Thank you Carlos for sharing an empirical gem! This seems opening a new and fascinating area in the quant trading field.
I am no expert in long/short system, but still I would like to ask whether the trading cost (commissions, slippage, short cost) will eat up the overall returns by much if we use a more harsher trading environment simulation? Thanks.
Thanks! You’re absolutely right: trading costs can quickly erode performance, especially in fast-rebalancing strategies.
In my tests, I already include commissions. Slippage can be largely ignored since trades are executed with MOC orders, and short borrow costs are negligible given the 24-hour holding period.
That said, if you raise commissions to typical retail levels, a daily-rebalanced strategy likely won’t hold up. You’d need to trade less frequently — say weekly or monthly — to keep it viable.
Great article. The method gives quants focusing on mid and large caps new strategies to codify smart money behavior. I assume that for small and micro caps (my focus) the data is too sparse to be useful?
There are several advantages to using word embeddings instead of character embeddings when training a deep neural network. First, word embeddings provide a higher level of abstraction than character embeddings. This allows the network to learn the relationships between words, rather than the individual characters that make up those words. This can lead to improved performance on tasks such as language modeling and machine translation.
Second, word embeddings are typically much smaller than character embeddings. This is because each word is represented by a single vector, rather than a vector for each character in the word. This can make training faster and more efficient.
Third, word embeddings are already available for many languages, which can save time when training a new model.
There are also some disadvantages to using word embeddings. One is that they can be less accurate than character embeddings, especially for rare words. Another is that they can be less effective for tasks that require understanding of the syntactic structure of a sentence, such as parsing.
Very nice explanation of the paper and hands on replication work! One question I have, if an institutional portfolio manager runs a long/short equity portfolio, the pairs of stock being long/short would be closely correlated but the position is opposite sign. Then the ordering of his position sizing would not properly reflect stock's adjacency to each other right?
Excellent work, Quantitativo. Your analysis is consistently sharp and engaging, and this strategy shows real promise!
Cool! Seems the hardest part would be data handling at first
it always is :)
Also, it would be hard to trade in real life for such a large portfolio with equal weighting like what you back testing
That’s really interesting! Do you guys usually discuss and try to implement this paper and others like it in your group?
Yes! That is THE PURPOSE of the community :)
Thank you Carlos for sharing an empirical gem! This seems opening a new and fascinating area in the quant trading field.
I am no expert in long/short system, but still I would like to ask whether the trading cost (commissions, slippage, short cost) will eat up the overall returns by much if we use a more harsher trading environment simulation? Thanks.
Thanks! You’re absolutely right: trading costs can quickly erode performance, especially in fast-rebalancing strategies.
In my tests, I already include commissions. Slippage can be largely ignored since trades are executed with MOC orders, and short borrow costs are negligible given the 24-hour holding period.
That said, if you raise commissions to typical retail levels, a daily-rebalanced strategy likely won’t hold up. You’d need to trade less frequently — say weekly or monthly — to keep it viable.
This is really interesting! Thank you.
Great article. The method gives quants focusing on mid and large caps new strategies to codify smart money behavior. I assume that for small and micro caps (my focus) the data is too sparse to be useful?
There are several advantages to using word embeddings instead of character embeddings when training a deep neural network. First, word embeddings provide a higher level of abstraction than character embeddings. This allows the network to learn the relationships between words, rather than the individual characters that make up those words. This can lead to improved performance on tasks such as language modeling and machine translation.
Second, word embeddings are typically much smaller than character embeddings. This is because each word is represented by a single vector, rather than a vector for each character in the word. This can make training faster and more efficient.
Third, word embeddings are already available for many languages, which can save time when training a new model.
There are also some disadvantages to using word embeddings. One is that they can be less accurate than character embeddings, especially for rare words. Another is that they can be less effective for tasks that require understanding of the syntactic structure of a sentence, such as parsing.
Very nice explanation of the paper and hands on replication work! One question I have, if an institutional portfolio manager runs a long/short equity portfolio, the pairs of stock being long/short would be closely correlated but the position is opposite sign. Then the ordering of his position sizing would not properly reflect stock's adjacency to each other right?
Now do none cherry picked years for robustness or nah?
Hi John! Thank you for your question! I testedI only in the years reported because that’s ALL the data I have ;)