Quantocracy’s Daily Wrap for 11/14/2021

Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis were the most widely used methods for topic modeling for the past 20 years. However, they rely on heavy pre-processing of the text content (custom stop-word lists, stemming, and lemmatization), and require the number of topics to be known. As a result, results of these approaches are often unstable. Moreover, they rely on

Value opportunities arise when market prices deviate from contracts present values of all associated entitlements or obligations. However, this theoretical concept is difficult and expensive to apply. Instead, simple valuation ratios, such as real interest rates or equity earnings yields with varying enhancements, have remained popular. Moreover, value strategies can take a long time to pay off

Now before we dive into testing a strategy we must first define what makes a good pair to test in the first place. This is a question that has not one answer but several depending on the approach you want to take. We will discuss some of the possibilities and the weapons of choice if you want to code a good pairs trading strategy. Testing for Cointegration The idea: While it may be difficult to

Ben and Cameron, which host the excellent Rational Reminder podcast, sit down with Jack Vogel and go through a laundry list of factor investing questions. The topics discussed: 0:27 Do long-only factor premiums survive transaction costs? 2:28 Would the market impact of rebalancing a fund like MTUM also show up in the index? 3:45 What are the capacity constraints for factors? 5:32 What would AA do

Nobody would argue that nowadays, we live in an information-rich society the amount of available information (data) is constantly rising, and news is becoming more accessible and frequent. It is indisputable that this evolvement has also affected financial markets. Machine learning algorithms can chew up big chunks of data. We can analyze the sentiment (which is frequently related to the