Modelling of S&amp;P 500 Index Price Based on U.S. Economic Indicators: Machine Learning Approach

Gasparėnienė, Ligita; Remeikienė, Rita; Sosidko, Aleksejus; Вебрайте, Вігіта

doi:10.5755/j01.ee.32.4.27985

Cited by 6 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using data from 1992 to 2015, they compare LSTM to other ML methods including DNN, logistic regression classifier, and RF. In Gaspareniene et al (2021), the authors predict the monthly value of the S&P 500 index using the decision tree, RF, and feedforward neural network methods. They find that RF is 19% more accurate than a baseline model that uses linear regression.…”

Section: Related Workmentioning

confidence: 99%

Forecasting relative returns for S&P 500 stocks using machine learning

Htun,

Biehl,

Petkov

2024

Financ Innov

View full text Add to dashboard Cite

Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to fluctuate. The random walk hypothesis and efficient market hypothesis essentially state that it is not possible to systematically, reliably predict future stock prices or forecast changes in the stock market overall. Nonetheless, machine learning (ML) techniques that use historical data have been applied to make such predictions. Previous studies focused on a small number of stocks and claimed success with limited statistical confidence. In this study, we construct feature vectors composed of multiple previous relative returns and apply the random forest (RF), support vector machine (SVM), and long short-term memory (LSTM) ML methods as classifiers to predict whether a stock can return 2% more than its index in the following 10 days. We apply this approach to all S&P 500 companies for the period 2017–2022. We assess performance using accuracy, precision, and recall and compare our results with a random choice strategy. We observe that the LSTM classifier outperforms RF and SVM, and the data-driven ML methods outperform the random choice classifier (p = 8.46e−17 for accuracy of LSTM). Thus, we demonstrate that the probability that the random walk and efficient market hypotheses hold in the considered context is negligibly small.

show abstract

Section: Related Workmentioning

confidence: 99%

Forecasting relative returns for S&P 500 stocks using machine learning

Htun,

Biehl,

Petkov

2024

Financ Innov

View full text Add to dashboard Cite

show abstract

“…The S&P500, also known as Standard & Poor's 500, is a widely monitored stock market index within the United States. It stands as one of the most commonly used benchmarks for assessing the performance of the U.S. stock market [22]. The index is deliberately designed to represent various sectors of the U.S. economy, thus encompassing companies from diverse industries, such as technology, healthcare, finance and more [23].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Dynamics of Brent Crude Oil, S&P500 and Bitcoin Prices Amid Economic Instability

Bâra,

Georgescu,

Oprea

et al. 2024

IEEE Access

View full text Add to dashboard Cite

In this paper, we mainly investigate three variables from the price volatility point of view: Brent crude oil, S&P500 and Bitcoin (BTCUSD), aiming to underline the impact of price volatility. Brent crude oil accounts for two-thirds of the oil market. Its price volatility has a significant impact on environmental, transportation, mobility, economic and social aspects that affect sustainability. This paper conducts an extensive examination of the forecasting capabilities of various GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, identifying the most suitable GARCH model for estimating Value at Risk (VaR) for Brent crude oil price. The assessment of VaR for different GARCH models is carried out using Kupiec's Probability of Failure (POF) test and Christoffersen's test. This study leverages Brent crude oil data spanning from 2019 to 2023. Additionally, to prove the robustness of the GARCH models, we further consider the West Texas Intermediate (WTI) and Dubai oil prices that are the dominant in the U.S and Asian market. The investigation identifies the TGARCH(1,1) Skewed Student model as the optimal choice among 9 models considered for VaR estimation. The results show that TGARCH Skewed Student model surpasses the other models in the study, proving its superiority in forecasting Brent crude oil price volatility and facilitating VaR estimation. A VaR of 0.044 with a 95% confidence level means that there is a 95% chance that the portfolio will not lose more than 4.4% of its value. By incorporating skewness in addition to volatility asymmetry, the Skewed GARCH-type models provide a more realistic representation of the underlying return distribution. Furthermore, the most appropriate GARCH-type model for WTI crude oil is EGARCH(1,1) Skewed Student, with a VaR coverage of 0.39. The most appropriate GARCH-type model for Dubai crude oil is TGARCH(1,1) Skewed Student, with a VaR coverage of 0.17. Both WTI oil and Dubai crude oil have a coverage that exceeds 5%, implying a more conservative approach to estimating potential losses. Furthermore, the unidirectional causalities BTCUSD→BRENT and BTCUSD→S&P500 are identified. The results of the current research have practical implications for both importing and exporting countries, policy makers and investors. For companies in the oil sector, VaR informs operational decisions, such as production levels, capital expenditure and inventory management, by providing insights into market risk. Moreover, understanding the risks associated with oil aids in long-term strategic planning.INDEX TERMS backtesting, Bitcoin, Brent crude oil, GARCH model, price volatility, S&P500

show abstract

“…Unfortunately, the target was unable to achieve due to several challenges and they will likely to create an obstacle for 2030 goals too. Factors such as higher public expectation for better living standard, steady economic growth, socio economic issues and high foreign tensions are creating hindrance for China to navigate the economy to its next development phase (Gasparėnienė et al, 2021;Jonah & Kanyangale, 2021;Wosiek, 2021).…”

mentioning

confidence: 99%

The Perceived Relationship Between Sustainable Energy Technologies, Eco-Innovation, Economic Growth and Social Sustainability: Evidence From China

Chien,

Chau,

Huang

2024

Technological and Economic Development of Economy

View full text Add to dashboard Cite

Social sustainability is a global necessity because of environmental and economic uncertainty. This issue needs the foremost solution, and for this purpose, researchers’ and policymakers’ emphasis is required. Thereby, the present paper investigates sustainable energy technologies such as solar and hydroelectric, eco-innovation and EG and their impact on social sustainability in China. The study also used industrialization and inflation as the control variables in the time span of 1981 to 2020. The present study also applied the Dynamic Auto-regressive Distributed Lags (DARDL) model to evaluate the association between the outlined variables. The results indicated that sustainable energy technologies such as solar and hydroelectric, eco-innovation, economic growth, industrialization and inflation are significantly associated with social sustainability in China. The present paper offers standard policies to regulators in making regulations related to maintaining social sustainability by using effective sustainable energy technologies and eco-innovation.

show abstract

Modelling of S&P 500 Index Price Based on U.S. Economic Indicators: Machine Learning Approach

Cited by 6 publications

References 25 publications

Forecasting relative returns for S&P 500 stocks using machine learning

Forecasting relative returns for S&P 500 stocks using machine learning

Exploring the Dynamics of Brent Crude Oil, S&P500 and Bitcoin Prices Amid Economic Instability

The Perceived Relationship Between Sustainable Energy Technologies, Eco-Innovation, Economic Growth and Social Sustainability: Evidence From China

Contact Info

Product

Resources

About