Price Trend Prediction Research Articles

Generalized visible curvature: An indicator for bubble identification and price trend prediction in cryptocurrencies

We propose a novel curvature-based indicator constructed on log-price time series that captures an interplay between trend, acceleration, and volatility found relevant to quantify risks and improve trading strategies. We apply it to diagnose explosive bubble-like behaviors in cryptocurrency price time series and provide early warning signals of impending market shifts or increased volatility. This improves significantly on standard statistical tests such as the Generalized Supremum Augmented Dickey–Fuller (GSADF) and the Backward SADF tests. Furthermore, the incorporation of our curvature-based indicator as a feature into the Light Gradient Boosting Machine enhances its predictive capabilities, as measured by classification accuracy and trading performance.

Enhancing Stock Trading Strategies: Integrating Discrete Wavelet Transformation with Deep Q-Network

The prediction of price trends in the stock market has always been a hot research topic in the financial field. However, due to the high instability and volatility of stock prices, it is very difficult to accurately predict stock trends. How to remove the noise of stock data, extract effective features, and pursue maximum value returns has always been a challenge. This paper proposes a hybrid model (DWT-DQN) that combines discrete wavelet transform with deep reinforcement learning to improve the accuracy and return rate of stock predictions. First, the model captures price fluctuation information on different scales by performing discrete wavelet transformation on the difference between long- and short-term moving averages of stock prices, and well extracts the changing characteristics of stock price data in the time domain and frequency domain. Then the feature data are input into the built DQN network for model training. The network can select the optimal trading action based on market status and historical experience and returns. At the same time, during the data sampling process, an attention mechanism is introduced to allow the model to further learn in the direction of maximizing returns. Through testing and verification on SSEC, HSI, NDX and SPX data sets, experiments show that the hybrid model proposed in this paper has excellent performance in terms of accuracy and return rate.

Seeing is believing: Forecasting crude oil price trend from the perspective of images

AbstractIn this paper, we propose a novel imaging method to forecast the daily price data of West Texas Intermediate (WTI) crude oil futures. We use convolutional neural networks (CNNs) for future price trend prediction and obtain higher prediction accuracy than other benchmark forecasting methods. The results show that images can contain more nonlinear information, which is beneficial for energy price forecasting. Nonlinear factors also have a strong influence during drastic fluctuations in crude oil prices. In the robustness tests, we find that the image‐based CNN is the most stable approach and can be applied in various futures forecasting scenarios. In the prediction of low‐frequency models for high‐frequency data, the CNN method still retains considerable predictive power, indicating the possibility of transfer learning of our novel approach. By unleashing the power of the picture, we open up a whole new perspective for forecasting future energy trends.

Using Generative Pre-Trained Transformers (GPT) for Electricity Price Trend Forecasting in the Spanish Market

The electricity market in Spain holds significant importance in the nation’s economy and sustainability efforts due to its diverse energy mix that encompasses renewables, fossil fuels, and nuclear power. Accurate energy price prediction is crucial in Spain, influencing the country’s ability to meet its climate goals and ensure energy security and affecting economic stakeholders. We have explored how leveraging advanced GPT tools like OpenAI’s ChatGPT to analyze energy news and expert reports can extract valuable insights and generate additional variables for electricity price trend prediction in the Spanish market. Our research proposes two different training and modelling approaches of generative pre-trained transformers (GPT) with specialized news feeds specific to the Spanish market: in-context example prompts and fine-tuned GPT models. We aim to shed light on the capabilities of GPT solutions and demonstrate how they can augment prediction models by introducing additional variables. Our findings suggest that insights derived from GPT analysis of electricity news and specialized reports align closely with price fluctuations post-publication, indicating their potential to improve predictions and offer deeper insights into market dynamics. This endeavor can support informed decision-making for stakeholders in the Spanish electricity market and companies reliant on electricity costs and price volatility for their margins.

Lob-based deep learning models for stock price trend prediction: a benchmark study

The recent advancements in Deep Learning (DL) research have notably influenced the finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL models focusing on Stock Price Trend Prediction (SPTP) based on Limit Order Book (LOB) data. To carry out this study, we developed LOBCAST, an open-source framework that incorporates data preprocessing, DL model training, evaluation, and profit analysis. Our extensive experiments reveal that all models exhibit a significant performance drop when exposed to new data, thereby raising questions about their real-world market applicability. Our work serves as a benchmark, illuminating the potential and the limitations of current approaches and providing insight for innovative solutions.

