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215 Years of Global Multi-Asset Momentum: 1800-2014 (Equities, Sectors, Currencies, Bonds, Commodities and Stocks)
Momentum and Markowitz: A Golden Combination


Market Timing with Moving Averages: Anatomy and Performance of Trading Rules
Relative Strength Strategies for Investing



A Century of Evidence on Trend-Following Investing


Avoiding The Big Drawdown: Is Downside Protection Helpful Or Heresy?

Learning to Play Offense and Defense: Combining Value and Momentum from the Bottom Up, and the Top Down
Anatomy of Market Timing with Moving Averages




215 Years of Global Multi-Asset Momentum: 1800-2014
(Equities, Sectors, Currencies, Bonds, Commodities and Stocks)

Christopher C. Geczy Mikhail Samonov

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This Version: May 18, 2015

ABSTRACT:

Extending price return momentum tests to the longest available histories of global financial asset returns, including country-specific sectors and stocks, fixed income, currencies, and commodities, as well as U.S. stocks, we create a 215-year history of multi-asset momentum, and we confirm the significance of the momentum premium inside and across asset classes. Consistent with stock-level results, we document a large variation of momentum portfolio betas, conditional on the direction and duration of the return of the asset class in which the momentum portfolio is built. A significant recent rise in pair-wise momentum portfolio correlations suggests features of the data important for empiricists, theoreticians and practitioners alike.


Momentum and Markowitz: A Golden Combination

Wouter J. Keller, Adam Butler, and Ilya Kipnis

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This Version: June 4, 2015

ABSTRACT:

Mean-Variance Optimization (MVO) as introduced by Markowitz (1952) is often presented as an elegant but impractical theory. MVO “is an unstable and error-maximizing” procedure (Michaud 1989), and “is nearly always beaten by simple 1/N portfolios” (DeMiguel, 2007). And to quote Ang (2014): “Mean-variance weights perform horribly… The optimal mean-variance portfolio is a complex function of estimated means, volatilities, and correlations of asset returns. There are many parameters to estimate. Optimized mean-variance portfolios can blow up when there are tiny errors in any of these inputs. .. “.

In our opinion, MVO is a great concept, but previous studies were doomed to fail because they allowed for short-sales, and applied poorly specified estimation horizons. For example, Ang used a 60 month formation period for estimation of means and variances, while Asness (2012) clearly demonstrated that prices mean-revert at this time scale, where the best assets in the past often become the worst assets in the future.

In this paper we apply short lookback periods (maximum of 12 months) to estimate MVO parameters in order to best harvest the momentum factor. In addition, we will introduce common-sense constraints, such as long-only portfolio weights, to stabilize the optimization. We also introduce a public implementation of Markowitz’s Critical Line Algorithm (CLA) programmed in R to handle the case when the number of assets is much larger than the number of lookback periods.

We call our momentum-based, long-only MVO model Classical Asset Allocation (CAA) and compare its performance against the simple 1/N equal weighted portfolio using various global multi-asset universes over a century of data (Jan 1915-Dec 2014). At the risk of spoiling the ending, we demonstrate that CAA always beats the simple 1/N model by a wide margin.


Market Timing with Moving Averages: Anatomy and Performance of Trading Rules

Valeriy Zakamulin

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This Version: March 25, 2015

ABSTRACT:

In this paper, we contribute to the literature in two important ways. The first contribution is to demonstrate the anatomy of market timing rules with moving averages. Our analysis offers a broad and clear perspective on the relationship between different rules and reveals that all technical trading indicators considered in this paper are computed in the same general manner. In particular, the computation of every technical trading indicator can be equivalently interpreted as the computation of the weighted moving average of price changes. The second contribution of this paper is to perform the longest out-of-sample testing of a set of trading rules. The trading rules in this set are selected to have clearly distinct weighting schemes. We report the detailed historical performance of the trading rules over the period from 1870 to 2010 and debunk several myths and common beliefs about market timing with moving averages.


Relative Strength Strategies for Investing

Mebane Faber

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This Version: April, 2010

ABSTRACT:

The purpose of this paper is to present simple quantitative methods that improve risk-adjusted returns for investing in US equity sector and global asset class portfolios. A relative strength model is tested on the French-Tama US equity sector data back to the 1920s that results in increased absolute returns with equity-like risk. The relative strength portfolios outperform the buy and hold benchmark in approximately 70% of all years and returns are persistent across time. The addition of a trendfollowing parameter to dynamically hedge the portfolio decreases both volatility and drawdown. The relative strength model is then tested across a portfolio of global asset classes with supporting results.


A Century of Evidence on Trend-Following Investing

Hurst, Ooi and Pederson, AQR

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This Version: Fall 2012

ABSTRACT:

We study the performance of trend-following investing across global markets since 1903, extending the existing evidence by more than 80 years. We find that trend-following has delivered strong positive returns and realized a low correlation to traditional asset classes each decade for more than a century. We analyze trend-following returns though various economic environments and highlight the diversification benefits the strategy has historically provided in equity bear markets. Finally we evaluate the recent environment for the strategy in the context of these long-term results.


Avoiding The Big Drawdown: Is Downside Protection Helpful Or Heresy?

Wesley R. Gray, Ph.D.

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This Version: Aug. 14, 2015

ABSTRACT:

Having your cake and eating it too is a great way to go. It’s great to have the cake, and it’s also great to eat the cake. But you can’t have it both ways. This trend continues when we speak with fellow investors: “Give me high, after-tax, net of fee returns, but with limited risk and volatility.” Now, we certainly love high returns with low risk. We also love high reward with low effort and high calories with low weight gain. Unfortunately, this brings us to our first problem with the investing unicorn.


Learning to Play Offense and Defense: Combining Value and Momentum from the Bottom Up, and the Top Down

Mebane T. Faber

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This Version: Sept., 2015

ABSTRACT:

Sorting stocks based on value and momentum factors historically has led to outperformance over the broad US stock market. However, any long-only strategy is subject to similar volatility and drawdowns as the S&P 500. Drawdowns of 50%, or even 60-90% make implementation of a stock strategy very challenging. Is there a way to add value on stock selection, but also reduce volatility and drawdowns of a long only strategy with hedging techniques? In this paper we examine the possibility of following a strategy that combines aggressive offense and smart defense to target outsized returns with manageable risk and drawdowns.


Anatomy of Market Timing with Moving Averages

Valeriy Zakamulin

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This Version: Aug., 2015

ABSTRACT:

The underlying concept behind the technical trading indicators based on moving aver-ages of prices has remained unaltered for more than half of a century. The development in this field has consisted in proposing new ad-hoc rules and using more elaborate types of moving averages in the existing rules, without any deeper analysis of commonalities and differences between miscellaneous choices for trading rules and moving averages. In this paper we uncover the anatomy of market timing rules with moving averages. Our analysis offers a new and very insightful reinterpretation of the existing rules and demonstrates that the computation of every trading indicator can equivalently be interpreted as the computation of the weighted moving average of price changes. This knowledge enables a trader to clearly understand the response characteristics of trading indicators and simplify dramatically the search for the best trading rule. As a straightforward application of the useful knowledge revealed by our analysis, in this paper we also entertain a method of finding the most robust moving average weighting scheme. The method is illustrated using the long-run historical data on the Standard and Poor’s Composite stock price index. We find the most robust moving average weighting scheme and demonstrates its advantages.

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