The article was written by David Shabotinsky, a Financial Analyst at I Know First, and enrolled at an undergraduate Finance program at the Interdisciplinary Center, Herzliya.

Quant Hedge Fund

“The punches you miss are the ones that wear you out”. —Boxing trainer Angelo Dundee

-The Intelligent Investor, by Benjamin Graham.

Active Investors have over the past years been experiencing large losses, as a result of volatile markets and recent market bubbles. The losses result in human error, that greatly hurts returns. This article will explain how hedge funds and investors alike, should begin to utilize machine-learning AI algorithms in their trading strategies.

Summary:

Background of Hedge Funds and their role in the market

Hedge fund and investor issues and how it affects the financial world today

Development of AI based algorithms and their advantages

I Know First Algorithm’s competitive advantages and usage in the market

Background

The first modern hedge fund was formed by Alfred Winslow Jones, in 1949. He initially had used two main strategies with his fund, short-selling and leveraging. Whereas in short selling he would profit from a seemingly riskless “spread” of the profits between his long position and short position. During his first year he had earned a sizeable return of 17.3% on investment. This had become the base for modern long/short equities model. Using a combination of these strategies, Jones secured sizeable returns in the first few years, and soon began taking 20% of profits (earned for investors) as compensation. Hedge funds today, have used the same basis of what Jones had used, however; have adapted to new innovating financial tools in the market.

Most hedge funds can be divided into two areas of investing: fundamental and quantitative/technical analysis. Whereas fundamental analysis refers to picking financial instruments based off of different metrics that refer to investigating financial statements of a company and other business metrics, to find instruments undervalued (in price). Quantitative, refers to complex mathematical formulas and computer models to create time-series models of the market in order to identify ideal short and long-term positions on different assets. This can as well involve technical analysis, whereas analysts use chart analytics to determine buying and selling points. Other strategies include various arbitrage metrics, such as convertible bond arbitrage or merger arbitrage.

Although Hedge funds may seem like an ordinary money management institution, they require large amounts of capital, to invest in. They as well are under-regulated by the SEC, and as they are not a public corporation, requiring even less regulation. As a result, they today control over $3.1 trillion dollars’ worth of assets worldwide, with upward of ten thousand different funds actively operate today. Many, investors, especially high-net-worth ones, trust these managers to actively invest at returns that offer high premiums on the market, usually benchmarked off the S&P500 Index. Fund managers have for years boasted of their high compounding returns that have constantly been shown to beat the market, and as a result charge high fees.

Problems Arising Today for Hedge Funds & Investors

The classic fee model that hedge funds have charged is called the ‘Two and Twenty’ Model, whereas, investors have a yearly 2% fee charged, in addition to 20% fee charged after reaching a certain profit threshold.

Most people have for years believed that on average the fund managers beat the market by a high premium, enough to charge the high fees. However, empirical evidence found recently, shows that on average these active hedge funds, perform below the market (after fees are included). A large explanation to this is as a result of the Efficient Market Hypothesis (EMH), that explains how the price of a security accurately reflects the current market price. For example, if you take a low p/e stock, it is not undervalued rather correctly valued by the market. Thus, actively investing will not give an investor any abnormal returns, as opposed to passively investing in the market. Although there is disputing evidence as to whether or not this theory is completely true, most believe it to be somewhat applicable in the market. In addition, new inorganic market forces, such as quantitative easing by the central bank, have caused the market to become harder to predict. Therefore, many people are not finding it more profitable to invest passively in newly found ETFs or passively managed funds, such as Vanguard’s passive fund.

Hedge Funds, today, are in a decline, as more and more people have lost faith in fund manager’s ability to outperform the market consistently. This had led to many cut fees, and institute a modern fee model of a flat 1.5% a year; however, as any intelligent investor understands, as revenues decline in a firm, one can only cut expenses so far before there is a realization that the business is deteriorating.

AI Based Algorithms

Therefore, Hedge Funds have realized they need to implement modern innovations to maintain their high premiums above the market. Many are beginning to add AI-based genetic algorithms to their quantitative departments.

In past articles by I Know First, there are many at length articles that explain in depth what the new advanced AI algorithms are.

In this article, we have a history of algorithm trading and how it has advanced today. Artificial Intelligence (AI), was first created in 1956 by Arthur Samuel, who wanted his computer to be able to beat him at checkers. Samuel then programmed his computer to play against itself thousands of times to the extent that the program accumulated sufficient knowledge of the game. Today, of course AI has advanced to much higher degree, and is used in most business, as well as financial institutions.

