In addition to a Pitch Book, every Private Fund should have these documents always updated and ready to go out in a Fedex and/or zip file:
Part I – Hedge Fund ETFs
Enclosed a list of Hedge Fund ETFs that replicate specific strategies and/or indices:
|QAI||IQ Hedge Multi-Strategy Tracker|
|WDTI||WisdomTree Managed Futures Strategy Fund|
|RLY||SPDR Multi-Asset Real Return ETF|
|CSMA||Credit Suisse Merger Arbitrage Liquid Index|
|MCRO||IQ Hedge Macro Tracker ETF|
|CPI||IQ CPI Inflation Hedged ETF|
|HDG||ProShares Hedge Replication ETF|
|ALT||iShares Diversified Alternatives Trust|
|CSMB||Credit Suisse Merger Arbitrage Index Leveraged Exchange Traded Notes|
|CSLS||Credit Suisse Long/Short Liquid Index|
|ALFA||AlphaClone Hedge Fund Long/Short Index|
|MNA||IQ ARB Merger Arbitrage ETF|
|MRGR||ProShares Merger ETF|
|QMN||IQ Hedge Market Neutral Tracker ETF|
|RRF||WisdomTree Global Real Return Fund|
|QEH||AdvisorShares QAM Equity Hedge ETF|
|HHF.TO||Horizons Morningstar HF Index ETF Comm|
This list is from the ETF Dabase and can be found here: http://etfdb.com/etfdb-category/hedge-fund/ along with details on performance, returns, expenses, and holdings
Part II – ’40Act Funds and Replication Funds (GARTX, Natixis Funds) coming shortly
Based on the below headline from today’s (5/8/13) WSJ below, I decided to run a comparison on SP500 vs Hedge Fund Indices for 2 time periods. Period1 – Jan 2003 to Sep 2007. Period 2 – March 2009 to end of April 2013. As you can see from the image below, these were periods of outsized returns.
I used the S&P500 index rather than the DJIA as it gives a broader market picture. I also compared the S&P500 to two hedge fund indices – the HFRX Global and Dow Jones Credit Suisse Broad Index.
First, let’s look at period1 by viewing Growth of a $1:
Now, Period 2
Now, let’s combine periods on the same graph:
So, what does this tell me:
1) S&P500 looking a bit overheated in comparison to the run-up of 2003 – 2007 (updated 5/14/2013). I’ve seen the “this time is different” chatter, but even “different” markets follow “laws of gravity”.
2) In relation to Hedge Funds, 2003 to 2007 looks like a long leverage bet on equities. Data confirms this. A Period1 times series analysis of S&P500 vs HRFX Global Hedge Fund Index transformed for stationarity shows a correlation of .74. The same time series analysis transformed for stationarity on Period2 index data has only a .01 correlation.
3) In terms of an uncorrelated return to equities, at a macro-level, hedge funds have delivered.
This is just a quick “back of the spreadsheet” analysis. It would be interesting to include bond and commodity indices in the mix here.
NEXT POST – A COMPARISON TO 1995, WHY TODAY’S RALLY HAS LEGS.
Risk-Parity investment strategies focus on greater allocation to less risky investment to achieve a given return goal. Leverage is used to “supercharge” the investment in the least risky asset class. Conceptually, a great plan.
In practice, most risk-parity strategies use a long bond strategy. Levering up long bonds via the use of futures and derivatives. During the 10-year bond bull market and over the past 5-years with easy money, this has been a great tactic and led to excess returns.
The question for risk-parity investors is does this strategy hold going forward? One school of thought says that coordinated Central Bank cheap money has inflated prices to artificial levels for both bonds and stocks. Others say that while the bond market has peaked, the equity and commodity markets still have legs to climb higher. No one really knows. However, there are some factors that should lead risk parity investors to delve further into exposure analysis. The main factor is that risk parity strategies have never been tested in a bear market for bonds.
To this end, I would recommend a r-Dex analysis of any risk-parity strategy. Never heard of r-Dex? r-Dex or Risk Downside Exposure is similar to Value@Risk in that it measures downside risk to the expected value of a portfolio. r-Dex measures this risk by stress-testing the “left tail” of portfolio returns. The value of r-Dex is that it allows you to test for highly-correlated negative movements across all asset classes (dependent testing) or simulate a “break” in a single asset class (independent testing).
From a risk-parity standpoint, r-Dex would allow you to simulate risk of a simultaneous break in both equity, bond, and commodity markets. Given that almost everyone agrees that the bond market has minimal upside potential, at a minimum, a r-Dex Independent test could be run only on fixed income assets.
Either way, using r-Dex would allow you to evaluate the Risk in Risk Parity.
The news that Reinhart and Rogoff’s study of Austerity Economics has flawed conclusions due to basic Excel errors has perked the interest of mainstream media. One article states that up to 80% of spreadsheets have some form of error.
My answer is…..So What? Excel is a tool and it can be used well or poorly. I can use my hammer correctly or hit my thumb instead of the nail. As a financial analyst, I also make mistakes in Excel as I parse data, change columns, add data, etc… However, I have learned through the years to cross-calculate my work as well as stress and back-test important findings. I have also run simulations to look at best case, worst case, likely case scenarios. Any “out-of-balance” findings are reviewed for accuracy. Furthermore, when presenting findings, I always prefer to have a “peer review” of my calculations and methodologies.
The real issue of excel errors is not fat-fingering errors but what I will term “fat-heading” or the failure to test one’s conclusions. Reinhart and Rogoff had a pre-disposed vision. When the data validated their initial hypothesis, their rush to judgment (and to publish) became a higher priority than validation. Back to the tool example here, measure twice cut once, should have been the go-forward metaphor. However, I am sure I have made my share of fat-headed mistakes as I rushed to deliver findings as well.
So, here is a list of ways to minimize fat-headed mistakes and keep excel humming along as a calculation engine (Listed in increasingly complexity and time commitment):
1. Use Ctrl + ‘ keys to show formulas in all cells
2. If you are creating a shared workbook, use the Track Changes functionality in Excel (http://office.microsoft.com/en-us/excel-help/track-changes-in-a-shared-workbook-HP010342961.aspx)
3. Cross-calculate summary cells
4. Create a validation worksheet that re-calculates and compares key formulas
5. Use a validation tool such as XLTest to validate calculations
6. Use a Monte Carlo simulation tool to test worst case, best case scenarios
7. For time-series investment and trading related calculations, back-test results with real-world data
8. Peer review of spreadsheets.
Enclosed is a link to a comprehensive summary on OTC Swap Trading and new Dodd-Frank requirements:
Approximately $626 trillion in notional credit and currency derivatives are outstanding, a majority likely traded OTC. Last week (March 11) all OTC IRS and CDS swaps had to be centrally cleared via approved centralized clearing mechanisms (ICElink, CMEclear, etc..). Centrally clearing these products now involves posting margin (initial average calculations at 150 bps) of notional value in addition to collateral posted to CCPs.
This money on the sidelines will deleverage a number of non-equity players in the Private Fund industry as well as hit performance. It will be interesting to see the “true” incremental margin rates as OTC clearing it still in a flux as the June 10 deadline for Private Fund central clearing of OTC IRS and CDS swaps approaches.