Sustainability

Research

16 pages, 4118 KiB

Open AccessArticle

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

by Seung Hwan Jeong, Hee Soo Lee, Hyun Nam and Kyong Joo Oh

Sustainability 2021, 13(3), 1011; https://doi.org/10.3390/su13031011 - 20 Jan 2021

Cited by 6 | Viewed by 4437

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. [...] Read more.

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. Full article

(This article belongs to the Special Issue Sustainability with Robo-Advisor and Artificial Intelligence in Finance)

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30 pages, 5363 KiB

Open AccessArticle

How Green FinTech Can Alleviate the Impact of Climate Change—The Case of Switzerland

by Thomas Puschmann, Christian Hugo Hoffmann and Valentyn Khmarskyi

Sustainability 2020, 12(24), 10691; https://doi.org/10.3390/su122410691 - 21 Dec 2020

Cited by 84 | Viewed by 14970

Abstract

The financial services industry is currently undergoing a major transformation, with digitization and sustainability being the core drivers. While both concepts have been researched in recent years, their intersection, often conceived as “green FinTech,” remains under-determined. Therefore, this paper contributes to this important [...] Read more.

The financial services industry is currently undergoing a major transformation, with digitization and sustainability being the core drivers. While both concepts have been researched in recent years, their intersection, often conceived as “green FinTech,” remains under-determined. Therefore, this paper contributes to this important discussion about green FinTech by, first, synthesizing the relevant literature systematically. Second, it shows the results of an empirical, in-depth analysis of the Swiss FinTech landscape both in terms of green FinTech startups as well as the services offered by the incumbents. The research results show that literature in this new domain has only emerged recently, is mostly characterized by a specific focus on isolated aspects of green FinTech and does not provide a comprehensive perspective on the topic yet. In addition, the results from the literature and the market analysis indicate that green FinTech has an impact along the whole value chain of financial services covering customer-to-customer (c2c), business-to-customer (b2c), and business-to-business (b2b) services. Today the field is predominantly captured by startup companies in contrast to the incumbents whose solutions are still rare. Full article

(This article belongs to the Special Issue Sustainability with Robo-Advisor and Artificial Intelligence in Finance)

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16 pages, 2369 KiB

Open AccessArticle

Momentum Investment Strategy Using a Hidden Markov Model

by Hosun Ryou, Han Hee Bae, Hee Soo Lee and Kyong Joo Oh

Sustainability 2020, 12(17), 7031; https://doi.org/10.3390/su12177031 - 28 Aug 2020

Cited by 6 | Viewed by 4735

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Sustainability with Robo-Advisor and Artificial Intelligence in Finance)

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24 pages, 1399 KiB

Open AccessArticle

Machine Learning and Algorithmic Pairs Trading in Futures Markets

by Seungho Baek, Mina Glambosky, Seok Hee Oh and Jeong Lee

Sustainability 2020, 12(17), 6791; https://doi.org/10.3390/su12176791 - 21 Aug 2020

Cited by 7 | Viewed by 5360

Abstract

This study applies machine learning methods to develop a sustainable pairs trading market-neutral investment strategy across multiple futures markets. Cointegrated pairs with similar price trends are identified, and a hedge ratio is determined using an Error Correction Model (ECM) framework and support vector [...] Read more.

This study applies machine learning methods to develop a sustainable pairs trading market-neutral investment strategy across multiple futures markets. Cointegrated pairs with similar price trends are identified, and a hedge ratio is determined using an Error Correction Model (ECM) framework and support vector machine algorithm based upon the two-step Engle–Granger method. The study shows that normal backwardation and contango do not consistently characterize futures markets, and an algorithmic pairs trading strategy is effective, given the unique predominant price trends of each futures market. Across multiple futures markets, the pairs trading strategy results in larger risk-adjusted returns and lower exposure to market risk, relative to an appropriate benchmark. Backtesting is employed and results show that the pairs trading strategy may hedge against unexpected negative systemic events, specifically the COVID-19 pandemic, remaining profitable over the period examined. Full article

(This article belongs to the Special Issue Sustainability with Robo-Advisor and Artificial Intelligence in Finance)

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19 pages, 1947 KiB

Open AccessArticle

Exploring the Initial Impact of COVID-19 Sentiment on US Stock Market Using Big Data

by Hee Soo Lee

Sustainability 2020, 12(16), 6648; https://doi.org/10.3390/su12166648 - 17 Aug 2020

Cited by 57 | Viewed by 8616

Abstract

This study explores the initial impact of COVID-19 sentiment on US stock market using big data. Using the Daily News Sentiment Index (DNSI) and Google Trends data on coronavirus-related searches, this study investigates the correlation between COVID-19 sentiment and 11 select sector indices [...] Read more.

