Quantpedia Premium Update – 7th April

New strategies:

#846 – Antonacci’s Dual Momentum

Period of rebalancing: Monthly
Markets traded: bonds, commodities, equities, REITs
Instruments used for trading: ETFs
Complexity: Simple strategy
Backtest period: 1974-2011
Indicative performance: 14.9%
Estimated volatility: 13.93%

Source paper:

Antonacci, Gary: Risk Premia Harvesting Through Dual Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2042750
Abstract:
Momentum is the premier market anomaly. It is nearly universal in its applicability. This paper examines multi-asset momentum with respect to what can make it most effective for momentum investors. We show that both absolute and relative momentum can enhance returns, but that absolute momentum does far more to lessen volatility and drawdown. We see that combining absolute and relative momentum gives the best results.

#847 – Keller’s & Keunig’s Protective Asset Allocation

Period of rebalancing: Monthly
Markets traded: bonds, commodities, equities, REITs
Instruments used for trading: bonds, CFDs, ETFs, funds
Complexity: Simple strategy
Backtest period: 1970-2015
Indicative performance: 12.2%
Estimated volatility: 6.8%

Source paper:

Keller, Wouter J. and Keuning, Jan Willem: Protective Asset Allocation (PAA): A Simple Momentum-Based Alternative for Term Deposits
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2759734
Abstract:
Since the financial crisis of 2008 and the recent (end of 2015) pull back, investors are searching for less risky investments. Therefore, there is a growing demand for low risk/absolute return portfolios. In this paper we describe a simple dual-momentum model (called Protective Asset Allocation or PAA) with a vigorous “crash protection” which might fit this bill. It is a tactical variation on the traditional 60/40 stock/bond portfolio where the optimal stock/bond mix is determined by multi-market breadth using dual momentum. We backtested the model with several global multi-asset ETF-proxies. Starting from Dec 1970 allows us to investigate the behavior of PAA in periods with rate hikes as well. The in-sample (Dec 1970-Dec 1992) and out-of-sample returns of the most protective variant of our PAA strategy satisfy our absolute return requirement without compromising high returns. This makes PAA an appealing alternative for a 1-year term deposit.

#848 – Keller’s & Keunig’s Vigilant Asset Allocation

Period of rebalancing: Monthly
Markets traded: bonds, commodities, equities, REITs
Instruments used for trading: ETFs
Complexity: Simple strategy
Backtest period: 1970-2016
Indicative performance: 15.6%
Estimated volatility: 10.2%

Source paper:

Keller, Wouter J. and Keuning, Jan Willem: Breadth Momentum and Vigilant Asset Allocation (VAA): Winning More by Losing Less
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3002624
Abstract:
VAA (Vigilant Asset Allocation) is a dual-momentum based investment strategy with a vigorous crash protection and a fast momentum filter. Dual momentum combines absolute (trendfollowing) and relative (strength) momentum. Compared to the traditional dual momentum approaches, we have replaced the usual crash protection through trendfollowing on the asset level by our breadth momentum on the universe level instead. As a result, the VAA strategy is on average often more than 50% out of the market. We show, however, that the resulting momentum strategy is by no means sluggish. By using large and small universes with US and global ETF-like monthly data starting 1925 and 1969 respectively, we arrive out-of-sample at annual returns above 10% with max drawdowns below 15% for each of these four universes.

#849 – Keller’s & Keunig’s Defensive Asset Allocation

Period of rebalancing: Monthly
Markets traded: bonds, commodities, equities, REITs
Instruments used for trading: ETFs
Complexity: Simple strategy
Backtest period: 1970-2018
Indicative performance: 16%
Estimated volatility: –

Source paper:

Keller, Wouter J. and Keuning, Jan Willem: Breadth Momentum and the Canary Universe: Defensive Asset Allocation (DAA)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3212862
Abstract:
We improve on our Vigilant Asset Allocation (VAA) by the introduction of a separate “canary” universe for signaling the need for crash protection, using the concept of breadth momentum. The amount of cash is now governed by the number of canary assets with bad (non-positive) momentum. The risky part is still based on relative momentum (or relative strength), just like VAA. We call this strategy Defensive Assets Allocation (DAA). The aim of DAA is to lower the average cash (or bond) fraction while keeping nearly the same degree of crash protection as with VAA. Using a very simple model from Dec 1926 to Dec 1970 with only the SP500 index as risky asset, we find an optimal canary universe of VWO and BND (aka EEM and AGG), which turns out to be rather effective also for nearly all our VAA universes, from Dec 1970 to Mar 2018. The average cash fraction of DAA is often less than half that of VAA’s, while return and risk are similar and for recent years even better. The usage of a separate “canary” universe for signaling the need for crash protection also improves the tracking error with respect to the passive (buy-and-hold) benchmark and limits turnover.

