maximum Sharpe ratio portfolios. Both the minimum expected variance and maximum expected Sharpe ratio portfolios are highly diversified across the five factors. With respect to the maximum Sharpe ratio portfolio, the value factor is weighted most heavily, followed by momentum, whereas the size factor has the highest weighting in the minimum variance portfolio. While the results reported in Table 8 are not necessarily indicative of the optimal allocation across factors given the reliance on historical data, what this table does clearly illustrate is that diversified factor exposures are superior to smart beta portfolios constructed using a single factor. For example, a value investor would gain utility by also having additional exposure to the momentum, quality or low volatility factor as part of their complete portfolio.

There is an ongoing debate about whether equity market factors are priced domestically or globally. Karolyi and Stulz (2003) argue that international equity flows and cross-country correlations should result in global factors being the key determinant of returns. They propose a model whereby equity prices are determined by a stock’s sensitivity to global risk factors. If smart beta factors were priced globally, investors could rely on the plethora of US-focused research to support their decision making. However the home bias, whereby market participants over-invest in their domestic market, may cause a degree of segmentation as the marginal investor is likely to be a domestic investor who is sensitive to local influences. The correlations between the Australian and global smart beta factors are reported in Table 9. While positively correlated as expected, the magnitude of these correlations is relatively modest, particularly when compared with the correlations between the respective market portfolios. These results show that local, as opposed to global, factors are a substantial driver of the variation in smart beta factor returns. The differences between local and global factor returns, as evidenced by their modest correlations, also illustrates the need for Australian-focused research as a means of justifying local smart beta strategies."

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A related paper has been added to:

#20 – Volatility Risk Premium Effect

Authors: Israelov, Nielsen

Title: Covered Calls Uncovered

Link: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2444999

Abstract:

Equity index covered calls have historically provided attractive risk-adjusted returns largely because they collect equity and volatility risk premia from their long equity and short volatility exposures. However, they also embed exposure to an uncompensated risk, a naïve equity market reversal strategy. This paper presents a novel performance attribution methodology, which deconstructs the strategy into these three identified exposures, in order to measure each’s contribution to the covered call’s return. The covered call’s equity exposure is responsible for most of the strategy’s risk and return. The strategy’s short volatility exposure has had a realized Sharpe ratio close to 1.0, but its contribution to risk has been less than 10 percent. The equity reversal exposure is responsible for about one-quarter of the covered call’s risk, but provides little reward. Finally, we propose a risk-managed covered call strategy that hedges the equity reversal exposure in an attempt to eliminate this uncompensated risk. Our proposed strategy improved the covered call’s Sharpe ratio, and reduced its volatility and downside equity beta.

Notable quotations from the academic research paper:

"Equity index covered calls are the most easily accessible source of the volatility risk premium to most investors. The volatility risk premium, which is absent from most investors’ portfolios, has had more than double the risk-adjusted returns (Sharpe ratio) of the equity risk premium, which is the dominant source of return for most investors. By providing the equity and volatility risk premia, equity index covered calls returns have been historically attractive, nearly matching the returns of their underlying index with significantly lower volatility.

One source of confusion on covered calls may be due to the opacity of the strategy’s risk exposures. Our paper’s first contribution is a novel performance attribution methodology for portfolios holding options, such as the covered call strategy. We demonstrate how to decompose the portfolio return into three distinct risk exposures: passive equity, equity market timing, and short volatility.

We demonstrate our proposed performance attribution by analyzing and comparing two covered call strategies. The first strategy mimics the CBOE S&P 500 BuyWrite Index (BXM), selling one-month at-the-money call options on option expiration dates. The second strategy mimics the CBOE S&P 500 2% OTM BuyWrite Index (BXY), selling one-month 2% out-of-the money call options on option expiration dates. Our performance attribution shows that passive equity is the dominant exposure for both covered call strategies. Short volatility contributes less than 10% of the risk, but with a Sharpe ratio near 1.0, adds approximately 2% annualized return to the covered call strategies.

Option-savvy market participants, such as market makers, are well aware that options include market timing, an active equity exposure. In fact, they often employ a delta-hedging program specifically designed to reduce the risk arising from this dynamic exposure. However, the covered call benchmark (CBOE BuyWrite Index) and most covered call funds do not hedge the time varying equity exposure arising from option convexity. Further, the risk and return contribution of an unhedged short option position’s dynamic equity exposure is by-and-large not reported by those who manage to those who invest in covered call strategies and is unaddressed in the covered call literature.

We employ our performance attribution to document that market timing is responsible for about one-quarter of the at-the-money covered call’s risk. The timing bet is smallest immediately after option expiration and largest just prior to option expiration. In fact, on the day before the call option expires, the equity timing position provides on average nearly the same risk as the passive equity exposure. We further show that covered call investors have not been compensated for bearing this risk. Because the embedded market timing is hedgeable by trading the underlying equity, covered call investors do not need to take that bet to earn the volatility risk premium. In other words, by shorting an option, covered calls include a market timing exposure that bets on equity reversals whose risk is material, uncompensated, and unnecessary for earning the volatility risk premium.

Having identified the covered call’s active equity exposure as an uncompensated contributor to risk, our final contribution analyzes a risk-managed covered call strategy that hedges away the identified dynamic equity exposure. On each day, the covered call’s active equity exposure may be measured by computing the delta of the strategy’s call option. The strategy trades an offsetting amount of the S&P 500 so that the covered call’s equity exposure remains constant. This risk management exercise mimics the delta-hedging approach taken by volatility desks. In so doing, the risk-managed covered call achieves higher risk-adjusted returns than does the traditional covered call because it continues to collect the same amount of equity and volatility risk premium, but is no longer exposed to equity market timing risk."

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