Eoin Treacy's view -
Machine learning is everywhere
“Machine learning” repeatedly appears in the news, from the game of go to autonomous cars: what can those algorithms do for us in finance?
Supervised learning and its pitfalls in finance
In this first report in the series, we focus on supervised learning and note that while machine learning is very relevant to us, there are dangerous pitfalls, sometimes specific to the type of data we deal with. In particular, we examine penalized regression (lasso and elastic net), decision trees, and boosting – we also mention, in passing, support vector machines and random forests.
Application to the Japanese equity market
To make things more concrete, we try to use those algorithms to combine the investment factors in our database in order to build a stock ranking system for the Japanese market; this shows the limitations and pitfalls of traditional machine learning practices in finance.
A link to the full report is posted in the Subscriber's Area.
Long Term Capital Management represented something of a genesis for quantitative strategies and their sophistication has been enhanced considerably since. The pace of adoption has accelerated in the last few years as the breadth of data from both conventional and unconventional sources has increased at an exponential rate and companies like Google and Baidu have demonstrated in real terms what is possible with these tools.
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