Hedge fund investors want to see a track record of fund returns and solid history of managing risk and losses. Over the last decade, pension funds (and their coveted investments) have only thrown up more gatekeepers and increased their due diligence process by adding layers of consultants to mine through a fund’s financial statements and asset allocations before giving the go ahead. 

Regulators are also tightening rules around buy side firms. As they say, past performance is not an indicator of future returns. So what if you could shore up your benchmarks and maintain steady returns through managing data? Flexible data analytics plays a crucial role in this.

Generating Alpha – operational or otherwise 

Beating the market benchmarks, or a self-generated benchmark, is the name of the game. Any incremental gains a fund can make on a daily basis will appear in the bottom line. The way to do that is to have as much visibility into and control over moving intraday risk metrics as possible.

These are the daily issues that hedge funds face:

• The need to optimize VaR levels based on exposure limits within specific parameters. 
• The need to perform historical VaR analysis and report results to investors.
• The need to benchmark performance attribution across levels and since inception by fund, or across the portfolio (i.e what is your standard deviation of risk over X number of years).
• The need to optimize portfolio allocation relative to its benchmark.

These are the things hedge funds need to solve for these issues:

• Dynamic bucketing and fast analysis of non-linear metrics applied at the trade and portfolio level.
• Replicate fund factsheets and create dynamic environments to update any metrics and generate client reports on a daily, weekly, monthly or ad-hoc basis. 
• Ability to detect opportunities while managing risk in real time using “what-if” scenarios.
• Ability to explain discrepancies in the risk numbers based on real-time market data and/or end-of-day prices.

Real-time visibility of data, non-linear analytics and the ability to assess complex scenarios without compromising on speed or accuracy in one solution is key to ticking all of these boxes. A user that is able to  easily drill down by instrument and position, across funds, or an individual fund considering a particular fund allocation, and calculate risk measures (such as VaR and vol) for each business line within those subsets or across business lines results in more accurate analytics. For a portfolio manager who wants a fuller view of risk you’d want to think about running decades of historical data, for example, for stress testing or benchmarking.

The devil is always in the details – so the ability to analyze on the fly complex metrics on streaming data such as VAR requires dynamic bucketing of trades based on whichever way the non-linear metrics move. So a trader or portfolio manager needs the ability to drill down to the smallest measure and see those details – it creates an ecosystem of security at  the end of the day.  Visit ActiveViam’s website for more information about our buyside solutions

Jeanine Prezioso

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Jeanine Prezioso is the Content Marketing Manager for Financial Services at ActiveViam based in New York. She brings with her nearly two decades of writing and editing experience as an award-winning financial journalist, editor and researcher. Jeanine has written about global energy trends, monetary policy and the impact of regulatory changes on the swaps and derivatives markets. Jeanine has also planned and managed events, conferences and press briefings and has conceptualized, outlined and tailored content for them. She is fluent in Italian and is a graduate of Columbia University’s Graduate School of Journalism in New York.

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