One new strategy has been added:
#418 – Momentum and the Firm Fundamental Cycle
Three new related research papers have been included into existing strategy reviews. And three additional related research papers have been included into existing free strategy reviews during last few weeks.
Benjamin Franklin once said "… in this world nothing can be said to be certain, except death and taxes." and we completely agree with that quote. Traders and portfolio managers often strongly concentrate on a process of building the strategy which delivers the highest outperformance. But a lot of them forget to include taxes into that building process. And this can be a significant mistake as the following research paper shows:
Authors: Goldberg, Hand, Cai
Title: Tax-Managed Factor Strategies
Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3309974
Abstract:
We examine the tax efficiency of an indexing strategy and six factor tilts. Between June 1995 and March 2018, average value added by tax management exceeded 1.4% per year at a 10- year horizon for all the strategies we considered. Tax-managed factor tilts that are beta 1 to the market generated average tax alpha between 1.6% and 1.9% per year, while average tax alpha for the tax-managed indexing strategy was 2.3% per year. These remarkable results depend on the availability of short-term capital gains to offset. To a great extent, they can be attributed to loss harvesting and the tax rate differential.
Notable quotations from the academic research paper:
"In 1993, Rob Jeffrey and Rob Arnott asked a provocative question: Is an investor’s alpha big enough to cover its taxes? Arnott and Jeffrey pointed out that alpha generation typically requires high turnover, which erodes pre-tax alpha by increasing taxes, but this important fact tended to be overlooked by investors and researchers. Twenty-five years later, the situation has not changed too much.
Some principles of tax-aware investing, such as locating high-tax investments in tax-deferred accounts or using tax-free municipal bonds (instead of their taxable counterparts) as investments and benchmarks, are no more than common sense. Other principles of taxaware investing may rely on more sophisticated mathematics and economics, as well as more detailed knowledge of the complex and ever-changing US tax code. An example of the latter would be loss harvesting, which is a tax-aware option that combines delayed realization of capital gains with immediate realization of capital losses. A second timing option, which depends on the tax rate differential, involves the realization of long-term gains in order to facilitate the harvesting of short-term losses.
In the present study, we document the performance of after-tax return and risk profiles of an indexing strategy and six factor tilts over the period June 1995 to March 2018.7 We focus on active return, and our results rely on a number of methodological innovations. We mitigate the substantial impact of period dependence on results by launching each strategy at regular intervals over a long horizon, generating ranges of outcomes obtained in different market climates. We construct each portfolio with a one-step optimization that balances the competing imperatives of constraining factor exposures, harvesting losses, and minimizing tracking error (TE) to a diversified benchmark. We develop an after-tax performance attribution scheme that decomposes estate/donation and liquidation active returns into factor alpha, tax alpha, and tracking return. We measure the impact of the tax rate differential that affects tax-managed factor tilts.
Our results span several dimensions. First, we compare after-tax performance of tax-managed versions to tax-indifferent versions of each strategy. In back-tests, average value added by tax management during the period studied exceeded 1.50% per year at 10-year horizon for all the strategies we considered. This finding illustrates the potential power of loss harvesting and lets us move on to the more nuanced topic of the loss-harvesting capacities of different strategies.
Figure 1 presents the average after-tax active return of the tax-managed versions of the strategies graphically. Overall, the best average performance was delivered by the Small Value strategy, but more than half the after-tax active return was due to factor alpha. On the basis of tax alpha, the strategies divide into the three groups. The highest average tax alpha was delivered by the indexing strategy. Each of the four beta-1 strategies captured at least 70% of the tax alpha in the indexing strategy, but the two lower-risk strategies captured less than 35%. The division is marked in the performance charts."
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A new research paper related mainly to:
#26 – Value (Book-to-Market) Anomaly
Authors: Zhou
Title: Can Cash-Flow Beta Explain the Value Premium?
Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3244791
Abstract:
It is well documented that the cash flow beta can partly explain the source of the value premium. This paper presents an empirical test that cast doubt on this widely accepted belief. We double sort the stocks with their value and quality dimension and obtain four corner portfolios: (A) expensive quality, (B) cheap junk, (C) cheap quality and (D) expensive junk stocks. Prior research has shown that the value premium concentrates on cheap quality minus expensive junk (i.e. undervalued minus overvalued) but is not significant in cheap junk minus expensive quality stocks. If cash-flow beta is the source of the value premium, we would expect a larger cash-flow beta difference between the cheap quality and expensive junk portfolio. However, our empirical test shows that β_CF ((B) cheap junk) – β_CF ((A) expensive quality) >>β_CF ((C) cheap quality)-β_CF ((D) expensive junk). In other words, B minus A does not contribute to the profit of the value premium but contribute most to the difference of the cash flow beta between value and growth portfolios. Therefore, our result may serve as evidence that the cash flow beta may only spuriously explain the value premium. Or, at least, the cash-flow risk premium estimated in the portfolio regression approach is biased.
