Vignette: Momentum and trend following: Time Series Momentum

[jaymon0703]

Create a Static Vignette. Save the vignette .Rmd file to the parser folder, and then follow the instructions referenced in issue #20.

Paper & Data

Link to Journal of Financial Econoimcs paper Time Series Momentum

Parser containing updated momentum factor data
Parser containing updated HML Devil factor data (use for devil factors and Global MKT Factor for MSCI)
Parser for long run commodity index data (use for GSCI)
Fama-French Factors in data/ folder.
Intermediate-Term bond index for BOND via FRED.

Figure 3

Plots the cumulative excess return to the diversified time series momentum strategy over time (on a log scale)

Figure 4

Plots the TSMOM returns against the market index returns.

Table 2

t-statistics of the alphas of time series momentum strategies with different look-back and holding periods.

Reported are the t-statistics of the alphas (intercepts) from time series regressions of the returns of time series momentum strategies over various look-back and holding periods on the following factor portfolios: MSCI World Index (AQR Global mkt fator), Lehman Brothers/Barclays Bond Index (generic intermediate-term bond), S&P GSCI Index (commodities for the long run index), and HML, SMB, and UMD Fama and French factors from Ken French's Web site.

• Panel A reports results for all asset classes
• Panel B for commodity futures
• Panel C for equity index futures
• Panel D for bond futures
• Panel E for currency forwards.

Table 3

Performance of the diversified time series momentum strategy

Panel A reports results from time series regressions of monthly and non-overlapping quarterly returns on the diversified time series momentum strategy that takes an equal-weighted average of the time series momentum strategies across all futures contracts in all asset classes, on the returns of the MSCI World Index (AQR Global MKT factor) and the Fama and French factors SMB, HML, and UMD, representing the size, value, and cross-sectional momentum premiums in US stocks.

Panel B reports results using the Asness, Moskowitz, and Pedersen (2010) value and momentum “everywhere“ factors instead of the Fama and French factors, which capture the premiums to value and cross-sectional momentum globally across asset classes.

Panel C reports results from regressions of the time series momentum returns on the market (AQR Global Mkt), volatility (VIX), funding liquidity (TED spread), and sentiment variables from Baker and Wurgler, 2006, Baker and Wurgler, 2007, as well as their extremes.

Table 5. Time series momentum vs. cross-sectional momentum.

Panel A reports results from regressions of the 12-month time series momentum strategies by asset class (TSMOM) on 12-month cross-sectional momentum strategies (XSMOM) of Asness, Moskowitz, and Pedersen (2010).

Panel B reports results from the decomposition of cross-sectional momentum and time series momentum strategies according to Section 4.2, where Auto is the component of profits coming from the auto-covariance of returns, Cross is the component coming from cross-serial correlations or lead-lag effects across the asset returns, Mean is the component coming from cross-sectional variation in unconditional mean returns, and Mean squared is the component coming from squared mean returns.

Panel C reports results from regressions of several XSMOM strategies in different asset classes, the Fama-French momentum, value, and size factors, and two hedge fund indexes obtained from Dow Jones/Credit Suisse on our benchmark TSMOM factor.