Prediction of Weekly Price Trend of Garlic Based on Classification Algorithm and Combined Features

To promote the sustainable development of the garlic industry and provide a reference for the prediction of agricultural product price trends, this study used the garlic price in Jinxiang, China as the research object. First, the feature combination De was obtained by extracting the sequence obtained using VMD decomposition. Then, the De_Vo combined feature was constructed by combining the volatility feature Vo. Classification algorithms, such as logistic regression, SVM, and XGBoost, were used to classify and predict the garlic price trend. The results showed that the prediction results based on the combined features were better than those based on the single De or Vo features. In the binary classification prediction, the accuracy values for LR, SVM, and XGBoost were 62.6%, 71.4%, and 72.9%, respectively. Among them, the XGBoost algorithm performed better than the LR and SVM algorithms in the three-class, four-class, and five-class predictions.

Validation study of Yunnan ethnic culture industry stock model under epidemic situation based on chaotic particle swarm optimization neural networks

The new crown pneumonia epidemic is raging, in the context of global integration, the scope of the impact of this sudden event spread around the world, the stock market has not been spared, the financial risk has increased dramatically compared with the past, the emergence of the epidemic has led to the spread of investor panic, March 2020, the U.S. S&P 500 index appeared in the four plunge, and led to the market trading meltdown, the world’s financial markets have had an extremely serious impact. The study of the impact of Xin Guan Pneumonia on the company’s stock returns is not only conducive to enriching the theoretical study of public health emergencies, but also conducive to improving the coping strategy, stabilizing the general economic market, and enhancing the public’s awareness of risk response. This paper compares the effect of the four intelligent algorithms of chaotic particle swarm algorithm, chaotic bee colony algorithm, chaotic fruit fly algorithm and chaotic ant colony algorithm combined with neural network on the prediction of the stock price trend of Yunnan national culture, and the study shows that the speed of convergence of the chaotic particle swarm optimization neural network and the speed of descent is better than that of the two models of chaotic fruit fly and chaotic bee colony, and the coefficients of decision of the chaotic particle swarm optimization neural network are higher than that of the other three models, and the errors are lower than the other three models. Indexes are lower than the other three models and have high accuracy in stock prediction of Yunnan ethnic culture, this finding emphasizes the potential of PSO-BP model to provide robust stock market prediction, which is important for both investors and policy makers in dealing with volatile market conditions.

A hybrid model for stock price prediction based on multi-view heterogeneous data

Literature shows that both market data and financial media impact stock prices; however, using only one kind of data may lead to information bias. Therefore, this study uses market data and news to investigate their joint impact on stock price trends. However, combining these two types of information is difficult because of their completely different characteristics. This study develops a hybrid model called MVL-SVM for stock price trend prediction by integrating multi-view learning with a support vector machine (SVM). It works by simply inputting heterogeneous multi-view data simultaneously, which may reduce information loss. Compared with the ARIMA and classic SVM models based on single- and multi-view data, our hybrid model shows statistically significant advantages. In the robustness test, our model outperforms the others by at least 10% accuracy when the sliding windows of news and market data are set to 1–5 days, which confirms our model’s effectiveness. Finally, trading strategies based on single stock and investment portfolios are constructed separately, and the simulations show that MVL-SVM has better profitability and risk control performance than the benchmarks.

Comparative research on multi-source heterogeneous data fusion technologies

Both structured market data and unstructured financial news data can significantly affect stock price fluctuations. Therefore, relying only on a single data source for stock price trend prediction may produce information bias. This article combines these two types of heterogeneous data and systematically compares the effectiveness of different data fusion technologies to establish the optimal data integration strategy, thereby improving the accuracy of stock price prediction. Specifically, this paper innovatively proposes a stock price trend prediction model named MVL-SVM. The model successfully combines multi-perspective learning with support vector machine, and realizes the effective fusion of stock market data and financial news data. It is worth mentioning that the prediction accuracy of MVL-SVM model using direct fusion method is more than 30% higher than that of index modeling method, which indicates that direct fusion method can fully learn effective information from multiple information sources, thus significantly improving the prediction effect.

Frequency-domain enhanced bi-directional recurrent quantum network for stock price trend prediction

Frequency-domain enhanced bi-directional recurrent quantum network for stock price trend prediction

Modelling Prediction of Cities Real Estate Price Trend Using Recurrent Neural Network: A Case of Dar es Salaam City

