11 Proven Reasons Why Ai is the Future of Cryptocurrency Trading
These 11 reasons are all based on scientific research and studies, condensed and interpreted here. The possible applications of Ai and machine learning are still being explored across the world, but their ability to shape the way we trade cryptocurrencies is already being proven.
Here are 11 reasons why Ai is the future of cryptocurrency trading.
- Computer algorithms are generating the majority of trading
- Ai is a necessary tool to parse through vast streams of digital data
- Ai hedge funds outperform other hedge funds
- Neural trading systems produce superior information for the next trading day
- Ai is great at spotting market manipulations
- Artificial neural networks outperform buy-and-hold approach
- Ai performs consistently at times of financial turmoil
- Ai maximizes percentage of winning trades
- Ai is a reliable way to price financial instruments
- Ai predicts prices for various traditional and emerging asset classes
- Ai-powered crypto portfolios consistently beat the market
Table of Contents
1. Computer algorithms are generating the majority of trading
According to the Financial Technology Conference at Michigan Law School, “regulators and academics estimated that computers are now generating around 50-70 percent of trading in equity markets, 60 percent of future and more than 50 percent of treasuries.”
Competing with traders using discretionary human trading skills becomes more difficult as computers are being utilized on a more consistent basis. In order to stay competitive, fast decision making is an absolute must.
“Machine learning and artificial intelligence are increasingly being used to analyse data, trade securities and provide investment advice.”
2. Ai is a necessary tool to parse through vast streams of digital data
IDC estimates that digital data will surpass 44 zettabytes by 2020, that is over 44 trillion gigabytes. To visualize, this is “an amount so big that if it was all put in iPad Air tablets, the stack would reach from earth to the moon more than six times over.”
Without artificial intelligence to parse and make sense of this data, we would be left in a digital world too complex to grasp, and would ultimately struggle to make informed decisions about their money.
3. Ai hedge funds outperform other hedge funds
Research conducted by Eurekahedge found that AI hedge funds outperform other quantitative and traditional hedge funds.
“Over both the five, three and two year annualized period, AI/machine learning hedge funds have outperformed both traditional quants and the average global hedge fund delivering annualized gains of 7.35%, 9.57% and 10.56% respectively over these periods.”
The application of AI in the hedge fund industry is still at an early stage – with some hedge fund managers utilizing AI as a partial input into their trading process, others, what are called ‘pure Ai hedge funds’, have outsourced both the trading and risk management aspect to Ai with minimal input from the fund manager.
4. Neural trading systems produce superior information for the next trading day
Kuang-Hsun Shih wrote a doctorate dissertation at Nova Southeastern University titled “Taiwanese High-Tech Stocks: Using Artificial Neural Networks to Test Weak-Form Market Efficiency in the Taiwan Electronic Index and to Develop an Evaluation Model of Investment Strategy in Taiwanese Stock Markets” in 2003.
The goal of this study was to test whether neural networks could predict the rate of future daily returns based on historical market data. With 5 years of data between 1995 and 2000 being fed to the Ai, it generated market predictions for the year of 2001.
The empirical results confirmed that neural trading systems fed with historical market data may produce superior information for the next trading day outperforming the buy-and-hold portfolio by more than 150% in just one year.
This is significant for a couple of reasons. Firstly, and most importantly, it shows that neural trading systems are effective and successful. Secondly, it shows that the current strategy of many of the more emotional and inexperienced traders struggle mightily when put up against the power and precision of a neural trading system.
5. Ai is great at spotting market manipulations
The Economist magazine published an article on machine-learning applications in finance in May 2017. Aside from mentioning that future CFAs (Chartered Financial Analysts) will need Ai expertise to pass exams starting as early as this year, the article goes through some interesting findings regarding Ai applications in trading.
One notable asset management company, Castle Ridge Asset Management, was able to gross an annual average return of 32% since 2013 by using sophisticated genetic machine-learning systems. Such high returns were partly attributed to the Ai’s ability to pick up 24 acquisitions before they were even announced. Ai algorithms spotted these acquisitions due to telltale signals pointing to small amount of insider trading.
Irregularities are a natural part of any market, but getting on top of these irregularities in a market with the potential upside of crypto is where the elite traders differentiate themselves from the average trader.
