Investors, just like animals, make their best bets based on the information available to them and their previous experience. Working on quantitative neuroscience and animal behaviour for almost 10 years has given me a rich view on decision making.In my personal view, animals collect information from their senses, directly or by interacting with other individuals, and make consequent decisions. These decisions may allow them to eat, mate, hide, catch prey or simply survive another day, just like investors. Animals make their best bets based on the available information, their instincts, and previous knowledge. Their instincts, encoded in their genome, constitute a general idea of how the world works, what specific sensory inputs may mean and the consequences of a given action.

Machine Learning is a hot topic in the science world right now. By combining the powers and capabilities of both computers and humans, perplexing and unimaginable problems are being resolved as we speak. Machines nowadays can more easily handle the ginormous amount of data constantly being produced, and decipher the complexity of scientific discoveries. Researchers have begun to recognise the potential this science can have in a vast variety of fields, and it's finally being put into practice. On researching the topic, many of the techniques and algorithms will seem familiar to a lot of statisticians, engineers, programmers, mathematicians and quants.

Ladies and gentlemen, place your bets, today we are going to do our best to beat one of the most frightening opponents that you can face in finance: a monkey. As you probably already know, in this blog we are all quite obsessed with predicting trends and returns, you can find other nice attempts in'Markov Switching Regimes say… bear or bullish?' by mplanaslasa or'Predict returns using historical patterns' by fjrodriguez2. Today, we are trying to predict the sign of tomorrow's return for different currency pairs, and I can assure you that a monkey making random bets on the sign and getting it right 50% of the time is going to be a tough benchmark. We are going to use an off the shelf machine learning algorithm, the support vector classifier. Support Vector Machines are an incredibly powerful method to solve regression and classification tasks.

Machine Learning is a hot topic in the science world right now. By combining the powers and capabilities of both computers and humans, perplexing and unimaginable problems are being resolved as we speak. Machines nowadays can more easily handle the ginormous amount of data constantly being produced and decipher the complexity of scientific discoveries. On the other hand, researchers have begun to recognise the potential this science can have in a vast variety of fields and finally it is being put into practice. On researching the topic, many of the techniques and algorithms will seem familiar to a lot of statisticians, engineers, programmers, mathematicians and quants.

We are going to introduce the Markov Switching Regimes (MSR) model which, as its name indicates, tries to capture when a regimen has changed to another one. This would be a change between opposite trends or it could consist in passing from "being in trend" to "not being in trend" and vice versa. The name of Markov could sound familiar to some of you as j3 introduced what the Markov chains were a couple of years ago. The main characteristic of this stochastic process is that in a stage t, the probability of occurrence only depends on what happened in the immediately previous stage, t-1. In our post we will assume that the trend of an index today will depend only on which trend was living yesterday, this means, the index will be governed by a Markov chain.

Is it possible to classify and predict (yes, predict!) if market trends will be bullish, bear or ranged by using a method called "naïve" and based on something as simple as Bayes' theorem is? Let's see! Our main objective is to explore techniques of machine learning that can help us not only to label series in a posteriori analysis, but also to predict to which class a new value given of the serie belongs to. The Naïve Bayesian Classifier is a supervised learning method of machine learning as well as a statistical method for classification. Although this method is including in its name a word as rare as "naïve" is, it will be our tool chosen to predict different trends of a market represented by an index. Bayesian classification provides practical learning algorithms where prior knowledge and observed data can be combined.

It is known that data mining algorithms are not perfect and they can fail under certain conditions. K-Means is an example of that triviality but there is a good alternative, K-Medoids. In a previous post, "Machine Learning: A Brief Breakdown" we already mentioned that K-Means is the cluster analysis algorithm par excellence and it is one of the most important data mining and machine learning techniques; even psanchezcri used it to analyze the direction of a financial time series, in his post "Returns clustering with K-means algorithm". Nevertheless, it's difficult to find discussions about the algorithm's unexpected results in certain cases. The algorithm documentation is too broad in Internet, so the main objective of this post is to focus on showing a financial example of the problem.

Bagging and Boosting are both ensemble methods in Machine Learning, but what is the key behind them? Bagging and Boosting are similar as they are both ensemble techniques, where a set of weak learners are combined to create a strong learner that obtains better performance than a single one. So, let's start from the beginning: Ensemble is a Machine Learning concept in which the idea is to train multiple models using the same learning algorithm. The ensembles take part in a bigger group of methods, called multiclassifiers where a set of hundreds or thousands of learners with a common objective are fused together to solve the problem. In the second group of multiclassifiers are the hybrid methods.

How about trying to find any use of the famous Monty Hall problem in a stock index context? First of all, some of you may be confused because neither "Monty Hall problem" nor "Let's make a deal" are familiar to you so I will refresh you what these names are concerned to. Monty Hall was a TV presenter for "Let's make a deal", a famous American show in the sixties. Suppose you're on this game show and you're given the choice of three doors: behind one door there is a prize; behind the others, there is nothing. You pick a door, say number 1, and the host, who knows what's behind the doors, opens another door, say number 3, which results to be empty.