机器学习在股票市场预测方面有多有效?
Nazif Ayyildiz1, Omer Iskenderoglu2
1Harran University, Suruc Vocational School, 63800, Sanliurfa, Turkey.
Heliyon
|January 31, 2024
概括
人工神经网络 (ANN) 和物流回归是预测发达国家的股票市场指数走势的顶级机器学习算法. 在多个指数中,ANN显示了最高的平均预测性能.
科学领域:
- 量化金融 量化金融
- 计算经济学的计算经济学
- 机器学习应用 机器学习应用
背景情况:
- 准确预测股票市场指数的变动对于财务决策至关重要.
- 机器学习算法 (MLM) 为分析复杂的财务数据提供了先进的功能.
- 之前的研究已经探讨了各种MLM,但需要对发达国家指数进行全面比较.
研究的目的:
- 为了比较多个机器学习算法对股票市场指数变动的预测性能.
- 确定最佳的机器学习算法,用于预测主要发达国家股票指数的方向变化.
- 评估不同多媒体市场在预测股票市场趋势方面的准确性.
主要方法:
- 采用了六种机器学习算法:决策树,随机森林,k-最近邻居,天真贝叶斯,后勤回归和人工神经网络 (ANN).
- 应用这些算法来预测发达国家的股票市场指数的方向移动,包括NYSE 100,NIKKEI 225,FTSE 100,CAC 40,DAX 30,FTSE MIB和TSX.
- 评估并比较每个算法的预测准确度,跨越所选的指数.
主要成果:
- 人工神经网络 (ANN) 展示了最高的平均预测性能,被确定为NYSE 100,FTSE 100,DAX 30和FTSE MIB的最佳算法.
- 后勤回归是NIKKEI 225,CAC 40和TSX指数的表现最好的算法.
- 对于所有测试的指数,ANN,后勤回归和支向量机实现了超过70%的预测准确率.
结论:
- 人工神经网络是最有效的机器学习算法,用于预测发达经济体的股票市场指数走势.
- 后勤回归和支向量机也提供可靠的定向运动预测,准确度超过70%.
- 这些发现为投资者和金融分析师提供了宝贵的见解,他们利用机器学习进行市场预测.
更多相关视频
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
8.2K
06:37Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
Published on: December 15, 2023
3.8K
相关概念视频
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
01:11Regression Analysis
5.7K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.7K
01:18Microsoft Excel: Regression Analysis
608
Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
To perform regression...
608
01:11Residuals and Least-Squares Property
7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
01:52Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
00:53Correlation and Regression
1.3K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
1.3K