#850 – Keller’s & van Putten’s Generalized Momentum and Flexible Asset Allocation

Period of rebalancing: Monthly
Markets traded: bonds, commodities, currencies, equities, REITs
Instruments used for trading: bonds, ETFs
Complexity: Simple strategy
Backtest period: 1998-2012
Indicative performance: 14.2%
Estimated volatility: 8.5%

Source paper:

Keller, Wouter J. and van Putten, Hugo: Generalized Momentum and Flexible Asset Allocation (FAA): An Heuristic Approach
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2193735
Abstract:
In this paper we extend the timeseries momentum (or trendfollowing) model towards a generalized momentum model, called Flexible Asset Allocation (FAA). This is done by adding new momentum factors to the traditional momentum factor R based on the relative returns among assets. These new factors are called Absolute momentum (A), Volatility momentum (V) and Correlation momentum (C). Each asset is ranked on each of the four factors R, A, V and C. By using a linearised representation of a loss function representing risk/return, we are able to arrive at simple closed form solutions for our flexible asset allocation strategy based on these four factors. We demonstrate the generalized momentum model by using a 7 asset portfolio model, which we backtest from 1998-2012, both in- and out-of-sample.

#851 – Adaptive Asset Allocation v.2

Period of rebalancing: Monthly
Markets traded: bonds, commodities, equities, REITs
Instruments used for trading: ETFs
Complexity: Simple strategy
Backtest period: 2003-2022
Indicative performance: 8.02%
Estimated volatility: 7.61%

Source paper:

Zambrano, Enrique A. and Rizzolo, Carlos: Long-Only Multi-Asset Momentum: Searching for Absolute Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4199648
Abstract:
The forward perspectives for the 60/40 portfolio have deteriorated, since secular tailwinds (that have benefited equities) are fading and stock-bond correlation may become positive. For this reason, allocating to uncorrelated strategies seems rational. In this paper we develop a long-only multi-asset momentum strategy that shows attractive risk-adjusted returns and the ability to generate positive returns on a rolling basis, while having low correlation with traditional portfolios. The strategy is based on a robust approach that considers several momentum measures and formation periods. It can be used in a standalone basis or combined with a 60/40 portfolio, generating higher returns with lower volatility and a fraction of the drawdowns.

#852 – Lethargic Asset Allocation

Period of rebalancing: Monthly
Markets traded: bonds, commodities, equities, REITs
Instruments used for trading: ETFs
Complexity: Complex strategy
Backtest period: 1949-2019
Indicative performance: 10.5%
Estimated volatility: 8.5%

Source paper:

Keller, Wouter J.: Growth-Trend Timing and 60-40 Variations: Lethargic Asset Allocation (LAA)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3498092
Abstract:
Growth-Trend (GT) timing from Philosophical Economics is a brilliant timing strategy which only signals a bear market when both the trend in the unemployment (UE) rate and the SP500 index are bearish. As a result, it captures most market downturns while switching to cash in less than 15% of the time. In this sense, its crash protection is much less drastic than our own “canary” protection in our DAA strategy (25% in cash) or the breadth protection in our VAA strategy (around 50% in cash). In this paper we apply GT timing to the well-known 60-40 static benchmark (60% SPY – 40% IEF), and search in-sample for variations on 60-40 with GT timing. For these variations, we in particular consider risky portfolios which are also agnostic for inflation and yield, inspired by the various static portfolio like the Permanent Portfolio and its siblings. Our final strategy switches between two static portfolios based on GT timing. This strategy is called the Lethargic Asset Allocation (LAA).

