There is something wrong with economics. We have (had) plenty of economic theories. Many attempts to describe what happens there. Often contradictory. Yet, none of them proved reliable. At least, for a longer period. Can we then speak of economics as of a science? If it leads to nothing as trustworthy as physics, for instance? To what degree? Is the complicated economic mathematics justified? If we answer these questions, we can then ask what does it tell us about our reality. Why is it, as it is.
Science should answer two fundamental questions: why? and how?. If we know why and how something happens, we should be able to make the correct predictions. We should be able to understand. To know what (and when) may or may not happen. There should be no surprises. If there is a surprise, it means we should rework our ‘why’ and ‘how’, because we must have overlooked something. Economics walks from one surprise to another. Its predictions are mostly worthless. So, why do we keep calling it “science”?
First of all, let us notice that in economy, the most fundamental role plays faith. That means: what people believe, or disbelieve (that happens or will happen). If people’s believes would have similar strength in the engineering field (that is applied physics), the Titanic would end its long life in 1950’s or 60’s. Up to the very day of its sinking, everyone believed it is an unsinkable, titanic construction. Yet, people’s believe or disbelieve means nothing in the material world.
On the other hand, in the economy we see something opposite. The faith (conviction) is everything. The market bubbles can grow and grow, until people finally lose their faith in their further successful growth. Or, to put it differently: until people realize that they deal with a bubble – something inflated beyond any sense.
The same is true in case of companies, businesses. The company or business is successful for as long as people believe it is successful. Of course, it requires a ‘non-empty’ faith. A conviction that is being manifested: by buying products, services, shares. By lending money. There are many examples of such companies (businesses). Like Enron. Able to thrive, thanks to the good press, creative accountancy, etc, while they were in fact bankrupt (dead) for many months, according to what is called the economic fundamental analysis.
Bernard Madoff is a great example of what the faith (conviction) means in business. And the same is true for all of the other pyramid schemes. Yes, there can be businesses, that are fundamentally wrong. Even from the very beginning. And they are a part of the economy. Sometimes, they are nominally bigger, than for example the entire wood industry.
The highly developed stock markets make the role of human trust/distrust, believe/disbelieve even stronger. It is their ‘merit’, that the modern economy gets more and more trust/believe based. But I return to them later, as they are a great example to prove my point.
Now, let’s take a look at the other end. What happens when people lose their trust in a company. If they start to believe the company is a bankrupt. Suppose, there is a firm quite strong and healthy, but the market (that is: people) got convinced/believed, that its condition is very bad. What would happen, then? That’s easy to predict. The clients would stop buying, or at least (strongly) decrease their orders. Who would want to buy anything from a prospective bankrupt? The bankrupt can give no real guarantee on its goods/services. So, it is better to avoid them.
The suppliers would do much to decrease their involvement, too. They would think, that if they provide supplies with delayed payments (as usual), they may never see their money. Their client is going bankrupt, isn’t it? So, even negotiating a long term supply contract could be seen as a waste of time. Why to bother negotiating something for two years ahead, if the client may vanish in the next quarter?
The investors/money lenders would not only stop further financing, but they would also try to withdraw what they can before it becomes impossible. They would ask for an immediate credit pay off, or an additional loan security. And the otherwise strong and healthy firm would in a short time turn into a real bankrupt. Faith determines the economic reality. Both ways. In good and bad.
Could anything like that happen in the material (physical) reality? Could a well designed and built bridge fell down simply because most people believe it has a serious construction flaw? Or could a bridge without the required support survive months or years because people believe it is practically indestructible?
Similar problems we see on the highest level. Plenty of contradictory economic theories. Praising regulation free market on one side, and wise government interventionism on the other. Between them: lots of other theories. Some of them even used in practice. Sometimes, yielding good results for some period. Basically, for as long as people believed in them: following their recipes with optimism. But when their attitude changed, when more and more people got convinced that the future will bring more bad than good – they failed. And there is almost no mathematics at this level. These are just general, high level theories – attempts to somehow describe or model what is going on in the economy. And even these basic efforts fail.
‘Sciences’ like economics, which subject is a product of the human behavior (mind) are something completely different from physics, chemistry, biology or engineering. That is sciences dealing with ‘the behaviors’ of the matter. This is the main thought contained in this text.
