An Empirical Test for Semi-strong form Efficient Market Hypothesis of the Nigeria Stock Market

Gbanador, Monday A.

doi:10.9734/ajeba/2021/v21i2330532

Cited by 1 publication

(1 citation statement)

References 9 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tan [4] pointed out that China's stock market does not conform to the efficient market hypothesis, and there are significant differences in the efficiency of information disclosure, regulatory efficiency, and the efficiency of individual investor behavior and trading mechanism, so the slow response of stock prices to information leads to momentum effect and long memory. Gbanador [5] argues that capital markets are considered effective if new information is quickly reflected in stock prices, such as the Nigerian stock market, which is semi-effective in the study. Therefore, many researchers are committed to building various quantitative models aiming to accurately grasp the transformation of market conditions and capture arbitrage opportunities.…”

Section: Introductionmentioning

confidence: 99%

Research on stock trend prediction based on grey wolf optimized RF and SVM models

2024

HBEM

View full text Add to dashboard Cite

The volatility of the stock market has a profound impact on the stability of the financial market, and then has a significant effect on the overall economic situation, so the accurate prediction of stock trend has always been the focus of research in the financial field. In this paper, the raw minute-level stock data of SSE 50 and CSI 300 in 2021 are normalized, and the historical training and test sets of daily stocks for model training and testing are established based on the datasets. The RF-SVM model based on the gray wolf optimization algorithm is constructed to compare the traditional support vector machine and random forest model, and the relevant performance indexes are calculated. In this study, the RF-SVM model based on gray wolf optimization is highly correlated with the characteristics of stock data, and the prediction accuracy of stock index data is more than 85%. The cross-validation score is over 0.77 and the overall weighted score is as high as 0.98. Compared with the SVM and random forest models under the default parameters, the RF-SVM model based on gray wolf optimization has an average advantage of 25% in performance indicators. The generalization ability is increased by about 26%; The fitting error was reduced by about 51.15%; The overall average score advantage is about 34%. These results prove that the RF-SVM model of gray wolf optimization has superior performance and significant optimization effect in stock prediction, and compared with the traditional grid search optimization algorithm, gray wolf optimization has excellent adaptability and can gradually obtain the global optimal parameter configuration.

show abstract

Section: Introductionmentioning

confidence: 99%

Research on stock trend prediction based on grey wolf optimized RF and SVM models

2024

HBEM

View full text Add to dashboard Cite

show abstract

An Empirical Test for Semi-strong form Efficient Market Hypothesis of the Nigeria Stock Market

Cited by 1 publication

References 9 publications

Research on stock trend prediction based on grey wolf optimized RF and SVM models

Research on stock trend prediction based on grey wolf optimized RF and SVM models

Contact Info

Product

Resources

About