Real estate refers to a class of real property such as land and its associated infrastructure. The prediction of real estate prices in cities, which is affected by a number of parameters, is an open research problem. The lack of reliable and effective tools for price forecasting in real estate, especially in residential housing, can adversely affect investment flows and the growth of the real estate sector. Taking Tanzania as an example, the price prediction practices rely on human suggestions that are prone to personal bias and subjective to price hysteria for personal gain and impact consumer expectations. To address the challenge, this paper designed a real estate price trend prediction model for the cities using Recurrent Neural Networks (RNN) with a Long Short-Term Memory (LSTM). The study identified the factors influencing real estate property prices, including size, location, time, property quality, accompanied services, market nature, price of land, cost of building materials, and value for money. However, the study spotted the size, price, location, and time as key factors in predicting price trends when using RNN-LSTM. The results show that the proposed RNN-LSTM model performed better with 50% MSE less compared to the Convolutional Neural Network (CNN). In computing the price trend per location, the model prediction accuracy was 97.45%, 79.23%, and 53.8% for the high class, middle class, and low class, respectively, resulting in an average prediction accuracy of 76.8%

Short-term stock price trend prediction with imaging high frequency limit order book data

Predicting price movements over a short period is a challenging problem in high-frequency trading. Deep learning methods have recently been used to forecast short-term prices via limit order book (LOB) data. In this paper, we propose a framework to convert LOB data into a series of standard images in 2D matrices and predict the mid-price movements via an image-based convolutional neural network (CNN). The empirical study shows that the image-based CNN model outperforms other traditional machine learning and deep learning methods based on raw LOB data. Our findings suggest that the additional information implicit in LOB images contributes to short-term price forecasting.

Interpretable image-based deep learning for price trend prediction in ETF markets

Image-based deep learning models excel at extracting spatial information from images but their potential in financial applications has not been fully explored. Here we propose the channel and spatial attention convolutional neural network (CS-ACNN) for price trend prediction. It utilizes the attention mechanisms to focus on specific areas of input images that are the most relevant for prices. Using exchange-traded funds (ETF) data from three different markets, we show that CS-ACNN – using images constructed from financial time series – achieves on-par and, in some cases, superior performances compared to models that use time series data only. This holds true for both model classification metrics and investment profitability, and the out-of-sample Sharpe ratios range from 1.57 to 3.03 after accounting for transaction costs. The model learns visual patterns that are consistent with traditional technical analysis, providing an economic rationale for learned patterns and allowing investors to interpret the model.

Attention based adaptive spatial–temporal hypergraph convolutional networks for stock price trend prediction

Stock price trend prediction is an important and challenging issue, and accurate forecasting will effectively improve investment decisions and contribute to investment returns. Improving prediction accuracy by exploring stock correlations has received much attention in recent studies. However, there are still some issues that have not been fully considered, such as the impact of invalid correlations, low sensitivity to minor price fluctuations and dependence on priors expert information. To solve the above issues, we propose a novel spatial–temporal framework, which has several characteristics: (1) the noise-aware spatial–temporal attention that dynamically filters out invalid associations from traditional spatial attention and combines temporal attention to capture the spatial–temporal patterns of different stock series; (2) the adaptive stock hypergraph generation maps the intrinsic associations of stocks into a trainable dense matrix via adaptive node embedding; (3) the adaptive graph convolution extends the graph convolution operation from static graphs to adaptive hypergraphs for exploring the dynamic correlations. (4) Multiple stacked attention-based adaptive spatial–temporal Blocks form the end-to-end prediction framework, which uses time-aware cascaded convolution to extract fine-grained temporal features. Convincing experimental results on two stock datasets, studies on the performance on various simulation investments and the model interpretability confirm the advantages of our approach.

An integrated approach of ensemble learning methods for stock index prediction using investor sentiments

It has been evidenced by numerous studies that irrational investor sentiment is one of the critical factors leading to dramatic volatility in financial market prices. Therefore, how to effectively predict market prices by information on investor sentiment is a popular but complex topic for researchers, market investors, and financial regulators. In this research, we aim to investigate the effectiveness of stock index prediction using multiple investor sentiment features, and we propose an advanced price trend prediction and trading simulation approach for the Shanghai Stock Exchange index and the Shenzhen Component index by integrating the Boosting, Bagging, and NSGA-II methods. Additionally, the SHAP method is employed as a model interpretation approach to analyze the importance of the sentiment variables and quantify their contributions to the predictions from both local and global perspectives. According to the experimental results, it can be found that the proposed method outperforms the benchmark methods in terms of the hit ratio, accumulated return, and maximum drawdown. It indicates that the proposed method is capable of achieving high accuracy, low risk, and stable profit in price trend prediction and trading simulation of the Chinese stock indexes. Moreover, the SHAP approach incorporated in the proposed method improved the interpretability of the proposed model, which can provide a beneficial reference for market participants to clarify the important sentiment factors and make relative decisions.