Notable quotations from the academic research paper:
"The value premium is one of the most important anomalies in the field of asset pricing. It is well known that the market beta fails to explain the value premium in the dataset after 1963.
Campbell and Vuolteenaho (2004) first proposed a “good beta, bad beta” model to solve this dilemma. They decompose the traditional market beta into two components: A good beta is the beta that measures a stock’s covariance with the temporary market movement or discount rate news, which is usually induced by changing market sentiment and varying risk aversion; A bad beta measures a stock’s comovement with market-wide fundamental cash-flow news. Campbell and Vuolteenaho (2004) and Cohen, Polk, Vuolteenaho(2009) argue that investors will regard wealth decrease induced by discount rate news as less significant because it tends to be temporary and the investors will be compensated by better future investment opportunity in an increased discount rate environment. A rational investor will demand higher return for the bad beta than the good beta.
Together with Campbell and Vuolteenaho (2004), Cohen, Polk, Vuolteenaho(2009), Campbell, Polk and Vuolteenaho (2010) and Da and Warachka (2009) among others, use different proxy for the cash-flow news and find that value stocks have a higher cash-flow beta than growth stocks. They conclude that cash-flow beta is one of the sources of the value premium. In this paper, we present an empirical test that question this widely accepted belief.
Our test double-sorts the stocks by value and quality dimension. In a conceptual simplified picture, Figure 1 illustrates four groups of stocks: (A) high quality, high price (expensive quality), (B) low quality, low price (cheap junk), (C) high quality, low price (cheap quality), and (D) low quality, high price (expensive junk). The price of portfolio A and B is thought to be “right” as their price is more aligned with the quality. Portfolio C (D) is the undervalued (overvalued) stocks.
High price portfolio A and D are growth stocks, and low price portfolio B and C are value stocks. The value premium is the return of ( + ) − ( + ) = ( − ) + ( − ) . ( − ) and ( − ) are represented respectively by the light blue and dark blue arrow in Figure 1.
When the price is “right”, the value premium is not significant. The value premium is concentrated on ( − ), but not on ( − ). The return of the four portfolio have the relationship: R > R ≈ R > R .
If the cash flow beta is the source of the value premium and the value premium is concentrated on ( − ), one would naturally expect that f( ) − f( ) ≫ f( ) − f( ), in which, f is the cash flow beta. However, in our
test, we find the opposite results: f( ) − f( ) ≫ f( ) − f( ). ( − ) does not contribute to the profit of the value premium while f( ) − f( ) contribute the most to the cash-flow beta difference between the value and growth portfolio.
If the cash-flow beta represents a risk, we take most of the risk in the value-junk minus growth quality portfolio, but we earn no profit or even negative profit. We take very little or negative risk in the value-quality minus growth-junk portfolio, but we earn most of the profit of the value premium. We need to find a plausible explanation to this phenomena before we conclude that the cash-flow risk is the source of the value premium. A fundamental reason of our result is that, on the value dimension, higher return links to a higher cash-flow beta, while on the quality dimension, higher return links to a lower cash-flow beta."
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A new research paper related to multiple currency risk factors:
#5 – FX Carry Trade
#129 – Dollar Carry Trade
Authors: Opie, Riddiough
Title: Global Currency Hedging with Common Risk Factors
Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3264531
Abstract:
We propose a novel method for dynamically hedging foreign exchange exposure in international equity and bond portfolios. The method exploits time-series predictability in currency returns that we find emerges from a forecastable component in currency factor returns. The hedging strategy outperforms leading alternative approaches out-of-sample across a large set of performance metrics. Moreover, we find that exploiting the predictability of currency returns via an independent currency portfolio delivers a high risk-adjusted return and provides superior diversification gains to global equity and bond investors relative to currency carry, value, and momentum investment strategies.
Notable quotations from the academic research paper:
"How should global investors manage their foreign exchange (FX) exposure? The classical approach to currency hedging via mean-variance optimization is theoretically appealing and encompasses both risk management and speculative hedging demands. However, this approach, when applied out of sample, suff ers from acute estimation error in currency return forecasts, which leads to poor hedging performance.
In this paper we devise a novel method for dynamically hedging FX exposure using mean-variance optimization, in which we predict currency returns using common currency risk factors.
Recent breakthroughs in international macro-nance have documented that the cross-section of currency returns can be explained as compensation for risk, in a linear two-factor model that includes dollar and carry currency factors. The dollar factor corresponds to the average return of a portfolio of currencies against the U.S. dollar, while the carry factor corresponds to the returns on the currency carry trade.
We take the perspective of a mean-variance U.S. investor who can invest in a portfolio of `G10' developed economies. We adopt the standard assumption that the investor has a predetermined long position in either foreign equities or bonds and desires to optimally manage the FX exposure using forward contracts. We form estimates of currency returns using a conditional version of the two-factor model where both factor returns and factor betas are time-varying.
