Related to all major factor strategies (trend, momentum, value, carry, seasonality and low beta/volatility):

Authors: Baltussen, Swinkels, van Vliet

Title: Global Factor Premiums

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3325720

Abstract:

We examine 24 global factor premiums across the main asset classes via replication and new-sample evidence spanning more than 200 years of data. Replication yields ambiguous evidence within a unified testing framework with methods that account for p-hacking. The new-sample evidence reveals that the large majority of global factors are strongly present under conservative p-hacking perspectives, with limited out-of-sample decay of the premiums. Further, utilizing our deep sample, we find global factor premiums to be not driven by market, downside, or macroeconomic risks. These results reveal strong global factor premiums that present a challenge to asset pricing theories.

Notable quotations from the academic research paper:

"In this paper we study global factors premiums over a long and wide sample spanning the recent 217 years across equity index (but not single securities), bond, currency, and commodity markets.

The first objective of this study is to robustly and rigorously examine these global factor premiums from the perspective of ‘p-hacking’.

We take as our starting point the main global return factors published in the Journal of Finance and the Journal of Financial Economics during the period 2012-2018: time-series momentum (henceforth ‘trend’), cross-sectional momentum (henceforth ‘momentum’), value, carry, return seasonality and betting-against-beta (henceforth ‘BAB’). We examine these global factors in four major asset classes: equity indices, government bonds, commodities and currencies, hence resulting in a total of 24 global return factors.4

We work from the idea that these published factor premiums could be influenced by p-hacking and that an extended sample period is useful for falsification or verification tests. Figure 1, Panel A summarizes the main results of these studies.

Shown are the reported Sharpe ratio’s in previous publications, as well as the 5% significance cutoff in the grey-colored dashed line. In general, the studies show evidence on the global factor premiums, with 14 of the 22 factors (return seasonality is not tested in bonds and currencies) displaying significant Sharpe ratio’s at the conventional 5% significance level.

Further, most of the studies have differences in, amongst others, testing methodologies, investment universes and sample periods, choices that introduce degrees of freedom to the researcher. To mitigate the impact of such degrees of freedom, we reexamine the global return factors using uniform choices on testing methodology and investment universe over their average sample period (1981-2011). Figure 1, Panel B shows the results of this replicating exercise. We find that Sharpe ratios are marginally lower, with 12 of the 24 factor premiums being significant at the conventional 5% level.

The second objective of this study is to provide rigorous new sample evidence on the global return factors. To this end, we construct a deep, largely uncovered historical global database on the global return factors in the four major asset classes. This data consists of pre-sample data spanning the period 1800- 1980, supplemented with post-sample data from 2012-2016, such that we have an extensive new sample to conduct further analyses. If the global return factors were unintentionally the result of p-hacking, we would expect them to disappear for this new sample period.

Our new sample findings reveal consistent and ubiquitous evidence for the large majority of global return factors. Figure 1, Panel C summarizes our main findings by depicting the historical Sharpe ratio’s in the new sample period. In terms of economic significance, the Sharpe ratios are substantial, with an average of 0.41. Remarkably, in contrast to most out-of-sample studies we see very limited ‘out-of-sample’ decay of factor premiums.

In terms of statistical significance and p-hacking perspectives, 19 of the 24 t-values are above 3.0,19 Bayesian p-values are below 5%, and the break-even prior odds generally need to be above 9,999 to have less than 5% probability that the null hypothesis is true."

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A new academic paper related to:

#25 – Size Factor
#26 – Value (Book-to-Market) Factor

Authors: Hung, Song, Xiang

Title: Fragile Factor Premia

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3312837

Abstract:

We demonstrate that returns and volatilities of the Fama-French size and value factors are significantly determined by non-fundamental flow-induced trading from actively managed equity mutual funds. Mutual fund flows are largely ignorant about systematic risks. These non-fundamental shifts in demand induce large return heterogeneity within and across the Fama-French size and book-to-market portfolios. We show that aggregate mutual fund flow- induced trades across the size and book-to-market spectrum significantly influence the size and value premia, followed by large subsequent reversals. We also find that the expected volatilities of mutual funds’ flow-induced trades strongly predict future factor volatilities. Our results highlight the importance of non-fundamental demand shocks in determining factor premia and factor volatilities.

Notable quotations from the academic research paper:

"Mutual fund trading has a considerable price impact on individual stocks. However, some more recent work suggests that mutual fund investors are largely ignorant about systematic risks, when allocating capitals among mutual funds. Empirically, it remains unclear how trades induced by the non-fundamental mutual fund flows impact returns and volatilities of size and value, the two prominent factors. This paper aims to fill this gap.

In our study, we use a bottom-up approach and estimate mutual fund flow-induced trading (FIT) for each stock-quarter from 1980 to 2017. In a nutshell, FIT measures the magnitude of flow-driven trading by the aggregate equity mutual fund industry on a particular stock in a given quarter. We use FIT rather than the entire realized trading of mutual funds because FIT only captures those trades that are driven by the demand shifts from mutual fund investors, which are largely ignorant about fundamentals

Our main findings are as follows.

We find that returns of the six FF size and book-to-market portfolios are largely determined by the uninformed mutual fund flow-induced trades. Within each of the six FF portfolios, stocks with higher FIT have higher return performance.

Mutual funds' flow-driven trades can even revert the positive size and value premia. That is, within the same book-to-market portfolios, we find large-cap stocks with above-median FIT outperform small-cap stocks with below-median FIT. Within the same size portfolios, growth stocks with above-median FIT outperform value stocks with below-median FIT.

We decompose the value minus growth returns (HML) into two components: (i) value-inflow minus growth-outflow returns (HMLInflow) and (ii) value-outflow minus growth-inflow (HMLOutflow). We decompose the small minus big returns (SMB) into the sum of (i) small-inflow minus big-outflow returns (SMBInflow) and (ii) small-outflow minus big-inflow returns (SMBOutflow). Figure 2 report the average monthly returns and alphas of SMB, HML, and their inflow and outflow components.

In sum, we find that the size premium is due to the component of small-inflow stocks minus big-outflow stocks, while the value premium is due to the component of value-inflow stocks minus growth-outflow stocks."

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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, suffers 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. Speciffically, 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-specific, 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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