It is symptomatic, that the Age of Enlightenment, known also as the Age of Reason gave rise to many false ideas based on ideological prejudices, which common denominator is reduction of the World to matter – materialism. And it is still the foundation of our Western philosophy of life, and the ideological prejudices stand strong, instead of being weakened. The proud Enlightenment, contemptuous to all the preceding epochs as not sufficiently based on logic and reasoning, was full of a priori taken assumptions, which survived much, much longer than anyone using pure, unbiased reasoning could expect. This feature of our post-Enlightenment thinking alone, deserves a closer analysis.
But let’s return to our current topic, to see on this example what such incorrect, materialistically-biased thinking leads to. Because the fundamental assumption of the modern economics is materialism. It shows clearly in the assumption that we can treat the phenomena happening in the economy (in the behavior of humans) as we treat the phenomena of the material (physical) world. That means the unquestioned assumption that we can model it mathematically and receive (discover) laws similar to the physical energy/mass preservation, or equations like the E=mc2.
But we cannot. The theoretical (mathematical) physics gave us the neutrino detector, space crafts and many others. The same is true in the field of chemistry or biology. Mathematics works there. The physical world, however most of us consider it imperfect, is in fact a perfect thing. Its behavior can be described in a clean, ideal, perfect mathematics. Mathematics which is miraculously perfect: huge and complicated, having many distinct areas, yet perfectly coherent. Everything fits together in mathematics. And in the physical world. It works like a perfect mechanism. Has anyone heard of a ‘theoretical economics’?
To some point, we could hold such assumptions saying that “the future should prove it is right” or “we need more sophisticated models”, etc. But the future is now. The XXI century started long ago. Computers can be used to solve mathematical problems unimaginable 50-70 years ago. And the answers we keep getting in economics are deeply unsatisfactory.
The economic models we have, tend to become invalid – one after another. Sometimes it takes several years, sometimes a few decades. While the Newton’s model of physics is still valid. After 300 years! However, now we know it is a generalization not suitable for the micro world or for the very high velocities (comparable to the light speed). But it gives answers correct to the third or more digits. What can we expect from the mathematical models of the economy world, I’ll try to show in the following chapter.
But before that, I want to admit that mathematics is useful in the economic statistics. The GDP, the amount of steel produced, the employment/unemployment and so on. But the same can be done in physics. We can have statistics on the amount of sun energy reaching the earth surface (day by day), average air pressure, steel densities, etc. But it does not answer the questions like: why and how. It gives no real understanding. It is not a science. It is just statistics. Plain facts assembled. Nothing more. (Of course, the statistics as a part of mathematics is a science. But not its application to data.)
Anyway, why do I argue? Because words matter. “Science” sounds proudly. It puts economics to one bag with physics or mathematics. It makes people believe, that economics gives scientific answers. That we have knowledge of what, how, and why happens in the economy. That our models there get better and better, more and more precise. Cause we use very advanced mathematics here, don’t we? And if we do, the answers must be precise, mustn’t they? Besides, economics tells us ‘scientifically’ which system is better and which is worse. Which solutions are good and which are bad. And that it somehow is wrong time after time? That the solutions considered as ‘rubbish’ by wise economics professors seem to work? That wise economics’ models applied to a real economy bring results often opposite to what was expected? “It does not matter” – they say – “exceptions are always and everywhere”. If only they could present an exception to gravitation. How much fuel it could save...
Someone might ask: “What’s the aim of this text? What’s the benefit of saying that economics is wrong? Wrong in its basic paradigm, wrong in its methods and results?”. My answer would be: I think, the real knowledge is always better than an illusion. Even if the illusion fits so nicely into our ‘philosophy about the World’. Embracing the reality as it is, not as we want/like it to be, is the only sane thing to do. Otherwise we are doomed to failures, disappointments and common errors happening time and time again. On the false economic ideologies, which shine as perfect in the world of the economics illusion. Staying in error is simply impractical. Not to mention, it is merely stupid.
