Prediction of Stock Performance on the Ghana Stock Exchange Using Financial Ratios: A Logistic Regression Approach

Dedu, Vincent Kofi; Saforo, F.K.

doi:10.18535/ijsrm/v4i6.13

Cited by 13 publications

(15 citation statements)

References 6 publications

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“…Table VI shows that the constant variance (Chi-square= 2.409) is insignificant (P = 0.121). A large Chi-square value greater than 9.22 would indicate the presence of heteroscedasticity (Dutta et al, 2012). Thus, we fail to reject.…”

Section: ) Multicollinearitymentioning

confidence: 76%

Innovativeness and Performance: Evidence from Kenyan SMEs

Kiiru

Mukulu²,

Ngatia³

2023

EJBMR

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Entrepreneurship has acknowledged innovation as a force behind successful business operations. As a result, the study explores how innovation affects Kenyan enterprise performance. The primary data used were obtained through surveys given out to respondents. The target population was animal fed manufacturing SMEs and unit of analysis were Owners and directors. Structural equation modeling (SEM) was the analytical technique employed. The findings indicated that innovativeness has a positive and significant impact on company performance. According to the study's findings, innovative businesses perform better. In order to thrive and succeed in the market, the study advises management and business owners of SMEs to foster an innovative culture.

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Section: ) Multicollinearitymentioning

confidence: 76%

Innovativeness and Performance: Evidence from Kenyan SMEs

Kiiru

Mukulu²,

Ngatia³

2023

EJBMR

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“…In 2012, Dutta applied the logistic model in Indian stock market to fit 30 large capitalization corporations' eight financial ratios during a four-year period to predict the rate of return of stocks as good or bad, and the final accuracy of Dutta's model reached 74.6% [10,11].…”

Section: Model Building In Stocks Predictionmentioning

confidence: 99%

Prediction Of Stock Performance in The U.S. Stock Market Based on Logistic Regression Model

Hong¹

2023

BCPBM

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How to select stocks as investment targets is a hot issue of concern in the investment market. Some researchers have found that the quintile ranking of payout, valuation, profitability, and historical growth in the market can be used as a screening basis to help investors make initial selections. However, the differences in ranking within the same quintile are still obvious, which does not allow for a fine and clear selection. Therefore, in this paper, by collecting the data profile of more than five thousand US stocks from 2015 to 2021 and fitting a logistic model to the data, the author examines price to book ratio, sales growth rate, price to earnings ratio, gross profit to asset ratio, return on equity, dividend yield and earnings per share to predict whether the stocks will outperform in the future, and further derive the result of whether it is worth investing or not under different decision thresholds to help differently risk-averse investors to screen stocks. The results show that for the prediction of whether stocks would “outperform or not”, the final model’s prediction was 71.28 percent accurate. This excellent result shows that this logistic model can be applied to the real market to help general investors and investment institutions to select the right stocks.

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“…statistical models and artificial intelligence (AI)-based models. Under the statistical model category, the widely used models are autoregressive integrated moving average (Banerjee, 2014; Babu & Reddy, 2014) and regression methods (Dutta, Bandopadhyay, & Sengupta, 2012). Under AI technique, primarily machine learning algorithms and artificial neural network (ANN) which have been rarely used to forecast time series or the Indian stock market.…”

Section: Introductionmentioning

confidence: 99%

Stock market prediction, COVID-19 pandemic and neural networks: an SCG algorithm application

Goel

Som

2023

ECON

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PurposeThis study aims to predict the Indian stock market (Nifty 50) by employing macroeconomic variables as input variables identified from the literature for two sub periods, i.e. the pre-coronavirus disease 2019 (COVID-19) (June 2011–February 2020) and during the COVID-19 (March 2020–June 2021).Design/methodology/approachSecondary data on macroeconomic variables and Nifty 50 index spanning a period of last ten years starting from 2011 to 2021 have been from various government and regulatory websites. Also, an artificial neural network (ANN) model was trained with the scaled conjugate gradient algorithm for predicting the National Stock exchange's (NSE) flagship index Nifty 50.FindingsThe findings of the study reveal that Scaled Conjugate Gradient (SCG) algorithm achieved 96.99% accuracy in predicting the Indian stock market in the pre-COVID-19 scenario. On the contrary, the proposed ANN model achieved 99.85% accuracy in during the COVID-19 period. The findings of this study have implications for investors, portfolio managers, domestic and foreign institution investors, etc.Originality/valueThe novelty of this study lies in the fact that are hardly any studies that forecasts the Indian stock market using artificial neural networks in the pre and during COVID-19 periods.

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Prediction of Stock Performance on the Ghana Stock Exchange Using Financial Ratios: A Logistic Regression Approach

Cited by 13 publications

References 6 publications

Innovativeness and Performance: Evidence from Kenyan SMEs

Innovativeness and Performance: Evidence from Kenyan SMEs

Prediction Of Stock Performance in The U.S. Stock Market Based on Logistic Regression Model

Stock market prediction, COVID-19 pandemic and neural networks: an SCG algorithm application

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