What's the distinction between organized and unstructured data? We will learn how to work with unstructured financial data and then transform it into a structured dataset that can be used by $mathrmML$ algorithms. In general, you should avoid consuming someone else's processed dataset because you will most likely find what someone else already knows or will figure out shortly. Ideally, your starting point will be a collection of unstructured, raw data that you will analyse in order to generate relevant characteristics.

What's the distinction between organized and unstructured data? We will learn how to work with unstructured financial data and then transform it into a structured dataset that can be used by $mathrmML$ algorithms. In general, you should avoid consuming someone else's processed dataset because you will most likely find what someone else already knows or will figure out shortly. Ideally, your starting point will be a collection of unstructured, raw data that you will analyse in order to generate relevant characteristics.

We spoke about how to create a $X$-dimensional matrix of financial variables from an unstructured dataset. Unsupervised learning algorithms can learn patterns from that matrix $X$, such as whether or not it has hierarchical clusters. Supervised learning techniques, on the other hand, require that the rows in $X$ be associated with an array of labels or values $y$, so that those labels or values may be predicted on unseen feature samples. We'll talk about how to classify financial data in this section.

What's the distinction between organized and unstructured data? We will learn how to work with unstructured financial data and then transform it into a structured dataset that can be used by $mathrmML$ algorithms. In general, you should avoid consuming someone else's processed dataset because you will most likely find what someone else already knows or will figure out shortly. Ideally, your starting point will be a collection of unstructured, raw data that you will analyse in order to generate relevant characteristics.

What's the distinction between organized and unstructured data? We will learn how to work with unstructured financial data and then transform it into a structured dataset that can be used by $mathrmML$ algorithms. In general, you should avoid consuming someone else's processed dataset because you will most likely find what someone else already knows or will figure out shortly. Ideally, your starting point will be a collection of unstructured, raw data that you will analyse in order to generate relevant characteristics.

What's the distinction between organized and unstructured data? We will learn how to work with unstructured financial data and then transform it into a structured dataset that can be used by $mathrmML$ algorithms. In general, you should avoid consuming someone else's processed dataset because you will most likely find what someone else already knows or will figure out shortly. Ideally, your starting point will be a collection of unstructured, raw data that you will analyse in order to generate relevant characteristics.

What's the distinction between organized and unstructured data? We will learn how to work with unstructured financial data and then transform it into a structured dataset that can be used by $mathrmML$ algorithms. In general, you should avoid consuming someone else's processed dataset because you will most likely find what someone else already knows or will figure out shortly. Ideally, your starting point will be a collection of unstructured, raw data that you will analyse in order to generate relevant characteristics.

What's the distinction between organized and unstructured data? We will learn how to work with unstructured financial data and then transform it into a structured dataset that can be used by $mathrmML$ algorithms. In general, you should avoid consuming someone else's processed dataset because you will most likely find what someone else already knows or will figure out shortly. Ideally, your starting point will be a collection of unstructured, raw data that you will analyse in order to generate relevant characteristics.

What's the distinction between organized and unstructured data? We will learn how to work with unstructured financial data and then transform it into a structured dataset that can be used by $mathrmML$ algorithms. In general, you should avoid consuming someone else's processed dataset because you will most likely find what someone else already knows or will figure out shortly. Ideally, your starting point will be a collection of unstructured, raw data that you will analyse in order to generate relevant characteristics.

What's the distinction between organized and unstructured data? We will learn how to work with unstructured financial data and then transform it into a structured dataset that can be used by $mathrmML$ algorithms. In general, you should avoid consuming someone else's processed dataset because you will most likely find what someone else already knows or will figure out shortly. Ideally, your starting point will be a collection of unstructured, raw data that you will analyse in order to generate relevant characteristics.

What's the distinction between organized and unstructured data? We will learn how to work with unstructured financial data and then transform it into a structured dataset that can be used by $mathrmML$ algorithms. In general, you should avoid consuming someone else's processed dataset because you will most likely find what someone else already knows or will figure out shortly. Ideally, your starting point will be a collection of unstructured, raw data that you will analyse in order to generate relevant characteristics.

One of the most common errors in financial research is taking some data, running it through an ML algorithm, backtesting the predictions, and repeating the process until a nice-looking backtest appears. Such pseudo-discoveries abound in academic journals, and even significant hedge funds are prone to falling into this trap. It makes no difference if the backtest is an out-of-sample walk-forward. The fact that we are repeating a test on the same data will almost certainly result in a discovery. This methodological error is so well-known among statisticians that the American Statistical Association warns against it in its ethical guidelines (American Statistical Association [2016], Discussion #4). It usually takes around 20 iterations to find a () investment strategy with a standard significance level ( positive rate) of 5%.