Work detail

Price gaps in the stock market

Author: Mgr. Jakub Vosmanský
Year: 2022 - summer
Leaders: prof. PhDr. Ladislav Krištoufek Ph.D.
Work type: Finance, Financial Markets and Banking
Language: English
Pages: 98
Awards and prizes:
Abstract: This thesis aims to scrutinise price gaps in the stock market. The key objective
is to analyse candlestick charts surrounding price gaps and determine whether
any patterns accompany their presence. Firstly, the thesis briefly describes
candlestick patterns, literature relevant to price gaps and Convolutional Neural
Network (CNN) as the method of choice. Price gaps are studied in a 5-minute
time frame in the data of all S&P 500 constituents in the years from 2015 to
2021. By feeding images of the candlestick chart into the CNN, the proposed
model reaches an Accuracy of 74.2% in predicting whether a future price will
be higher or lower than the price at the gap. This result can be translated
into a statement that the CNN detects hidden patterns around the price gaps.
Furthermore, the thesis finds that these patterns dier across individual stocks.
The thesis also shows that including news sentiment in the analysis does not
improve the ability to discover patterns.




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