Our client, South Africa’s leading fixed-income asset manager, for more than 20 years, has an opportunity for a Quantitative Credit Analyst in the Investment Team. Candidates should be passionate for investments and have relevant technical and market experience and skills – including strong communication & interpersonal skills, be highly organized and adaptable.
Our client is committed to transformation, and preference will be given to suitably qualified previously disadvantaged South African candidates. The position is based in Cape Town.
This person is responsible, as part of a team, for:
Creating and revising models of aspects of the credit process, at inception a) the creation of a macro-credit spread model, b) assisting with the formulation of a multi-variable credit rating tool for use across deals, and c) other projects as they arise; and
In the area of credit analysis - all aspects of the credit analysis process across specified counterparties and industries predominantly in the listed debt space, although there is a requirement to get involved in unlisted debt and equity transactions as needed.
More broadly, the role is to find sources of investment returns for clients’ funds, build investment processes, and work with the team to extend such alpha sources and processes to all funds under management.
Delivery of specific projects – initially a macro credit spread model and a credit ratings model – to be used as tools by the credit team to assess, price, rank, value, and analyse credit assets. Work may extend to reviewing credit-pricing models and methods, valuation methods, and participating in the pricing-for-liquidity modelling;
- Making investment recommendations in respect of listed corporate bonds and money market instruments;
- Performing detailed investment analysis (including an assessment of financial, operational, management, competitive, industry, governance, environment and social factors);
- Performing global, local as well as industry specific research and analysis;
- Participation in ad hoc projects (related to listed bonds, process improvements, ASISA initiatives etc)
- Analysis of legal documentation and review of key terms and conditions for appropriateness; Working with internal and external legal advisors to ensure adequate protections are captured in the legal documentation;
- Proposing appropriate risk-adjusted pricing and working with the dealing desk in the pricing and valuation of listed bond instruments;
- Monitoring pricing and issuance trends in listed debt auctions to inform the broader investment decision making process;
- Working with the team to further develop and subsequently maintain internal relative value pricing databases;
- Regular contact with borrower management teams, rating agencies, deal originators, legal advisors, internal portfolio managers, ASISA, the JSE and other stakeholders;
- Deal origination;
- Sourcing transactions via reverse enquiry;
- Developing and maintaining counterparty and arranger relationships;
- Analysis of appropriateness of deal structures and pricing;
- Reviewing existing transactions to assess evolving risks and rewards and recommend if our client funds should continue to hold the investment, buy more or sell;
- Maintenance of all investment related data;
- Participation in valuation methods and processes, Listed and Unlisted Credit Committees and other internal team meetings;
- Participation in client investor presentations, discussions and report backs; and
- To develop the use of derivative instruments, either stand-alone or embedded, in the management of instruments and funds.
Technical/professional knowledge and skills:
- Relevant commerce or financial markets degree (quantitative / financial mathematics / statistics subjects);
- Prior asset management or banking experience (3 years) in quantitative analysis, deal implementation, monitoring and instrument valuations would be an advantage;
- Prior experience with financial instruments and in financial markets an advantage;
- Experience with fixed income investments;
- Experience with Python and R programming languages
- Prior experience in statistical modelling and credit risk modelling techniques; and