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For being here in this site now, it means you've taken a step beyond the average. You are getting ready to enter a fascinating world where only the most select traders can understand how to use the power of artificial intelligence to be a step ahead in the markets.
In this new series of Oustpoken Market we show examples of the main models of machine learning applied to trading! This post is about an application of a Logistic Regression for trading. Download of all files
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US investment bank JP Morgan has created a crypto-currency to help settle payments between clients in its wholesale payments business.  US investment bank JP Morgan has created a crypto-currency called JPM Coin JPMorgan (JPM) said it is the first American bank to create and successfully test a digital coin. The so-called JPM Coinis the first digital currency to be backed by a major US bank. The crypto-currency, which runs on blockchain technology, has been used successfully to move money between the bank and a client account. Based on a Blockchain technology, the JPM Coin has several characteristics:
 This news shocked many people who remember when, in 2017, JPMorgan CEO Jamie Dimon described Bitcoin as a "fraud," "stupid" and "far too dangerous" to people who traded it.
Moreover, Dimon said that he supported blockchain technology for tracking payments and his company would fire anyone at the bank that traded in bitcoin "in a second". He soon after backed off of that harsh assessment, saying that he regretted his comments, adding that he believes cryptocurrencies are real and had to be examined individually. But... Does JP Morgan really need a crypto currency? Not everyone is convinced that JP Morgan needed to create its own digital currency. JP Morgan says that it is trialling crypto-currency and blockchain to speed up payment transfers, as well as reducing clients' counter-party and settlement risk, and decreasing capital requirements. However, a Blockchain is designed to be decentralized, so no one party has control over transactions being sent over the network. This is the opposite of the JPM Coin concept. Mr Gerard - the author of Attack of the 50 Foot Blockchain: Bitcoin, Blockchain, Ethereum & Smart Contracts - is skeptical and does not believe that the bank needs the technology to speed up transactions and he told the BBC: "It doesn't even need a blockchain at all because JP Morgan runs it. They could do it on a website and database they run. It isn't like Bitcoin that aren't under anybody's control - it's a centrally controlled thing that sounds vaguely like crypto-currency." What will happen in next months is not easy to be predicted, but Blockchain technology now is officially in the paradise of technologies used by the most important US Investment Bank. 
Algorithmic Trading - or Algo Trading - is more than a technological breakthrough in the stock market. It is a programmed process executed on a computer, which follows a specific set of instructions (an algorithm) to perform a trading operation. The markets of the moment are the US, India, the UK, and South Korea. Accuracy, exceptional speed, and liquidity are the unique characteristics of algorithmic trading, which will result in the exponential growth of the algorithmic trading market soon.
Algorithmic and quantitative trading strategies are becoming more popular as they can increase the likelihood of success with the support of statistics and machine learning. Nearly 50% of global Forex volumes are negotiated through algorithms. An expanding market
According to a Coherent Market Insights survey, the global algorithmic trading market was valued at $ 9 billion in 2017 with a projected growth of 10.1% pa over the forecast period (2018-2026).
Among the algorithmic applications in the financial market, the hedge funds segment is expected to show the highest growth rate until 2026. For those who do not yet know, a hedge fund is formed by the partnership between different investors, where the fund groups assets of individuals accredited or institutional investors to leverage funds and use various strategies (such as derivatives, futures, etc.) to obtain returns in various markets. Who is using Algo Trading?
For example, in 2017 in the US, the S&P 500 index reached an accumulated high of 19.42% in three years. At the same time, the traditional fund SH Capital Partners recorded returns of 234.1%. Meanwhile, Silver8 Partners and Global Advisors Bitcoin Investment Fund reached 771% and 330.1%, respectively, based on algorithmic trading.
In addition to these two examples, funds such as Blackrock, Renaissance Technologies, Two Sigma, and others are using artificial intelligence (AI) to select the bests stocks to trade. According to the same study in 2018, about 37% of financial institutions in India have invested in technologies focused on artificial intelligence and about 68% plan to adopt it soon. However, the US Algo Trading market was responsible for the largest slice in 2017 and will maintain its dominance over the period envisaged by the survey. This is due to the strong technological advances and to the considerable application of the trading algorithms in several verticals, like banks and financial institutions in the whole region. Algorithmic trading accounts are about 60-73% of all US stock trading, accounting for 7.5% (or $ 1.45 trillion) of the US gross domestic product. Some of the key players in the algorithmic trading market include:
Beyond the US
It is also expected that the Asia-Pacific region will offer the greatest growth opportunities for market participants. For example, the SEBI allowed for algorithmic trading in India in April 2008 by opening up direct market access to institutions. Since then, it has grown rapidly in the various asset classes. In April 2018, SEBI announced plans for new standards to make algorithmic trading more accessible to investors.
