Not All Hedge Funds Are Created Equal

Exploring The Past, Present and Possible Future of Hedge Funds

Abhishek Kothari
9 min readJun 26, 2017

Markets are constantly in a state of uncertainty and flux and money is made by discounting the obvious and betting on the unexpected — George Soros

Hedge funds have always elicited two primary emotions in society — awe and contempt. They were born out of a necessity to hedge risks by taking an equal and opposite bet. The word hedge means to:

“protect oneself against loss on (a bet or investment) by making balancing or compensating transactions”

This article introduces readers to the history and fundamentals of hedge funds, the state of affairs today and a discussion around the future of hedge funds. Today, it is almost impossible to talk about finance without talking about the Blockchain in the same breath. A new breed of hedge funds seeks to harness the power of Blockchain to evolve beyond current models. This is what I call IPaaS(Intellectual Property as a Service).

Building Blocks

Simply put, hedge funds are alternative investments that pool together funds from various kinds of accredited investors such as pension funds, high net worth individuals, university endowments, charitable institutions. As you may have realized, hedge funds are not for the common investors. They are open only to accredited investors. The typical minimum investment is between $500,000 and $ 1million.

Hedge funds are open to “qualified investors” only i.e. Investors with income greater than $200,000 in the past 2 years or assets of $1 million and above excluding primary residence.

The first hedge fund was believed to have started by writer and sociologist Alfred Winslow Jones in 1949 through his company — A.W. Jones & Co. Jones gathered about $100,000 (including $40,000) of his own. He intended to hedge the long positions he held in stocks by short selling other stocks. Today, this is a classic strategy called the long/short strategy. For example, you could be long (bullish) on clean tech stocks and be short (bearish) on coal stocks.

A long (or long position) is the buying of a security such as a stock, commodity or currency with the expectation the asset will rise in value (investopedia)

A short position is exactly the opposite. Short selling means selling a stock you don’t possess. An investor typically borrows the stock and sells it believing that it will go down in value.


Hedge funds are typically set up as a limited liability company. There is lesser transparency into hedge fund operations compared to other SEC monitored securities.

A manager manages the investments and typically makes 2% of the assets under management and a 20% cut of the profits.Therefore, this structure is known as 2/20.


Hedge funds can employ a plethora of strategies on a wide variety of assets such as stocks, bonds, currencies, sovereigns, commodities and for that matter anything that has a market is fair game. However, some of the most common strategies that hedge funds employ are:

1. Long/Short: simultaneously maintaining long and short positions in equity and /or derivative securities.

2. Event Driven: Maintain positions in companies that are currently or will in the future be involved in mergers, financial distress, takeovers, buybacks etc.

3. Global Macro: analyze the impact of macro economic variables on various markets or asset classes and then take positions accordingly.

4. Credit funds: make huge investments in fixed income securities and then use the ownership stake to participate in management of the company.

5. Quant Funds: take positions based on computer/mathematical models

6. Relative Value: maintain positions based on differences in relative value of securities.

7. Multi strategy funds: funds that use a combination of quant or fundamental strategies.

Understanding Greek

1. Alpha: This is the most important measure of a hedge funds performance. It measures the extra return on investment that the fund generated over the broader market.

2. Beta: Measures the hedge funds sensitivity to the broader market

3. Sigma: this is the variance of a funds performance from a defined norm

4. Omega: this measures how the funds bad returns stack against its good returns with am idea to know how likely a fund will provide a certain % return on investment.


Below are the top 10 hedge funds by AUM

A Brief History

In the 1920's , one of the earliest known hedge funds was formed by Benjamin Graham and Jerry Newman. Warren Buffet cited the hedge fund in a 2006 letter to the Museum of American Finance.

During the 1990's, the hedge fund industry saw a significant rise in AUM. Hedge fund strategies expanded into quant funds and multi strategy funds. By 2008, the AUM touched almost $2 trillion.

Post the financial crisis, hedge fund AUM’s rebounded and reached $2.13 trillion in April 2012.

The Man Who Broke the Bank of England

George Soros is almost synonymous with hedge funds. Any article on hedge funds is incomplete without mentioning him. In the 1992 Black Wednesday UK Currency crisis, he is known to have shorted $10 billion in pound sterling which yielded him a $1bn profit. Because of a massive position held against the Bank of England, it was not possible for the Bank of England to buy pound Sterlings that could boost up the Sterling’s value which resulted in Soros being known as ‘the man who broke the Bank of England

Activist Investors

Hedge funds have gained notoriety recently because of a spate of investor activism in various companies. Activist investors utilize many strategies including raising issues with the corporate governance of the company.

As per an article published in the Harvard Law School forum in January 2017:

There are more than 100 hedge funds currently engaged in frequent activism and over 300 others that have launched activism campaigns in recent years.

The Next Wave: Meta Models

Established Wall Street players such as Bridgewater Associates (the biggest hedge fund of all) which is managed by Ray Dalio were already looking at using Machine Learning Algorithms to create mathematical models for their hedge funds. However, a new breed of pioneers is crowdsourcing hedge fund models from data scientists all across the world. The data scientists get paid in tokens of the hedge fund. The idea is similar to stocks. Just as a portfolio minimizes variance and can sometimes provide better risk adjusted returns than a stock, the idea behind crowd sourcing models is to improve (reduce risk of) the traditional hedge fund model by creating a portfolio of uncorrelated models. Whether these models will transform the hedge fund industry, only time will tell.