This study explores the initial impact of COVID-19 sentiment on US stock market using big data. Using the Daily News Sentiment Index (DNSI) and Google Trends data on coronavirus-related searches, this study investigates the correlation between COVID-19 sentiment and 11 select sector indices of the Unites States (US) stock market over the period from 21st of January 2020 to 20th of May 2020. While extensive research on sentiment analysis for predicting stock market movement use tweeter data, not much has used DNSI or Google Trends data. In addition, this study examines whether changes in DNSI predict US industry returns differently by estimating the time series regression model with excess returns of industry as the dependent variable. The excess returns are obtained from the Fama-French three factor model. The results of this study offer a comprehensive view of the initial impact of COVID-19 sentiment on the US stock market by industry and furthermore suggests the strategic investment planning considering the time lag perspectives by visualizing changes in the correlation level by time lag differences. Full article

(This article belongs to the Special Issue Sustainability with Robo-Advisor and Artificial Intelligence in Finance)

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15 pages, 1755 KiB

Open AccessArticle

Robo-Advisors: Machine Learning in Trend-Following ETF Investments

by Seungho Baek, Kwan Yong Lee, Merih Uctum and Seok Hee Oh

Sustainability 2020, 12(16), 6399; https://doi.org/10.3390/su12166399 - 9 Aug 2020

Cited by 4 | Viewed by 6687

Abstract

We examine an application of machine learning to exchange traded fund investments in the U.S. market. To find how the changes in exchange traded fund prices are associated with expected market fundamentals, we propose three parsimonious risk factors extracted from various U.S. economic [...] Read more.

We examine an application of machine learning to exchange traded fund investments in the U.S. market. To find how the changes in exchange traded fund prices are associated with expected market fundamentals, we propose three parsimonious risk factors extracted from various U.S. economic and market indicators. Based on the information set including these three factors, we build a predictive support vector machine model that can detect long or short investment signals. We find that the high probability of an upward momentum from our forecasting model suggests a long exchange traded fund signal, whereas the low probability of a downward momentum indicates a short exchange traded fund signal. We further design an algorithmic trading system with the support vector machine factor model. We find that the trading system shows practically desirable and robust performances over in-sample and out-of-sample trading periods Full article

(This article belongs to the Special Issue Sustainability with Robo-Advisor and Artificial Intelligence in Finance)

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19 pages, 1402 KiB

Open AccessArticle

Developing a Forecasting Model for Real Estate Auction Prices Using Artificial Intelligence

by Jun Kang, Hyun Jun Lee, Seung Hwan Jeong, Hee Soo Lee and Kyong Joo Oh

Sustainability 2020, 12(7), 2899; https://doi.org/10.3390/su12072899 - 5 Apr 2020

Cited by 34 | Viewed by 5881

Abstract

The real estate auction market has become increasingly important in the financial, economic and investment fields, but few artificial intelligence-based studies have attempted to forecast the auction prices of real estate. The purpose of this study is to develop forecasting models of real [...] Read more.

The real estate auction market has become increasingly important in the financial, economic and investment fields, but few artificial intelligence-based studies have attempted to forecast the auction prices of real estate. The purpose of this study is to develop forecasting models of real estate auction prices using artificial intelligence and statistical methodologies. The forecasting models are developed through a regression model, an artificial neural network and a genetic algorithm. For empirical analysis, we use Seoul apartment auction data from 2013 to 2017 to predict the auction prices and compare the forecasting accuracy of the models. The genetic algorithm model has the best performance, and effective regional segmentation based on the auction appraisal price improves the predictive accuracy. Full article

(This article belongs to the Special Issue Sustainability with Robo-Advisor and Artificial Intelligence in Finance)

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15 pages, 4290 KiB

Open AccessArticle

Asset Allocation Model for a Robo-Advisor Using the Financial Market Instability Index and Genetic Algorithms

by Wonbin Ahn, Hee Soo Lee, Hosun Ryou and Kyong Joo Oh

Sustainability 2020, 12(3), 849; https://doi.org/10.3390/su12030849 - 23 Jan 2020

Cited by 12 | Viewed by 7001

Abstract

There has been a growing demand for portfolio management using robo-advisors, and hence, research on the automation of portfolio composition has been increasing. In this study, we propose a model that automates the portfolio structure by using the instability index of the financial [...] Read more.

There has been a growing demand for portfolio management using robo-advisors, and hence, research on the automation of portfolio composition has been increasing. In this study, we propose a model that automates the portfolio structure by using the instability index of the financial time series and genetic algorithms (GAs). We use the instability index to filter the investment assets and optimize the threshold value used as a filtering criterion by applying a GA. For an empirical analysis, we use stocks, bonds, commodities exchange traded funds (ETFs), and exchange rate. We compare the performance of our model with that of risk parity and mean-variance models and find our model has better performance. Several additional experiments with our model using various internal parameters are conducted, and the proposed model with a one-month test period after one year of learning is found to provide the highest Sharpe ratio. Full article

(This article belongs to the Special Issue Sustainability with Robo-Advisor and Artificial Intelligence in Finance)

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15 pages, 2423 KiB

Open AccessArticle

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

by Jiwoo Kim, Sanghun Shin, Hee Soo Lee and Kyong Joo Oh

Sustainability 2019, 11(23), 6803; https://doi.org/10.3390/su11236803 - 30 Nov 2019

Cited by 6 | Viewed by 3676

Abstract

An initial public offering (IPO) is a type of public offering in which a company’s shares are sold to institutional and individual investors. While the majority of studies on IPOs have focused on the efficiency of raising capital and price adequacy in IPOs, [...] Read more.

