Ethereum trends analyzed through blockchain transactions and Google searches.
problem Identifying market manipulation in crypto prices.
method Big data analysis of Ethereum transactions, smart contracts, and search volumes.
result Big players manipulate crypto markets after price drops.
Using non-linear machine learning methods and a proper backtest procedure, we critically examine the claim that Google Trends can predict future price returns. We first review the many potential biases that may influence backtests with this kind of data positively, the choice of keywords being by far the greatest culpr…
TLOB predicts stock prices better than existing models by adapting a simple MLP to LOB data.
problem Predicting stock prices from LOB data is challenging and complex.
method TLOB uses a transformer model with dual attention to capture spatial and temporal dependencies.
result TLOB outperforms state-of-the-art models across multiple datasets and horizons.
This paper compares LSTM, GRU, and Transformer models for stock price prediction.
problem Improving stock price prediction accuracy in fast-paced financial markets.
method Training models on Tesla stock data from 2015 to 2024, comparing LSTM, GRU, and Transformer.
result LSTM model achieved 94% accuracy in predicting stock prices.
ST-GAN predicts stock trends using financial news and data.
problem Predicting financial trends in stock markets.
method ST-GAN combines NLP and technical indicators using GAN technology.
result Significant improvement over existing models in stock price forecasting.
Kriging predicts futures prices by accounting for trends and bid-ask spreads.
problem Predicting futures prices with trends and bid-ask spreads.
method Bayesian Kriging technique to model term structure.
result Kriging accurately predicts futures prices with embedded trends and bid-ask spreads.
Deep learning models struggle with new data in stock price trend prediction.
problem Stock price trend prediction using Deep Learning models.
method Examination of fifteen state-of-the-art DL models on LOB data, using LOBCAST framework.
result All models show significant performance drop with new data, questioning their market applicability.
Paper builds a supervised learning model for Chinese futures price prediction.
problem Predicting the trend of Chinese futures prices accurately.
method Supervised learning model designed for futures price movement classification.
result The model meets accuracy requirements for classifying futures price movements.
This paper evaluates random forest models for predicting stock price trends.
problem Predicting stock price trends to assist investors in making informed decisions.
method Random forest models combined with artificial intelligence, using optimal parameters.
result Random forest models show better predictive performance and time efficiency.
Enhances trading signals using image analysis and weighted moving averages.
problem Improving price trend trading strategies in financial markets.
method Image-induced importance weights applied to weighted moving averages of trading signals.
result Significant enhancement of price trend trading signals with improved portfolio selection.
In April 2009, we introduced a model representing the evolution of motor fuel price (a subcategory of the consumer price index of transportation) relative to the overall CPI as a linear function of time. Under our framework, all price deviations from the linear trend are transient and the price must promptly return to …
A novel approach predicts long-term stock price trends using 2D-convolutional encoders and semantic segmentation.
problem Predicting long-term daily stock price changes with deep learning models.
method Proposes a hierarchical CNN structure with Atrous Spatial Pyramid Pooling blocks to capture both long and short-term temporal relationships.
result Achieved overall accuracy and AUC of 78.18% and 0.88 for predicting trends over the next 20 days.
Study predicts cryptocurrency trends using LSTM model.
problem Predicting cryptocurrency price trends.
method Combination of window-sliding and prediction range method with LSTM model.
result Established model for cryptocurrency price trend prediction.
This study introduces a new GAS blending ensemble model for Bitcoin price prediction.
problem Predicting Bitcoin price fluctuations in the cryptocurrency market.
method Integrates advanced ensemble learning methods, feature selection algorithms, and sentiment analysis.
result The GAS model demonstrates excellent performance in daily Bitcoin trend prediction.
The model predicts stock price trends and opening, minimum, and maximum prices with reasonable accuracy.
problem Forecasting stock prices and trends for investment decisions.
method Improvement of a model based on the association of three LSTM neural networks.
result The model predicts stock price trends and opening, minimum, and maximum prices with reasonable accuracy.
Study uses machine learning to predict stock trends based on fundamental data.
problem Predicting stock trends using fundamental analysis.
method Used LSTM, 1D CNN, and LR models on financial data.
result Logistic Regression models outperformed other models.