The real knowledge in so called “social sciences” is accumulated for centuries. In stories. In myths. In religions. The story of Cinderella tells us a deeper truth about men and women than heavy tomes of the modern psychology. But could the contemporary, post-Enlightenment humans admit that they cannot ‘scientifically’ explain a large part of the reality? We have made a mockery of such ‘sources of knowledge’ (like religion) for a long time. So, how could we now acknowledge we were wrong? That the real answers here are not mathematical, ‘scientific’, but the common sense ones. That predictions and understanding here are as good as good is our understanding of the human beings. And it cannot get better, no matter what we would do.
Today’s economics is a large subject. It contains many specializations. It is perhaps the only aspect in which it is similar to physics, which spans from astronomy to the quantum mechanics. Including many additional, independent divisions. That makes it hard for me to prove (show) the correctness of my thinking on the economics as such. There are simply too many branches and divisions belonging to the contemporary economics. Yet, I’m pretty sure, that what (and how) I show in this chapter can be easily adopted to all the remaining parts of the economics, and as such – to the economics as a whole. My assumption is based on the two simple facts:
Therefore, the painful results of the fundamental inapplicability of mathematics to the world of the human minds should be observable throughout the entire economics.
Now, let’s get to the point. Let see, what the contemporary economics provides as tools to describe, analyze, and most importantly – to predict what will happen in stock markets. Nowadays, we are given three options to deal with the stock markets behavior. First and intuitively the most appropriate is the fundamental analysis. We simply read the market (company) reports and data, and try to figure out the “big picture”. To guess (predict) the expected outcome of the current state. It’s logical and reasonable approach. At least, at first glance. Anyway, such analysis requires lot’s of knowledge and brilliant intelligence to be successful. But there is a catch: “the market can stay irrational longer than you can stay solvent”. Human behavior does not have to adhere to what someone thinks is logical, reasonable and expected.
You may have a really good understanding of the market situation. You might correctly (in terms of logic and obvious expectations) guess the most probable outcome. Nevertheless, you may get astonished time after time, when you see that things are going in a completely different way. That other people somehow do not see and understand what you see and understand. It may be true. More often, there are simply different calculations, certain unobvious dependencies, etc. Anyway, the result is that you fail time and time again.
That is the reason, by the way, why Marxism and all other ‘perfect’ social constructs fail. And have to fail. People’s behavior; however often being logical, reasonable, common sense; is not the behavior of a machine (mechanism). Free will, various aspirations and life goals, cultural differences make all predictions very hard. The more precise the prediction is (= easier to monetize), the more often it fails. We (as humans) learn it the hard way. Time after time, generation after generation.
Anyway, at this point we may simply agree with the already mentioned stock market’s common wisdom about the market’s ‘irrationality’. It does not mean, that the fundamental analysis is useless. It means, that there are no ‘laws’ in economy, as they are in physics for example. Knowledge, logic and reason are not enough to be successful. But they often help, of course. If only one has the final, vital ingredient – humility.
The quantitative mathematics.
The second approach is the kingdom of mathematics. It is known as the quantitative math. It stems from the acceptance of the failure of mentioned above fundamental analysis. And the paradigm is quite opposite: “we do not care what do we value, we just care about the quantitative (statistical – probability based) properties”. Here we have an abundance of equations and formulas. Among them, the most popular, Nobel prized Black-Scholes equation for “pricing accurately” the plain-vanilla options.
I admit, I expected much from the famous, fundamental, Nobel-prized(!) thing. I was eager to learn the successful attempt to describe and model mathematically what happens on the stock market. And it was very disappointing for me, to find out that all the human ingenuity, all what happens there got squeezed into one, single, real value. Among, more or less, reasonable real values like the interest rate, the price of the underlying (asset), the strike price of the option and (of course!) time; there is “a volatility”. A simple, plain number like 0.123 is there to represent everything what is really interesting, mysterious – unknown. The market’s (that is: people’s) behavior.
It’s like I would say that the behavior of lions in Africa is described by the real number 0.27, while the behavior of apes is 0.151. What would a biologist say to that? And here we pretend that the human thinking, human decisions, human behavior can be described by one 3-digit number. Is it really a science?
Of course, I know the ‘official’ definitions. Like, that it is an “annualized volatility of the asset” or “the current/expected market volatility”. None of these is true. In fact, everything what we have here is untrue, far from the truth, or (at most) showing only a resemblance to the truth. Starting from the very beginning. From the very basic assumptions.
