• The Journal of Information Systems
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• v.27 no.3
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• pp.123-140
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• 2018

Purpose The purpose of this study is to explore the optimal trading frequency which is useful for stock price prediction by using deep learning for charting image data. We also want to identify the appropriate time for accurate forecasting of stock price when performing pattern analysis. Design/methodology/approach In order to find the optimal trading frequency patterns and forecast timings, this study is performed as follows. First, stock price data is collected using OpenAPI provided by Daishin Securities, and candle chart images are created by data frequency and forecasting time. Second, the patterns are generated by the charting images and the learning is performed using the CNN. Finally, we find the optimal trading frequency patterns and forecasting timings. Findings According to the experiment results, this study confirmed that when the 10 minute frequency data is judged to be a decline pattern at previous 1 tick, the accuracy of predicting the market frequency pattern at which the market decreasing is 76%, which is determined by the optimal frequency pattern. In addition, we confirmed that forecasting of the sales frequency pattern at previous 1 tick shows higher accuracy than previous 2 tick and 3 tick.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.255-267
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• 2011

The aim of this study is to utilize system trading for making investment decisions and use technical analysis and Dynamic Time Warping (DTW) to determine similar patterns in the frequency of stock data and ascertain the optimal timing for trade. The study will examine some of the most common patterns in the futures market and use DTW in terms of their frequency (10, 30, 60 minutes, and daily) to discover similar patterns. The recognized similar patterns were verified by executing trade simulation after applying specific strategies to the technical indicators. The most profitable strategies among the set of strategies applied to common patterns were again applied to the similar patterns and the results from DTW pattern recognition were examined. The outcome produced useful information on determining the optimal timing for trade by using DTW pattern recognition through system trading, and by applying distinct strategies depending on data frequency.

• Asia-Pacific Journal of Business
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• v.14 no.2
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• pp.33-49
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• 2023

Purpose - The purpose of this study is to examine the option selection and optimal trading of informed traders in KOSPI 200 options market based on the PIN (probability of informed trading) model of Easley et al.(2002). Design/methodology/approach - This study uses TAQ (trade and quote) data provided by Korean Exchanges (KRX) which contains all the bids and trades recorded during the continuous auction trading hours for the KOSPI 200 options between May 2019 and September 2020. Findings - First, there was no difference in the PIN between call and put options in the 2019 data, but the PIN of put options was slightly higher in 2020. Second, regardless of the type of option, the PIN was higher for in-the-money (ITM) options, and the PIN of out-of-the-money (OTM) options was the same as or slightly higher than that of at-the-money (ATM) options. Third, we found that the PIN decreases as trading liquidity increases, and fourth, the PIN increased sharply as the expiration date approached, especially for OTM options, while ITM and ATM options showed relatively weak effects. Fifth, for foreign and institutional investors, the periodicity of orders was observed in milliseconds, especially for foreign investors, where the periodicity of orders was clear and frequent in OTM options. The results suggest that the purpose of option trading varies depending on the moneyness from the perspective of the informed trader.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.15-27
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• 2012

We have studied the realized variance(RV) of intra-day returns and market microstructure noise based on high-frequency stock transaction data for the four largest companies in terms of market capitalization in the KOSPI. First, non-negligible biases are observed for the RV and for the bias-corrected realized variance($RV_{AC_1}$) which is constructed by adjusting RV for the first order autocorrelation in intra-day returns. Bias is more obvious for the RV and the $RV_{AC_1}$ when intra-day returns are sampled more frequently than every 2 minutes. Transaction Time Sampling(TTS) is shown to be better than Calendar Time Sampling(CTS) in terms of biases of the RV and the $RV_{AC_1}$ for the 4 companies. The analysis reveals that market microstructure noise is temporally dependent. Second, by using the Noise-to-Signal Ratio(NSR), we estimate sampling frequencies that are optimal in terms of the Mean Square Errors(MSE) of the RV and the $RV_{AC_1}$. The optimal sampling frequencies are around 200 for RV and is around 5000 for the $RV_{AC_1}$ for all the four stock prices. For the 6 hour transaction period of the Korean stock trading, these correspond to about 2 minutes and 6 seconds.