Modeling and predicting earnings per share via regression tree approaches in banking sector: Middle East and North African countries case
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DOIhttp://dx.doi.org/10.21511/imfi.17(2).2020.05
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Article InfoVolume 17 2020, Issue #2, pp. 51-68
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The regression tree approach is an effective and easy to interpret technique where it utilizes a recursive binary partitioning algorithm that divides the sample into partitioning variables with the strongest correlation to the response variable. Earnings per share can be considered as one of the main factors in making the investment decision. This study aims to build a predictive model for earnings per share in the context of the Middle East and North African countries (MENA) . The sample of the study consists of sixty-three banks, which were chosen from eight countries, with a total of six-hundred thirty observations. The simple regression, regression tree, and its pruned regression tree, conditional inference tree, and cubist regression are used to build the predictive model for earnings per share that depends on total assets, total liability, bank book value, stock volatility, age of the bank, and net cash. The results show that the cubist regression is outperforming other approaches where it improves root mean square error for the predictive model by approximately double in comparison with other methods. More interesting results are obtained from the important scores, where it shows that the total assets of the bank, bank book value, and total liability have the biggest impact on the prediction of earnings per share. Also, the cubist regression gives an improvement in R-squared over other methods by at least 30% and 23% using training and testing data, respectively.
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JEL Classification (Paper profile tab)C53, D22, F47, M10
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References47
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Tables9
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Figures9
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- Figure 1. The correlation matrix, histogram, and scatter plots for the study variables
- Figure 2. Linear regression variable importance scores for EPS model
- Figure 3. Basic regression tree for EPS model
- Figure 4. Basic regression tree variable importance scores for EPS model
- Figure 5. Pruned regression tree for EPS model
- Figure 6. Pruned regression tree variable importance scores for EPS model
- Figure 7. Conditional inference tree for EPS model
- Figure 8. Conditional inference tree variable importance scores for EPS model
- Figure 9. Cubist regression variable importance scores for EPS model
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- Table 1. Descriptive statistics for the study variables
- Table 2. Linear regression analysis for EPS model
- Table 3. Linear regression variable importance scores and performance metrics for EPS model
- Table 4. Basic regression tree variable importance scores and performance metrics for EPS
- Table 5. Pruned regression tree variable importance scores and performance metric for EPS
- Table 6. Conditional inference tree variable importance scores and performance metrics for EPS model
- Table 7. Cubist resampling results across tuning parameters for 566 samples and 6 predictors
- Table 8. Cubist regression approach variable importance scores and performance metrics for EPS model
- Table 9. Performance metrics for the study methods
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The risk management practices in the manufacturing SMEs in Cape Town
Problems and Perspectives in Management Volume 15, 2017 Issue #2 (cont. 2) pp. 386-403 Views: 3390 Downloads: 536 TO CITE АНОТАЦІЯRisk management is one of the prominent issues which are pivotal to the success of a business and may adversely affect profitability if not properly practised. Therefore, the main objective of this paper was to determine risk management practices in manufacturing SMEs in Cape Town. The research conducted was quantitative in nature and constituted the collection of data from 74 SME leaders, all of whom had to adhere to a list of strict delineation criteria. All data collected were thoroughly analyzed through means of descriptive statistics. From the findings made, it is clear that SMEs in the manufacturing sector do in fact understand risk management initiatives applicable to ‘manage’ their respective businesses towards sustainability, but not to a large extent. It was found that respondents are unaware of the elements which make risk management effective, which ultimately aids to the development of problems for SMEs. All employees, managers and owners must coordinate their efforts together to identify and manage organizational risks within their ambit to obtain total risk coverage, as well as provide assurance that these risks are effectively managed from a coordinated approach. Further studies may be carried out to identify measures that can be taken to improve the effectiveness of risk management practices in SMEs.
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Machine learning for robo-advisors: testing for neurons specialization
Roman Semko
doi: http://dx.doi.org/10.21511/imfi.16(4).2019.18
Investment Management and Financial Innovations Volume 16, 2019 Issue #4 pp. 205-214 Views: 2685 Downloads: 384 TO CITE АНОТАЦІЯThe rise of robo-advisor wealth management services, which constitute a key element of fintech revolution, unveils the question whether they can dominate human-based advice, namely how to address the client’s behavioral biases in an automated way. One approach to it would be the application of machine learning tools during client profiling. However, trained neural network is often considered as a black box, which may raise concerns from the customers and regulators in terms of model validity, transparency, and related risks. In order to address these issues and shed more light on how neurons work, especially to figure out how they perform computation at intermediate layers, this paper visualizes and estimates the neurons’ sensitivity to different input parameters. Before it, the comprehensive review of the most popular optimization algorithms is presented and based on them respective data set is generated to train convolutional neural network. It was found that selected hidden units to some extent are not only specializing in the reaction to such features as, for example, risk, return or risk-aversion level but also they are learning more complex concepts like Sharpe ratio. These findings should help to understand robo-advisor mechanics deeper, which finally will provide more room to improve and significantly innovate the automated wealth management process and make it more transparent.
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Understanding the preference of individual retail investors on green bond in India: An empirical study
Dhaval Prajapati , Dipen Paul ,
Sushant Malik ,
Dharmesh K. Mishra
doi: http://dx.doi.org/10.21511/imfi.18(1).2021.15
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