The first assumption here is the “random walk”. Normal or lognormal random walk. The ‘normal’ here stands for the normal (Gaussian) distribution. The prices go up and down, which (in time) may be seen as “a walk” (of the price). And here the problem starts. The word “random” here means something different from what it means in the physical world. That is when it is used to describe tossing a coin or throwing dice. This ‘random’ only seems to adhere to the physical laws of probability.
The normal probability density graph looks like a bell. Ideally symmetric along its vertical axis. But the real life data acquired from the market only ‘resembles’ the normal density bell. Its shape differs much, even at first glance. From the mathematical point of view, it is even worse. This market taken ‘normal distribution’ has so called “fat tails”. Both its ends are much fatter, than they should be. How much ‘fatter’ they are? Well, there are examples showing that they are like 1070 fatter. 10 to the power of 70. 1 and 70 zeroes. It is an absurdly high error level. It makes a mouse bigger than a galaxy, or even the universe! ‘Science’ containing such errors is simply ridiculous. And such error level is hidden under the vague term “fat tails”.
On top of this “random walk” assumption, the Black-Scholes equation with its “volatility” is founded. As I mentioned, in the economics literature it is called “annualized volatility”. If it would be really “annualized” – monthly, quarterly or yearly, it would change a little on a day-to-day basis. But people dealing with derivatives know, it may change strongly in a couple of days. This falsifies the “annualized” definition of the volatility.
The wiser authors write about the “current/expected market volatility”. But is this a scientific approach? “Current” and “expected” relate to future, which starts now. Taking coefficients from future or present stands in opposition to what a real science is. Science is about making future predictions based on the past. Science have to be objective. Otherwise it is not a science. If we guess a coefficient and put it into the equation, it is no longer science. It is no longer a real mathematics, either. Where did this number come from? – From my private, subjective experience. From what I learnt through years as a trader. With such approach, we can make a science of anything. We could harness mathematics in psychology or psychiatry. To have equations for people’s insanity.
So, let’s have a look at the final feature of so called ‘volatility’. If it would be any kind of scientific value (no matter: annualized, current, expected, or whatever else), if only it would be a value somehow describing the reality, it should be a single value. That’s what it is in the Black-Scholes equation in the first place. I’ll use an example to show what I mean.
Let’s assume that there is really something like “the volatility” – describing the situation on the market. An objective property. The likelihood the prices will change, the amount of such change, etc. For instance, for a single asset. Let it be a Foo company share. If we assume the volatility of the Foo shares to be 0.15, which means the perspective (projected, expected) uncertainty (the likelihood and amount of the price change), then it should be an objective feature of the Foo shares on the market. Or the market itself. Simply, the Foo shares have the current volatility of 0.15. Then, this very 0.15 should be used (as an objective property of the Foo shares) in any and all calculations of the Foo derivatives. Because this is how much the Foo shares are volatile. It is logical, isn’t it?
But it is not so. The ‘volatility’ of the Foo shares can be 0.15 for one option on the Foo shares, and 0.142 for another at the same time. It is called “volatility smile”, “volatility skew” depending on how such different volatility values look like when put on the graph. How can we then say that the Black-Scholes equation is used to price the derivatives “accurately”, if we need to manually change coefficients for the calculated prices to match the reality?
What is the Black-Scholes equation, then? The answer is: The B-S equation is a tool for practitioners (market-makers, traders) to help in pricing derivatives consistently. The B-S only helps. It gives no correct, objective or accurate price. It helps to consistently price all the various options on one underlying, providing you know that for example the strike price 120 call on March 15th should cost 5. Then the B-S gives you a clue on the prices of all the remaining calls and puts on different strikes and maturity dates. But you need to manually fix the results for some options. How do you get the first (base) price? Exactly as I wrote before: it comes from the private, subjective experience of a seasoned trader.
The Black-Scholes equation does not answer why or how. Instead, it gives a clue for a reasonable prices consistency. Since practitioners have nothing better (or at least easier), they use what they have. Anything is better than nothing. The alternative would be to take all prices out of thin air. But such prices are often inconsistent. Therefore, it is perfectly understandable why the traders use the B-S equation. But the fact, that something is widely used in practice does not mean it is scientific. The bookmakers pricing bets on the Champions League, NBA matches, horse races, etc. certainly have some tools to price them consistently. But would we call it science?
