Cnn bitcoin price
The Centre will bring a bill in the upcoming Winter Session of Parliament to regulate cryptocurrency. Image for representation. As markets and experts speculate on the consequences of the decision, sources in the security establishment told CNN-News18 that the regulation will not be an outright ban. They added that cryptocurrency will not be recognised as legal tender as this is dangerous for currency and taxation system of the country.
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- Bitcoin, ethereum prices tumble as cryptocurrencies continue their downward slide
- Coinbase Valued at $86 Billion in ‘Landmark Moment’ for Crypto
- Robinhood and Coinbase shares fall to their lowest levels ever
- Bitcoin falls further as China cracks down on crypto-currencies
- Guggenheim CIO Says Bitcoin Could Eventually Climb to $600,000
- Crypto fallout: Robinhood, Coinbase shares plunge to lowest values but rebound
- Bitcoin's terrible run isn't over yet
- Bitcoin ticks back up to $34,000 after a dizzying 48 hours
Bitcoin, ethereum prices tumble as cryptocurrencies continue their downward slide
Try out PMC Labs and tell us what you think. Learn More. In last decade, cryptocurrency has emerged in financial area as a key factor in businesses and financial market opportunities. Accurate predictions can assist cryptocurrency investors towards right investing decisions and lead to potential increased profits.
Additionally, they can also support policy makers and financial researchers in studying cryptocurrency markets behavior. Nevertheless, cryptocurrency price prediction is considered a very challenging task, due to its chaotic and very complex nature.
In this study we evaluate some of the most successful and widely used deep learning algorithms forecasting cryptocurrency prices. The results obtained, provide significant evidence that deep learning models are not able to solve this problem efficiently and effectively.
Conducting detailed experimentation and results analysis, we conclude that it is essential to invent and incorporate new techniques, strategies and alternative approaches such as: more sophisticated prediction algorithms, advanced ensemble methods, feature engineering techniques and other validation metrics.
Cryptocurrency is a new type of digital currency which utilizes blockchain technology and cryptographic functions to gain transparency, decentralization and immutability [ 12 ].
Bitcoin BTC is considered the first and the most popular cryptocurrency, which was invented by an anonymous group or person in Since then, alternative cryptocurrencies like Etherium ETH and Ripple XRP were created proving that the cryptocurrency market has emerged in financial area.
Cryptocurrency price prediction can provide a lending hand to cryptocurrency investors for making proper investment decisions in order to acquire higher profits while it can also support policy decision-making and financial researchers for studying cryptocurrency markets behavior.
Cryptocurrency price prediction can be considered as a common type of time series problems, like the stock price prediction. Traditional time series methods such as the well-known AutoRegressive Integrated Moving Average ARIMA model, have been applied for cryptocurrencies price and movement prediction [ 13 ].
However, these models are not able to capture non-linear patterns of very complicated prediction problems in contrast to Deep Learning algorithms which achieve greater performance on forecasting time series problems [ 17 ]. Deep Learning DL refers to powerful machine learning algorithms which specialize in solving nonlinear and complex problems exploiting most of the times big amounts of data in order to become efficient predictor models. The accurate cryptocurrency price prediction is by nature a significantly challenging and complex problem since its values have very big fluctuations over time following an almost chaotic and unpredictable behavior.
Therefore, deep learning techniques may constitute the proper methodology to solve this problem. Recent research efforts have adopted deep learning techniques for predicting cryptocurrency price. Ji et al. Their results demonstrated slightly better accuracy of LSTM compared to other models for regression problem while DNNs outperformed all models on price movement prediction.
Shintate and Pichl [ 16 ] developed a trend prediction classification framework for predicting non-stationary cryptocurrency time series utilizing deep learning. Their results revealed that their proposed model outperformed LSTM baseline model while the profitability analysis showed that simple buy-and-hold strategy was superior to their model and thus it cannot yet be used for algorithmic trading.
Their results showed that LSTM was superior to the generalized regression neural architecture concluding that deep learning is a very efficient method in predicting the inherent chaotic dynamics of cryptocurrency prices.
