Quantocracy’s Daily Wrap for 01/04/2018

As traders, we of course need money to make money, but not everyone has 10-50k of capital lying around to start one's trading journey. Perhaps the starting capital is only 1k or less. This article describes how one can take a small amount of capital and multiply it as much as 10 fold in one year by taking advantage of large market inefficiencies (leading to arbitrage opportunities) in the

Back in June of 2016, I wrote this post about random entries and trailing exits. It turns out (on average) that you can beat buy-and-hold of the S&P 500 by simply buying members of the S&P 100 randomly, as long as you a) have a market-timing filter, and 2) have a trailing stop of 20%. Yes thats right, just pick them at random! Here are the details of that original system (its

The January effect, in common with the Halloween indicator and sell in May and go away, is a catchy, get-rich-quick investment idea adored by financial commentators because it is so easy to explain to unsophisticated readers. It rests on the claim that the U.S. stock market performs better in January, compared to the other months in the year. Unfortunately, financial reports

Last nights subscriber letter featured (an expanded version of) the following study, which looks at performance in the 1st couple of days following a positive 1st day of a new year. 2018-01-03 The stats and curve all suggest some immediate follow-through has been typical. There have now been 9 winners in a row, with the last loser occurring in 1998. Also notable is that 24 of the 26 instances

Deep learning is an active area of research in machine learning. I train deep feedforward neural networks (DFN) based on a set of 68 firm characteristics (FC) to predict the US cross-section of stock returns. After applying a network optimization strategy, I find that DFN long-short portfolios can generate attractive risk-adjusted returns compared to a linear benchmark. These findings underscore