Trading styles affect long-run variance of asset prices, increasing under trend-following and decreasing under mean-reverting.
problem Understanding how different trading styles impact the long-run variance of asset prices.
method Probabilistic models designed to capture the direction of trading were used.
result Trading styles increase long-run variance under trend-following and decrease it under mean-reverting conditions.
Deep learning predicts NFT prices with high accuracy.
problem Dynamic valuation of non-fungible tokens (NFTs).
method Trained deep learning model on Ethereum blockchain data.
result Highly accurate price predictions of NFTs.
New hybrid model predicts carbon prices using blockchain data.
problem Predicting carbon prices with fluctuation.
method DILATED CNN-LSTM framework with L1/L2 regularization.
result DILATED CNN-LSTM outperforms traditional models.
It is hypothesized that price charts can be empirically decomposed into two components as random and non random. The non random component, which can be treated as approximately regular behavior of the prices (trend) in an epoch, is a geometric line. Thus, the random component fluctuates around the non random component …
Study uses deep learning to predict stock trends with superior performance.
problem Predicting short-term equity trends with high accuracy.
method Dual-task multilayer perceptron (MLP) integrating technical signals and deep learning.
result Deep learning model outperforms linear baselines in multi-factor stock selection.
This paper proposes a framework to predict long-term trends and short-term fluctuations in multivariate time series.
problem Existing prediction methods often ignore the distinction between long-term trends and short-term fluctuations.
method The paper introduces a MTS forecasting framework that uses both original time series and its first difference to capture long-term trends and short-term fluctuations.
result The proposed method improves forecasting performance by using more supervision information.
Predict stock trends using news sentiment and technical indicators in Spark.
problem Predicting the stock market trend is challenging due to multiple influencing factors.
method Created a machine learning classification problem with features from technical indicators and news sentiment scores.
result Random Forest model achieved 63.58% test accuracy in Spark.
Investor attention predicts global equity market volatility during Ukraine invasion.
problem Predicting global equity market volatility during geopolitical events.
method Event-specific attention indices based on Google Trends, analyzed across 51 global equity markets.
result Investor attention significantly predicts volatility in countries with higher economic openness to Russia and closer to it.
Three years ago we found a statistically reliable link between ConocoPhillips' (NYSE: COP) stock price and the difference between the core and headline CPI in the United States. In this article, the original relationship is revisited with new data available since 2009. The agreement between the observed monthly closing…
Hierarchical hidden Markov models predict market trends in financial time series.
problem Misinterpretation of short-term price fluctuations as long-term trend changes.
method Hierarchical hidden Markov models to capture both short- and long-term trends.
result Hierarchical models provide a comprehensive picture of financial markets.
Improved crypto market forecasting using historical price reactions to tweets.
problem Challenges in inferring market impact from human sentiment labels.
method Market-derived labeling approach to assign tweet sentiment labels based on historical price trends. Fine-tuned language model with context-aware prompt-tuning.
result 89.6% accuracy on Bitcoin news events, outperforming traditional fusion models.
AI models predict stock trends using historical data and public sentiment.
problem Improving stock market prediction accuracy using AI.
method Employed regression and classification ML algorithms for technical and fundamental analysis respectively.
result Median performance suggests AI is not yet superior to stock markets.
Study uses multiple online media to predict crude oil prices.
problem Forecasting crude oil prices using online media.
method Semantic analysis and ARIMAX models on Twitter, Google Trends, Wikipedia, and GDELT.
result Combined analysis from four platforms improves price prediction.
The Efficient Market Hypothesis has been a staple of economics research for decades. In particular, weak-form market efficiency -- the notion that past prices cannot predict future performance -- is strongly supported by econometric evidence. In contrast, machine learning algorithms implemented to predict stock price h…
Proposes LSR-IGRU for improved stock trend prediction.
problem Challenges in stock price prediction due to complex relationships and nonlinear dynamics.
method Long short-term relationships matrix and improved GRU input for better temporal and relationship integration.
result Significantly improved accuracy in predicting stock trend changes.
