Can algorithmic trading beat the market?: An experiment with S&P 500, FTSE 100, OMX Stockholm 30 Index
Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
The research at hand aims to define effectiveness of algorithmic trading, comparing with different benchmarks represented by several types of indexes. How big returns can be gotten by algorithmic trading, taking into account the costs of informational and trading infrastructure needed for robot trading implementation?
To get the result, it’s necessary to compare two opposite trading strategies:
1) Algorithmic trading (implemented by high-frequency trading robot (based on statistic arbitrage strategy) and trend-following trading robot (based on the indicator Exponential Moving Average with the Variable Factor of Smoothing))
2) Index investing strategy (classical index strategies “buy and hold”, implemented by four different types of indexes: Capitalization weight index, Fundamental indexing, Equal-weighted indexing, Risk-based indexation/minimal variance).
According to the results, it was found that at the current phase of markets’ development, it is theoretically possible for algorithmic trading (and especially high-frequency strategies) to exceed the returns of index strategy, but we should note two important factors:
1) Taking into account all of the costs of organization of high-frequency trading (brokerage and stock exchanges commissions, trade-related infrastructure maintenance, etc.), the difference in returns (with superiority of high-frequency strategy) will be much less .
2) Given the fact that “markets’ efficiency” is growing every year (see more about it further in thesis), and the returns of high-frequency strategies tends to decrease with time (see more about it further in thesis), it is quite logical to assume that it will be necessary to invest more and more in trading infrastructure to “fix” the returns of high-frequency trading strategies on a higher level, than the results of index investing strategies.
Place, publisher, year, edition, pages
2012. , 77 p.
Algorithmic trading, high-frequency trading, investment strategy
IdentifiersURN: urn:nbn:se:hj:diva-19495OAI: oai:DiVA.org:hj-19495DiVA: diva2:555559
Subject / course
2012-09-03, B5062, Jonkoping, 11:00 (English)
UppsokSocial and Behavioural Science, Law