Support Vector Machine for Cryptocurrency Forecasting

Support Vector Machine (SVM) is a powerful tool for data classification that uses a non-linear, non-parametric approach. It operates in a high-dimensional space to identify a hyperplane that minimizes error rates. This makes SVM highly effective for forecasting tasks, including the prediction of cryptocurrency market movements.

The recent growth in Bitcoin prices between 2016–2018 has drawn significant interest from investors and researchers alike. Forecasting cryptocurrency prices is now considered essential to improve market performance. In this context, integrating SVM with other machine learning methods — such as sentiment analysis — offers enhanced forecasting accuracy.

This article examines the use of SVM in cryptocurrency forecasting, with a focus on combining it with sentiment analysis techniques to predict financial movements.

Research Objectives 

The research objectives are as follows:

• Explore existing research on SVM in financial forecasting.
• Gather sentiment-based dataset used for cryptocurrencies in social media.
• Develop a sentiment-based SVM that is optimized by Particle Swarm Optimization (PSO) for cryptocurrency forecasting algorithms.
• Evaluate the sentiment-based SVM for cryptocurrency forecast.
• Compare the performance of sentiment-based SVM optimized by PSO with benchmarked algorithms.

 

Research Questions 

Looking at the research objectives above, the following research questions have been made:

• RQ1: Describe the drawbacks or limitations of the support vector machine used in financial forecasting.
• RQ2: Demonstrate the effects of sentiment on social media towards financial forecasting.
• RQ3: In which process sentiment can enhance the support vector machine?
• RQ4: Does the performance of optimized PSO with sentiment improved the cryptocurrency forecasting compared to other machine learning algorithms

 

Literature Review 

Every classification system has its own advantages and drawbacks depending on the nature of the data being analyzed. SVM, for instance, is well-suited for liquidation analysis when the data distribution is unknown. However, its limitations include:

• Lack of transparency in results due to high-dimensional data.
• Difficulty in representing all companies’ scores as a general parametric function of economic ratios.
• High processing time for large datasets.
• Poor performance when dealing with overlapping classes.
• Complexity in selecting the appropriate kernel, increasing time and potential risks in predictions.

Researchers have explored ways to improve financial forecasting accuracy, with sentiment analysis being one of the most promising approaches. Sentiment analysis — a branch of Natural Language Processing (NLP) — categorizes feedback, opinions, and attitudes as positive or negative.
Studies (He, Guo, Shen & Akula, 2016) show that:

• Negative social media sentiment is associated with falling stock prices.
• A 1% increase in negative Twitter sentiment can lead to a 3.7% drop in stock prices.
• Sentiment data can be valuable in cryptocurrency price forecasting.

 

Enhancing SVM with Sentiment Analysis

Sentiment analysis can be conducted through:

• MLSA (Machine Learning Sentiment Analysis)
• LBSA (Lexicon-Based Sentiment Analysis)

To improve SVM performance in sentiment-driven forecasting:

• Feature Extraction: Transform raw data into a series of features that are essential metrics.
• Pre-Processing: Clean and prepare the dataset to remove noise.
• Training: Use labeled data to train the SVM to classify sentiments.
• Post-Processing: Fine-tune predictions using methods like hyperparameter optimization.
• Optimization: Apply PSO or other algorithms to enhance classification accuracy.

 

SVM in Cryptocurrency Forecasting

Bitcoin datasets typically include features such as:

• Market price
• Block size
• Cost per transaction
• Miner revenue
• Market supply
• Output volume
• Trade volume

SVM models can forecast Bitcoin prices using these datasets, often achieving:

• High accuracy with smaller datasets.
• Superior performance compared to some other ML algorithms.

 

Performance Evaluation: SVM vs Other Methods

Other popular cryptocurrency forecasting techniques include:

• Artificial Neural Networks (ANN): Powers like the human brain, but more complicated, expensive and time consuming.
• Deep Learning: Extracts features using many layers, returning good results at the cost of additional computational power.

Advantages of SVM:

• Performs well in creating clear decision boundaries.
• Outperforms in generalization with smaller datasets.
• Reliable for short-term predictions.

Limitations of SVM:

Struggles with extremely large datasets.
Requires more processing time for complex, overlapping data.

 

Conclusion

The cryptocurrency market is interesting because of the history of fast technological growth. Support Vector Machine, Neural Networks, Deep Learning were among the techniques of machine learning that could be priceless in terms of trend analysis and market trend predictions.

These include SVM however, standing out due to being simple, robust and also highly accurate – typically reaching over 70 percentage in accuracy for short currency of cryptocurrencies prediction. For students or professionals working on similar forecasting models, expert Machine Learning Assignment Help can be invaluable in implementing and optimizing these algorithms for academic or real-world projects.
 
References 

• Akyildirim, E., Goncu, A., & Sensoy, A. (2018). Prediction of Cryptocurrency Returns using Machine Learning, 2-24.
• Auria, L., & Moro, R. A. (2008). Support vector machines (SVM) as a technique for solvency analysis, 1-16.
• Han, S., & Chen, R. C. (2007). Using svm with financial statement analysis for prediction of stocks. Communications of the IIMA, 7(4), 8.
• He, W., Guo, L., Shen, J., & Akula, V. (2016). Social media-based forecasting: A case study of tweets and stock prices in the financial services industry. Journal of Organizational and End User Computing (JOEUC), 28(2), 74-91.
• Hitam, N., & Ismail, A. (2018). Comparative Performance of Machine Learning Algorithms for Cryptocurrency Forecasting. Indonesian Journal Of Electrical Engineering And Computer Science, 11(3), 1121. doi: 10.11591/ijeecs.v11.i3.pp1121-1128
• Jadav, B. M., & Vaghela, V. B. (2016). Sentiment analysis using support vector machine based on feature selection and semantic analysis. International Journal of Computer Applications, 146(13).
• Raghava-Raju, A. (2018). A Machine Learning Approach to Forecast Bitcoin Prices. International Journal of Computer Applications, 182(24).
• Zaini, N., Malek, M. A., Yusoff, M., Mardi, N. H., & Norhisham, S. (2018, April). Daily River Flow Forecasting with Hybrid Support Vector Machine–Particle Swarm Optimization. In IOP Conference Series: Earth and Environmental Science (Vol. 140, No. 1, p. 012035). IOP Publishing.
• Zainuddin, N., & Selamat, A. (2014, September). Sentiment analysis using support vector machine. In 2014 International Conference on Computer, Communications, and Control Technology (I4CT) (pp. 333-337). IEEE.