The Long-Term Predictive Effect of SVM Financial Crisis Early-Warning Model

Hu, Yanjie; Su, Peng; Hao, Xuchun; Tang, Fei

doi:10.1109/icise.2009.1230

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…This is an aspect that we take into consideration in our work, by building different models for different sectors within the U.S. market. Similarly, in Hu et al (2009) and Ding et al (2008), SVMs based on fundamental data predict quite successfully stock crises and the financial conditions of the companies in the Chinese market.…”

Section: Previous Applications Of Svms To Financial Datamentioning

confidence: 91%

Nonlinear support vector machines can systematically identify stocks with high and low future returns

Huerta

Corbacho

Elkan

2013

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This paper investigates the profitability of a trading strategy based on training a model to identify stocks with high or low predicted returns. A tail set is defined to be a group of stocks whose volatility-adjusted price change is in the highest or lowest quantile, for example the highest or lowest 5%. Each stock is represented by a set of technical and fundamental features computed using CRSP and Compustat data. A classifier is trained on historical tail sets and tested on future data. The classifier is chosen to be a nonlinear support vector machine (SVM) due to its simplicity and effectiveness. The SVM is trained once per month, in order to adjust to changing market conditions. Portfolios are formed by ranking stocks using the classifier output. The highest ranked stocks are used for long positions and the lowest ranked ones for short sales. The Global Industry Classification Standard is used to build a model for each sector such that a total of 8 long-short portfolios for Energy, Materials, Industrials, Consumer Discretionary, Consumer Staples, Health Care, Financials, and Information Technology are formed. The data range from 1981 to 2010. Without measuring trading costs, but using 91 day holding periods to minimize these, the strategy leads to annual excess returns (Jensen alpha) of 15% with volatilities under 8% using the top 25% of the stocks of the distribution for training long positions and the bottom 25% for the short ones.

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Section: Previous Applications Of Svms To Financial Datamentioning

confidence: 91%

Nonlinear support vector machines can systematically identify stocks with high and low future returns

Huerta

Corbacho

Elkan

2013

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“…SVR is a machine learning technique for forecasting continuous numerical outcomes based on SVM (support vector machine). SVR (Rubio & Alba, 2022, Kumar et al, 2021Hu et al, 2009;Huang et al, 2022;Virigineni et al, 2022) essentially seeks to identify an optimal hyperplane to maximise margins while accommodating a preset error level, effectively capturing patterns and trends (Dash et al, 2021). Kernel tricks facilitate nonlinear relationship modelling by projecting data into a higher-dimensional space.…”

Section: Introductionmentioning

confidence: 99%

Optimized Ensemble Support Vector Regression Models for Predicting Stock Prices with Multiple Kernels

Thumu,

Nellore

2024

AIP

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Stock forecasting is a complicated and daily challenge for investors because of the non-linearity of the market and the high volatility of financial assets such as stocks, bonds and other commodities. There is a need for a powerful and adaptive stock prediction model that handles complexities and provides accurate predictions. The support vector regression (SVR) model is one of the most prominent machine learning models for forecasting time series data. An ensemble hyperbolic tangent kernel SVR (HTK-SVR-BO) is proposed in this paper, combining Tanh and inverse Tanh kernels with Bayesian optimization. Combining the strengths of multiple kernels using the ensemble technique and then using optimization to identify the optimal values for each SVR model to enhance the ensemble model performance is possible. Our proposed model is compared with an ensemble SVR model (LPR-SVR-BO), which uses well-known SVR kernel types, including linear, polynomial and radial basis function (RBF). We apply the proposed models to Microsoft Corporation (MSFT) stock prices. The mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R2 score (model accuracy) and mean absolute percentage error (MAPE) are the regression metrics used to compare the effectiveness of each ensemble model. In our comparison, HTK-SVR-BO performs better in terms of regression metrics compared to LPR-SVR-BO and achieves results of 0.27424, 0.13392, 0.36595, 0.99997 and 5.2331 respectively. According to the analysis, the proposed model is more predictive and may generalize to previously unknown data more effectively, so it can be accurate when forecasting future stock prices.

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The efficiency assessment of internal accounting controls: A new perspective on the financial firms in China

Zhu

2014

The 26th Chinese Control and Decision Conference (2014 CCDC)

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The Long-Term Predictive Effect of SVM Financial Crisis Early-Warning Model

Cited by 5 publications

References 5 publications

Nonlinear support vector machines can systematically identify stocks with high and low future returns

Nonlinear support vector machines can systematically identify stocks with high and low future returns

Optimized Ensemble Support Vector Regression Models for Predicting Stock Prices with Multiple Kernels

The efficiency assessment of internal accounting controls: A new perspective on the financial firms in China

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