Price Prediction Research Articles

Unlocking ETF price forecasting: Exploring the interconnections with statistical dependence-based graphs and xAI techniques

In the complex landscape of financial markets, accurately predicting Exchange-Traded Fund (ETF) price movements requires advanced methodologies. This research introduces a practical approach that integrates network analysis with graph embeddings, specifically utilizing Node2Vec, to enhance financial prediction models' performance and interpretability. By representing the intricate relationships within financial markets in a lower-dimensional space, we improve the efficiency of AI-driven predictions. A key component of our method is applying the SHAP Explainable AI (xAI) framework, which helps interpret our tree-based models' decision-making process. Using six different tree-based models, our approach delivers accurate predictions while maintaining transparency in model interpretation. This combination of graph embeddings and explainability tools enables stakeholders to understand better the factors influencing financial market behavior, improving decision-making based on AI models.

Artificial intelligence-based power market price prediction in smart renewable energy systems: Combining prophet and transformer models

With the increasing integration of smart renewable energy systems and power electronic converters, electricity market price prediction is particularly important. It is not only crucial for the interests of power suppliers and market regulators but also plays a key role in ensuring the reliable and flexible operation of the power system, particularly during extreme weather events or abnormal conditions. This study develops a hybrid time series forecasting model that combines Prophet and Transformer, which takes advantage of deep learning to provide a new solution for electricity market price forecasting. By introducing the Stacking optimization strategy, this study improves the accuracy and stability of electricity market price sequence prediction. In addition, the study tries to integrate traditional time series forecasting methods (such as the Prophet model) with deep learning models (such as the Transformer model), aiming to make full use of their respective advantages to achieve more accurate and stable predictions. Through experimental evaluation on four electricity market data sets, this study finds that the hybrid forecast model exhibits significant performance improvements in enhancing the accuracy and stability of electricity market price predictions. This method not only provides a more accurate tool for power market price prediction, but also provides solid technical support for the efficient operation and sustainable development of smart renewable energy systems. Experimental results also show that the combination of deep learning models with traditional time series methods and the introduction of Stacking strategies is crucial to improving the performance of power market price prediction, and also helps us better understand and design smart renewable energy systems, price and energy management strategies, thereby providing an effective method for achieving efficient and reliable power and energy transmission.

Carbon Price Point–Interval Forecasting Based on Two-Layer Decomposition and Deep Learning Combined Model Using Weight Assignment

With the intensification of global warming, the demand for carbon emissions reduction has gradually increased in various countries. Carbon price is crucial for promoting the activation of the carbon trading market and facilitating emissions reduction. However, the current carbon price has non-linear characteristics, large fluctuations, and high complexity, making accurate predictions challenging. To effectively predict the trends and change of carbon price, this study proposed a hybrid deep learning point–interval prediction model. First, an improved variational mode decomposition–symplectic geometry mode decomposition (IVMD–SGMD) two-layer decomposition model was constructed to decompose the carbon prices into regular subsequences. Then, attention–temporal convolutional network–bidirectional gated recursive unit (Attention-TCN-BiGRU) and Encoder–Decoder long short-term memory (LSTM) combined prediction models were constructed for the prediction of subsequences. The entropy method (EM) was used to assign weights to the predictions of two models to achieve model complementarity and a linear reconstruction of the models’ results. Then the error correction was performed to obtain the final prediction results. This study conducted an experiment on carbon prices in the Guangdong and Shenzhen markets. The mean absolute error (MAE) of the two datasets were reduced by 89.69% and 87.43% lower than that for LSTM. To demonstrate the model's adaptability, prediction experiments conducted on natural gas and crude oil prices were employed, confirming its strong predictive accuracy in energy price forecasting. Based on the point prediction error, the interval prediction using the improved kernel density estimation (IKDE) provides more carbon market information for decision makers. The proposed model aids government energy policy formulation and fosters ongoing efforts to reduce carbon emissions.

