Be data-focused, and include seasonality options in the model calibration process — Time and seasonality features are often assumed in time series analysis, ignoring their crucial role as an input in model calibration. More recently, cyclic seasonality variables have become highly popular in data science. …

Using regression & statistical inference to assess the changes since the pandemic — Bitcoin is the largest cryptocurrency by far, and there has been a marked change in its relationship to broader financial markets since the pandemic. Bitcoin is moving more in sync with the stock market in particular, and to some extent to the US dollar too. This article is not focused…

With Bitcoin time series data — Finance workers have long relied on the trusty Excel spreadsheet. But times they are a-changin’, and there is a shift to Python among other coding languages. …

Dynamic factor modelling of US large-cap equities — Much of what impels human behaviour is not directly observable. This is a common refrain in data analysis of social phenomenon, such as the socio-cultural drivers of education and income. Or in financial markets where many of the underlying factors that drive the buying or selling of particular securities at…

A case study on optimizing class probability in the Portuguese bank marketing dataset — Classification algorithms are often able to output predicted probabilities. Sometimes these predicted probabilities are of interest themselves, such as when assessing betting odds. Predicted probabilities may also help with imbalanced data by giving us the option of adjusting the classification threshold to improve model predictions.

Testing mean-variance optimization with Bayesian probabilistic modeling — We have little to no control over the actual return from our financial investments. We hope for a positive return, but there is no guarantee. The only thing we can control throughout the process is the volatility in the value of those investments, and we do this through diversification. It…

Using unsupervised machine learning to identify behavioral currency clusters — How might we deal with a jumble of jigsaw puzzle pieces? A tried-and-tested strategy is to start by spreading out the pieces so they may be taken in with a glance. Then we sort them into recognizable groups, first of edge pieces and then by color. Grouping the pieces according…

How relying on historical daily returns statistics may provide false inferences about drawdown risk in the S&P 500 Index — Financial markets are a cornucopia of delights for data scientists, disgorging endless reams of data. Yet that temptation can be a siren song for the unwary because financial market statistics are slippery and treacherous. …

Crafting & testing a dynamic Recency-Frequency-Monetary (RFM) model — The 2012 news story that Target could predict its customers’ pregnancies was arguably a watershed moment in machine learning’s rise to mass consciousness. Apocryphal or not, the story went viral, swathed in those layers of wonder and anxiety that embody the popular view of AI. …