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: 5499 Downloads: 1088 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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Understanding the preference of individual retail investors on green bond in India: An empirical study
Dhaval Prajapati , Dipen Paul
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Sushant Malik 
,
Dharmesh K. Mishra
doi: http://dx.doi.org/10.21511/imfi.18(1).2021.15
Investment Management and Financial Innovations Volume 18, 2021 Issue #1 pp. 177-189 Views: 5291 Downloads: 1823 TO CITE АНОТАЦІЯThe biggest challenge facing countries, including India, is creating and managing an LCR (low carbon resilient) economy, which balances the need for high growth rates and is environmentally sustainable. The green bond market provides investors the means to help change the economy into an LCR economy. The study was undertaken to understand the key drivers and the factors influencing the individual retail investor’s decision to invest in green bonds. A survey instrument was designed and administered through the snowball sampling technique to 125 Indian respondents of various age groups who were eligible to invest in the Indian bond market. SPSS software was used to conduct a descriptive analysis followed by regression and conjoint analyses. The study results suggest that the Environmental, Social, and Governance (ESG) rating and credit rating of the green bond issuers are the key factors that influence an individual’s investment decision. The findings also highlight that incentives such as tax exemptions and awareness of green bonds also affect an investor’s decision. This research stands out as one of the first attempts to understand the Indian retail investors’ perception of a green bond.
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ESG or financial metrics? What retail investors really look for in decision-making
Suresh Gopal ,
Saravanakrishnan V. ,
Elangovan N.
doi: http://dx.doi.org/10.21511/imfi.22(1).2025.27
Investment Management and Financial Innovations Volume 22, 2025 Issue #1 pp. 351-368 Views: 4347 Downloads: 1697 TO CITE АНОТАЦІЯWith the increasing global emphasis on responsible investing, this study explores the tradeoff between ESG and traditional financial metrics in shaping the investment decisions of retail investors in India. A within-subject experimental design was employed at Christ University, India, involving an initial sample of 75 participants, with 55 completing all three experiment rounds. The sample respondents evaluated masked stock profiles across three rounds, where updated financial and ESG information on masked stock was provided at each round. The results indicate that though ESG metrics are getting attention among retail investors, financial metrics are still the main determining factor for investment. It was found that ROE (52 responses), 3-year CAGR Net Profit (36 responses), and P/E ratios (48 responses) are the most influencing factors to make investment decisions. Similarly, ESG factors (Governance, Environmental, and Sustainability scores) are also frequently mentioned, with 74 citations. Retail investors mainly consider profitability and view ESG as risk-mitigating or neutralizing factors. While evaluating the ESG factors, retailers mainly look at the firm’s environmental concerns, followed by governance and social factors. This result contrasts with the previous studies in this domain, where the literature emphasized governance factors more than environmental factors. These results highlight the integration of ESG elements, as retail investors remain with favorable returns and sacrifice sustainability. Further, this study spots the need for better and quantifiable ESG performance reports to consider alternative data comparable to financial data for better investment decisions.
