- CFA Level 2: Alternative Assets – Introduction
- Real Estate: Types of Properties
- Real Estate Valuation: NPV & IRR Analysis
- Income Property Valuation Using Capitalization Rate
- Gross Income Multiplier (GIM) for Real Estate Comps
- Private Equity: Venture Capital, Leveraged Buyouts and Exit Strategies
- Private Equity Fund Structures
- Venture Capital and Leveraged Buyout Valuation
- About Hedge Funds: What You Need to Know

# About Hedge Funds: What You Need to Know

Hedge funds are privately organized investment entities that commonly take both long and short positions in securities and derivatives contracts and employ the use of financial leverage.

Hedge funds are usually organized as limited partnerships or limited liability companies and have strict minimum investment and net worth requirements.

**Equity Hedge Funds:** Common strategies are market neutral (zero beta portfolios), net short, and long/short.

**Fixed Income Hedge Funds:** May use leverage to generate returns on yield spread relationships.

## Performance Measurement Challenges

Historically, hedge funds are thought of as investments designed to generate positive returns in both bull and bear markets.

Because of their unique characteristics, it is difficult to measure hedge fund performance with traditional benchmarks and risk-adjusted return measures. Some of these unique characteristics include:

- In the large universe of hedge funds, there are many styles and sub-styles.
- Limited public disclosure requirements for funds.
- High portfolio turnover.
- Large and/or changing degrees of leverage.
- Style drift.

## Hedge Fund Benchmarks

**Hedge Fund Indexes:**CSFB/Tremont offers several style based hedge fund benchmark indexes.- Hedge Fund Index pitfalls: Voluntary fund listing, potential exclusion of large funds, no way to validate data.
- Pitfall sources: Turnover of index funds, short histories for some funds, survivorship bias, autocorrelation causing misestimates of volatility, and closure of some funds to new investors.

**Market Indexes:**some market indexes can be useful in assessing hedge fund returns, while others not so much. The Merrill Lynch High Yield Index may work well for assessing returns on some fixed income hedge funds and the Russell 3000 Index may work well for assessing returns on some equity hedge funds. With this in mind, market indexes generally cannot be used to explain hedge fund returns.**Positive Risk Free Rate:**Given the expectations of hedge funds to generate positive returns in any market, the risk free rate plus some additional margin is sometimes used as a benchmark. Funds may be benchmarked against an absolute return or an index return plus a margin. A positive risk free rate of return may be technically appropriate for only market neutral funds.

## Hedge Fund Risks

Hedge fund risks include:

- Fraud (remember Bernie Madoff?)
- Operational risks: breakdown in operations related to insufficient employee supervision, infrastructure weakness, technology shortfalls, or counterparty risk have lead to the demise of some hedge funds over the years.
- Investment strategy risk: different hedge funds expose their investors to the risks of the strategies pursued (imagine being invested in a short bias equity fund when a bull market suddenly takes off).

**Reviewing Operational Risk**

Investors should take care to monitor hedge funds for the following red flags: high personnel turnover, changes in lifestyle or behavior of key fund managers, controlled asset pricing, and absence of third party audits on the fund’s financial statements.

**Reviewing Investment Risk**

In order to monitor investment risk associated with hedge funds, investors should be alert for a high degree of investment concentration (either in a sector or single investment) and increases in the amount of leverage.

## Measuring Downside Risk

- Hedge fund investors are commonly focused on loss avoidance (this is “left tail” risk, on a distribution of returns), so downside measures of risk are appropriate when evaluating funds.
**Maximum Drawdown:**This is a fund’s largest percentage loss over a specified period of returns. While this provides some insight about risk in a historical context to investors, maximum drawdown does not give any indication of probability for a comparable loss in the future.**Value at Risk (VaR):**This measure attempts to incorporate probability into the left tail risk assessment by informing investors how much the fund could lose if a significant event transpired.**Example:**A hedge fund has assets of $100 million. The probability of losing $20 million or more over the next month is 5%. VaR in minimum loss terms: the fund has a 5% probability of losing at least $20 million. VaR in maximum loss terms: there is a 95% probability that the fund will not lose more than $20 million.- VaR can be flawed in that: past data may not be indicative of future return patterns; changes to a fund’s strategy reduces the insightfulness of VaR; it can be easy to inaccurately account for overlapping risks when performing a VaR calculation; and VaR assumes a normal distribution of returns but many hedge funds employ derivatives strategies that do not have symmetrical returns.

**Loss Standard Deviation:**This is a modification of a fund’s standard deviation of returns, calculated from only loss making periods.**Downside Deviation:**Another variation on standard deviation, downside deviation begins with an investor’s minimum acceptable return and then calculates a standard deviation based on the periods where a fund’s return was below the investor’s minimum acceptable return.**Sortino Ratio:**This is a modified version of the Sharpe Ratio, where fund excess returns above the minimum acceptable return are divided by the downside deviation.

## Data Science in Finance: 9-Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

### What's Included:

- Getting Started with R
- R Programming for Data Science
- Data Visualization with R
- Financial Time Series Analysis with R
- Quantitative Trading Strategies with R
- Derivatives with R
- Credit Risk Modelling With R
- Python for Data Science
- Machine Learning in Finance using Python

Each book includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $39 (Regular $57)## Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.