In the prior post Tracking Posts on WallStreetBets - Part I, we demonstrated how relatively easy it is to extract reddit activities related to a given stock ticker - in their raw form. If you haven't already read that post, you may want to take a moment to skim that article. In this post, we

The news story in 2021 that captured the complete attention of the financial press was the Gamestop / WallStreetBets / RoaringKitty episode of late January. A group of presumably small, retail traders banded together on Reddit's r/wallstreetbets forum to drive the price of $GME, $AMC and other

You may have noticed that this blog has been mostly inactive in 2019. Earlier this year, I took a full-time position with one of my buy-side consulting clients focused on researching methodologies for application of ML / data science to alpha research. While I'm thoroughly enjoying the work, it

Markets are, in my view, mostly random. However, they're not completely random. Many small inefficiencies and patterns exist in markets which can be identified and used to gain slight edge on the market. These edges are rarely large enough to trade in isolation - transaction costs and overhead

The field of deep learning is frequently described as a mix of art and science. One of the most "art-sy" parts of the field, in my experience, is the subject of network topology design - i.e., choosing the right geometry, size, depth, and type of the network. Machine learning practitioners

Several weeks back, I posted some work I had done on ETF fund flows and what they could tell us about how investors, on average, fare with respect to timing their entries and exits. TL;DR: Most investors are terrible at timing inflows and outflows to the market. They badly trail benchmarks because

To most investors, short selling is a shadowy, mysterious corner of the markets. Many do not make use of shorting - and I suspect a majority don't understand how to glean insights from trends in short selling activity. Over the past several years, I've traded short about as often as long

"We have met the enemy, and he is us" - Walt Kelly It has long been noted that investors - individual and institutional - tend to be their own worst enemies. They have an uncanny ability to buy stocks near market tops and sell near market bottoms. As a consequence, investor returns, in

Use of machine learning in the quantitative investment field is, by all indications, skyrocketing. The proliferation of easily accessible data - both traditional and alternative - along with some very approachable frameworks for machine learning models - is encouraging many to explore the arena.

It's easy to forget that virtually all asset classes we hold are priced in US dollar terms. Portfolio valuations are as impacted by the denominator (US dollars) as by the numerator (asset value) Commodity assets like oil and gold are highly (negatively) correlated to US dollar strength. Certain

Key Takeaways: Traditional methods of validation and cross-validation are problematic for time series prediction problems The solution is to use a "walk-forward" approach which incorporates new information as it becomes available. This approach gives us a more realistic view of how

Most investors have heard the adage "Sell in May and go away" which reflects the common wisdom that markets perform less well during the summer months than during the winter. This anomaly is well described here. Many widely held beliefs go away, precisely because they're widely held

This is the third post in my series on transforming data into alpha. If you haven't yet see the data management and guide to feature engineering, please take a minute to read those first... This post is going to delve into the mechanics of feature selection to help choose between the many