#853 – Resilient Asset Allocation

Period of rebalancing: Monthly
Markets traded: bonds, commodities, equities, REITs
Instruments used for trading: ETFs
Complexity: Complex strategy
Backtest period: 1970-2020
Indicative performance: 12.3%
Estimated volatility: 8.9%

Source paper:

Keller, Wouter J.: Lazy Momentum with Growth-Trend timing: Resilient Asset Allocation (RAA)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3752294
Abstract:
Resilient Asset Allocation (RAA) is a more aggressive version of our Lethargic Asset Allocation (LAA) strategy. It combines a more robust “All Weather” portfolio with even slower growth-trend (GT) filter and a faster market crash-protection. GT timing goes risk-off only when both the US unemployment (UE) and the US capital markets are bearish. To arrive at RAA, we adapt LAA in three steps. First, the (risky, near-static) portfolio is changed to an even more robust and more diversified “all-weather” portfolio, now with five (instead of four) equal weighted assets and with only bonds as risk-off assets (“cash”). Second, the “canary” technology from our Defensive Asset Allocation (DAA) paper is used for determining the market trend with a faster filter. Third, we change the unemployment trend filter to a slower one, where we simply compare the recent unemployment rate with that of one year ago. As a result, RAA is more aggressive and more robust than LAA, while at the same time nearly as “lazy” with respect to trading and turnover (on average one trading month per year).

#854 – Bold Asset Allocation

Period of rebalancing: Monthly
Markets traded: bonds, commodities, equities, REITs
Instruments used for trading: ETFs
Complexity: Complex strategy
Backtest period: 1970-2022
Indicative performance: 14.6%
Estimated volatility: 8.5%

Source paper:

Keller, Wouter J.: Relative and Absolute Momentum in Times of Rising/Low Yields: Bold Asset Allocation (BAA)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4166845
Abstract
Our aim is to develop a very offensive (‘aggressive’) tactical asset allocation strategy, by combining some of our previous models like Protected- (PAA), Vigilant- (VAA) and Defensive (DAA) Asset Allocation. We will call this new strategy the ‘Bold Asset Allocation’ (BAA). BAA combines a slow relative momentum with a fast absolute momentum and crash protection, based on the concept of the ‘canary’ universe, where we switch from our offensive to the defensive universe when any of the assets in the canary universe has negative absolute momentum. As a result, BAA spends ca 60% in the defensive universe. By enhancing this defensive universe beyond cash, we find very impressive returns (>=20%) with low monthly max drawdowns (<=15%) over Dec 1970 – Jun 2022.

#855 – Avoid Equity Bear Markets with a Market Timing Strategy

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: CFDs, ETFs, funds, futures
Complexity: Simple strategy
Backtest period: 1927-2022
Indicative performance: 7.05%
Estimated volatility: 11.83%

Source paper:

Ďurian, Ladislav and Vojtko, Radovan: Avoid Equity Bear Markets with a Market Timing Strategy
https://ssrn.com/abstract=4397638
Abstract:
In this paper, our goal is to construct a market timing strategy that would reliably sidestep the equity market during bear markets and thereby reduce market volatility and boost risk-adjusted returns. We build trading signals based on price-based indicators, macroeconomic indicators, and a leading indicator, a yield curve, that can predict recessions and bear markets in advance. Our best-performing strategy uses signals from the Treasury spread, a 200-day SMA, and an alternative risk metric, the Rachev ratio, as trend indicators, while Real Retail Sales Growth, Industrial Production Growth, and S&P Composite dividends as macroeconomic indicators. Based on the sample period from 1927 to 2022, it yields an annual excess return of 7.05%, well above the market return, while doubling the market Sharpe ratio to 0.60 and cutting the maximal drawdown by two-thirds to -25.13%.

#856 – Forecasted Unemployment Beta Predicts the Cross-Section of Stock Returns

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1974-2019
Indicative performance: 5.54%
Estimated volatility: 16.10%

Source paper:

Ince, Baris: Forecasted Unemployment and the Cross-Section of Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4264131
Abstract:
We introduce forecasted unemployment as a state variable that forecasts future macroeconomic activity. Hence, forecasted unemployment is expected to be priced in the crosssection of stock returns. Consistently, we quantify stock exposure to forecasted unemployment and document the importance of unemployment beta in the pricing of individual stocks. Stocks in the lowest unemployment beta decile generate 7% more annualized riskadjusted return than stocks in the highest unemployment beta decile. The unemployment premium is driven by the outperformance (underperformance) by stocks with negative (positive) unemployment beta. The premium is robust to controls for firm-specific characteristics, risk factors, macroeconomic and financial variables.