IMPLEMENTASI MACHINE LEARNING ALGORITMA NEURAL NETWORK DALAM MEMPREDIKSI HARGA SAHAM MENGGUNAKAN RAPIDMINER

The research is aimed at implementing the Neural Network algorithm in predicting stock prices using RapidMiner. Historical stock price data is analyzed to train neural network models in identifying patterns and trends that can be used to predict future stock prices. The research phases include data collection, data preprocessing, Neural Network model development, model training, and model performance evaluation. Data used include daily closing prices, trade volumes, and other technical indicators. The data is processed through several stages of preprocesing, including normalization and filling of missing values, before being used to train Neural network models. The model was built with several layers and the number of neurons adjusted based on early experiments to find the optimal configuration. Models are trained using datasets that are divided into training sets and testing sets. The predicted result is compared to the actual value to evaluate the performance of the model. The results showed that the Neural Network model was able to predict stock prices with a fairly high accuracy rate, with an RMSE of 0.989 indicating a low prediction error rate. The research concludes that the Neural Network algorithm implemented in RapidMiner is effective in predicting stock prices and has significant potential to be used in investment decision-making. The use of RapidMiner as a data processing tool has proven to be efficient and facilitating the development and evaluation of predictive models. Keywords: Neural Network, Stock Price Prediction, RapidMiner, Machine Learning, Investment

Housing Price Prediction - Machine Learning and Geostatistical Methods

Abstract Machine learning algorithms are increasingly often used to predict real estate prices because they generate more accurate results than conventional statistical or geostatistical methods. This study proposes a methodology for incorporating information about the spatial distribution of residuals, estimated by kriging, into selected machine learning algorithms. The analysis was based on apartment prices quoted in the Polish capital of Warsaw. The study demonstrated that machine learning combined with geostatistical methods significantly improves the accuracy of housing price predictions. Local factors that influence housing prices can be directly incorporated into the model with the use of dedicated maps.

Stock Price Prediction and Investment Simulation - Based on SSA-CNN-LSTM Method

Stock price fluctuations, which are time-series in nature. At the same time, based on the machine learning Long Short-Term Memory Network (LSTM) has excellent processing ability in predicting long time series, this paper proposes a stock price prediction method using CNN-LSTM optimized for LSTM hyperparameters using Sparrow algorithm. The data used in this study covers a total of 3403 trading days from March 1, 2010 to March 29, 2024 and about 40 technical indicator factors were selected. CNN model was firstly used to obtain features in the data. And later, CNN-LSTM network using SSA for parameter optimization uses the extracted features for stock price prediction and simulated trading based on the predicted up and down signals. The experimental results show that the SSA-CNN-LSTM network is able to provide better prediction accuracy than the CNN-LSTM network for price derivation. This prediction method not only provides a better op-eration idea for actual trading, but also provides practical experience for scholars to study time se-ries in finance.

Explainable Artificial Intelligence methods for financial time series

We consider the problem of developing explainable Artificial Intelligence methods to interpret the results of Artificial Intelligence models for time series data, taking time dependency into account. To this end, we extend the Shapley-Lorenz method, normalised by construction, to rtificial Intelligence for time series, such as neural networks and recurrent neural networks. We illustrate the application of our proposal to a time series of Bitcoin prices, which acts as the response variable, along with time series of classical financial prices, which act as explanatory variables.Three main findings emerge from the analysis. First, recurrent neural networks lead to a better performance, in terms of accuracy and robustness, with respect to classic neural networks. Second, the best performing models indicate that Bitcoin prices are affected mostly by their lagged values, and that their explainability, in terms of classical financial assets, is limited. Third, although limited, the contribution of classical assets to Bitcoin price prediction is well captured by recurrent neural networks.

Navigating financial risk: Unlocking the power of modeling and risk quantification in episode-based payment models.

