Demystify systematic macro?

I studied quite a few papers during my masters. 2 that seem relevant to this question are:

• Time Series Momentum (TSMOM) – Moskowitz, Ooi, Pedersen (MOP)
• Demystifying Managed Futures – Hurst, Ooi, Pedersen (HOP)

The HOP paper is an extension of MOP – it's key finding is that TSMOM (time series momentum) is the main driver of managed futures returns (systematic macro, or CTAs)

Here are my summary notes for those two papers:

MOP

• A diversified portfolio of time series momentum strategies across all asset classes

  delivers substantial abnormal returns

• Little exposure to standard asset pricing factors and performs best during extreme

  markets

• speculators profit from time series momentum at the expense of hedgers: speculators

  trade in the same direction as a return shock and reduce their positions as the shock

  dissipates, whereas hedgers take the opposite side of these trades

• paper includes country equity indexes, currencies, commodities, and sovereign bonds over more than

  25 years of data

• past 12-month excess return of each instrument is a positive predictor of its future

  return

• time series momentum or ‘‘trend’’ effect persists for about a year and then partially

  reverses over longer horizons – too far past fundamental = end of trend

• findings are robust across a number of subsamples, look-back periods, and holding

  periods

• Rather than focus on the relative returns of securities in the cross-section, time series

  momentum focuses purely on a security’s own past return

• correlations of time series momentum strategies across asset classes are larger than the

  correlations of the asset classes themselves: stronger common component to time

  series momentum across different assets than is present among the assets themselves

• driving force: significant positive auto-covariance between a security’s excess return

  next month and it’s lagged 1-year return. (covar between itself at different times)

• TSMOM challenges the random walk hypothesis. Not a compensation for risk. Performs

  best in extreme market moves

• Size each position (long or short) so that it has an ex ante annualized volatility of 40%

• the markets we study vary widely in terms of the type of investors, yet the pattern of

  returns remains remarkably consistent across these markets and is highly correlated across very different asset classes

HOP

Extension of MOP – TSMOM is the main driver of managed futures returns

• potential sources of profit due to initial under-reaction and delayed over-reaction to

  news

• time series momentum strategies produce large correlations and high Rsquares with

  Managed Futures indices and individual manager returns, including the largest and most

  successful managers.

• While the largest Managed Futures managers have realized significant alphas to

  traditional long-only benchmarks, controlling for time series momentum strategies

  drives their alphas to zero

• Fung and Hsieh (2001) find that portfolios of look-back straddles have explanatory

  power for Managed Futures returns, but these look-back straddles are not

  implementable as they use data from future time periods

• Finds strong evidence of trends across different look-back horizons and asset classes.

• A time series momentum strategy diversified across all assets and trend horizons

  realizes a gross Sharpe ratio of 1.8 with little correlation to traditional asset classes.

• in prolonged bear markets, time series momentum takes short positions as markets

  begin to decline and thus profits as markets continue to fall

• Regress Managed Futures indices and manager returns on time series momentum

  returns, we find large R-squares and very significant loadings on time series momentum

  at each trend horizon and in each asset class.

• In addition to explaining the time-variation of Managed Futures returns, time series

  momentum also explains the average excess return.

• Controlling for time series momentum drives the alphas of most managers and indices

  below zero. The negative alphas relative to the hypothetical time series momentum

  strategies show the importance of fees and transaction costs.

• strategy goes long if the preceding 1-month excess return was positive, and short if it

  was negative. The 3-month and 12-month strategies are constructed analogously. Each

  strategy always holds a long or a short position in each of 58 markets.

• Constant volatility position sizing (MOP):

  1. Diversified portfolio not overly dependent on the riskier assets (important given large dispersion in vol for different assets: nat gas vs bonds)

  2. Risk of each asset stable over time

  3. Minimises risk of data mining

They scale positions so that each asset has the same ex ante volatility at each time

• means that, the higher the volatility of an asset, the smaller a position it has in the portfolio, creating a stable and risk-balanced portfolio

I can dig out some more later if this is useful!