And the actual price of DASH, and all other
cryptocurrencies can be relatively strongly affected
by sentiment indicators, which cannot be considered
using the models in this research. In the meantime,
the analysis was conducted using daily data from a
specific period. The inclusion of additional data, such
as intraday prices could potentially improve model
accuracy. Future research could explore the
integration of sentiment analysis using data from
social media platforms like Twitter and Reddit.
Additionally, experimenting with other advanced
machine learning models, such as LSTM networks,
could further enhance the accuracy of price
predictions. The success of machine learning models
in this analysis opens the door for developing and
testing algorithmic trading strategies. By automating
the trading process based on predictive analytics,
investors could capitalize on short-term price
movements with greater precision.
4 CONCLUSIONS
To sum up, this study explored the application of
various machine learning models, including Ordinary
Least Squares (OLS) regression, Random Forest, and
LightGBM, to predict the price of DASH
cryptocurrency. The analysis revealed that while OLS
provided a basic understanding of the linear
relationships between technical indicators and DASH
prices, more sophisticated models like Random
Forest and LightGBM significantly outperformed it
in terms of accuracy and predictive power.
LightGBM, in particular, demonstrated superior
performance, effectively capturing the complex, non-
linear dynamics of the DASH market. The model’s
ability to integrate trend-following indicators such as
the 50-day Simple Moving Average (SMA_50) and
momentum indicators like MACD and RSI_14
allowed it to provide accurate price predictions,
offering valuable insights for traders and investors.
However, the study also highlighted certain
limitations. The exclusion of sentiment indicators and
intraday data, as well as the focus on a specific time
period, may have constrained the model's predictive
accuracy. Future research should consider
incorporating these factors to enhance the robustness
of predictions. Additionally, the development of
hybrid models that com-bine machine learning with
sentiment analysis could offer further improvements.
Overall, this research contributes to the literature on
cryptocurrency price prediction by filling a gap in the
analysis of DASH and demonstrating the efficacy of
machine learning models in this domain. The findings
underscore the potential of these models to inform
trading strategies, ultimately helping investors
navigate the volatile cryptocurrency market with
greater precision.
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