Earlier this year, RoninAi, a project focusing on AI algorithms for cryptocurrencies, spotted multiple market manipulations due to unusual behavior of social sentiment indicators.
You can read about them individually here:
- Unusual Cryptocurrency Market Behavior Detected Before Big Crypto Drop
- Breaking News: Bitcoin Market Manipulation Caught By AI
- Neural Network Detects Bitcoin Market Manipulation in Recent Dip
6. Artificial neural networks outperform buy-and-hold approach
Researchers wanted to test the effectiveness of the Backpropagation Neural Network (BPN) approach in forecasting stock prices. More importantly, they also wanted to see how Ai-powered trading strategies perform relative to buy-and-hold portfolios, very much considered the baseline strategy of inexperienced traders.
A study by Chiang, Lin, Chen, and Lin titled “Backpropagation Neural Network Model for Stock Trading Points Prediction” was published in the International Research Journal of Applied Finance in October 2016. 7 stocks trading on the Taiwan stock market were selected for the study. Market data coupled with technical indicators served as an input to enhance the Ai’s predictability results.
The results of this research showed that the combination of different indicators using the BPN approach is superior to the buy-and-hold strategy. While this may not come as a surprise to those already using Ai as a centerpiece of their trading strategy, this was an important finding for those who were not. For some, there tends to be a misconception that Ai only lessens the time needed to trade, but studies such as this one show definitively that they also should be used to identify and execute on more complex and lucrative strategies.
7. Ai performs consistently at times of financial turmoil
A study by Krauss, Do, and Huck titled “Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500” was published in the European Journal of Operational Research in October 2016.
The goal of the study was to implement and analyze the effectiveness of gradient-boosted-trees, random forests, and several ensembles of these methods in the context of statistical arbitrage.
In their study, researchers have shown that algorithms based on artificial intelligence are able to make profitable investment decisions. When applied to the S&P 500 constituents from 1992 to 2015, their stock selections generated annual returns in the double digits – whereas the highest profits were made at times of financial turmoil. Yes, the neural networks were able to turn their highest profits when the market as a whole was in trouble.
Here is an excerpt from this study
“In particular, AI-driven algorithms observed particularly strong annual returns of 334 percent in 1999, the year leading up to the dot-com bubble. These were outstripped in 2000 with annual returns of 545 percent, the time when the bubble finally burst and technology stocks lost billions in market capitalization.
The largest outlier was in the year 2008, when annual returns of 681 percent fall together with the outbreak of the global financial crisis.
In particular, returns in October 2008, the month following the bust of Lehman Brothers, exceed 100 percent – by far the strongest period from December 1992 until October 2015. Finally, October 2011 resulted in a positive return of 35 percent, coinciding with the peak of the European debt crisis. Thus it is fair to state that machine learning algorithms were effective in capturing relative mispricings between securities at times of high market turmoil.”
8. Ai maximizes percentage of winning trades
A study was conducted to see whether fuzzy neural networks could outperform conventional technical analysis trading systems. The study by Tan, Quek, Yow titled “Maximizing winning trades using a novel RSPOP fuzzy neural network intelligent stock trading system” was published in June 2007.
Here’s what they found:
Daily close prices of 5 stocks from the Stock Exchange of Singapore were used to generate empirical results. The price series from January 1991 to December 2000 were used for training, and the price series from January 2001 to December 2004 were used for testing.
“Empirical results demonstrated that neural networks are able to outperform the benchmark trading system dramatically due to its ability to ﬁlter out false or erroneous trading signals and capitalizing on swings in the stock counters.
The proposed trading system also increased the percentage of winning trades to over 90%; there were very little losing trades.
Moreover, losing trades are preventive in nature; they are exited due to an imminent loss, all losing trades are due to the transaction costs.”
A 90% success rate is extremely impressive, especially when you translate it to the potential profits that this success rate can yield in the cryptocurrency market. Even the most impressive of traders are, percentage-wise, only barely more successful than average. The presence of Ai in this equation completely blows the status quo out of the water, and allows any trader, with the right Ai tools, to find the successful trading they are aiming to achieve.
9. Ai is a reliable way to price financial instruments
The crypto space has troubles assigning proper fundamental value to the most popular cryptocurrencies such as Bitcoin, Ether, and Litecoin. While there are a number of theories floating around regarding more proper pricing methodologies, nobody came up with one broadly accepted method.