Amjad and Shah [ 3 ] used live streaming Bitcoin data for predicting price changes increase, decrease or no-change , building a model based on the most confident predictions, in order to perform profitable trades. In this work, we evaluate the performance of advanced deep learning algorithms for predicting the price and movement of the three most popular cryptocurrencies BTC, ETH and XRP.
The main contribution of this research lies in investigating three major questions: i Can deep learning efficiently predict cryptocurrency prices? Furthermore, it also lies in the recommendation for new algorithms and alternative approaches for the cryptocurrency prediction problem.
The remainder of this research is organized as follows: Sect. Section 3 presents our research methodology and experimental results. Section 4 discusses and answers the three research questions, while Sect. Finally, Sect. Deep learning algorithms constitute one of the most powerful machine learning algorithms categories which have been successfully applied on a multitude of commercial applications.
Long Short Term Memory and Convolutional Neural Networks are probably the most popular, successful and widely used deep learning techniques. These networks have become very popular since they have been successfully applied on a wide range of applications and have shown remarkable performance on time series forecasting [ 5 ]. More specifically, LSTM networks are composed by a memory cell, an input, output and forget gate.
The input gate controls the new stored information into the memory cell, while the forget gate controls the information which must be vanished. Finally, the output gate controls the final output information value which is given after a delay into the forget, input gate utilizing a feedback connection loop. In this way, LSTM is able to create a controlled information flow filtering unnecessary information and thus achieving to learn long term dependencies.
The principle idea is that each training sequence is presented forwards and backwards into two separate LSTM layers aiming in accessing both past and future contexts for a given time. More specifically, the first hidden layer possesses recurrent connections from the past time steps; while in the second one, the recurrent connections are reversed, transferring activation backwards along the sequence. Convolutional Neural Networks CNN [ 2 ] constitute another type of deep neural networks which utilize convolution and pooling layers in order to filter the raw input data and extract valuable features, which will feed a fully connected layer in order to produce the final output.
More specifically, they apply convolution operations in the input data and in order to produce new more useful features. The convolutional layers are usually followed by a pooling layer which extracts values from the convolved features producing a lower dimension instance. In fact, a pooling layer produces new features which can be considered as summarized versions of the convolved features produced by the convolutional layer. This implies that pooling operations can significantly assist the network to be more robust since small changes of the inputs, which are usually detected by the convolutional layers, will become approximately invariant.
Table 1 depicts our DL models for the best identified topologies. We have to mention that exhaustive and thorough experiments were performed in order to identify the DL topologies which incur the best performance results. We recall that the basic idea of utilizing LSTM and BiLSTM on cryptocurrency price prediction problems, is that they might be able to capture useful long or short sequence pattern dependencies, due to their special architecture design, assisting on prediction performance, while the convolutional layers of a CNN model might filter out the noise of the raw input data and extract valuable features producing a less complicated dataset which would be more useful for the final prediction model [ 9 ].
Therefore, we expect that a noticeable performance increase will be achieved by the incorporation of these advanced models comparing to classic machine learning algorithms. The implementation code was written in Python 3. Since the cryptocurrency price prediction problem can be considered a regression problem, in our experiments we utilized these two evaluation metrics.
Moreover, by comparing the predicted prices of our models, with the real ones, we managed to compute the classification accuracy of price movement direction prediction if the price will increase or decrease. Therefore, we utilized two additional performance metrics: Accuracy Acc and -score. For the purpose of this research, we utilized data from Jan to Aug, concerning the hourly prices in USD and were divided into training set consisting of data from Jan to Feb values and testing set from Mar to Aug values.
This data were taken from www. Also, we utilized four forecasting horizons F number of past prices taken into consideration , i. An extended report which includes all experimental results can be found in [ 14 ].
Tables 2 and 3 present the experimental results of our DL models ML models. Nevertheless, the performance variations for all DL models seem to be minimal. Furthermore, the 3NN model managed to achieve the best overall performance in Acc score almost in all cases.
In summary, advanced DL models seem to slightly outperform ML models while they did not manage to achieve a noticeable performance increase comparing to our ML models. Performance of DL and ML forecasting models for. In the sequel, we evaluate the forecasting reliability of the proposed prediction models, by performing a test of autocorrelation in the residuals [ 11 ]. This test examines the presence of autocorrelation between the residuals differences between predicted and real values.
