Predicting Bitcoin Trends Through Machine Learning Using Sentiment Analysis with Technical Indicators

Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market.As the history of the Bitcoin market is short and price volatility is high,studies have been conducted on the factors affecting changes in Bitcoin prices.Experiments have been conducted to predict Bitcoin prices using Twitter content.However,the amount of data was limited,and prices were predicted for only a short period(less than two years).In this study,data from Reddit and LexisNexis,covering a period of more than four years,were collected.These data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of posts.An accuracy of 90.57%and an area under the receiver operating characteristic curve value(AUC)of 97.48%were obtained using the extreme gradient boosting(XGBoost).It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner(VADER)and 11 technical indicators utilizing moving average,relative strength index(RSI),stochastic oscillators in predicting Bitcoin price trends can produce significant results.Thus,the input features used in the paper can be applied on Bitcoin price prediction.Furthermore,this approach allows investors to make better decisions regarding Bitcoin-related investments.

Trend of Price and Economic Development in 2005

The situation of the four major kinds of price in 2004-residents' con sumption price, price of production means, interest rate, foreign exchange rate-became main concern from the domestic and international personages. By reviewing the market price in 2004, and analyzing the current price variables, we will easily find the prospects of Chinese economy and people's life in 2005.……

A Machine Learning Approach: Enhancing the Predictive Performance of Pharmaceutical Stock Price Movement during COVID

Predicting stock price movement direction is a challenging problem influenced by different factors and capricious events. The conventional stock price prediction machine learning models heavily rely on the internal financial features, especially the stock price history. However, there are many outside-of-company features that deeply interact with the companies’ stock price performance, especially during the COVID period. In this study, we selected 9 COVID vaccine companies and collected their relevant features over the past 20 months. We added handcrafted external information, including COVID-related statistics and company-specific vaccine progress information. We implemented, evaluated, and compared several machine learning models, including Multilayer Perceptron Neural Networks with logistic regression and decision trees with boosting and bagging algorithms. The results suggest that the application of feature engineering and data mining techniques can effectively enhance the performance of models predicting stock price movement during the COVID period. The results show that COVID-related handcrafted features help to increase the model prediction accuracy by 7.3% and AUROC by 6.5% on average. Further exploration showed that with data selection the decision tree model with gradient, boosting algorithm achieved 70% in AUROC and 66% in the accuracy.

NY futures & A Index turn lower Chinese prices unchanged

2015.5 Recent price movement After trending higher from midMarch through the end of April,values for NY futures and the A Index turned lower in May.Chinese prices have been stable,while Indian and Pakistani prices increased.Prices for the nearby July contract(NY futures)met resistance near 68 cents/

An Agent-Based Approach for Time-Series Momentum and Reversal

This paper proposes a novel agent-based model combining private information diffusion to explain time-series momentum and reversal.Private information transmission allows heterogeneous trading strategies coexist in the artificial market.The experiments reproduce momentum in short horizon and reversal in long horizon in the artificial financial market.Moreover,the authors also analyze how the private information contagion affects the momentum.Meanwhile,the authors find the significant price trend and excess volatility of volume when private information diffuses gradually.