Can algorithmic trading beat the market? : An experiment with S&P 500, FTSE 100, OMX Stockholm 30 Index

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 trad...

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Main Author: Kiselev, Ilya
Format: Others
Language:English
Published: Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Economics, Finance and Statistics 2012
Subjects:
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-19495
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spelling ndltd-UPSALLA1-oai-DiVA.org-hj-194952013-01-08T13:43:58ZCan algorithmic trading beat the market? : An experiment with S&P 500, FTSE 100, OMX Stockholm 30 IndexengKiselev, IlyaInternationella Handelshögskolan, Högskolan i Jönköping, IHH, Economics, Finance and StatisticsInternationella Handelshögskolan, Högskolan i Jönköping, IHH, Nationalekonomi2012Algorithmic tradinghigh-frequency tradinginvestment strategyThe 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. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-19495application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
topic Algorithmic trading
high-frequency trading
investment strategy
spellingShingle Algorithmic trading
high-frequency trading
investment strategy
Kiselev, Ilya
Can algorithmic trading beat the market? : An experiment with S&P 500, FTSE 100, OMX Stockholm 30 Index
description 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.
author Kiselev, Ilya
author_facet Kiselev, Ilya
author_sort Kiselev, Ilya
title Can algorithmic trading beat the market? : An experiment with S&P 500, FTSE 100, OMX Stockholm 30 Index
title_short Can algorithmic trading beat the market? : An experiment with S&P 500, FTSE 100, OMX Stockholm 30 Index
title_full Can algorithmic trading beat the market? : An experiment with S&P 500, FTSE 100, OMX Stockholm 30 Index
title_fullStr Can algorithmic trading beat the market? : An experiment with S&P 500, FTSE 100, OMX Stockholm 30 Index
title_full_unstemmed Can algorithmic trading beat the market? : An experiment with S&P 500, FTSE 100, OMX Stockholm 30 Index
title_sort can algorithmic trading beat the market? : an experiment with s&p 500, ftse 100, omx stockholm 30 index
publisher Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Economics, Finance and Statistics
publishDate 2012
url http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-19495
work_keys_str_mv AT kiselevilya canalgorithmictradingbeatthemarketanexperimentwithsampp500ftse100omxstockholm30index
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