A related literature provides strong empirical evidence, with underpinning theoretical support, that the dollar and carry factor returns are partly predictable. We exploit this predictability to forecast currency returns. Speciffically, we estimate factor betas and 1-month ahead dollar and carry factor returns in the time series, and then form expected bilateral currency returns using these estimates. This vector of expected currency returns enters the mean-variance optimizer to produce optimal, currency-specific, hedge positions. We update the positions monthly and refer to the approach as Dynamic Currency Factor (DCF) hedging.
We evaluate the performance of DCF hedging, over a 20-year out-of-sample period, against nine leading alternative approaches ranging from naive solutions in which FX exposure is either fully hedged or never hedged, through to the most sophisticated techniques that also adopt mean-variance optimization. We nd DCF hedging generates systematically superior out-of-sample performance compared to all alternative approaches across a range of statistical and economic performance measures for both international equity and bond portfolios. As a preview, in Figure 2 we show the cumulative payoff to a $1 investment in international equity and bond portfolios in January 1997. When adopting DCF hedging, the $1 investment grows to over $5 by July 2017 for the global equity portfolio, and to almost $4 for the global bond portfolio. These values contrast with $2 and $1.5, which a U.S. investor would have obtained, if the FX exposure in the equity or bond portfolios was left unhedged."
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Two new strategies have been added:
#416 – Using Baltic Dry Index to Trade Tanker Shipping Companies
#417 – Analyst Days
Three new related research papers have been included into existing strategy reviews. And three additional related research papers have been included into existing free strategy reviews during last few weeks.
A new research paper related mainly to:
Authors: Novy-Marx, Velikov
Title: Betting Against Betting Against Beta
Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3300965
Abstract:
Frazzini and Pedersen’s (2014) Betting Against Beta (BAB) factor is based on the same basic idea as Black’s (1972) beta-arbitrage, but its astonishing performance has generated academic interest and made it highly influential with practitioners. This performance is driven by non-standard procedures used in its construction that effectively, but non-transparently, equal weight stock returns. For each dollar invested in BAB, the strategy commits on average $1.05 to stocks in the bottom 1% of total market capitalization. BAB earns positive returns after accounting for transaction costs, but earns these by tilting toward profitability and investment, exposures for which it is fairly compensated. Predictable biases resulting from the paper’s non-standard beta estimation procedure drive results presented as evidence supporting its underlying theory.
Notable quotations from the academic research paper:
" Frazzini and Pedersen’s (FP) Betting Against Beta (BAB, 2014) is an unmitigated academic success. Despite being widely read, and based on a fairly simple idea, BAB is not well understood. This is because the authors use three unconventional procedures to construct their factor. All three departures from standard factor construction contribute to the paper’s strong empirical results. None is important for understanding the underlying economics, and each obscures the mechanisms driving reported effects.
Two of these non-standard procedures drive BAB’s astonishing “paper” performance, which cannot be achieved in practice, while the other drives results FP present as evidence supporting their theory. The two responsible for driving performance can be summarized as follows:
Non-standard procedure #1, rank-weighted portfolio construction: Instead of simply sorting stocks when constructing the beta portfolios underlying BAB, FP use a “rank-weighting” procedure that assigns each stock to either the “high” portfolio or the “low” portfolio with a weight proportional to the cross-sectional deviation of the stock’s estimated beta rank from the median rank.
Non-standard procedure #2, hedging by leveraging: Instead of hedging the low beta-minus-high beta strategy underlying BAB by buying the market in proportion to the underlying strategy’s observed short market tilt, FP attempt to achieve market-neutrality by leveraging the low beta portfolio and deleveraging the high beta portfolio using these portfolios’ predicted betas, with the intention that the scaled portfolios’ betas are each equal to one and thus net to zero in the long/short strategy.
FP’s first of these non-standard procedures, rank-weighting, drives BAB’s performance not by what it does, i.e., put more weight on stocks with extreme betas, but by what it does not do, i.e., weight stocks in proportion to their market capitalizations, as is standard in asset pricing. The procedure creates portfolios that are almost indistinguishable from simple, equal-weighted portfolios. Their second non-standard procedure, hedging with leverage, uses these same portfolios to hedge the low beta-minus-high beta strategy underlying BAB. That is, the rank-weighting procedure is a backdoor to equal-weighting the underlying beta portfolios, and the leveraging procedure is a backdoor to using equal-weighted portfolios for hedging.
BAB achieves its high Sharpe ratio, and large, highly significant alpha relative to the common factor models, by hugely overweighting micro- and nano-cap stocks. For each dollar invested in BAB, the strategy commits on average $1.05 to stocks in the bottom 1% of total market capitalization. These stocks have limited capacity and are expensive to trade. As a result, while BAB’s “paper” performance is impressive, it is not something an investor can actually realize. Accounting for transaction costs reduces BAB’s profitability by almost 60%. While it still earns significant positive returns, it earns these by tilting toward profitability and investment, exposures for which it is fairly compensated."
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