28 Background: The Centers for Medicare and Medicaid Innovation’s (CMMI) Enhancing Oncology Model (EOM) is a voluntary value-based care (VBC) model that requires physician group practices (PGPs) to assume financial risk and performance accountability for episodes of care related to seven common cancers. However, PGPs often lack the means to assess the financial impact and risks associated with these alternative payment models. To address this, we employed Monte Carlo Simulations (MCS) to calculate the likelihood of outcomes and associated risks. This information can assist PGPs in making informed decisions about assuming risk and participating in the EOM. Methods: Using historical episode files from 2019 to 2022 for 23 PGPs in The US Oncology Network, along with the EOM Price Prediction Model provided by CMMI, we calculated the predicted baseline price and savings per episode. We then determined the average savings for each PGP per time-period and utilized the MCS technique to simulate savings and risk, under each EOM risk arrangement. We shared the simulation results with the PGPs to support their decision-making process and participation. Results: Despite having enough episodes per time-period (ranging from 105 to 6100) for the 23 PGPs, the limited number of time-based populations (only 6) based on case mix and patient volume made traditional probability assessments inadequate. To overcome this, we utilized MCS with 5000 simulations per PGP, assuming a normal distribution based on population characteristics. By assessing the savings outcomes of each simulation, we developed probability distributions and confidence intervals to determine the likelihood of generating shared savings, recoupment, or landing in the neutral zone within the EOM. Factors such as episode counts, baseline price, and risk arrangement had the greatest impact on the range of outcomes. We shared the MCS results with all 23 PGPs, highlighting the limitations of the original data and facilitating their decision-making process. Ultimately, 12 PGPs chose to assume risk based on the probability of financial outcomes with the model, and to select the appropriate risk arrangement in the EOM. Conclusions: Compared to the status quo measures of central dispersion, MCS yields a wide range of expected financial outcomes per PGP. MCS is a robust technique for quantifying outcome probabilities in VBC models despite low data volumes but relies on the representativeness and quality of the original data. Conducting sensitivity analysis with MCS to assess the impact of individual assumptions or data inputs on outcome probabilities is an area for further exploration.

A Comparative Analysis of Deep Learning and Traditional Statistics for Stock Price and Return Forecasting

A Comparative Analysis of Deep Learning and Traditional Statistics for Stock Price and Return Forecasting

CNN-BiLSTM-Attention Based Stock Price Prediction and Quantitative Investment Strategy

With the rapid development of technology and the increasing maturity of financial markets, stock price prediction has become a hot topic and an important trend in the financial field. However, the stock market has complexity and volatility, in order to reduce the investment risk and ensure the maximization of benefits, selecting the optimal stock to predict the stock price and formulating the quantitative trading strategy are extremely important issues for financial academics and investors. For the research of stock price prediction and quantitative trading, firstly, the quantitative stock selection model combining XGBoost and multi-factor stock selection model is constructed, and four stocks are screened out, and then the CNN-BiLSTM-Attention model is proposed to predict the stock price trend of the selected stocks, and it is found from the prediction results of the four stocks that the prediction accuracies all reach more than 95%, which is higher than that of the single model prediction. accuracy and passed the validity test of the fitted model. Secondly, based on the quantitative investment portfolio given in the above analysis and the quantitative stock trading strategy of MACD, the prediction results are verified, and the total return of most stocks based on the strategy is higher than 40% at the highest, and the loss is controlled within 10%, which indicates that the trading strategy in this paper is effective and feasible. The study concludes that greater returns can be obtained by calculating the total returns of stock portfolios based on different portfolio approaches.

Evaluating Volatility Using an ANFIS Model for Financial Time Series Prediction

This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the future behavior of the price and its volatility. The proposed ARIMA-ANFIS model is compared with a benchmark ARIMA-GARCH model. To evaluated the adequacy of the models in terms of risk assessment, we compare the confidence intervals of the price and accuracy measures for the testing sample. Additionally, we implement the diebold and Mariano test to compare the accuracy of the two volatility forecasts. The results revealed that each volatility model focuses on different aspects of the data dynamics. The ANFIS model, while effective in certain scenarios, may expose one to unexpected risks due to its underestimation of volatility during turbulent periods. On the other hand, the GARCH(1,1) model, by producing higher volatility estimates, may lead to excessive caution, potentially reducing returns.

Price predictability in limit order book with deep learning model

ABSTRACT This study explores the prediction of high-frequency price changes using deep learning models. Although state-of-the-art methods perform well, their complexity impedes the understanding of successful predictions. We found that an inadequately defined target price process may render predictions meaningless by incorporating past information. The commonly used three-class problem in asset price prediction can generally be divided into volatility and directional prediction. When relying solely on the price process, directional prediction performance is not substantial. However, volume imbalance improves directional prediction performance.

