Machine Learning (ML) is a form of data processing in which computers are trained to recognize patterns in large volumes of data by having them navigate through a set of simulated situations that are repeatedly shown to them. Machine learning allows humans to take data from the past and present and create predictive models that can more accurately anticipate the future.
In other words, Machine Learning is the quest of computers to learn to do certain tasks in a self-learning manner. The best way to explain it is to use an example. Imagine that every time a new customer walks in the door, you like to know that customer’s name, when they’re buying your products, what their demographics are and how many sales they are bringing to your business. You also like to know that they are returning customers, which is usually referred to as a repeat customer. So that’s all you want to know about customers. If the customers are return customers you want to know how many times they have bought from you, that would be how to keep on returning customers. It’s a small number. And you want to know what your competition is doing. You don’t want them copying you, you want to know what their marketing and sales is doing and how they are marketing and how they are treating customers.
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Before we move ahead and learn more about what is machine learning, let us understand a few machine learning algorithms.
Machine Learning Algorithms:
Machine learning algorithms are a form of rules-based systems that use algorithms to create and refine the models that are relevant to a certain situation. The development of algorithms can be attributed to seminal works of the 1960s, such as the “Linear Algorithm” developed by Herbert A. Simon and “Karp’s algorithm” by Paul Karp. This article was first published by Investopedia, a self-directed educational resource and the world’s largest financial community. Investopedia is where investors go for trusted information about investing.
In brief, A Machine Learning Algorithm is nothing but a set of rules, which helps to select which variables will be most significant in the predictive model. These rules are specified and enforced by the algorithm that is trained over the data set. This is a type of artificial intelligence used to simulate the human learning process. In general, a machine learning algorithm has two inputs that can be the value of the input variable and the predicted value of the output variable. The output is usually presented in a mathematical format in a system called a table. The output data set is usually used for solving different types of mathematical problems. The reason why the output can be presented in a mathematical form is because the output of the machine learning algorithm is normally compared to the output value to determine which variables predicted better. This is known as validation, which is the process of validating the predictions of the machine learning algorithm.
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Although Machine Learning algorithms can be very powerful and they are being implemented by the financial analysts at a very large scale, they have a tendency to underperform. It is because of two major reasons. The first is the limited scope of the decision making.
Rather than using the input variables to generate the predicted outputs, a machine learning algorithm is trained on the data to generate predictions. The algorithm is not able to analyse the impact of one variable on the performance of the other. This process is known as control. So, the final output is not always a reliable indication of the future performance of the portfolio.
The second reason is that while we take an analytical approach to analysing a business and its financial performance, most businesses do not give us information about their fundamental performance or market indicators. A lot of research is required to understand if a business will succeed or not. Also, we need to look at the market conditions to determine if the market conditions are conducive for that business. This is known as exogenous, which is an external force that influences a specific outcome. Exogenous is any factor or situation that the firm can neither fully predict nor understand.
There is also another reason why machine learning does not perform as well as an analytical approach. Most of the models require two input variables, which are known as auxiliary variables. A common auxiliary variable is capital expenditure. Capital expenditure may increase or decrease. So, if capital expenditure rises, then sales would decline. But this does not hold true in case of most business models. A firm must meet the sales targets or it has to stop operations. So, capital expenditure is usually a critical input. But these auxiliary variables are ignored while the model is designed. This could result in incorrect predictions. This aspect is a big area of research that many researchers are working on.
Now that we know what is machine learning, let us understand how it can be used in portfolio strategy.
Use of Machine learning in Portfolio Strategy :
Machine learning can be applied to portfolio analysis. It gives you the capability to understand what could happen to different parts of your portfolio over the coming year. A collection of data points, which helps to analyze returns over a period of time is called a predictive model. Predictive models could include macroeconomic data, as well as data on stock prices and movements, etc. Machine learning identifies patterns within data points to gain information about the future. In fact, it’s important to note that forecasting over long periods of time with data sets is not an exact science. Instead, it’s usually a study in understanding how the market moves and the movements that create these movements.
This brings us to the end of the blog on what is machine learning. We hope that you were able to gain valuable insights from te same.