An initial public offering (IPO) is a type of public offering in which a company’s shares are sold to institutional and individual investors. While the majority of studies on IPOs have focused on the efficiency of raising capital and price adequacy in IPOs, studies on portfolio allocation strategies for IPO stocks are relatively scarce. This paper develops a machine learning investment strategy for IPO stocks based on rough set theory and a genetic algorithm (GA-rough set theory). To reduce issues of information asymmetry, we use nonfinancial data that are publicly available to individual and institutional investors in the IPO process. Based on the rule sets generated from the training sets, we conduct 120 tests with various conditions involving the target days and the partition of the training and testing sets, and we find excess returns of the constructed portfolios compared to the benchmark portfolios. Investors in IPO stocks can formulate more efficient investment strategies using our system. In this sense, the system developed in this paper contributes to the efficiency of financial markets and helps achieve sustained economic growth. Full article

(This article belongs to the Special Issue Sustainability with Robo-Advisor and Artificial Intelligence in Finance)

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12 pages, 1559 KiB

Open AccessArticle

Using Genetic Algorithms to Develop a Dynamic Guaranteed Option Hedge System

by Hyounggun Song, Sung Kwon Han, Seung Hwan Jeong, Hee Soo Lee and Kyong Joo Oh

Sustainability 2019, 11(15), 4100; https://doi.org/10.3390/su11154100 - 29 Jul 2019

Cited by 3 | Viewed by 2886

Abstract

In this research, we develop a guaranteed option hedge system to protect against capital market risks using a genetic algorithm (GA). We test the hedge effectiveness of our guaranteed option hedge strategy by comparing the performance of our system with those of other [...] Read more.

In this research, we develop a guaranteed option hedge system to protect against capital market risks using a genetic algorithm (GA). We test the hedge effectiveness of our guaranteed option hedge strategy by comparing the performance of our system with those of other strategies. A genetic algorithm heuristic trading method for the optimization of a non-linear problem is applied to each system to improve the hedge effectiveness. The GA dynamic hedge system developed in this research is found to improve hedge effectiveness by reducing the option value volatility and increasing the total profit. Insurance companies are able to make more efficient investment strategies by using our guaranteed option hedge system. It contributes to the investment efficiency of the insurance companies and helps to achieve efficiency for financial markets. In addition, it helps to achieve sustained economic benefits to policyholders. In this sense, the system developed in this paper plays a role in sustaining economic growth. Full article

(This article belongs to the Special Issue Sustainability with Robo-Advisor and Artificial Intelligence in Finance)

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17 pages, 2668 KiB

Open AccessArticle

An Investigation on Factors Affecting Stock Valuation Using Text Mining for Automated Trading

by Xusen Cheng, Danya Huang, Jin Chen, Xiangsong Meng and Chengyao Li

Sustainability 2019, 11(7), 1938; https://doi.org/10.3390/su11071938 - 1 Apr 2019

Cited by 1 | Viewed by 4581

Abstract

Predicted price-to-book value ratios (P/BV) are widely used for the valuation of listed common stocks. However, with the application of automated trading system (ATS), the existing indicators that are applied in the method are losing their effectiveness in the Chinese market. Combining qualitative [...] Read more.

Predicted price-to-book value ratios (P/BV) are widely used for the valuation of listed common stocks. However, with the application of automated trading system (ATS), the existing indicators that are applied in the method are losing their effectiveness in the Chinese market. Combining qualitative research with the text mining method, this study explores and validates those ignored factors to improve the accuracy of the stock valuation. On the basis of the principal of the existing valuation method, we clarify the scope of the factors that affects the P/BV ratio prediction. Through semi-structured interviews that are designed with six first-level factors which are taken from the literature, we then excavate some second-level factors. After that, with three corpuses including samples form Sina.com.cn, Xueqiu.com, and CSDN.net, four first-level factors and thirteen second-level factors have been verified step by step through the Latent Dirichlet Allocation (LDA) model. In the process, two other new factors and three sub-factors are also found. Furthermore, based on the factor correlation that was found in a data analysis, a factor relationship model was built. The results can be used in a stock valuation in future work as the basis of the indicator system for the prediction of P/BV ratio. Full article

(This article belongs to the Special Issue Sustainability with Robo-Advisor and Artificial Intelligence in Finance)

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