Estimating the positioning of trend followers

There is a simple method of approximating trend follower positioning in real-time and without lag. It is based on normalized returns in liquid futures markets over plausible lookback windows, under consideration of a leverage constraint, and uses estimated assets under management as a scale factor. For optimization and out-of-sample analysis, the approach can be enhanced by sequential estimation of some key parameters, such as the momentum lookback, the normalized momentum cap and the lookback for realized volatility calculation. Trend follower positions are an important factor of endogenous market risk due to the size of assets under management in dedicated funds and the informal use of trend rules across many trading accounts.

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Crowded trades: measure and effect

One measure of the crowdedness of trades in a portfolio is centrality. Centrality is a concept of network analysis that measures how similar one institution’s portfolio is to its peers by assessing its importance as a network node. Empirical analysis suggests that [1] the centrality of individual portfolios is negatively related to future returns, [2] mutual fund holdings become more similar when volatility is high, and [3] the centrality of portfolios seems to reflect lack of information advantage. This evidence cautions against exposure to crowded trades that rely upon others’ information leadership or are motivated by widely publicized persuasive views.

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The passive investment boom and its consequences

Passive investment vehicles have been expanding rapidly over the past 10 years, with assets reaching about USD8 trillion or 20% of aggregate investment funds last year. ETFs alone now account for 14% of fund assets. Beyond, the share of informal passive investing, such as ‘closet indexing’, is probably even larger. The plausible consequences of the passive investment boom include [i] less information efficiency of markets, [ii] greater incentive for low-quality issuance and corporate leverage, [iii] greater price correlation across securities, and [iv] stronger transmission of financial shocks into emerging economies.

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Trend following and the headwinds of rising yields

The decline in bond yields over the past decades has supported profitability and diversification value of trend followers. Returns have been boosted by a persistent secular downward drift in interest rates and persistent positive carry. Diversification value owed to negative correlation of long duration positions with equity and credit returns, reflecting the dominance of deflationary over inflationary shocks. However, in a rising yield environment carry would work against the trend follower, while diversification value is dubious. A simple simulation that reverses the yield dynamics over the past 15 years suggests that a generic trend following strategy could perform poorly in a rising yield environment.

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Cash hoarding and market dynamics

Institutional asset managers can aggravate market swings due to the pro-cyclicality of redemptions, internal leverage and cash positions. A new empirical analysis shows that cash hoarding, a rise in funds’ cash positions in times of redemptions, is the norm. Cash hoarding seems to be particularly pronounced in less liquid markets and is a rational response if fire sale haircuts are prone to escalate with growing flows, i.e. if liquidating late is disproportionately costly. Investment opportunities arise initially from timely positioning and subsequently from the detection of flow-driven price distortions.

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Fake alpha

Statistical alpha can be divided into fake alpha, which is a premium for non-directional systematic risk, and true alpha, which reflects the quality of the investment process. Fake alpha arises from exposure to conventional factors that are not correlated with the market portfolio. Failing to distinguish fake and true alpha can be costly for investors and strategy developers. Fake alpha is easy and cheap to produce and after periods of high risk premia on conventional factors it can post impressive performance statistics (or backtests). Subsequently, investors overload on managers or strategies that use these factors and related performance inevitably deteriorates. This goes some way in explaining the negative historic alpha on actively managed funds.

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Lessons from long-term global equity performance

A truly global and long-term (116 years) data set for both successful and failed financial markets shows that equity has delivered positive long-term performance in each and every country that did not expropriate capital owners, even those that were ravaged by wars. Also, equity significantly outperformed government bonds in every country, with a world average annual return of 5% versus 1.8%. The long-term Sharpe ratio on world equity has been 0.24 versus 0.09 for bonds. Valuation-based strategies for market timing have historically struggled to improve equity portfolio performance. Active management strategies that rely on both valuation and momentum would have been more useful.

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The 1×1 of trend-following

Trend-following is the dominant alternative investment strategy. Its historical return profile has been attractive on its own and for diversification purposes. It is suitable for rising and falling prices, albeit not for range-bound and “gapping” markets. A basic trend-following algorithm is easy to build. Trend-following commands over USD300 billion in dedicated assets and a lot more are managed by informal trend-followers. The style is itself a major force of price trends, with no direct ties to fundamental asset value.

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Why fund managers share trade ideas

Through sharing research and ideas fund managers can increase both the number and quality of their trading strategies. Empirical evidence suggests that managers share ideas particularly with peers that have both the ability and the intention to provide useful feedback. This implies that portfolio managers’ communication and good intentions are critical for their success in a network of idea generation.

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Pension funds and herding

Pension funds have three types of motivations for herding: rebalancing rules, the effects of regulatory changes and peer pressure of senior executives. A new empirical study detects all of these in the trading flows of the large Dutch pension funds. These flows offer opportunities for contrarian traders that provide liquidity to the “herd”.

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