Modeling Stock Price Changes Based on Microstructural Market Data
In modern electronic stock exchanges there is an opportunity to analyze event driven market microstructure data. This data is highly informative and describes physical price formation which makes it possible to find complex patterns in price dynamics. It is very time consuming and hard to find this...
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| Format: | Article |
| Language: | Russian |
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Government of the Russian Federation, Financial University
2018-11-01
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| Series: | Финансы: теория и практика |
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| Online Access: | https://financetp.fa.ru/jour/article/view/757 |
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| author | N. A. Bilev |
| author_facet | N. A. Bilev |
| author_sort | N. A. Bilev |
| collection | DOAJ |
| description | In modern electronic stock exchanges there is an opportunity to analyze event driven market microstructure data. This data is highly informative and describes physical price formation which makes it possible to find complex patterns in price dynamics. It is very time consuming and hard to find this kind of patterns by handcrafted rules. However, modern machine learning models are able to solve such issues automatically by learning price behavior which is always changing. The present study presents profitable trading system based on a machine learning model and market microstructure data. Data for the research was collected from Moscow stock exchange MICEX and represents a limit order book change log and all market trades of a liquid security for a certain period. Logistic regression model was used and compared to neural network models with different configuration. According to the study results logistic regression model has almost the same prediction quality as neural network models have but also has a high speed of response which is very important for stock market trading. The developed trading system has medium frequency of deals submission that lets it to avoid expensive infrastructure which is usually needed in high-frequency trading systems. At the same time, the system uses the potential of high quality market microstructure data to the full extent. This paper describes the entire process of trading system development including feature engineering, models behavior comparison and creation of trading strategy with testing on historical data. |
| format | Article |
| id | doaj-art-e36f28c01aea47baab82067ea0c05ed9 |
| institution | DOAJ |
| issn | 2587-5671 2587-7089 |
| language | Russian |
| publishDate | 2018-11-01 |
| publisher | Government of the Russian Federation, Financial University |
| record_format | Article |
| series | Финансы: теория и практика |
| spelling | doaj-art-e36f28c01aea47baab82067ea0c05ed92025-08-20T03:00:44ZrusGovernment of the Russian Federation, Financial UniversityФинансы: теория и практика2587-56712587-70892018-11-0122514115310.26794/2587-5671-2018-22-5-141-153682Modeling Stock Price Changes Based on Microstructural Market DataN. A. Bilev0Lomonosov Moscow State university, MosocowIn modern electronic stock exchanges there is an opportunity to analyze event driven market microstructure data. This data is highly informative and describes physical price formation which makes it possible to find complex patterns in price dynamics. It is very time consuming and hard to find this kind of patterns by handcrafted rules. However, modern machine learning models are able to solve such issues automatically by learning price behavior which is always changing. The present study presents profitable trading system based on a machine learning model and market microstructure data. Data for the research was collected from Moscow stock exchange MICEX and represents a limit order book change log and all market trades of a liquid security for a certain period. Logistic regression model was used and compared to neural network models with different configuration. According to the study results logistic regression model has almost the same prediction quality as neural network models have but also has a high speed of response which is very important for stock market trading. The developed trading system has medium frequency of deals submission that lets it to avoid expensive infrastructure which is usually needed in high-frequency trading systems. At the same time, the system uses the potential of high quality market microstructure data to the full extent. This paper describes the entire process of trading system development including feature engineering, models behavior comparison and creation of trading strategy with testing on historical data.https://financetp.fa.ru/jour/article/view/757stock market tradingalgorithmic tradingmarket microstructurelimit order bookmachine learningtime seriesinvestmentcapital management |
| spellingShingle | N. A. Bilev Modeling Stock Price Changes Based on Microstructural Market Data Финансы: теория и практика stock market trading algorithmic trading market microstructure limit order book machine learning time series investment capital management |
| title | Modeling Stock Price Changes Based on Microstructural Market Data |
| title_full | Modeling Stock Price Changes Based on Microstructural Market Data |
| title_fullStr | Modeling Stock Price Changes Based on Microstructural Market Data |
| title_full_unstemmed | Modeling Stock Price Changes Based on Microstructural Market Data |
| title_short | Modeling Stock Price Changes Based on Microstructural Market Data |
| title_sort | modeling stock price changes based on microstructural market data |
| topic | stock market trading algorithmic trading market microstructure limit order book machine learning time series investment capital management |
| url | https://financetp.fa.ru/jour/article/view/757 |
| work_keys_str_mv | AT nabilev modelingstockpricechangesbasedonmicrostructuralmarketdata |