In general, there are two different types of algorithms and algorithmic trading. There is a basic formatting type algorithm that is programed to do specific functions. Then there is a self-learning algorithm, that takes historical data (empirical) and is able to adjust accordingly for a trade.

The new type of self-learning AI based algorithms are able to build trends that are often unknown even to the most intelligent analyst. The reason is because they are able to eliminate “noise” in the market. This refers to short-term (daily or intra-day) fears, worries, and negative fueled perception regarding the price of a security or general market atmosphere. By ignoring it once is able to identify trends in the market. The AI based algorithms, are able to adapt as a result of neural networks built to allow for deep learning, which then allows the algorithm to adapt accordingly. More on how specifically it goes about adjusting can be found here, in a past I Know First article.

Today, some hedge funds have secretly begun incorporating these types of algorithms to maintain an edge over other market players. According to Preqin, a well-known provider of financial industry data, there are about 1,360 hedge funds that are currently making a majority of their trades with help from computer models. These funds are managing about $197 billion in total. Additionally, approximately 40% of funds that opened last year use “systematic” trading. As a result, these hedge funds have had an upper hand in this past year’s high volatile trading days. For example, in January, most hedge funds and other money management firms had seen losses across the spectrum, as there were huge sell-offs and further declining oil prices. However, funds that did manage to maintain positive returns, were those that on average had implemented machine-learning systems in addition to their own personal strategies. Even more recent, during Brexit, when most financial institutions had lost significant sums of money, many hedge funds that have begun implementing AI-based algorithms such as Winton Capital Management and Aspect, which run computer-driven funds, actually saw positive returns after Brexit. The algorithms had recognized the trend that was emerging with regards to volatility in currencies such as the pound and other commodities such as the gold. Although most investors and some have noted the market as well, ultimately ‘believed’ that the UK would remain the EU, they let their emotions get the better.

I Know First Algorithm’s competitive advantages

Investors, should adapt to technological innovations, and begin using AI based algorithms as a tool to for their investment strategies. I Know First has a premium algorithm that incorporates why investors need to be able to achieve high returns with cost efficiency as well. The algorithm, was developed by Dr. Roitman, who has a vast 35+ years pf experience in the field of AI and machine learning. Every day, the self-learning and self-adjusting algorithm produces market forecasts with trends of stocks, commodities, and indices over 6 different time horizons ranging between a few days and a year. The kind of analysis and pattern recognition that the system does each day could never be accomplished by a human in any amount of time. On August 25, 2016, an article was published detailing the competitive advantages that directly apply to investors. Through a mixture of chaotic system and efficient patterns found in the market, the algorithm is able to successfully detect trends to enable investors to understand entry and exit points in investing.

For the past ten years, I Know First has built a track record of providing investors with large premiums above the benchmark of the S&P500 Index, for time ranges as short as 3 days, up to 12 months. Over the past year, I Know First had begun implementing a new short-term trading strategy, that has resulted in superb results of an alpha up to 35.75% above the market. The main strategy can be broken down into five different sub-types. Whereas, initially a predictability filter is applied to focus on the more predictable stocks within the S&P 500 universe. The daily threshold changes daily and depends on the predictability level of all the stocks in the system on a given day. Usually approximately half of the S&P 500 stocks pass the filter, based on the highest predictability. The algorithm as well utilizes signals, that shows the strength of the movement of a security, forecasted by the algorithm.

Hedge funds and other active investors need to adapt to changes in technology in order to sustain and maintain their status quo in the market today. They are already under intense scrutiny for underperforming other cheaper and more passive alternatives today. Quantitative-based hedge funds, such as Bridgewater Associates and Renaissance Technologies are implementing these types of advanced AI algorithms. However, they still charge high fees, and there is still little data to show that they outperform the market after fees.

I Know First’s algorithm is being used to help hedge funds and active investors beat the market at a substantially lower fee model.

Conclusion

Even after, an investor does his/her due diligence, by performing extensive research regarding a firm, they are still susceptible to errors (i.e. fear/greed). Machine-learning algorithms make far fewer mistakes than humans would. Although many say that a big con to using algorithms is they find it difficult to react to systematic events, due to new financial uncertainties being seen on the macro level, especially from Central Banks, individual investors lack this quality as well. Additionally, unlike most AI-based algorithms, I Know First has much more capabilities that have allowed it to react better than other algorithms to systematic changes. As well, the algorithm is able to trade forex, which is a valuable instrument used strongly by hedge funds across the globe. The cost the reduction and the higher premiums offered by these new type of algorithms should convince investors to utilize this new tools today.