One of our core tenants at STATS4TRADE is to always be open and transparent. As a result the first point in the article is salient – namely no algorithm can ever claim risk-free trading with only positive returns and no losses. In fact the article prominently quotes Matthew Griffen, CEO and founder of the 311 Institute, who correctly states that no model is perfect. We fully agree – our models are certainly far from perfect. Moreover and as we have stated multiple times before, if you expect to "get rich quick" with our approach, you will be sorely disappointed.
So what is the value of algorithmic approaches such as STATS4TRADE, if they are imperfect? Well as in much of life, the goal is not to be perfect but simply better – in our case statistically better than pure chance (e.g., greater than 50% probability) at picking winning stocks in the short-term and better than benchmark indices with respect to both return and risk over the long-term. We do this by using machine-learning engines to make data-driven forecasts about upcoming price movements that eliminate subjective yet very common human biases*.
Of course our forecasts are not always right and we incur losses – but the key is that we are right more than we are wrong! In fact if you consider the results for any market on our Peformance pages, we typically have at least a 60:40 win-loss ratio. Compare this result to a casino's probability of taking a customer's money with roulette – almost a 53:47 win-loss ratio on an even/odd bet for the American version! In this case we perform significantly better than even a roulette table in a casino**.
The second critical point in the article concerns human emotions and their effect on markets and ultimately prices. Markets are not logical inanimate objects; instead markets are social communities with emotions ranging from joy to fear and every feeling in between. Investors often behave irrationally with suprising regularity. Therefore and to the probable chagrin of some financial academicians, markets are not fully efficient. As a result we can tease obsure statistical patterns of behavior out of long-term historical price-data. In turn this enables us to raise our probability of making a winning trade above chance.
However and as Greyfeather Capital's Matthew Sandretto accurately notes in the article, the trick for any algorithm is the balance between illogical behavior and logical conclusions. Again no algorithm is perfect at drawing conclusions from irrational human behavior – especially over short timespans. Therefore the models from our machine-learning engines strive to balance accuracy with consistency over varying timespans – all verified by rigorous testing with both training- as well as test-data. In effect we statistically optimize our models for both return and risk over the long-term instead of the short-term.
Machines on the March...
Now comes the third and fourth points. Clearly trading algorithms are on the march with much potential for growth. As a result the financial services industry faces drastic changes in transitioning from human-centric to machine-centric decision-making processes. Just like managers in industries such as transportation and manufacturing, fund managers now face a stark choice: either adapt to the oncoming disruptions or likely perish.
Bluntly stated – in comparison to highly-paid analysts and traders, machines are reliable, consistent and cheap. Moreover and as Alessandro Di Soccio of A.I. Machines cites in the article, when expenses are considered, only about 1% of today's asset managers actually outpeform benchmark indices while 25% underperform. No wonder investors are frustrated and flock to passive index funds! As a result it will be increasingly difficult for any fund to justify high fees and mediocre performance in the future.
...but Humans Still Needed
Nonetheless this trend towards machines in trading does not mean that humans will become completely superfluous. As Andrej Rusakov of Capital Data Management mentions in the article, it will always be the responsibility of a human – say a fund-manager – to set the parameters for any automated investment strategy. At STATS4TRADE we use the analogy of an automatic pilot. In modern aircraft a computer normally pilots the plane; however if an emergency occurs, a human pilot is always available to intervene and control the aircraft herself. It will be no different in the future for "financial pilots".
No Magic Wands
The article's ends with an important quote from Alessandro Di Soccio of A.I. Machines. He states about artifical intelligence (AI):
Of course we do not ask that you fully accept our analysis or conclusions. If you have different ideas, please do not hesitate to post a comment or contact us via email at firstname.lastname@example.org. As always we look forward to hearing from you.
**Why anyone would ever place such an irrational bet with losing odds is a question for human psychology.