Why Banks May Build or Buy Algorithmic Trading Capabilities

# Is it likely that banks would become clients of algotrading companies?


# Is it likely that banks would become clients of algotrading companies?












Algotrading is growing, while banks don't currently have the HR to continually develop sophisticated algorithms on their own.

Is it likely that banks (and governments) would become clients of algotrading companies, or instead open their own algo dev departments to compete agains everyone else?

## Answer by Barnaby (score 1)

https://quant.stackexchange.com/a/17633

I dont think neither of both ideas are to be very fertile in the present structures of banks or asset management firms. There are several factors that have influenced the birth of algo trading.

1) The development of computer processing capacity behyond human capabilities which provided the hability to process more information than any team would. This will eliminate or substitute all present personnel as per it requires mostly different knowledge than present.

2) Lack of knowledge at the management level required in banks to formulate and perform and implement team building as per the complexity and knowledge required to have real quantitative prediction and modeling. Deriving from good quant houses where their offices are stuffed by physicists, mathematicians and comp science phds.

3) Banks and most fund management companies internal investing today is driven by/sell side fundamental and technical driven ideas rooted in the investing ideas from prior and up to the 80s. These have short to long terms horizons with holing periods that range from weeks to years. In contrast algo trading and its development is usually focused on short to very short term holding periods of securities. There are some factors that may influence this.

4) Low frequency trading and high frequency trading statistical properties of the time series may differ as per the properties of non stationarity and noise types afecting the time series. Due to this reason some of the analytical methods developed for higher frequency may not be aplicable to low frequency. The complexity of good trading algo companies is beyond the comprehension of most personnel departments from funds and banks.

5) Fundamentally banks and asset management firms have been driven to focus on not diverging much from index returns and competitors and not providing alpha. This has been provided via tactical asset allocation which in general is marginal while structural asset allocation provided the backbone of the weight. In contrast algo companies are not index competing and their allocations and security selection is not index but alpha generation driven.

There is a culprit on all this and you can see what happens as per the example in high freq trading. In this segment the game is "the winner takes it all" and this depends on who has the fastest machines, fastests algorithms at any given time. so it depends on processing and routing speed as well as but to a lesser degree in the degree of algo sofistication (most firms use the similar algos with very few firms having the knowledge to formulate breakthroughs and get significantly ahead of competition). So if a segment such us low freq trading will implement such an approach it may have a similar effect.