In addition, according to QuantInsti's CXO, algorithmic trading potentially helps traders execute operations faster, expand strategic portfolios using more advanced quantitative tools, and remove human errors that often affect the performance of trading strategies. Leandro Guerra Sources: Business Wire and Coherent Market Insights In early 2019, ESMA published a Crypto-Assets Regulation report
Bitcoin’s price up and down has been one of the hottest topicin the most relevant worldwide business newspapers in the last 2 years. Unjustified enthusiasm and skepticism about this phenomenon have been several times said around crypto currencies and Distributed Ledger Technology, forgetting the total crypto-assets market capitalization of around EUR 110bn as of end-December 2018*that justify fully the investments on Crypto Currencies.
The ESMA(European Securities and Markets Authority) just published* an official document about Initial Coin Offerings and Crypto-Assets where it seems to confirm that the direction of ICOs and crypto-assets trading is a trend that they monitor with high attention. Being an official neutral institution ESMA cannot argue about Crypto-Asset future values, but we can say if even the European Regulator is asking which Law should regulate these transactions - exactly like all plain financial assets and derivates – they are convinced that a consolidated market will raise around Crypto-Asset trading. What are Crypto-assets? Crypto-assets are a type of private asset that depends primarily on cryptographyand Distributed Ledger Technology(DLT). There are a wide variety of crypto-assets. Examples of crypto-assets range from so-called cryptocurrencies or virtual currencies, like Bitcoin, to so-called digital tokens issued through Initial Coin Offerings (ICOs). Some crypto-assets have attached profit or governance rights while others provide some consumption value. Still others are meant to be used as a means of exchange. Many have hybrid features. Crypto-assets are relatively new and the market isevolving. There are more than 2,000 crypto-assets outstanding. 
And which is the current value of Crypto-Assets Market?
Hundreds of crypto-assets have been issued since Bitcoin was launched in 2009. There are more than 2,050 crypto-assets outstanding representing a total market capitalization of around EUR 110bn as of end-December 2018 – down from a peak of over EUR 700bn in January 2018.9 Bitcoin represents just over half of the total reported value of market capitalization, with the top five crypto-assets representing around 75% of the reported market capitalization. Why EU is working on a Crypto-Asset regulation? In its 2018 FinTech Action plan, the European Commission requested the European Supervisory Authorities (ESAs) assess the suitability of the EU regulatory framework with regards to ICOs and crypto-assets more generally. Crypto-assets raise specific challenges for regulators and market participants, as there may be a lack of clarity as to how the regulatory framework applies to such instruments. Where it does apply, there may be areas where crypto-assets require potential interpretation or re-consideration of specific requirements to allow an effective application of regulations. ESMA considers it important to take a technology-neutral approach, to ensure that similar activities and assets are subject to the same or very similar standards regardless of their form. So, which are EU next actions on Crypto-Assets Regulation? Where crypto-assets do not qualify as financial instruments (or where they do not fall within the scope of other EU rules applicable to non-financial instruments such as the e-money directive as identified in the EBA’s report and advice on crypto-assets3), ESMA believes that the absence of applicable financial rules leaves consumers exposed to substantial risks. ESMA believes that EU policymakers should consider possible ways to address the risks in a proportionate manner. Which are for EU the risk of Crypto-Assets Market? ESMA is concerned about the risks it poses to investor protection and market integrity. ESMA identifies the most significant risks as fraud, cyber-attacks, money laundering, and market manipulation. Meanwhile, there could be benefits in ICOs provided the appropriate safeguards are in place. The development of tokenisation, i.e., the representation of traditional assets on DLT, could bring benefits, although it is still at a very early stage. Crypto-assets are one application of DLT. ESMA sees many potential benefits in DLT but there are important challenges What’s Next on Crypto-Assets market? ESMA will continue actively monitoring market developments, in an effort to foster supervisory convergence among NCAs. ESMA will also continue to engage with global regulators, as we believe international cooperation is required to address this global phenomenon Finally, being neither a Crypto fundamentalist neither unjustified skeptical, OM thinks that Crypto-Assets market will continue to raise in Europe and we will help you to analyze trends and market movers’ decisions Aurthor: Giulio Mariani *European Securities and Markets Authority, 2019. ‘Advice. Initial Coin Offerings and Crypto-Assets’
I have been researching for some time how to create trading strategies using Quantitative Finance and Machine Learning.
