October 2018 has started with a frenzy of activity in the markets. This post explores which way the funds have been flowing in October so far. It is a follow-up to a prior post which found that fund flows can provide a contra-indicator of future market direction.
Short sellers are often thought to be among the most informed market participants. Can a systematic, quantitative trader find meaningful edge following (or fading) the actions of short sellers? This article crunches the numbers for 2000 stocks across a full year of daily data to find signal inside of shorting.
We've all had the feeling - buying or selling at the worst possible time. Rest assured, you're not alone. The average investor significantly underperforms the market indices because the market consistently lures them into buying high and selling low. This post delves into the data and finds that typical ETF investors underperform the funds they use by 1 to 4% per year.
This post is going to delve into the mechanics of walk-forward modeling which is, in my view, the most robust way to train and apply machine learning models in inherently sequential domains like finance. The overriding objective of the methods described here is to overcome the issues inherent in traditional cross validation approachs.
Investors in the US have the luxury of ignoring exchange rates when trading in markets quoted in US dollars. However, this can create a blind spot to the giant impact that exchange rates have on prices and returns.
This fourth post in a series on transforming data into alpha provides the mechanics of walk-forward modeling which is often regarded the most robust way to train and apply machine learning models in inherently sequential domains like finance.
There's an old wall street axiom which advises to "sell in May and go away". Has this advice ceased to work in modern markets? Are there other seasonal patterns in the broader market? This analysis evaluates seasonal patterns on the S&P 500 from 1993 to present.
This is the third post in my tutorial series on applying machine learning to stock prediction. This post is going to delve into the mechanics of feature selection - a critical step towards improving model robustness. This will cover a systematic approach for choosing between the many variations of features you've created during the feature engineering stage.
This post is going to delve into the mechanics of feature engineering for the sorts of time series data that you may use as part of a stock price prediction modeling system. I'll cover the basic concept, then offer some useful python code "recipes" for transforming your raw source data into features which can be fed directly into a ML algorithm or ML pipeline.
This first post of a tutorial on machine learning in finance will present a framework for organizing and working with data. Perhaps not the most electrifying of topics, but it's the foundation for later modeling tutorials. It's also of critical significance and importance. I've heard it said that 90% of time in real-world quant finance is spent on data rather than models.