#857 – Crypto Perpetual Futures Arbitrage

Period of rebalancing: Monthly
Markets traded: cryptocurrencies
Instruments used for trading: cryptocurrencies, futures
Complexity: Very complex strategy
Backtest period: 2020-2022
Indicative performance: 7.58%
Estimated volatility: 4.4%

Source paper:

He, Songrun and Manela, Asaf and Ross, Omri and von Wachter, Victor: Fundamentals of Perpetual Futures
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4301150
Abstract:
Perpetual futures — swap contracts that never expire — are by far the most popular derivative traded in cryptocurrency markets, with more than $100 billion traded daily. Perpetuals provide investors with leveraged exposure to cryptocurrencies, which does not require rollover or direct cryptocurrency holding. To keep the gap between perpetual futures and spot prices small, long position holders periodically pay short position holders a funding rate proportional to this gap. The funding rate incentivizes trades that tend to narrow the futures-spot gap. But unlike fixed-maturity futures, perpetuals are not guaranteed to converge to the spot price of their underlying asset at any time, and familiar no-arbitrage prices for perpetuals are not available, as the contracts have no expiry date to enforce arbitrage. Here, using a weaker notion of random-maturity arbitrage, we derive no-arbitrage prices for perpetual futures in frictionless markets, and no-arbitrage bounds for markets with trading costs. These no-arbitrage prices provide a useful benchmark for perpetual futures and simultaneously prescribe a strategy to exploit divergence from these fundamental values. Empirically, we find that deviations of crypto perpetual futures from no-arbitrage prices are considerably larger than those documented in traditional currency markets. These deviations comove across cryptocurrencies, and diminish over time as crypto markets develop and become more efficient. A simple trading strategy generates large Sharpe ratios even for investors paying the highest trading costs on Binance, which is currently the largest crypto exchange by volume.

#858 – Contrarian VIX strategy

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: ETFs, futures
Complexity: Simple strategy
Backtest period: 2017-2021
Indicative performance: 10.8%
Estimated volatility: 5.59%

Source paper:

Chandorkar, Pankaj and Brzeszczynski, Janusz: Do Not Fear the Fear Index: Evidence from the US, UK and European Markets
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4294487
Abstract:
The VIX index is commonly known as the “fear index”. Similar indices are introduced in the UK and in the European stock markets. In this study, we investigate whether such indices reflect investors’ fear. Our results from long-horizon predictive regressions show that the movements of these indices, as well as extreme jumps, fail to predict statistically significant negative market returns in the US, the UK and in the European markets. Moreover, the response of leading business cycle indicators to shocks in the fear indices are statistically insignificant. However, monetary policy in these countries appears to be sensitive to these shocks.

New research papers related to existing strategies:

#224 – Profitability Factor Combined with Value Factor

Enhancing the Fama-French five-factor model with informative factors
https://ssrn.com/abstract=4336292
Abstract:
Book-to-market, profitability, and investment – the characteristics underlying the Fama-French (2015) value, profitability, and investment factors – are imperfect indicators of expected returns. In this study, we propose to narrow down the characteristics’ variation that is informative about expected returns and to use their informative parts to construct enhanced versions of the factors. We find that our informative factors exhibit around 50% higher Sharpe ratios than their standard counterparts. A five-factor model using our informative factors strongly outperforms the standard model regarding the maximum Sharpe ratio criterion. Importantly, contrary to the standard factors, our informative factors exhibit positive risk prices and thus generate an upward-sloping multivariate security market line. Moreover, our informative model prices a large cross-section of characteristics-sorted portfolios much better than the standard model. Finally, our procedure to enhance the Fama-French (2015) factors largely outperforms other recently proposed enhancement procedures.

#567 – Low-risk Anomaly Index
#498 – Value in Anomalies

Jiang, Fuwei and Tang, Guohao and Yu, Jiasheng: Global Mispricing Matters
https://ssrn.com/abstract=4384265
Abstract:
This paper constructs a global anomaly index based on the 153 long-short portfolio returns of 33 stock markets. We find that global anomaly index is a strong negative predictor of future aggregate stock returns in international markets both in- and out-of-sample. It captures the common change in overpricing across stock markets, and is not subsumed by the extant well-known stock return predictors. The predictive power of global anomaly index arises from globally widespread stronger mispricing correction persistence for overpricing relative to underpricing, and partly from the predictive ability to forecast future sentiment-change. We provide evidence that global mispricing is an important pricing factor for predicting aggregate stock returns around the world.

