| dc.description.abstract | The research provides a deep exploration of cryptocurrency price dynamics by blending
technical analysis, sentiment analysis, and backtesting, aiming to reveal the
hidden patterns, drivers, and irregularities in their price behaviors. As the field
of cryptocurrencies gains importance, characterized by extreme price volatility and
sensitivity to sentiment shifts, understanding these dynamics is vital for developing
effective financial models and investment strategies. Cryptocurrencies are infamous
for their unpredictable nature, often influenced by market sentiment as much, if not
more, than fundamental or technical indications. This study aims to bridge the
gap by evaluating the effectiveness of combining sentiment analysis with traditional
technical analysis to enhance predictive accuracy and investment returns. We use
various predictive models, including Support Vector Machine (SVM) and Random
Forest, to evaluate their performance in different scenarios. Our findings reveal that
the SVM model significantly outperforms other methods when sentiment analysis
is merged. Specifically, sans sentiment analysis, the Random Forest model achieves
an annual return of 3.59. Nevertheless, with sentiment analysis, the SVM model
generates a distinctly higher annual return of 10.112. These results underscore the
crucial role of sentiment analysis in boosting the predictive power of financial models
concerning cryptocurrencies. Backtesting these models offers pragmatic insights into
their effectiveness. The backtesting results show that including sentiment analysis
in financial models not only enhances return metrics but also improves risk management.
The superior performance of the SVM model with sentiment analysis underscores
the impact of market sentiment on cryptocurrency prices, indicating that
investor sentiment is a potent force that should not be ignored. The implications
of these findings are substantial for both academia and practice. For researchers,
this study adds to the growing body of literature on financial modeling in unstable
and emerging markets, like cryptocurrencies. It presents empirical evidence supporting
the merging of sentiment analysis into predictive models, thereby advancing
theoretical understanding and methodological approaches in the field. For practitioners,
particularly investors and financial analysts, the results provide actionable
insights into optimizing investment strategies. By utilizing sentiment analysis, they
can develop sturdier models that better capture market movements and investor
behavior, leading to improved investment outcomes. The ability to predict price
movements with increased accuracy permits more effective portfolio management
and risk mitigation, which are crucial in the highly volatile cryptocurrency market. Additionally, the research accentuates the importance of continuous innovation in
financial modeling techniques. As the cryptocurrency market evolves, so must the
methods employed to analyze and predict its behavior. The integration of sentiment
analysis represents a significant leap forward in this aspect, offering a robust tool
to navigate the complexities of this emerging asset class. This research highlights
the value of integrating sentiment analysis into financial models for cryptocurrencies.
The findings indicate that such integration not only boosts predictive accuracy
but also enhances investment returns and risk management. By advancing financial
modeling techniques and providing practical insights for investment strategies, this
study presents a significant contribution to both academic research and practical
applications in the swiftly evolving world of cryptocurrencies. | en_US |