In case autocorrelation exists, then the prediction model may be inefficient since it did not manage to capture all the possible information which lies into the data. Notice that the confident limits blue dashed line are constructed assuming that the residuals follow a Gaussian probability distribution. Clearly, all present ACF plots reveal that some correlation coefficients were not within the confidence limits dashed lines , violating the assumption of no auto-correlation in the errors.
More specifically, the interpretation of Figs. Therefore, the presence of correlation indicates that the advanced DL models are unreliable for cryptocurrency price predictors since there exists some significant information left over which should be taken into account for obtaining better predictions. Following our experiments, this section is dedicated in providing a thorough and sufficiently detailed discussion of our findings with regard to the predefined three research questions: Can deep learning algorithms efficiently predict cryptocurrency prices?
Are cryptocurrency prices a random walk process? Which is a proper validation method of cryptocurrency price prediction models? Deep learning algorithms are considered to be the most powerful and the most effective methods in approximating extremely complex and non-linear classification and regression problems, therefore it was expected that a noticeable performance increase will be achieved by the incorporation of these models comparing to classic machine learning algorithms.
Surprisingly, our results demonstrated that the utilized DL algorithms, slightly outperformed the other ML algorithms utilized in our experiments, whereas instead a noticeable performance increase was anticipated. So, it is paramount importance to investigate the reason why that happened. To this end, we summarize two possible reasons: The problem we are trying to solve is a random walk process or very close to it, thus any attempt for prediction might be of poor quality or the problem is just too complicated that even advanced deep learning methods cannot find any pattern that would lead to any reliable prediction.
Thus, more sophisticated methodologies, techniques and innovative strategies are needed to be investigated. When a time series prediction problem follows a random walk process or it is so complicated that most models face it as a random process, then the more efficient method to face it, is the employment of present values as the prediction values for the next state [ 11 ].
In contrast, the deep learning models may attempt forecasting based on patterns that were traced and as a result are unable to achieve high performance because either those patterns are false or because there exist no such patterns at all, in the case that the cryptocurrency price prediction problem is a random walk process. Nevertheless, as mentioned before, the DL models did not manage to achieve a noticeable performance score in our experiments, since their score was almost the same with the ML models.
Towards the construction of a model which performs reliable and accurate predictions, firstly, we have to identify if the cryptocurrency price prediction problem is a random walk process. In a recent study, Stavroyiannis et al. However, since this problem is highly affected by time evolution and external changes, these results maybe temporary and reverse in future.
However, there are numerous technical strategies that the majority of the professional traders utilize in order to make trading decisions in stock market and cryptocurrency investments. A recent study utilized those technical indicators and trading patterns strategies in order to predict stock market and cryptocurrency prices [ 7 ].
Their results provide evidence that technical analysis strategies have strong predictive power and thus can be useful in cryptocurrencies markets like Bitcoin. Therefore, we conclude that the cryptocurrency prices in general are not totally a random walk process but they may be close to it, which means that probably exist some actual patterns on historic data that could assist on forecasting attempts.
As a result, more research is required for the discovery of alternative, innovative and more sophisticated methods such as the incorporation of new feature engineering strategies and the creation of new algorithmic and ensemble methods. However, finding a proper validation metric for cryptocurrency price prediction models can be a very complicated and tricky task and cannot be considered an easy and straightforward process.
The MAE and RMSE may constitute an incomplete way for validating cryptocurrency price prediction problems since a prediction model may have excellent MAE and RMSE performance but cannot properly predict the cryptocurrency price direction move classification problem.
A cryptocurrency trader or investor may be more interested in the future price direction movement rather than knowing the exact future cryptocurrency price.
Profitability analysis for algorithmic trading strategies reveal that classification prediction models were more effective than regression models [ 8 ]. Even if we utilize a third evaluation metric which will measure the performance accuracy of cryptocurrency price direction movement, that may still constitute an incomplete method for validating cryptocurrency prediction algorithms.
Consider the following example: Suppose we wish to validate 2 cryptocurrency prediction models utilizing a test set of questions, e. The second model answers only 5 from questions but it cannot answer the other 95 questions, while these 5 answers are correct. A cryptocurrency trader or investor will probably choose the second model since it acts in a more reliable way and it would be more valuable for him to possess a model which performs accurate predictions on random times specified by the model , rather than possessing a model which performs unreliable predictions on every moment specified by the user.