#415 – Sovereign CDS Predicts FX Market Return
#793 – Sovereign CDS Currency Factor

Giovanni Calice and Ming-Tsung Lin: Sovereign Momentum Currency Returns
https://www.efmaefm.org/0EFMAMEETINGS/EFMA%20ANNUAL%20MEETINGS/2022-Rome/papers/EFMA%202022_stage-3032_question-Full%20Paper_id-99.pdf
Abstract:
We study the relationship between cross-sectional sovereign credit risk and currency spot prices. We find that past sovereign credit risk, measured by sovereign credit default swap (CDS) spreads, predict future currency spot returns. In particular, we document a significant cross-sectional currency portfolio spread in excess of the risk-free rate of return (up to 9.4% p.a.) between the highest and the lowest quintile sovereign CDS spreads. These results suggest a new profitable currency return strategy based on sovereign credit risk.

#829 – Environmental Machine Learning Strategy in Equities

Fereydooni, Ali and Barak, Sasan and Sajadi, Seyed Mehrzad Asaad: A Novel Online Portfolio Selection Based on Pattern Matching and ESG Factors
https://ssrn.com/abstract=4355713
Abstract:
In modern finance, social investment portfolios have attracted the attention of researchers, investors, and practitioners. Regarding the long-term nature of this investment, the selection of the portfolios for a single period should be reconsidered as On-Line portfolio selection (OLPS) which focuses on the allocation of portfolios over multiple periods to maximize the expected growth rate of the portfolio. Besides common factors such as return of investment, many investors are willing to invest on assets complying with sustainability requirements. In this study, an OLPS strategy is developed which considers Environmental, Social, and Governance (ESG) factors in addition to return and risk. Due to the diversity of constructed portfolios, different assets are first clustered based on their mutual information (MI). Then, a novel pattern-matching approach is implemented on the clustered assets that not only considers the amount of profitability of previous windows, but also finds the optimal length and number of windows. After predicting the last groups of windows based on the pattern matching, superior assets in terms of return and Sharpe ratio in each cluster are chosen and the final portfolios are established regarding two scenarios; (i) a Mean-Variance (MV) strategy, and (ii) a developed MV strategy which considers ESG factors besides return and risk.The presented approaches are compared with several well-known benchmarks on four different datasets (i.e. 100 selective assets from S&P 500 index, S&P 500, Nikkei 225, and Dow Jones). The results indicate the superiority of the approach based on simple MV strategy over others in metrics such as Sharpe Ratio and Deflated Sharpe. Approaches containing ESG factors also show profit and can be considered a long-term investment opportunity by investors who seek more ESG-based decisions.

#615 – Machine Learning and Mutual Fund Characteristics

Broman, Markus S. and Moneta, Fabio: On the Anomaly Tilts of Factor Funds
https://ssrn.com/abstract=4358597
Abstract:
By analyzing portfolio holdings, we find that a significant subset of Hedged Mutual Funds (HMFs) and smart-beta Exchange-Traded Funds (ETFs) tilt their portfolios towards well-known anomaly characteristics and that such tilts are highly persistent. Short positions of HMFs amplify their factor tilts. Most single-factor ETFs target multiple factors, while many also exhibit offsetting tilts to other factors. HMFs with large factor tilts outperform corresponding ETFs, which is driven by short positions and higher factor-related returns. Overall, we show the superior factor replication ability of HMFs over ETFs, and that HMFs achieve similar (or better) performance as the academic factors.

And several interesting free blog posts have been published during last 2 weeks:

Overview of Different Short Volatility Strategies

The expected return on the “variance factor” known as variance risk premium (VRP) is nothing new to options markets. For any investor interested in benefitting from this phenomenon, we present the study of Dörries et al. (2021), which provides a clear overview of different VRP-earning strategies.

Is Gold a Safe Haven? It Depends on the Country

If you’re a regular reader of our blogs (and we hope you are!), you would not miss that we like to touch macro-economic subjects. One of that never-fading topics is the role of gold as a crisis hedge. The probably most known commodity is a popular choice for a portion of the total portfolio, from small investors to central banks, for various reasons (be it diversification or hedging). So let’s not further delay it, and today we ask: Is gold really a safe haven?

Can We Backtest Asset Allocation Trading Strategy in ChatGPT?

It’s always fun to push the boundaries of technology and see what it can do. The AI chatbots are the hot topic of current discussion in the quant blogosphere. So we have decided to test OpenAI’s ChatGPT abilities. Will we persuade it to become a data analyst for us? While we may not be there yet, it’s clear that AI language models like ChatGPT can soon revolutionize how we approach to finance and data analysis.