New Capital
Machine Learning Strategies Fund

»MOZAMIL AFZAL

A machine learning approach to investing in financial markets

A machine learning approach to investing in financial markets

Over the past few years a quiet investment revolution has been gathering pace: an explosion in the breadth and volume of new data sources, the advent of cloud-based computing, the arrival of new data providers, and substantial advances in machine learning algorithms, have together helped push out the boundaries of investing. The New Capital Machine Learning Strategies Fund has embraced these developments by creating a multi-manager product that leverages off the quantitative skill sets of hedge funds firmly tethered to this revolution.

The long term objective of the Fund is to outperform developed equity markets but with lower volatility and with minimal correlation to both traditional markets and other alternative hedge fund strategies. In order to achieve this triple objective, the Fund invests in a portfolio of highly differentiated hedge funds that use different but complimentary approaches, methodologies and styles from one another but all with the common theme of using predictive machine learning processes within their investment framework.

The Fund typically invests in 8-12 underlying hedge fund managers, but the number may grow over time as new funds are launched and identified by the research team. The Fund is expected to have low manager turnover, with an average holding period ranging from 3-5 years per manager.

The investment process is predominantly bottom-up, focusing on the experience and expertise of the key people within each hedge fund, the processes they employ and a validation of these qualitative aspects through a rigorous analysis of the underlying portfolio and track record of each hedge fund. The overarching objective is to identify persistence, consistency and resilience within each hedge fund and to identify any potential or inherent sources of weakness.

The top-down element is partly a contextualization of historic performance at both the individual hedge fund and overall Fund levels and partly a forward-looking evaluation of the anticipated market conditions over the period ahead, so as to allow the Fund to be optimally positioned in the hedge funds that stand to benefit most from that market evaluation. The forward-looking anticipation of market conditions is based on a well-developed macro-economic perspective, influenced by and spearheaded by the Head of Research and EFGAM’s CIO.

The Fund offers the potential of both high returns and diversification from a portfolio of funds that use differentiated processes to identifying return drivers and who typically operate at the shorter end (intraday to a few weeks) relative to more traditional approaches to investing.

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0.00%

Cumulative performance
USD Inst Acc share class Since inception (Jun 2018)
 
  1 Year YTD
Fund
Benchmark

PRICES AND LITERATURE

Class ISIN Price Currency Price Date Factsheet Datasheet KID
USD Ord Acc IE00BDFKRH13 98.55 USD 8/31/2018 - -
USD Inst Acc IE00BDFKRJ37 98.67 USD 8/31/2018 - -
GBP Ord Acc IE00BDFKRN72 99.36 GBP 8/31/2018 - -
GBP Inst Acc IE00BDFKRP96 98.74 GBP 8/31/2018 - -
EUR Ord Acc IE00BDFKRL58 99.20 EUR 8/31/2018 - -
EUR Inst Acc IE00BDFKRM65 98.03 EUR 8/31/2018 - -
CHF Ord Acc IE00BDFKRQ04 97.83 CHF 8/31/2018 - -
CHF Inst Acc IE00BDFKRR11 97.96 CHF 8/31/2018 - -

DOWNLOAD FUND DOCUMENTS FOR DUE DILIGENCE

Reports & Accounts
Annual Report-ENG
Semi-Annual Report-ENG
Prospectus
Full Prospectus-ENG
How to Invest
Application Form

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