Any discussion of finance, including hedge funds, today is meaningless without discussing the Blockchain. Consider this:

  1. A majority of countries recognize Bitcoin as legal. However, it is not yet an official currency.
  2. Investors in private company securities on Nasdaq can use a bank’s cross-border payments facility and blockchain to buy, sell and settle transactions
  3. The Ethereum Enterprise Alliance has a high representation of financial services firm. The alliance continues to grow.
  4. Hyperledger, run by the Linux foundation, has more than 140 members. It began with 30 members in February 2016.

Blockchain may result in the disintermediation and transformation of the whole financial services industry from consumer to investment banking. It has graduated from boardroom discussions to mainstream applications.

One such application is the hedge fund — a playground for sophisticated trading methods and now sophisticated technology.


Numerai utilizes machine intelligence to command the funds in a hedge fund. What is even more unique is that it pools together models crowdsourced from roughly 7500+ data scientists all across the world to create a ‘meta model’ i.e. A portfolio of model that reduces risk by combining crowdsourced uncorrelated models.

Thirdly, Numerai went live on the Ethereum platform and raised $15 million in its Initial Coin offering.

To understand how Numerai is building its Meta Model, watch the video below:

It’s token is known as numeraire. Numerai’s South African founder Richard Craib believes that its token the Numeraire will foster collaboration amongst faceless data scientists that contribute their intellectual property to the fund.

Richard in a way aims to turn Wall Street into an open source software platform. But, many have doubts that hedge funds don’t need something as complicated as Numerai.

Whatever be the case, innovation just got a shot in the arm.


While Quantopian doesn’t use Ethereum as a platform, it crowd sources algorithms from data scientists. It hosts an iPython environment where data scientists can perform research.

In addition, Quantopian provides data for back testing, capital if a particular algorithm is selected as well as a $5000 prize to the winning algorithm. All while ensuring that the data scientists retain rights to their models.

Two Sigma

As per its website:

Two Sigma Investments was founded in 2001 by David Siegel, John Overdeck and Mark Pickard. They shared the belief that innovative technology and data science could help discover value in the world’s data to consistently deliver value for clients.

Two Sigma is yet another investment management firm that utilizes machine learning and distributed computing to create a unique kind of investment manager.

It allows data scientists to use a notebook style the Beaker Notebook development environment to play with large data sets.

Winton, UK

Founded in 1997 by David Harding, Winton utilizes data and machine learning to manage $30 bn in assets from various investors (pension funds, endowments etc.)

Winton is a quant hedge fund that utilizes models derived from scientific and mathematical research to take decisions. Unsurprisingly, David is a physicist.

Such is the proliferation of quants that even Citadel has launched campus recruiting efforts to match modern competitors.


Founded by Igor Tulchinsky, WorldQuant is a quantitative investment management firm that manages investments using algorithms.

It has virtual summer training programs operating in India and China where participants are encouraged to create their own alpha generation algorithms while worldquant provides the necessary training.

An alpha generation platform is a technology solution used in algorithmic trading to develop quantitative financial models, or trading strategies, that generate consistent alpha, or absolute returns (source: wiki)

Artificial Neural Networks

Hedge funds are mimicking the human brain by creating artificial neural networks to promote deep learning and to come up with investment decisions:

Talent Wars

Data Scientists and machine learning engineers have never been in great demand. It is not just hedge funds but every single industry today is mining data and is either using or intends to use machine learning to create insights.

Hedge funds have resorted to different competitions and compensation structures to attract the best data scientists across the world.

Final Thoughts

Even if the hedge fund industry doesn’t evolve the way Numerai thinks it will, one thing is certain : Machine Learning, Blockchain, Crowdsourcing Wisdom and Big Data will be critical elements of next generation hedge funds.

If you conduct a thought experiment, it is not a fantasy to imagine the financial services business running with the Blockchain under its hood. The changes could be rapid and far reaching. Perhaps, the hedge funds of today will look very different tomorrow.

Alternative Investments, on the other hand, will always carry risks just like any other asset class.

Risk is not having the money when you need it.

If you owned a property in 2005 which was worth $1 Million then and is now worth $2 million, you would say that was a great investment. Well, it could be only if you have a seller. In other words-liquidity is essential to the market. Without buyers, there can be no sellers.

Similarly, without a large scale adoption of the Blockchain, all the innovation could end up being a great experiment. But then, as Steve Jobs once said:

The over-all point is that new technology will not necessarily replace old technology, but it will date it. By definition. Eventually, it will replace it. But it’s like people who had black-and-white TVs when color came out. They eventually decided whether or not the new technology was worth the investment.

One thing is certain: We do live in Interesting times and if you are not interested in technology, you will be left behind. In that sense, not all education can be treated equal. Data Science may very well be the profession of the future.

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Abhishek Kothari

Futurist@The Intersection of Finance, Tech & Humanity. Stories of a Global Language: “Money”. Contributor @ Startup Grind, HackerNoon, HBR