Impact of Artificial Intelligence And Machine Learning on Trading And Investing

 General impact of artificial intelligence and machine learning on trading

Artificial Intelligence (AI) is currently allowing replacing humans with machines. In the 1980s, AI researchers focused primarily on expert systems and fuzzy logic. With the power becoming way too cheaper, using great machines to solve large-scale optimization glitches became economically achievable. As a result of the advances in hardware and software, AI only focuses on the use of neural networks and other learning methods for identifying and analyzing predictors, also famously known as features, or factors, that have economic value or can be used with classifiers to develop profitable models. This specific application of AI often goes by the name of Machine Learning (ML).

The application of methods used for developing trading strategies completely based on AI, both in short-term time frames and for longer-term investing, is gaining huge popularity and there are a few verge funds that are very active in this field. However, extensive acceptance of this new technology is quite slow due to various factors, the most important being that AI requires a strong investment in new tools and human talent. There aren’t many hedge funds that rely solely on AI. Daisy AI trading is the newly launched program combination of both AI and machine learning. The application of AI is growing at the retail level but the majority of its traders still use the traditional methods that were proposed in the mid-twentieth century, including traditional technical analysis, because they were much easy to learn and apply.

Note:  AI and ML are not only used to develop trading strategies but are also in other areas, e.g.: In evolving liquidity searching and suggesting suitable portfolios to various clients. 

Impact of artificial intelligence and machine learning on alpha generation

During the initial phase of the adoption of AI technology, there will be many opportunities for those who understand it and know how to manage its upcoming risks. One identified problem with trading strategies based on AI is that they can profit models that are worse than random. 

I will try to explain what I mean by this: Traditional technical analysis is an unprofitable method of trading strongly because strategies that are based on chart patterns and indicators draw their returns from the way of distribution with zero mean before any transaction costs. Some traders are always found at the right tail of the distribution and this gives them a false impression that these methods carry economic value. My research shows that specifically in the futures and forex markets, longer-term profitability is very hard to achieve no matter which method is being used because these markets are mainly designed to benefit market makers. However, in shorter periods, some traders can also tend to realize large profits in leveraged markets due to their luck or any special skill.

With AI and ML, there are many unlisted additional effects, such as the bias-variance trade-off. Data-mining bias can also result in strategies that are over-fitted to past data but immediately fail on the new data, or strategies that are way too simple and do not capture important signals in the data that have economic value. The result of this trade-off is far worse-than-random strategies. This presents an opportunity for profit gainer for large funds and investors in the post-quantitative era. However, as the worse-than-random, AI traders are being removed from the market and only those with robust models are allowed to remain, the battle for profits will become extremely intense. It is too early to speculate whether AI traders or large investors will win this battle of gaining profit? As I mentioned above, Daisy AI trading is the program which you can used for gaining profit. 

I would also like to mention a common misconception in this area: few tend to believe that the value is in the ML algos used. This is not true. The true value is in the predictors used, is also known as features or factors. ML algorithms cannot find gold where there is none. One problem which is faced most is that most ML professionals use the same predictors and try to develop models in an iterative fashion that will produce the best results. This process is plagued by data-mining bias and eventually fails.

                                                                                      

Impact of artificial intelligence and machine learning on technical analysis

We have to make a wide difference between the traditional and quantitative technical analysis because all previous methods rely on the analysis of price and volume series which would fall under this subject. Traditional technical analysis, i.e., chart patterns, some simple indicators, certain theories of price action, etc., was not up to the mark to start with. Other than a few imperfect efforts of limited scope and reach, publications that advertised these methods never presented their longer-term statistical expectation but only wished to offer promises that if this or that rule is used there would be profit potential. Since profits and losses in the markets only follow some statistical distribution, there were always those who attributed their luck to these methods. 

In the early 1990s, some market professionals realized that a large number of retail traders were trading using these naive methods. Some developed algos and AI expert systems to identify the formations in advance and then traded against them, causing in the process volatility that retail traders, also known as weak hands, unfortunately, could not cope with. More fundamentally, the failure of traditional technical analysis can also be attributed to the disappearance of high serial correlation from the markets starting in the 1990s. It was the high serial correlation that offered the wrong impression that these methods worked. Daisy AI trading is the cryptocurrency smart contract based on blockchain. You can invest money in it and getting lots of benefits.