The paper combines Bitcoin price models with expert corrections for better predictions.
problem Improving Bitcoin price predictions using statistical and expert insights.
method Linear regression models combined with expert corrections, utilizing Bayesian approach for fat-tailed distributions.
result Better price prediction results compared to using either model or expert opinion alone.
A new model predicts price concavity and reversion after metaorder execution.
problem Modeling market response to exogenous trades on limit order books.
method Developed a Non-Markovian Zero Intelligence model with a time-weighted mid-price return function.
result The model predicts concave price paths and price reversion after metaorder execution.
Bitcoin volatility can be predicted from price and alternative data.
problem Predicting Bitcoin volatility from market data.
method Modeling Bitcoin volatility using price, volatility momentum, and alternative data like sentiment and engagement.
result Bitcoin volatility can be predicted with a lag of several hours.
A new GNN model predicts stock trends by learning historical and future correlations.
problem Limited improvement in stock trend prediction models due to ignoring future patterns.
method DishFT-GNN framework that trains a teacher and student model to capture historical and future data correlations.
result State-of-the-art performance on real-world datasets.
Study shows Twitter sentiments predict stock price fluctuations.
problem Predicting stock prices using public opinions.
method Time series analysis and natural language processing with LSTM model.
result Positive, negative, and subjective sentiments correlate with stock price changes.
The paper discovers and evaluates support and resistance levels in financial time series.
problem Understanding and predicting support and resistance levels in financial markets.
method Developed a heuristic discovery algorithm to identify SR levels in intraday price series.
result Discovered SR levels statistically significantly reverse price trends and have a decay aspect over time.
Study finds 'happiness' search data predicts stock returns, suggesting utility needs impact firm performance.
problem Investing in firms that meet societal utility needs.
method Used Google Trends data on 'happiness' search volume to predict stock returns.
result Happiness search exposure (HSE) explains future stock returns, particularly for big and value firms.
Over the past decade, the blockchain technology and its Bitcoin cryptocurrency have received considerable attention. Bitcoin has experienced significant price swings in daily and long-term valuations. In this paper, we propose a partial differential equation (PDE) model on the bitcoin transaction network for predicting…
We investigate possible origins of trends using a deterministic threshold model, where we refer to long-term variabilities of price changes (price movements) in financial markets as trends. From the investigation we find two phenomena. One is that the trend of monotonic increase and decrease can be generated by dealers…
Unified Bayesian framework predicts cryptocurrency market dynamics and volatility.
problem Predicting cryptocurrency market trends and volatility.
method Bayesian framework based on potential field theory and Gaussian Process.
result Attractors and repellers from the potential field are reliable market indicators.
In this paper we outline initial concepts for an immune inspired algorithm to evaluate price time series data. The proposed solution evolves a short term pool of trackers dynamically through a process of proliferation and mutation, with each member attempting to map to trends in price movements. Successful trackers fee…
Study uncovers financial trends from cross-lingual news data.
problem Understanding financial dynamics across diverse global economies.
method Sentiment analysis, NER, and semantic textual similarity for news articles.
result Meaningful correlation between stock price movements and cross-linguistic news sentiments.
Deep learning models predict financial market trends from social media leaders.
problem Predicting financial market trends using social media data.
method Deep learning models trained on NLP analysis of leaders' Twitter handles.
result Substantial improvement in financial market prediction accuracy.
NoxTrader predicts stock returns using LSTM for profitable trading.
problem Predicting profitable stock returns for quantitative trading.
method LSTM model for time-series analysis of stock data.
result Improved investment return from -60% to 325%.
The influence of the past price behaviour on the realized volatility is investigated in the present article. The results show that trending (drifting) prices lead to increased (decreased) realized volatility. This ``volatility induced by trend'' constitutes a new stylized fact. The past price behaviour is measured by a…
Since decades, the data science community tries to propose prediction models of financial time series. Yet, driven by the rapid development of information technology and machine intelligence, the velocity of today's information leads to high market efficiency. Sound financial theories demonstrate that in an efficient m…
In this paper, we propose a modified Levy jump diffusion model with market sentiment memory for stock prices, where the market sentiment comes from data mining implementation using Tweets on Twitter. We take the market sentiment process, which has memory, as the signal of Levy jumps in the stock price. An online learni…