A Machine Learning Portfolio Allocation System for IPOs in Korean Markets Using GA-Rough Set Theory

Kim, Jiwoo; Shin, Sei One; Lee, Hee Soo; Oh, Kyong Joo

doi:10.3390/su11236803

Cited by 6 publications

(9 citation statements)

References 18 publications

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“…More precisely, the authors conduct a classification task by developing models which try to identify IPOs with first-day returns of 50% or higher. In a similar fashion, several other studies use financial variables as inputs in machine learning algorithms to classify whether the IPOs will realize positive or negative first-day returns (Cheng et al, 2007;Chen et al, 2010;Kim et al, 2019).…”

Section: Machine Learning Approach Of Ipo Underpricingmentioning

confidence: 99%

“…As a second robustness test, we adopt two alternative sample splits for our training and testing datasets. First, we select 70% of our data as the training set, and the remaining 30% as the testing set (Veganzones and Severin, 2018;Kim et al, 2019). Second, we select 75% of our data as the training set, and the remaining 25% as the testing set (Gogas et al, 2018).…”

Section: Prediction With a Combination Of Lexicon Features And Financial Datamentioning

confidence: 99%

“…However, there are some recent papers that investigate this issue using machine learning algorithms (Basti et al, 2015;Quintana et al, 2017). Among them, there is a handful of studies which examine the IPO underpricing as a binary classification task (Cheng et al, 2007;Chen et al, 2010;Kim et al, 2019). In other words, these studies use machine learning algorithms in order to distinguish underpriced from overpriced IPOs.…”

Section: Introductionmentioning

confidence: 99%

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Textual Information and IPO Underpricing: A Machine Learning Approach

et al. 2020

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This study examines the predictive power of textual information from S-1 filings in explaining IPO underpricing. Our empirical approach differs from previous research, as we utilize several machine learning algorithms to predict whether an IPO will be underpriced, or not. We analyze a large sample of 2,481 U.S. IPOs from 1997 to 2016, and we find that textual information can effectively complement traditional financial variables in terms of prediction accuracy. In fact, models that use both textual data and financial variables as inputs have superior performance compared to models using a single type of input. We attribute our findings to the fact that textual information can reduce the ex-ante valuation uncertainty of IPO firms, thus leading to more accurate estimates.

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Section: Machine Learning Approach Of Ipo Underpricingmentioning

confidence: 99%

Section: Prediction With a Combination Of Lexicon Features And Financial Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Textual Information and IPO Underpricing: A Machine Learning Approach

et al. 2020

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“…Previous studies proposed a pattern matching trading system using DTW to predict exchange rates and stock prices [20][21][22][23]. Additionally, GA has been used for predicting stock indices, real estate auction prices and appraisals, and was also used to optimize IPO investment strategies or trading strategies that hedge options [24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market

et al. 2021

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Research on stock market prediction has been actively conducted over time. Pertaining to investment, stock prices and trading volume are important indicators. While extensive research on stocks has focused on predicting stock prices, not much focus has been applied to predicting trading volume. The extensive trading volume by large institutions, such as pension funds, has a great impact on the market liquidity. To reduce the impact on the stock market, it is essential for large institutions to correctly predict the intraday trading volume using the volume weighted average price (VWAP) method. In this study, we predict the intraday trading volume using various methods to properly conduct VWAP trading. With the trading volume data of the Korean stock price index 200 (KOSPI 200) futures index from December 2006 to September 2020, we predicted the trading volume using dynamic time warping (DTW) and a genetic algorithm (GA). The empirical results show that the model using the simple average of the trading volume during the optimal period constructed by GA achieved the best performance. As a result of this study, we expect that large institutions will perform more appropriate VWAP trading in a sustainable manner, leading the stock market to be revitalized by enhanced liquidity. In this sense, the model proposed in this paper would contribute to creating efficient stock markets and help to achieve sustainable economic growth.

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“…Meanwhile, few efforts to increase IC have been made by practitioners and academics. Researchers have recently reported that scientific methodology can be used to improve the accuracy of transition pattern recognition [11,12]. The hidden Markov model (HMM) shows good predictive power for transition pattern recognition [13].…”

Section: Introductionmentioning

confidence: 99%

Momentum Investment Strategy Using a Hidden Markov Model

et al. 2020

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There has been a growing demand for portfolio management using artificial intelligence (AI). To sustain a competitive advantage for portfolio management, stock market investors require a strategic investment decision that can realize better returns. In this study, we propose a momentum investment strategy that employs a hidden Markov model (HMM) to select stocks in the rising state. We construct an HMM momentum portfolio that includes 890 Korean stocks and analyze the performance of the stocks over the period of January 2000 to December 2018. By identifying states of stocks, sectors, and markets through HMM, our strategy buys shares in the rising state and proceeds with rebalancing after the holding period. The HMM momentum portfolio is determined to earn higher returns than traditional momentum portfolios and to achieve the best performance under the conditions of a short holding period (one week) and a short formation period (one month). In addition, our strategy exhibits competitive performance in market and sector index investment compared with market returns. This study implies that the momentum investment strategy using HMM is useful in the Korean stock market. Based on our HMM momentum strategy, future research can be enriched by applying the HMM to developing a new AI momentum strategy that can be utilized for other portfolios containing various types of financial assets on the global market.

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A Machine Learning Portfolio Allocation System for IPOs in Korean Markets Using GA-Rough Set Theory

Cited by 6 publications

References 18 publications

Textual Information and IPO Underpricing: A Machine Learning Approach

Textual Information and IPO Underpricing: A Machine Learning Approach

Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market

Momentum Investment Strategy Using a Hidden Markov Model

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