Experts believe that the solution lies in neural networks.
The first and most famous option pricing model is that proposed by Black and Scholes in 1973, designed to price European-style equity options. Economists received the Nobel Prize in Economics for this formula, confirming its importance to the world of economics. Given certain imperfections surrounding Black-Scholes in pricing of real-live options, researchers wanted to see whether neural networks could improve upon it.
A study by Bennell and Sutcliffe titled “Black-Scholes Versus Artificial Neural Networks in Pricing FTSE 100 Options” was published in 2005. What they found confirmed the power of neural networks.
“This paper compared the performance of Black–Scholes with an artificial neural network in pricing European-style call options on the FTSE 100 index. For out-of-the-money options, the AI turned out to be clearly superior to Black–Scholes. For in-the-money options the performance of the neural networks is comparable to that of Black–Scholes.
Researchers noted that the superiority of the neural networks was quite surprising given that European-style equity options are the home ground of Black–Scholes. This study suggests that AI may have an important role to play in pricing other options for which there is either no closed-form model, or the closed-form model is less successful than is Black–Scholes for equity options.”
While not the flashiest, this application of Ai is one that should not be overlooked. As the cryptocurrency market matures, most experts believe it will start to shape up similarly to more traditional markets, and the stability that Ai can bring to it will be invaluable, and will continue to help it stake its claim as a market worth devoting time and money to.
10. Ai successfully predicts prices for various traditional and emerging asset classes
Countless research papers have been published proving that Ai can dramatically outperform existing trading strategies and buy-and-hold portfolio on a wide range of asset classes, such as equities, futures, foreign exchange, emerging markets, and real estate.
- A study by Krauss, Do, and Huck titled “Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500”, previously mentioned above, was published in the European Journal of Operational Research in October 2016. Researchers found that machine learning algorithms generated much higher absolute returns, coupled with higher Sharpe ratio futures.
- A study by Lukas Schulze-Roebbecke titled “Using Artificial Neural Networks to generate trading signals for crude oil, copper, and gold futures” was published in July 2016. The study found that artificial neural networks could yield significant returns with low standard deviation for copper futures.
- A study by Jinxing Han Gould titled “Forex prediction using an artificial intelligence system” was published in December 2004 as a thesis for Oklahoma State University. The study found that back-propagation neural network Forex rates can be predicted, allowing maximal profits.
- Kuang-Hsun Shih wrote a doctorate dissertation at Nova Southeastern University, mentioned above, titled “Taiwanese High-Tech Stocks: Using Artificial Neural Networks to Test Weak-Form Market Efficiency in the Taiwan Electronic Index and to Develop an Evaluation Model of Investment Strategy in Taiwanese Stock Markets” in 2003. The empirical results confirmed that neural trading systems fed with historical market data may produce superior information for the next trading day outperforming the buy-and-hold portfolio by more than 150% in just one year.
- Published in the Emerald Journal in November 2016, “A survey of property valuation approaches in Nigeria,” by Abidoye and Chan, presented valuable insights as to reasons why advanced valuation approaches, such as artificial neural networks and fuzzy logic, are more desirable compared to traditional methods. The article provided a table summarizing strengths of some of the machine learning algorithms used as advanced valuation methods for real estate properties. The findings were very positive for artificial intelligence’s future in real estate.
11. Ai-powered crypto portfolios consistently beat the market
Magnus Erik Hvass Pedersen, PhD in Engineering Science from University of Southampton, conducted a study titled “Artificial Intelligence for Long-Term Investing” in January 2016. The purpose of the study was to determine optimal portfolio allocation using AI model for long-term investing.
- Stocks of 40 U.S. companies, S&P 500 Index, and U.S. government bonds with one-year maturity were selected for the study. The results were quite staggering.
“Between 1995 and 2015, the Ai model outperformed the S&P 500 by about 18% per year on average. The Ai model performed especially well when stocks were extremely mispriced, e.g. during the Dot-Com bubble around year 2000 where stocks were generally overpriced, and then again during the financial crisis where stocks were generally very cheap.”
Cryptocurrency traders have a reputation of being emotional and impulsive, but they have certainly never willfully ignored any way to ensure better profits. Employing artificial intelligence is undoubtedly the most consistently successful strategy currently available in crypto, and the products that make it most easily available to the average crypto trader will end up on top.