In this article I present a study made exclusively for the Bitcoin Portal to answer the following question: Is a machine learning model capable of overcoming the Buy & Hold in Bitcoin? This is an interesting question even more so in a year like 2018 where bears are dominating the cryptocurrency market. What is machine learning?
In plain English, machine learning is a subfield of computer science that has evolved from the study of pattern recognition and computational learning theories in artificial intelligence (Wikipedia / Encyclopedia Britannica).
That is, it is the ability to write an algorithm (via programming language) that uses an input database (training) to develop a mathematical equation that best represents a certain phenomenon, reducing as much as possible the difference between the predicted and the observed value (test / validation databases). What is Quantitative Finance?
According to an excellent definition of FGV, quantitative finance and financial engineering are the areas of finance that involve the application of tools and methods of traditional finance, mathematics, physics, computer science, economics and econometrics to solve problems of interest in areas such as investment management, corporate finance, risk management, pricing and hedging of derivative instruments, trading, economic finance, structured products and asset allocation.
In a definition I like to use, that's when math meets the investment world. Do past returns influence future returns?
This is a classic question when we talk about trading. The quest for the holy grail - the moneymaking machine - gets the attention of the brightest minds in the world. Even more at this moment where the power of processing, storage and access to data is the greatest of our entire history.
To help answer this question, I selected a database of Bitcoin quotes from every 15 minutes, from May 2017 to July 2018, and calculated the return for the period, going back to a total of 12 periods (or 3 hours, since we speak of intervals of 15 minutes). The central idea is to assess whether there is any interference or past return relationship for Bitcoin's current return. In the bottom chart, the current return is called Return_P0 (on the Y-axis) followed successively from the previous returns from P1 to P12 (on the X-axis of each chart). The scale is decimal representing the percentage of return. The blue line is an added trend line on each chart that represents: • If tilted down, it means that the current return tends to follow inversely past returns. That is, the stronger the market falls or rises, the opposite is perceived in the sequence, a trend to return to the mean; • If it is horizontal, past returns do not influence the current return at all - this is the expected behavior according to the theory of efficient markets; • If tilted up, it means that the current return tends to follow the same direction as past returns, presented what we call momentum; 
Analyzing the graphs, we see that there is not a really significant slope of the blue line in any of the 12 behaviors. However, they are not all exactly horizontal. There is a slight downward slope in some of them, especially the periods P4, P5 and P6. And a slightly positive slope in the first period P1. This is very interesting because it means that the price tends to follow the moment of the last period.
The existence of these small inclinations up or down are the evidence that I needed to develop the mathematical model. Developing the machine learning model
Using the statistical software R, I developed a simple linear regression. A linear regression is that of a kind Y = aX + b that we learn in high school, but no one has the ability to explain what it really means. If I had known these things since then, well ... this is another story.
I divided the database between training and testing. The training of the model consists of the data from 2017 and the test / validation of the model was done with the data of 2018. The target or dependent variable - the information we want to predict - is the (Period_P0) and the independent variables are the returns from P1 to P12. The first result of the model is as follows: 
Leaving aside the mathematical rigor to evaluate the results and to improve the model (they are not the purpose of this article), what matters at the moment is to understand the last information in the table, which are the 3 dots ***
Notice the fourth line of the image (from the bottom up) where we have Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 '' 1 This means that the variables accompanied by *** are the most relevant to explain the phenomenon of the model - our prediction of the return of the current period. And note that coincidence: they are the variables of returns P4, P5 and P6, precisely those with the greatest slope of the line in the first graph. As the other variables are not statistically significant, I will remove them and run a new regression. The result is as follow, cleaner and more organized. 
The moment when Machine Learning Outperforms Buy & Hold
Now I will apply this model in the test base (the year 2018). The result speaks for itself. Whoever bought Bitcoin in early 2018 and held it so far has a return of -44.6%. Anyone who had followed a model like this would have only -10.2% - without considering operational costs.
Of course, one can argue that the result is still negative. However, we must consider three very important points:
Recently, I have been following a thread on the www.forexfactory.com when I saw the post below from the user Cristiano Teixeira (tradista). In my opinion, it carries so many truths about a trader life, about the process on how really to become a trader, that I have decided to share with all readers from the Outspoken Market. Please, read it carefully and paying attention to the details.