Where is the boundary between science, which gives answers to why, how and what happens, which provides understanding; and merely practical means we use to deal with things which are beyond our ability to predict and fully understand?
I suppose, some people at this moment would point to things like weather or earthquakes. Both are hard to accurately predict, or fully understand. But the similarity is illusive.
In case of earthquakes, we know pretty much nothing. We have only a very general model of the Earth internal structure. We know close to nothing about what happens 10, 50 or 500 miles below the earth surface. We have no sensors or measuring devices there. No real base for reasonable modeling or predictions. In case of markets we know virtually everything, except what happens in the human minds. We know everything about every transaction, all this is available for research.
In case of weather – it is basically aerodynamics. Enormously complex aerodynamic environment. Aerodynamics is a tricky thing. But we managed to solve it pretty well for simple environments. But the entire planet? This task in nearly infinitely complex. It is not that we do not understand what, how and why happens there (except maybe for some very rare events). We know it pretty well. There are simply too many variables for our equations. And we still have too few sensors if we take into account the total volume of the Earth’s atmosphere. But at least we know what and how limits us in this area. And our weather forecasts are quite good. Not perfect, not always accurate, but still getting better. And our economics forecasts? Our markets forecasts? Wise economics professors are not (much) better than anyone else.
But these two examples show us one additional thing. A feature of the real science. In real science, we go from simple, small systems, up to the big and complex ones. We learn aerodynamics in a closed tube. Then in a bigger and bigger systems, and finally we try to predict what will be the weather next week on the eastern coast. A true scientist would never say: “I do not know what happens in that tube. I can say a little about the more complex systems. But I will give you equations for the weather in Asia.”
What is the economics about? It is about people trading. People exchanging goods. So, the simplest, most basic system in economics are two persons. Trading. Exchanging goods. The simplest model here is one human being. Can anyone model mathematically a human being (behavior)? Can we model 2, 3, 5 persons? No. We cannot. The science based on mathematics is helpless here.
It means, we start with something that cannot be modeled mathematically. Not in the basic case, nor in the more complex cases. Then, why do we keep believing, that we can model it mathematically when the scale is very big?
The phenomena of the physical world follow the laws of the probability at all levels. You can toss one coin, you can toss 5 millions. The mathematical probability works. The laws of the probability assume, that the phenomena being studied follow certain rules at every level. It is unreasonable, faith based assumption, that something, what does not follow the laws of the probability in the simple cases, will follow them in the ‘large numbers’ cases. We’ve learned that the human behavior eludes the mathematical modeling. But we refuse to accept it, and we keep saying that for ‘big numbers’, the probability works. The prejudice of materialism stands strong. No matter the facts.
But again, it is worse than that. The assumption that in case of stock markets, we deal with the simple decisions of millions of people is false. These legendary millions of individual market players are responsible for ca. 10% of the market. The contemporary markets are driven by so called “big players”. On a very liquid European Forex market several big financial institutions were accused of manipulating the market and prices setting. As I remember, they were found guilty and had to pay a fine.
How many people in these institutions were responsible for “setting the market”? 20? 50? 100? None of these is, what the probability calls “big numbers”. The same we see in other markets. Hedge funds, investment banks and similar financial sharks have hundreds of billions in their purses. Compared to them, a million of individual investors with 100.000 each means nothing.
Even if there are billions of individual trades each day on the market, they are all an emanation of the conscious, intelligent will. Human will. In most cases the market is shaped by the will of several dozens most influential traders. Often cooperating. They make it ‘drift’ in this or that direction. ‘Bending’ the probability (providing, that the physical world's probability is there at all). It does not have to happen each day on every market. But that makes things only harder, not easier to model.
In general, the problem is in misperceiving the common sense “average” with the probability. On average, the most probable outcome is to receive “hi!” in response to “hi!”. But it is something different than receiving 7 on average, while throwing 2 six sided dice. The first one is a product of the intelligent free will. The other is the result of the laws of physics. The mechanics. A perfect necessity.
