The growth versus value battle rages on. Growth investors seek companies that offer strong earnings growth, and value investors look for stocks that appear to be undervalued. Value, notwithstanding its incredible oracles, has performed relatively poorly over the last decade, and may now be on the wrong side of rampant technological disruption, driven, in part by COVID-19.

Of course, the jury is still out on the economic recovery. Value has seen some rotation into cyclicals, as is typically the case at the beginning of a recovery. …

Fundamental analysis continues to face numerous challenges. The COVID-19 crisis has led to delayed earnings, repeated analyst revisions, suspended dividends, and the absence of earnings guidance. Quantitative analysis has long sought to encroach on this territory with its rock-star math. But quant trading models and strategies that mostly rely on pattern recognition can run afoul of the cardinal sin of overfitting. They may also fail to adequately control for political, structural, and behavioural idiosyncrasies.

Given the recent volatility and relative underperformance of passive investment strategies, both fundamental and quantitative approaches are looking to reassert their active selves. But increasingly, the generation of alpha requires a combination of methods. …

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It’s notoriously difficult to extract any predictive power or “alpha” from your data in finance. When you do, it often quickly decays because the predictive power is also being mined by other Data Scientists or Quants out there.

Nevertheless, 80% of the daily moves in US stock markets are algorithmic trades and Quant hedge funds currently manage over $1 trillion, which means they must be doing something right?

This short article will share insights collected from some leading data scientists in finance to help you improve your productivity and the accuracy of your machine learning modelling.

1. Asking the right questions of data in finance

It can be very tempting to build models to forecast prices or exact values, yet this approach is extremely difficult and the utility of knowing the exact values is limited. …

Photo by Carlos Muza on Unsplash

According to the Economist and industry leaders such as Andrew Ng and Google’s CEO Sundar Pichai, data has become the new oil and machine learning the new electricity.

This has encouraged organizations to launch their own data science initiatives in order to make sense of the treasure trove of data they’ve built over the years and apply it in profitable ways. However, in many cases, there is so much data that organisations struggle to assess whether they are holding oil or mud.

This short article will outline how to solve three core data problems and turn your organization into a value-generating data refinery. …


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