"I wasted too many years of my life working a 9 to 5 just to see the unemployment line in my early forties, when the crisis took down half the world by storm. I saw my savings of many years go down the drain in a couple of years (not losses, just regular expenses, kids, mortgage, etc). I can't give advice, I can only tell what I know from my own perspective. Trading or any other endehavior won't save your life when you're in real complicated situation like I once was. What will save your life is keeping your head up, think by yourself (not from others opinions), get good at reading your own gut and instinct about people and about what you want to achieve, always believe you can handle whats coming (not always easy), read as many inspiring stories as you can, learn to trade without indicators and most importantly don't rush it, don't be in a hurry for anything when it comes to trading. Pick a strategy, stick to it and see if it fits your temperament (took me 2 years to find this out as I was so stubborn and obsessed that I should make money to pay the bills, so... best scenario is no rush! Believe me, you'll be glad you took your time, because it'll end being faster than forcing things to work out. Find something to support yourself, either by getting another job (temporarily) or doing your own thing, but don't really expect anything from your trading journey until you actually see it happen! People say "if you can see it in your mind you can achieve it", yeah sure... keep dreaming... of course, that has some truth to it, but that's not it... that's just the starting of the journey of hardship that you must be willing to endure. After seeing it in your mind you must work on your confidence, so you must see it with your eyes! How do you do that if you're a noob? Well, learn, study, trade every day without getting fixed with the profits, don't put your cash on the line, trade on demo for a while, but do it with a plan, a purpose, like "I'm testing this method for the next 30 days" and do it like if it's your bread and butter way of trading... keep track on your progress on that particular system. If it feels good and you didn't cheat or made some stupid non-related trades, then cool, that's the way to go... else, you're not taking it seriously enough, so no wonder you'll stay mediocre and never make money, you're just wasting time and playing the "wanna be" game.
If you really want to become a trader you must commit to becoming very extremely good at one particular method, and master it fully. This takes time. Then, little by little it will make sense and become part of you, of how you view your trading and the market. Put it to the test some more, bottom line being to try to trade under all market conditions possible, using always the same approach. When you start to see the profits based on an absolutely consistent approach, never deviated from the plan, never placed stupid "just trying" or "revenge" trades (which in a live account would cost you) then you'll start being confident in your ability, because you're seeing that you can be profitable each week, each month, time after time again. In the meantime, keep 'saving' money for a real account that will serve you to build on it (not to pay the market fee to learn the hard way!).
Then little by little work on growing the acount(s) by not taking money out too early for expenses, use compounding based solely on your consistency to make good trades. Focus should always be on the process of making the trades and never look at the balance once you're trading live. Then, little by little things start to pickup, people will come to you, you'll start making acquaintance with other traders, investors, whatever and opportunities will appear to grow your potential. Just give it time and stay at it (wanting to master your craft) like a maniac ! That's how I would have done if I could do it again." Cristiano Teixeira
Algorithm. Unless you live disconnected from the universe, you've probably heard or read this word that has changed the world and has more influence on your life than you think. It's never too late to understand it and know how it can help you!
