New Series to Employ Parala Capital’s Proprietary Methodology,
Utilizing Economic Factors as Inputs for Passive Asset Allocation Strategy
LONDON (May 23, 2012) – Dow Jones Indexes and Parala Capital LLP today announced the launch of the Dow Jones Parala Macro Allocation Indexes, a new family of stock indexes based on Parala Capital’s proprietary macro-allocation methodology.
Formed in 2007, Parala Capital focuses solely on developing investable strategies using rigorous quantitative methodologies based on proprietary and academic research. The London-based firm’s proprietary macro-allocation methodology seeks to identify the future performance of a universe of assets by utilizing economic factors as inputs for a passive asset allocation strategy.
“By working with Parala Capital on this unique family of indexes, we are joining with some of the financial community’s leading experts who have spent years analyzing how asset prices are affected by changing economic conditions,” said Michael A. Petronella, President, Dow Jones Indexes. “We believe the Dow Jones Parala Macro Allocation Indexes will appeal to market participants who appreciate innovative ideas that can help them better understand and measure the effects that macroeconomic conditions have on the financial markets.”
Parala Capital’s macro-allocation methodology uses monthly macroeconomic and risk-factor inputs to measure the state of the economy and capital markets, taking into account changing economic conditions by over- and under-weighting market segments based on their expected performance. The model rebalances regularly to create forward-looking rankings and scores.
Based on these scores, Dow Jones Indexes then assigns weightings to the market segments (subindexes) represented in each index.
The first index in the series, the Dow Jones Parala Global Sector Macro Allocation Index, allocates among 19 global sector indexes within the Dow Jones Sector Titans Index family.
It is expected that additional indexes will be added to the Dow Jones Parala Macro Allocation Index series over time.