Armelle Guizot outlines the importance of properly mining and evaluating data to the successful assessment of hedge fund performance.
Armelle Guizot outlines the importance of properly mining and evaluating data to the successful assessment of hedge fund performance.
In his book The Number, Alex Berenson best characterizes reality about data manipulation during decades primarily in Wall Street investment banks to produce the quarterly corporate earnings that most investors traded to get fatter salaries, fees, stock options, bonuses and perks.
While investment bankers are laying low now about data manipulation, calibrations and distortion, an army of compliance officers, risk managers, statisticians, mathematicians and actuaries are surfacing and concerned with providing qualitative information to reveal much about funds’ returns.
But while some paid their dues, with, by the way, still much left in their pockets, others such as hedge funds’ managers kept riding the bulls ever since the downfall of Long Term Capital Management in 1998, by cumulating profits from tweaking and twisting by little notches the Net Asset Valuation or NAV and by generating significant fees. At large, pricing and positions of funds’ clients get even more interesting within prime brokerage firms conducting their own hedge funds’ operations without Chinese Walls between entities.
Brokerage capacity is 50% of the global hedge funds’ industry with USD 500 billion. Interestingly enough, hedge funds’ data constitutes a global challenge of choice and excellence as it was never managed appropriately and mostly left up to individual managers to manipulate NAVs accordingly for marketing purposes or jurisdictions’ interests. Prioritizing on data’s accuracy and substance to accomplish pure risk management turned out to be the last item on managers’ minds.
Data is an embedded implicit operational risk arising from insufficient communication and connections between people, processes, technologies, cultures, systems.
Hedge funds’ databases come and vanished with matured markets on a rolling basis: Trading Advisors Selection System (TASS), Centre for International Securities and Derivatives Markets (CISDM) (ex_MAR/Hedge), Hedge Fund Research (HFR), Altvest Lipper Reuters, Edhec-Risk, or EurekaHedge. Some overlap while others have geographical biases. Strategic Financial Solutions reports that the biggest and the five largest databases account for 44% and 84% of 8,800 funds composing as of 2006 less than 10 databases. Poor returns goes hand and hand with industry data biases defined as:
- Survivorship bias : a statistical bias in performance aggregates due to inclusion of only live funds and exclusion of liquidated or non-reporting funds.
- (Self) selection bias : database represents a universe sample. Funds not reporting due to superior returns offset those reporting due to poor performance. Joining a database is for marketing.
- Instant history or backfill bias : this occurs when a fund enjoys positive returns to report.
- Liquidation bias : this arises from disappearing funds no longer reporting final periods near liquidation times.
- Static versus Dynamic Data Inaccuracy arises from on going data changes not dynamically updated.
To remedy the main operational risks affecting hedge funds, the author has produced a tool kit in attempt to solve macro and micro risks challenges :
The macro risk book "The Hedge Fund Compliance and Risk Management Guide" exposes an overview of "all risks": market, credit, legal, technologies, data, people, indices, volatilities, compliance. The micro tools includes the following books’ series. « Hedge Funds and Operational Risk » describes entreprise risks arising from miscommunications, systemic gaps between operations, processes, people, reporting disconnections and manual transactions. It defines new era risks’ types such as misappropriations, misrepresentations, mispricing, misvaluations, and provides a qualitative rating methodology to rate and grade infrastructures.
A capital adequacy methodology to link heavy trading speculations to markets’ illiquidity to operational erosion rates enabling managers enables to allocate minimum reserves in function of capital at risk. Global Unhedged Hedge funds is a microscopic review by markets at risk. Operational risks’ weaknesses in individual countries are defined due to national interest, laws, and cultural barriers. Operational erosion ratios by strategies and countries evaluate national capital at risk exposures.
About 45 nations accepting hedge funds’ trading observe markets’ illiquidity charts by strategies. It is a double sided approach : top down approach and bottom up detailing all aspects of operational risks in each country. Hedge Funds and Technology provides a library of tech products to improve operational infrastructures and funds’ corporate governance.
The book informs about products’ qualitative aspects and verifies that infrastructures are connected. Private market makers add liquidity solutions while direct exchanges’ connectivities are suggested to get rid of corrupted intermediaries’ entities generating fees from clearing and settling voluminous transactions without netting collateral assets’ valuations at hedge funds’ levels
The qualitative operational risk rating is applied to technologies and systems by countries of infrastructural distribution and measure their contribution to improve global financial corporate governance, to upscale transparencies and to crystalize global corporate capital at risk.
In conclusion, data alone is not likely to make a difference.
