Machine learning modeling to forecast uncertainty between capital sudden stop and boom

Park, Sangjin; Yang, Jae-Suk

doi:10.1016/j.eswa.2023.121662

Cited by 3 publications

(1 citation statement)

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“…The study offers a comprehensive framework for improved stock price forecasting. Park and Yang [14] focus on predicting sudden stops in capital flows using various ML techniques, including XGB. SHAP methods are employed for model interpretation, addressing the black box structure concern.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Stock Prices With Machine Learning Using Comparative Analysis of Random Forest Algorithm

Khandagale,

Patil,

Patil

et al. 2023

IJEAST

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The precise forecasting of stock market prices presents a tough challenge, owing to the crucial volatility and details of the market. In this research paper, we tackle this challenge by introducing a stock market prediction model grounded in the Random Forest algorithm. Our study centers on historical trading data encompassing a diverse array of stocks and ETF funds, harnessing the capabilities of AI technology and machine learning methodologies to forecast and scrutinize stock prices through regression analysis. The outcomes underscore the Random Forest model's capacity to achieve commendable accuracy in stock prediction, thereby offering invaluable insights for both institutional and individual stock investments. These models rely on technical indicators as inputs, with the closing value of stock prices serving as the predicted variable. The results not only underscore the effectiveness of our proposed approach in constructing predictive models for stock price projection but also highlight the potential of Machine Learning algorithms to reveal valuable insights into the dynamics of stock market activity. Moreover, our paper investigates the exploration of diverse Machine Learning models, encompassing Linear Regression, Support Vector Regression, Decision Tree, Random Forest Regressor, and Extra Tree Regressor. Their implementation has proven instrumental in achieving precision in stock price prediction and has furnished fresh perspectives into the intricate interplay between buyers and sellers in the stock market. The evaluation of these models is grounded in their accuracy in predicting stock prices, using both closing values and stock prices as crucial metrics. Consequently, our research shines a spotlight on the substantial potential of Machine Learning algorithms in decoding stock market price dynamics, thus contributing significantly to our comprehension of the stock market's intricate workings

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Section: Literature Reviewmentioning

confidence: 99%