Momentum Investment Strategy Using a Hidden Markov Model

Ryou, Hosun; Bae, Han Hee; Lee, Hee Soo; Oh, Kyong Joo

doi:10.3390/su12177031

Cited by 6 publications

(9 citation statements)

References 32 publications

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“…Chatwin et al [89] 1996 x Durrani and Forbes [90] 2018 x Eckert and Osterrieder [91] 2020 x x Khan et al [92] 2020 x Ryou et al [93] 2020 x Sawhney [94] 1991 x Sharma et al [95] 2014…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda

2021

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In the past decade, current literature and businesses have drawn attention to Artificial Intelligence (AI) tools and in particular to the advances in machine learning techniques. Nevertheless, while the AI technology offers great potential to solve difficulties, challenges remain implicated in practical implementation and lack of expertise in the strategic usage of AI to create business value. This paper aims to implement a systematic literature review analyzing convergence of the AI and corporate strategy and develop a theoretical model incorporating issues based on the existing research in this field. Eighty-one peer-reviewed articles were discussed on the basis of research methodology from Webster and Watson (2002). In addition to gaps in future research, a theoretical model is developed, discussing the four sources of value creation: AI and Machine Learning in organizations; alignment of AI tools and Information Technology (IT) with organizational strategy; AI, knowledge management and decision-making process; and AI, service innovation and value. These outcomes lead to both theoretical and managerial viewpoints, with extensive possibilities to generate new methods and types of management practices.

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“…Chatwin et al [89] 1996 x Durrani and Forbes [90] 2018 x Eckert and Osterrieder [91] 2020 x x Khan et al [92] 2020 x Ryou et al [93] 2020 x Sawhney [94] 1991 x Sharma et al [95] 2014…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda

2021

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“…This approach was taken by Talla (2013) for the Swedish equity market, using the OMXS30 index with encouraging results. Moreover, we have considered another three important researches for the applicability of HMM in order to forecast equity markets using macroeconomic variables: Nguyen and Nguyen (2015), Ryou et al (2020), Varenius (2020, and by Nguyen and Nguyen (2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Trading using Hidden Markov Models during COVID-19 turbulences

Lolea

Stamule

2021

Management &Amp; Marketing. Challenges for the Knowledge Society

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Obtaining higher than market returns is a difficult goal to achieve, especially in times of turbulence such as the COVID-19 crisis, which tested the resilience of many models and algorithms. We used a Hidden Markov Models (HMM) methodology based on monthly data (DAX returns, VSTOXX index Germany’s industrial production and Germany’s annual inflation rate) to calibrate a trading strategy in order to obtain higher returns than a buy-and-hold strategy for the DAX index., following Talla (2013) and Nguyen and Nguyen (2015). The stock selection was based on 26 stocks from DAX’s composition, which had enough data for this study, aiming to select the 15 best performing. The training period was January 2000 - December 2015, and the out-of-sample January 2016 - August 2021, including the period of high turbulence generated by COVID-19. Fitting the best model revealed that the following regimes are the most suitable: two regimes for DAX returns, two regimes for VSTOXX and three regimes for the inflation rate and for the industrial production, while the posterior transition probabilities were event-depending on the training sample. Furthermore, portfolios built using HMM strategy outperformed the DAX index for the out-of-sample period, both in terms of annualized returns and risk-adjusted returns. The results were in line with expectations and what other researchers like Talla (2013), Nguyen and Nguyen (2015) and Varenius (2020) found out. We managed to highlight that a strategy calibrated based on HMM methodology works well even in periods of extreme volatility such as the one generated in 2020 by COVID-19 pandemic.

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“…Other research in Korea by [16] reveals that investing using a momentum strategy on the Korean Stock Exchange can be done. They conclude their findings after testing the momentum strategy using the Hidden Markov Model (HMM), where investors get better returns than the traditional approach.…”

Section: A Research On Momentum Strategies In Asian Capital Marketsmentioning

confidence: 99%

A Review of Momentum Strategy in Capital Market

Syamni¹,

Wardhiah²,

Sari³

et al. 2021

Proceedings of the International Conference on Social Science, Political Science, and Humanities (ICoSPOLHUM 2020)

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This research is a literature research review that focuses on the topic of the momentum strategy in the capital market. This strategy is a trading strategy carried out by investors by buying stocks that have performed well in the hope that the performance will continue well in the future. The results of the study show that most of the capital market investors carried out a momentum strategy even though the implementation is still inconsistent. It indicates that the development of several capital markets leads to inefficient markets.

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Momentum Investment Strategy Using a Hidden Markov Model

Cited by 6 publications

References 32 publications

Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda

Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda

Trading using Hidden Markov Models during COVID-19 turbulences

A Review of Momentum Strategy in Capital Market

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