Therefore, we conclude that finding a proper validation metric for cryptocurrency price prediction models is a very challenging task and thus alternative and new methods for evaluating cryptocurrency prediction models are essential.
Coinbase Valued at $86 Billion in ‘Landmark Moment’ for Crypto
Bitcoin has lost almost half its value since its November high, with cryptocurrency prices continuing to plunge as major economies look to curb their growing popularity. Its peers have fared worse. Investors are getting jittery about digital currencies and other riskier assets ever since the US Federal Reserve signaled it may unwind economic stimulus more aggressively than expected. Governments are cracking down as well. The Russian proposal comes just a few months after China launched a full-scale clampdown on cryptocurrency, banning both trading and mining. Other countries are also flirting with a ban on crypto.
Robinhood and Coinbase shares fall to their lowest levels ever
It's Tuesday, and for the second day in a row, cryptocurrencies are going down. In a. EDT trading today:. Why is that? As we saw last week , and as we saw yesterday , the answer appears to be government regulation and action -- not just in the U. In the United Kingdom, authorities just announced a "significant" operation to seize what they called laundered "proceeds of crime" in the form of cryptocurrency, according to CNN. And although no one can fault authorities for investigating and confiscating any ill-gotten gains, the fact that police forces around the world are working to trace these funds suggests that crypto's untraceability and anonymity -- two big reasons why cryptocurrency got popular in the first place -- are now no longer assured. Nor is the U.
Bitcoin falls further as China cracks down on crypto-currencies
By Paul R. New York CNN Business Bitcoin prices have been on a wild ride this year, and they are set to finish sharply higher than where they began it. Bitcoin and other cryptos may become a little less volatile in By Paul R. More Videos
Guggenheim CIO Says Bitcoin Could Eventually Climb to $600,000
Bitcoin XBT has tripled in value during , growing steadily even as the stock market plunged in the early days of the pandemic. Investors have been drawn to it, as well as other cryptocurrencies , as the US dollar has weakened. With the US Federal Reserve expected to leave interest rates near zero for several more years, bitcoin may continue to win new fans. Rick Rieder, the chief investment officer of fixed income BlackRock BLK , has said the digital currency could replace gold. Yet even as Bitcoin is becoming mainstream, the currency is still commonly used by fraudsters, giving it negative attention.
Crypto fallout: Robinhood, Coinbase shares plunge to lowest values but rebound
Price stability allows that invention to work with minimal friction. Bitcoin has become a cultural and financial phenomenon. While many people have heard of Bitcoin, far fewer understand it. In short, Bitcoin is a digital currency, or "cryptocurrency," that allows person-to-person transactions independent of the banking system. Bitcoin is not a physical coin that you keep in your purse or wallet. Rather, it is a virtual currency—a digital computer code you store in a virtual wallet in cyberspace and access with a computer or smartphone app. Some see Bitcoin as revolutionary because it allows people to transfer money to each other very easily like sending an email , even across international borders. Lately, however, many people are buying this virtual currency purely as a financial investment, hoping it will appreciate, rather than using it for transactions.
Bitcoin's terrible run isn't over yet
Scott Minerd, chief investment officer of the multi-billion dollar investment firm Guggenheim Partners, has revised his previous prediction for bitcoin's long-term price potential. Further, the institutional levels of market participation, while growing, aren't yet big enough to support current price levels. Yet, cryptocurrency "has come into the realm of respectability and will continue to become more and more important in the global economy," Minerd said. The leader in news and information on cryptocurrency, digital assets and the future of money, CoinDesk is a media outlet that strives for the highest journalistic standards and abides by a strict set of editorial policies.
Bitcoin ticks back up to $34,000 after a dizzying 48 hours
Try out PMC Labs and tell us what you think. Learn More. In last decade, cryptocurrency has emerged in financial area as a key factor in businesses and financial market opportunities. Accurate predictions can assist cryptocurrency investors towards right investing decisions and lead to potential increased profits. Additionally, they can also support policy makers and financial researchers in studying cryptocurrency markets behavior.
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