Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets

Pyo, Sujin; Lee, Jaewook; Cha, Mincheol; Jang, Huisu

doi:10.1371/journal.pone.0188107

Cited by 65 publications

(28 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, various kinds of AI programs have been developed based on “big data” collected through the Internet of Things. AI programs have been widely used in the manufacturing sector, the information-communications industry [4], and the medical field [5-7]. The development and utilization of AI programs in the medical field are currently entering the stage of commercialization [8,9].…”

Section: Introductionmentioning

confidence: 99%

Physician Confidence in Artificial Intelligence: An Online Mobile Survey

Oh¹,

Kim²,

Choi³

et al. 2019

J Med Internet Res

237

174

View full text Add to dashboard Cite

Background It is expected that artificial intelligence (AI) will be used extensively in the medical field in the future. Objective The purpose of this study is to investigate the awareness of AI among Korean doctors and to assess physicians’ attitudes toward the medical application of AI. Methods We conducted an online survey composed of 11 closed-ended questions using Google Forms. The survey consisted of questions regarding the recognition of and attitudes toward AI, the development direction of AI in medicine, and the possible risks of using AI in the medical field. Results A total of 669 participants completed the survey. Only 40 (5.9%) answered that they had good familiarity with AI. However, most participants considered AI useful in the medical field (558/669, 83.4% agreement). The advantage of using AI was seen as the ability to analyze vast amounts of high-quality, clinically relevant data in real time. Respondents agreed that the area of medicine in which AI would be most useful is disease diagnosis (558/669, 83.4% agreement). One possible problem cited by the participants was that AI would not be able to assist in unexpected situations owing to inadequate information (196/669, 29.3%). Less than half of the participants(294/669, 43.9%) agreed that AI is diagnostically superior to human doctors. Only 237 (35.4%) answered that they agreed that AI could replace them in their jobs. Conclusions This study suggests that Korean doctors and medical students have favorable attitudes toward AI in the medical field. The majority of physicians surveyed believed that AI will not replace their roles in the future.

show abstract

Section: Introductionmentioning

confidence: 99%

Physician Confidence in Artificial Intelligence: An Online Mobile Survey

Oh¹,

Kim²,

Choi³

et al. 2019

J Med Internet Res

237

174

View full text Add to dashboard Cite

show abstract

“…The results indicate that RF outperforms the other tested methods. Pyo et al (2017) use ANN and SVM to predict the trend of the Korea Stock Price Index. The authors use price based indicators such as moving average as input features for classification.…”

Section: Literaturementioning

confidence: 99%

Financial Forecasting with Machine Learning: Price Vs Return

Kamalov¹,

Gurrib²,

Rajab³

2021

Journal of Computer Science

View full text Add to dashboard Cite

Forecasting directional movement of stock price using machine learning tools has attracted a considerable amount of research. Two of the most common input features in a directional forecasting model are stock price and return. The choice between the former and the latter variables is often subjective. In this study, we compare the effectiveness of stock price and return as input features in directional forecasting models. We perform an extensive comparison of the two input features using 10-year historical data of ten large cap US companies. We employ four popular classification algorithms as the basis of the forecasting models used in our study. The results show that stock price is a more effective standalone input feature than return. The effectiveness of stock price and return equalize when we add technical indicators to the input feature set. We conclude that price is generally a more potent input feature than return value in predicting the direction of price movement. Our results should aid researchers and practitioners interested in applying machine learning models to stock price forecasting.

show abstract

“…Pyo, Lee et al [38] investigated the movements of the KOSPI 200 prices applying the ANN and SVM. Their outcomes proved the instability and high variability of machine-learning approaches on market predictions that are explained in [39] and various other studies, by way of hypothesis tests utilizing the KOSPI 200 index market.…”

Section: Related Workmentioning

confidence: 99%

A Novel Hybrid Model for Stock Price Forecasting Based on Metaheuristics and Support Vector Machine

Sedighi

Jahangirnia

Gharakhani³

et al. 2019

Data

View full text Add to dashboard Cite

This paper intends to present a new model for the accurate forecast of the stock’s future price. Stock price forecasting is one of the most complicated issues in view of the high fluctuation of the stock exchange and also it is a key issue for traders and investors. Many predicting models were upgraded by academy investigators to predict stock price. Despite this, after reviewing the past research, there are several negative aspects in the previous approaches, namely: (1) stringent statistical hypotheses are essential; (2) human interventions take part in predicting process; and (3) an appropriate range is complex to be discovered. Due to the problems mentioned, we plan to provide a new integrated approach based on Artificial Bee Colony (ABC), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Support Vector Machine (SVM). ABC is employed to optimize the technical indicators for forecasting instruments. To achieve a more precise approach, ANFIS has been applied to predict long-run price fluctuations of the stocks. SVM was applied to create the nexus between the stock price and technical indicator and to further decrease the forecasting errors of the presented model, whose performance is examined by five criteria. The comparative outcomes, obtained by running on datasets taken from 50 largest companies of the U.S. Stock Exchange from 2008 to 2018, have clearly demonstrated that the suggested approach outperforms the other methods in accuracy and quality. The findings proved that our model is a successful instrument in stock price forecasting and will assist traders and investors to identify stock price trends, as well as it is an innovation in algorithmic trading.

show abstract

Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets

Cited by 65 publications

References 18 publications

Physician Confidence in Artificial Intelligence: An Online Mobile Survey

Physician Confidence in Artificial Intelligence: An Online Mobile Survey

Financial Forecasting with Machine Learning: Price Vs Return

A Novel Hybrid Model for Stock Price Forecasting Based on Metaheuristics and Support Vector Machine

Contact Info

Product

Resources

About