Financial insights: hedge funds and strategies

What are Hedge Funds?

A hedge fund is an alternative investment based on the so-called “Pooled fund” which aggregate many investors’ funds into a single portfolio in order to benefit from economies of scale, related to diversification (low beta) and lower trading costs. Various are the characteristics of a hedge fund, which can be described as follow:

 

  • Investment objectives
    Focus on capital protection (low risk) and absolute return, thus no benchmarks (like S&P500) are taken into consideration as comparison.
  • Flexible investment policy
    All asset classes and markets are used as investments in the fund and short selling/leverage are also allowed as integrated part of the fund’s policy.
  • Unregistered / unregulated
    Only qualified or institutional investors can be part of funds and in general less SEC (Security and exchange commission) regulations are applied to them compared to mutual or pension funds.

 

Many investment strategies are involved during a fund trading day, ranging from pure equity to systematic trading, trying to maximize active returns (alpha) for their investors. Examples of these investment strategies are:

Systematic trading: investment decisions are based on some sort of system, often automatically generated by a computer, which uses technical patterns, fundamental data, market anomalies while back-testing using historical data. Systematic traders are, essentially, hedge funds that trade ant macroeconomic market through algorithmic trading programs, which typically use technical signals (price or volume) in attempt to detect market trends while entering in position in order to gain profit. A wide range of instructions, which come from timing, price, quantity, or other mathematical model, are incorporated to place orders onto the market. Moreover, algo-trading allows the market to ensure more liquidity and more systematic trades without the impact of human activities. Nowadays, machine learning is considered a key aspect of several financial services including trading, which will drive the investment experience towards a new level for the next decades. As an application of Artificial Intelligence, machine learning focuses on developing system that can access pools of data and then automatically adjust its parameters to improve the performance. These machines are trained to draw insights and make predictions of the market when the volume of data is massive, enabling to make thousands of trades every day.

Equity long – short: long undervalued stocks and short overvalued ones, or in alternative trading index futures which are extremely more liquid. This strategy allows to gain from stocks mispricing, in contrast with the EMH (Efficient Market Hypothesis) which states that all information is already incorporated in the stock prices, thus believing that the latter ones are priced in a fair way for the market.

Global Macro: macro trades are based on managers’ perceptions of the prevailing macroeconomic conditions all over the world and their impact on financial markets. Directional positions (long or short) are established in different asset classes and regions. Major focus is frequently on interest rate positions, such as short on 10Y government bond and long on the 2Y ones which allows to gain from a possible steepening of the yield curve, since the 10Y interest rates will rise while bond prices falling and vice versa for the 2Y.

 

Graph on risk return by trading strategy compared to S&P500 and Treasury Bonds

 

A cura di Gianmarco Zuffranieri del VGen Finance Hub