Initial Coin Offering Prediction Comparison Using Ridge Regression, Artificial Neural Network, Random Forest Regression, and Hybrid ANN-Ridge
Abstract
Can machine learning take a prediction to win an investment in ICO (Initial Coin Offering)? In this research work, our objective is to answer this question. Four popular and lower computational demanding approaches including Ridge regression (RR), Artificial neural network (ANN), Random forest regression (RFR), and a hybrid ANN-Ridge regression are compared in terms of accuracy metrics to predict ICO value after six months. We use a dataset collected from 109 ICOs that were obtained from the cryptocurrency websites after data preprocessing. The dataset consists of 12 fields covering the main factors that affect the value of an ICO. One-hot encoding technique is applied to convert the alphanumeric form into a binary format to perform better predictions; thus, the dataset has been expanded to 128 columns and 109 rows. Input data (variables) and ICO value are non-linear dependent. The Artificial neural network algorithm offers a bio-inspired mathematical model to solve the complex non-linear relationship between input variables and ICO value. The linear regression model has problems with overfitting and multicollinearity that make the ICO prediction inaccurate. On the contrary, the Ridge regression algorithm overcomes the correlation problem that independent variables are highly correlated to the output value when dealing with ICO data. Random forest regression does avoid overfitting by growing a large decision tree to minimize the prediction error. Hybrid ANN-Ridge regression leverages the strengths of both algorithms to improve prediction accuracy. By combining ANN’s ability to capture complex non-linear relationships with the regularization capabilities of Ridge regression, the hybrid can potentially provide better predictive performance compared to using either algorithm individually. After the training process with the cross-validation technique and the parameter fitting process, we obtained several models but selected three of the best in each algorithm based on metrics of RMSE (Root Mean Square Error), R2 (R-squared), and MAE (Mean Absolute Error). The validation results show that the presented Ridge regression approach has an accuracy of at most 99% of the actual value. The Artificial neural network predicts the ICO value with an accuracy of up to 98% of the actual value after six months. Additionally, the Random forest regression and the hybrid ANN-Ridge regression improve the predictive accuracy to 98% actual value.
References
Adhikari, R., and Agrawal, R. K. An introductory study on time series modeling and forecasting. arXiv preprint arXiv:1302.6613 (2013).
Babu, C. N., and Reddy, B. E. A movingaverage filter based hybrid ARIMA-ANN model for forecasting time series data. Applied Soft Computing 23 (2014), 27–38.
Burns, L., and Moro, A. What makes an- ICO successful? an investigation of the role of ICO characteristics, team quality and market sentiment. SSRN Electronic Journal (2018).
Chursook, A., et al. Can tweets predict ICO success? sentiment analysis for success of ICO whitepaper: evidence from australia and singapore markets. In 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (2020), IEEE, pp. 1–5.
Fieri, B., and Suhartono, D. Offensive language detection using soft voting ensemble model. MENDEL 29, 1 (Jun. 2023), 1–6.
Fisch, C. Initial coin offerings (ICOs) to finance new ventures. Journal of Business Venturing 34, 1 (2019), 1–22.
Hartmann, F., Grottolo, G., Wang, X., and Lunesu, M. I. Alternative fundraising: success factors for blockchain-based vs. conventional crowdfunding. In 2019 IEEE international workshop on blockchain oriented software engineering (IWBOSE) (2019), IEEE, pp. 38–43.
Hartmann, F., Wang, X., and Lunesu, M. I. A hierarchical structure model of success factors for (blockchain-based) crowdfunding. In Blockchain and Web 3.0 (2019), pp. 270–308.
Ibrahim, A. Forecasting the early market movement in bitcoin using twitter’s sentiment analysis: An ensemble-based prediction model. In 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) (2021), IEEE, pp. 1–5.
James, G., et al. An introduction to statistical learning, vol. 112. Springer, 2013.
Liu, M., Li, G., Li, J., Zhu, X., and Yao, Y. Forecasting the price of bitcoin using deep learning. Finance research letters 40 (2021), 101755.
Liu, Y., and Wu, H. Prediction of road traffic congestion based on random forest. In 2017 10th International Symposium on Computational Intelligence and Design (ISCID) (2017), vol. 2, IEEE, pp. 361–364.
Liu, Z., Zhu, Z., Gao, J., and Xu, C. Forecast methods for time series data: A survey. IEEE Access 9 (2021), 91896–91912.
Lunesu, M. I., and Desogus, O. ICO evaluation websites analysis. In 2020 IEEE International Workshop on Blockchain Oriented Software Engineering (IWBOSE) (2020), IEEE, pp. 48–56.
Mahalakshmi, G., Sridevi, S., and Rajaram, S. A survey on forecasting of time series data. In 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE’16) (2016), IEEE, pp. 1–8.
Mendoza Uribe, I. Predictive model of the enso phenomenon based on regression trees. MENDEL 29, 1 (Jun. 2023), 7–14.
Muayad, A., and Neamah, I. Ridge regression using artificial neural network. Indian Journal of Science and Technology 9 (08 2016).
Myalo, A., and Glukhov, N. Success of initial coin offering. the empirical evidence from 2016- 2019. The Empirical Evidence from 2019 (2016). [19] Panin, A., Kemell, K.-K., and Hara, V. Initial coin offering (ICO) as a fundraising strategy: a multiple case study on success factors. In Software Business: 10th International Conference, ICSOB 2019, Jyv¨askyl¨a, Finland, November 18–20, 2019, Proceedings 10 (2019), Springer, pp. 237–251.
Peng, Y., Albuquerque, P. H. M., de S´a, J. M. C., Padula, A. J. A., and Montenegro, M. R. The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with support vector regression. Expert Systems with Applications 97 (2018), 177–192.
Sai, G., and Singh, V. Prediction of compressive strength using support vector regression. MENDEL 25, 1 (Jun. 2019), 51–56.
Salim, I., and Hamza, A. B. Ridge regression neural network for pediatric bone age assessment. CoRR abs/2104.07785 (2021).
Schreiber-Gregory, D. N. Ridge regression and multicollinearity: An in-depth review. Model Assisted Statistics and Applications 13, 4 (2018), 359–365.
Serra Deola, B., and Cetingok, B. Analysis of succ´es factors for initial coin offerings and automatisation of whitepaper analysis using textmining algorithms. Master’s thesis, Universitat Polit`ecnica de Catalunya, 2018.
Suthar, M., and Aggarwal, P. Modeling CBR value using RF and M5P techniques. MENDEL 25, 1 (Jun. 2019), 73–78.
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