During my studies I was taught: “If at the very beginning of your experiment/research you have the error level of say: 0.05, do not enter as the final result all that is displayed by your calculator, like: 1.305974538. Cause everything lesser than 0.05 is completely meaningless.” It was the basic, most obvious rule, which every good student knew and understood. One could say: “If you measure water in buckets, do not give the amount in cups”. Pretty obvious, isn’t it?
In the quantitative math, applied to economics, we have a cosmic level of error at the very beginning. Then we have the Nobel-prized equation with its volatility taken out of thin air. And this is the foundation for more sophisticated equations, which at the end give us a price for some bizarre derivative as 14.75. How much does it relate to reality? Well, as much as we believe it does. Economy is based on faith, isn’t it?
The technical analysis.
The starting point for the technical analysis is the same as in the quantitative mathematics. The base here are the market’s graphs. But the paradigm differs. It is no longer mathematics. It’s kind of astrology. The future is written in graphs. One needs to study them carefully, to find similarities, trends, patterns. If you are good at it, you will be able to predict future. This is the promise of the technical analysis. Quite like in the astrology, where the star-graphs, patterns and similarities tell the future of individuals, nations and the world.
The difference here is that astrology had been replaced long ago by the physics of the universe – astronomy. Astronomy shows us the cosmic space as it is. It gives true, verifiable answers and predictions. But there was nothing in the economics, true and verifiable enough, to falsify and replace the technical analysis. So, it is still being taught, used, sometimes praised. It works. In a way, all the self-fulfilling prophecies work.
Because it is the self-fulfilling what really stands behind the technical analysis. It suffices that enough people believe in it, or at least behave as if they would believe in it. And things do happen as ‘predicted’. If enough people believe that the price will go up, they start buying and the price really goes up. If only a few big traders join them for their own purpose. It is certainly a good (cheap) way of exploiting the less experienced market players.
Anyway, the technical analysis can be effective. Taking into account the cost/value factor, it seems to be the best choice. It is simple and easy to learn and use. At least, in comparison to the fundamental analysis or the quant mathematics. One does not have to be an expert in accountancy and evaluation, or a higher rank mathematician. Technical analysis is a method of dealing with the incomprehensible and unpredictable in a cheap, cost-effective way.
There is one more thing worth mentioning here. The fundamental assumptions. For the technical analysis the fundamental assumption is the price move dependency. Technical analysis states that the future price moves are strongly dependent on the previous (past) moves. An analyst needs to search for patterns that repeat themselves. If the future moves of the price would be independent from what happened in the past (especially in the most recent past), then the technical analysis would be a lie.
The quantitative math uses the opposite assumption. All the probability based mathematics, which is the foundation for the quantitative analysis, requires each price move to be totally independent from the preceding moves. Exactly as it happens with the coin tossing (the favorite example of all quants, btw). The next result is independent from what you got previously. The probability of receiving tails does not depend on the number of previously received heads. If this assumption fails, then all the sophisticated economic quant math analysis is a lie. At least, for a mathematician.
If for example chemistry would be like economics, we could have two classrooms full of students. In one, they would be taught that one element cannot be changed into another during a chemical reaction. That such change requires a nuclear fusion. In the neighboring classroom, they would learn, that there are chemical reactions which can change one element into another. Especially, every ‘heavy’ metal like iron or lead can be transformed into gold in a special, alchemical reaction. We only have to discover such reaction.
It would be a perfectly reasonable approach, if only we would remove the no contradiction rule from science. ‘A science’ containing contradictions is not a science, but an intellectual mess.
In fact, all the methods I described in this text, rely on the people’s believes. “The market can stay irrational longer than you can stay solvent”. The word “irrational” here means simply: “thinking differently”, believing in a different outcome. That’s what really counts. What people think and believe in. The success here is to convince people to your way of thinking. Being it the fundamental analysis, the quantitative math, the technical analysis or a crystal ball. There is no really significant difference between them. The contemporary economics, however being over 200 years old, could not conceive anything reliable. Nothing that could be seen as an unquestioned, objective answer. Its own versions of ‘astrology’ and ‘astronomy’ live side by side, cause everything in economics is a question of a subjective believe.
The stock markets, as the entire economy, are an incomprehensible jungle of various goals, tactics, prejudices and emotions, which may from time to time surprise even the ones, who usually consider themselves as the top level predators there. The success here is to influence other people. The disguise of ‘a science’ is as good as anything else.