A representation-learning-based approach to predict stock price trend via dynamic spatiotemporal feature embedding

Stock price trend prediction is a fascinating but difficult research topic. Recently, GNN-based models have been continuously proposed, which are believed to be more effective since they consider the information about stocks themselves and the information between stocks. However, the graph data are often static, unstructured and not all-inclusive, which cannot dynamically reflect all relationships between stocks. Therefore, we propose a novel model PriceExploration-Network (PE-Net), effectively utilizing both temporal and cross-sectional information contained in price to predict the price trend. PE-Net only requires the price data effectively saving the trouble of fetching alternative data and is able to capture the dynamic implicit relations between stocks by combining clustering techniques and GAT architecture. The effectiveness of PE-Net is examined on real-world S&P 500 constituents and the results demonstrate that PE-Net can outperform state-of-the-art models w.r.t. both accuracy and AUC.

EMD-BiLSTM Stock Price Trend Forecasting Model based on Investor Sentiment

The movement of stock prices is the focus of investors' attention in the stock market, so stock price trend prediction has always been a hot topic in quantitative investment research. Traditional machine learning forecasting models are difficult to process nonlinear, high-frequency, high-noise stock price time series, which makes the prediction accuracy of stock price trends low. In order to improve the prediction accuracy, according to the temporal characteristics of stock price data, it is proposed to use a combination of empirical mode decomposition (EMD), investor sentiment and two-way long short-term memory neural network to predict the rise and fall of stock prices. Firstly, the empirical mode decomposition algorithm is used to extract the characteristics of the stock price time series on different time scales, and the investor sentiment indicators of the text from the close of the previous stock trading day to the opening of the next trading day are extracted by constructing a financial sentiment dictionary, and finally the EMD-BiLSTM model is used to predict the rise and fall of the next index trading day. Experiments on the dataset of stock price series show that the optimized BiLSTM model has strong predictive ability for the trend of consumer sector indexes.

Analysis of Stock Price and Price Movement Prediction based on Machine Learning Models for E-Hualu

At present, many multi-information input models are used in various stock forecasts, but for most Chinese investors, it is difficult to obtain effective stock information, and most of them buy and sell stocks only based on stock trends and stock prices. Therefore, in order to get better prediction results based on basic information, the author tries to use some machine learning methods to predict E-Hualu in the Chinese stock market, and the aim is to select the best prediction model. In this study, five machine learning models were applied to predict stock prices, namely linear regression, SVR, random forest, XGBoost, and LSTM, then the close price of the previous 14 days were converted into the input of each model to estimate the close price of the next day. After the price prediction results were obtained, secondary processing was carried out, transforming previous outputs into trend prediction results. The research results indicate that the LSTM model has outstanding performance in both price prediction and trend prediction. Hopefully, the results of this research can provide some suggestions for investing in E-Hualu.

LSTM-ReGAT: A network-centric approach for cryptocurrency price trend prediction

Predicting price trends of cryptocurrencies is a challenging task due to the highly speculative cryptocurrency market. Prior studies mainly investigate predictors such as historical trading data, macroeconomic development, and public interests in cryptocurrencies for price trend prediction while ignoring the predictive role of the relations between cryptocurrencies. In fact, the price movement of a cryptocurrency may be affected by those of other cryptocurrencies, thus, incorporating cryptocurrency interrelations can further improve the prediction performance. In this paper, we propose a novel end-to-end model for price trend prediction using long short-term memory (LSTM) and relationwise graph attention network (ReGAT), in which both individual cryptocurrency features and cryptocurrency relations are considered. Specifically, a cryptocurrency network is built by using shared features between cryptocurrencies. Further, LSTM is used to profile sequential patterns of individual cryptocurrency features. To fully extract network features, ReGAT is designed to aggregate information conveyed by different types of relations while automatically differentiating their importance. Finally, the conventional and network features are concatenated to predict the price trend. The effectiveness of LSTM-ReGAT is validated using real-world cryptocurrency market data. The trading simulations for Bitcoin and portfolios reveal that our model obtains the highest profits. Our study provides insightful implications for investment decision support in cryptocurrency market.

Investor Sentiment Analysis Based on Comment Text for Stock Price Prediction

The sentiment of stock investors is affected by many factors, and the trend of stock price is reflected by the decision behavior of stock trades. Previous tendency can be seen in technical index, and people's sentiment towards the stock is closely related to the text of commentary on the stock. This paper adopts sentiment dictionary to classify the same stock comment text, and modify the corpus of SnowNLP to train a model that conforms to Chinese stock comment. Based on the stock technical index and the established sentiment index to achieve the stock price prediction, so as to achieve a better analysis and prediction of the stock price trend. According to the analysis, the established investor sentiment index has a good effect on stock prediction. To be specific, the predictive regression score of the investor sentiment index is significantly improved compared with those of purely technical indicators, and MAE, MSE and other error indicators are obviously decreased. These results shed light on guiding further exploration of stock forecast with investor sentiment.