What is an algorithm? It is something that is not just correlated with computer science or scientists. Really! An algorithm is nothing more than a sequence of actions taken to achieve a specific goal. Every day you run an algorithm, from the time you wake up to the moment you go to sleep. And when you sleep, your body also performs various algorithms. The action of preparing a juice or to get ready to go to work in the morning are just a few examples. And that favorite app on your smartphone? They are a set of algorithms! Most people tend to always do the same things. We call this a routine. One may like to have it and others do not. Even when you try to do something out of your routine or when you try not to have one, you are executing some tasks to get away from it. Try to google "how to get away from the routine". A high number of websites, books, and videos suggest you follow certain steps (tasks ?!) to try to do something different :) The same thing happens when you seek to be more productive: you look for tasks that will help you be more efficient, do more things with more quality in the same time. The more complex the objective, the more complex is the algorithm. In the end, what we are really looking for is a way - the best way - to do something we want. As simple as preparing breakfast or complex as having an app that helps us escape the traffic. To make it even clearer here is an example of an algorithm: Algorithm to call someone from a cell phone. 1 - Pick up the phone from your pocket and open the calls app 2 - I know the number of the person? 2.1 - If yes, enter the number 2.2 - If not, search for the number in the phone book 3 - Press Call 4 - Did the person answer? 4.1 - If yes, talk 4.2 - If not: 4.3 - Was it important? 4.3.1 - If yes, wait a while and call again in a few minutes or leave a message 4.3.2 - If no, call tomorrow 5 - End the call 6 - Put your cell phone back to your pocket Simple, right? And why should I care about algorithms? Well, there's no way to get away from them. But it is very easy to benefit from it! And we can not dissociate programming from algorithms either. Programming is to write algorithms in some specific language so that we can automate tasks or execute them faster. In a way, many people are already programmers, from executing simple jobs in Excel to professional applications developers and solutions like programs to do astronomical calculations. Particularly I think that in the near future programming will be a discipline in schools, that we will be learning from an early age (at least it should be). It does not mean that we should all be programmers. The world will always need other types of professionals. However, programming or knowing how to create algorithms don't need to be an exclusive knowledge from software developers or scientists. Given its basis in mathematics and logic, the benefits are numerous. As each day we collect and store more data, knowing what to do with them and how to extract meaning from it, becomes evident how important this subject is. For instance, if you are a psychologist and want to analyze data from a survey done with your patients, of course, you can hire a good professional to help you. But would he have the same knowledge in psychology as you have? There are 3 characteristics that define what a valid algorithm is:
Also, we said at the beginning of the post that the world is full of algorithms. In fact, it always was. What has happened over time is that we have found more efficient ways to run and automate our tasks. This is an endless process, and every time we take it one step further, life becomes easier and more beneficial to all of us. Thank you for the reading and see you next post!
Whether you like it or not, we all have to make bets. The bets we make may not involve gambling, lotteries or casinos, but they are sure involving money and uncertainty. Do you want an example? When you buy a car or a house and buy an insurance, you are not making anything more than a bet; You are exchanging an amount (the value of the policy) in exchange for a higher payment (premium) based on the possibility of an uncertain event (a car accident or problems with your house). This is a kind of gamblimg, as well as health insurance, life insurance and investments. The question here is: Are they worth it?
Common sense always leads us to respond an yes at first. But is that correct? Let's take a simpler example, such as the extended warranty offered by the store when we buy any king of electronic goods. I am sure enough that it's a big deal for retail chains, but is it for you too? Some people say no because the chance of any problem occurring with products that go beyond the factory warranty is very small. Others say that it is not only a chance of a problem to happen, but the time you save and the headache you avoid when you need to exchange the product is worth paying the extended warranty. Luckily the rule to understand whether it really is worth it or not has already been developed centuries ago.
And how do I do it? Very simple, you just have some basic understanding of probability theory.
Now imagine that you are in front of a new TV that costs U$ 600 and offer you the extended 5 year warranty for only "U$ 49". It may seems a small value regarding the total value of the TV...but regardless the apparently low value, this extended warrant does not worth it. Why? Just follow the formula: Honest Warranty = - Risk of Not Missing Nothing x Warranty Price + Risk of Loss x TV Price If the result of this formula is negative or equal to zero, the extended warranty does not worth it. Considering the value of the TV U$ 600, this would be your total expected loss in case of any problem. The extended warranty is only worth it if it is less than the value of the TV multiplied by the chance you having some kind of problem in the next 5 years. A quick search on Google shows that less than 5% of televisions give some trouble in five years. So your chance of having no problems with your new TV is 95% or more. Therefore, our formula is: Honest warranty= 95% x 49 + 5% x 600 Honest warranty= -16.55 As the result is negative, the warranty does not worth it. The fair price of the extended warranty based on the chance of any problem happening to you would be 5% x U$ 1,200, then U$ 30. Now, if you apply the same formula with your car or home insurance, is it worth paying? See you next post!
Unless you live in another reality or are a beginner and naive, you have realized that being a trader is not easy. I have posted on the blog a series of posts about some fundamental points that challenge you as a trader. In today's post I highlight the 5 most important points you need to keep in mind before you start doing your trades.
1. Know Yourself It sounds likea philosophy jargon, but it's not. You need to carefully define your risk tolerance and understand your needs. To profit consistently you must, before knowing the markets, know yourself. The first step is to gain self-awareness of your risk tolerance by allocating the adequate capital. I'm not just talking about the famous risk management, but I'm talking about carefully studying your own financial goals and what you really want to gain from trading. 2. Define your goals, keep your plan Once you know what you want, you should systematically set a deadline and a work plan for your career as a trader. What is failure or success for you? What is your deadline to accept a win/lose situation, perhaps more the lose ones than the winners, as long as you understand that this is an important part of your learning? How long can you devote yourself to your new journey? Do you aim for financial independence or simply want an extra income? These questions must be answered before you actually trade. Answering them is part of establishing your trading plan, your goals. Also, having clear and realistic goals is what will help you succeed. 3. Focus! Do not diversify Yes, it may seems crazy and go against the common sense. But the truth is that the markets are deep and complicated. It is difficult to master all the different kinds of events taking place in this world, so it is a good idea to restrict our vision to the asset we understand and are familiar with, always remembering to join the most liquid and widely traded assets, whether you are a beginner or an advanced trader. 4. Do not add to a losing position. Do not make average price Seriously, do not make average price. Sorry, but I had to be very clear about that. Nothing can be said about the future, so you do not know how long a stock will go up or down. If you are at a loss, the smallest one is the first. 5. Take notes. Study your success and your failure. Have you ever heard about the trading journal? If so, why have not you made one yet? If not, know that the successful trader will keep a journal of his operations, where he will carefully understand his mistakes and successes to find out what works and what does not. This is one of the most important trading tips you will receive from a good mentor. Thanks and see you next post!
As long as you cannot predict an event, it is considered random. However, is it true? What makes something be really random? The absence of a specific pattern? The inability to predict what the next event will be?
Humans have an almost uncontrollable desire to find patterns in things. Our evolutionary process has guaranteed our brain as a fantastic tool to classify things: from recognizing the face of your mother from the time you are born to knowing who you can trust or not (!?) Regardless of what we have learned to classify or not, the big point is that we are too bad at dealing with the lack of patterns. In the post "Predicting Rare Events and Financial Crises" we left open the question "how to calculate the probability of the occurrence of 17 heads in a row if this never happened in your sample? Do you think throwing a die or shuffling a deck are random events? Maybe not. The act of rolling a die is ruled by specific mathematical laws and if we knew precisely the rolling force, the direction of the wind, the friction with the air, initial position of the die, the throwing angle, the point of friction with the surface and all the other variables involved, it would be absolutely possible to predict what the outcome of the launch would be. The same rational can be applied to a deck or trading. So, possibly, randomness does not exist - and not even the free will - since everything is the result of a combination of previous events, and changing any one of them, lead us to have a totally different answer. Any change in the initial conditions of the die will influence the final result. Stopping to look at a bird can prevent you from being hit by a car a few minutes later or make you to meet the love of your life - the famous butterfly effect. If things are like this on a daily basis, why would they be different in the financial markets? When looking at a chart of any asset, the first thing that happens is to fall into the temptation to find a pattern in the prices. As our brain is a beautiful classification machine, we can find several of them already in the first 5 minutes looking at the chart ... we still have the ones who say: if you look at the chart and in less than 1 minute you find nothing, it's because there's nothing to be done! In fact, it is very easy to look at an already drawn chart and find a lot of things that "seem to have happened since others have happened," or better saying, the famous patterns of the technical analysis. What is really difficult is to find these same patterns live, in real time, while the chart is forming. Here is where the "men are separated from the boys". Everything that happened in the past chart was a consequence of past events and events that no one would even know would happen, others already partially expected and all the consequences of the others - remember the butterfly effect described above. Everything that will happen from this second will still be influenced by the speed of response to the events that are happening now, what other people are seeing on the same chart and the consequences of other possible infinite variables. Any change in price now influences the present so much that forecasting what will happen in the future is practically impossible, assuming that we do not know all the variables involved. This discussion brings us closer to chaos theory, as defined in one sentence: systems considered to be dynamic are extremely sensitive to their initial conditions. A personal example: one of the reasons for the 2008 financial crisis was the near-zero US interest rate cut by the Fed Chairman Alan Greenspan, to contain a chain effect caused by the dot-com crisis, the internet business bubble. The crisis of 2008 led the job offers and wages in the US to a huge reduction, a fact that made me choose another place to do my internship. This "new" place was close to a mountain region where I went one day to ski for the first time and I broke my knee! In other words, would I have broken my knee because of the dot-com bubble? Anyway, does randomness exist or does not? Like everything in life, it depends! By the point of view of the classical physics, no...while by the point of view of the quantum physics, yes. The subatomic universe is completely different from the Newtonian physics. With all this talk of randomness, classification and cause-and-effect relationships, we leave an open question for the next post: Is technical analysis a fallacy? |
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