Novel Custom Loss Functions and Metrics for Reinforced Forecasting of High and Low Day-Ahead Electricity Prices Using Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) and Ensemble Learning

Day-ahead electricity price forecasting (DAEPF) is vital for participants in energy markets, particularly in regions with high integration of renewable energy sources (RESs), where price volatility poses significant challenges. The accurate forecasting of high and low electricity prices is particularly essential, as market participants seek to optimize their strategies by selling electricity when prices are high and purchasing when prices are low to maximize profits and minimize costs. In Japan, the increasing integration of RES has caused day-ahead electricity prices to frequently fall to almost zero JPY/kWh during periods of high RES output, creating significant profitability challenges for electricity retailers. This paper introduces novel custom loss functions and metrics specifically designed to improve the forecasting accuracy of extreme prices (high and low prices) in DAEPF, with a focus on the Japanese wholesale electricity market, addressing the unique challenges posed by the volatility of RES. To implement this, we integrate these custom loss functions into a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model, augmented by an ensemble learning approach and multimodal features. The proposed custom loss functions and metrics were rigorously validated, demonstrating their effectiveness in accurately predicting high and low electricity prices, thereby indicating their practical application in enhancing the economic strategies of market participants.

Car Price Prediction Using Enhanced Technique

The price of a new car in the industry is fixed by the manufacturer with some additional costs incurred by the Government in the form of taxes. So, customers buying a new car can be assured of the money they invest to be worthy. But, due to the increased prices of new cars and the financial incapability of the customers to buy them, used car sales are on a global increase. Therefore, there is an urgent need for a used car price prediction system which effectively determines the worthiness of the car based on multiple aspects, including vehicle mileage, year of manufacturing, fuel consumption, transmission, road tax, fuel type, and engine size. We have developed a model which will be highly effective. This model can benefit sellers, buyers, and car manufacturers in the used cars market. Upon completion, it can output a relatively accurate price prediction based on the information that user‘s input. Various regression methods were applied in the research to achieve the highest accuracy. Because of which it will be possible to predict the actual price a car rather than the price range of a car. User Interface has also been developed which acquires input from any user and displays the Price of a car according to user‘s inputs. The price of a new car in the industry is fixed by the manufacturer with some additional costs incurred by the Government in the form of taxes. So, customers buying a new car can be assured of the money they invest to be worthy. But, due to the increased prices of new cars and the financial incapability of the customers to buy them, used car sales are on a global increase. Therefore, there is an urgent need for a used car price prediction system which effectively determines the worthiness of the car based on multiple aspects, including vehicle mileage, year of manufacturing, fuel consumption, transmission, road tax, fuel type, and engine size. We have developed a model which will be highly effective. This model can benefit sellers, buyers, and car manufacturers in the used cars market. Upon completion, it can output a relatively accurate price prediction based on the information that user‘s input. Various regression methods were applied in the research to achieve the highest accuracy. Because of which it will be possible to predict the actual price a car rather than the price range of a car. User Interface has also been developed which acquires input from any user and displays the Price of a car according to user‘s inputs.

Comparison of Machine Learning Models for Stock Price Forecasting

Comparison of Machine Learning Models for Stock Price Forecasting

Research on house price prediction in UK based on linear regression model

This paper aims to analyze the factors that affect housing prices and predict the trend of housing prices through a linear regression model. This article will use a linear regression model to analyze data on UK house prices from 1952 to 2022. And selected 12 different factors that affect housing prices, using a linear regression model to predict the fluctuations of housing prices. At the same time, a multiple prior regression model was used to explore the importance of each factor in affecting house prices. And the significance of these variables was also compared. The results show that the location of the house, per capita income, housing environment, immigration rate, actual deposit rate and actual loan rate have a significant linear relationship with house price changes, while per capita productivity and house type after the epidemic do not have a significant linear relationship with house price changes. Therefore, UK house prices can be predicted based on the above factors.

Development of a software service for stock price forecasting based on sentiment analysis and autoregressive models

This paper addresses the critical need for efficient market analysis tools in the era of big data and artificial intelligence. We present a novel software service that integrates real-time news sentiment analysis with stock market prediction, enhancing the accuracy and speed of trading decisions. The system employs APIs for data collection, FinBERT for sentiment analysis, and MongoDB for data storage, overcoming limitations of existing platforms like Investing.com and MarketWatch. Our methodology combines sentiment analysis with autoregressive models to forecast stock prices for 11 major companies. The experiment utilized 141 observations, applying multiple regression and binary outcome models. Results demonstrate that investor sentiment significantly affects stock prices for 2 out of 11 companies, with Meta showing a 70 % determination coefficient in price direction changes based on sentiment. The study reveals that incorporating both quantitative (previous stock prices) and qualitative (sentiment) data improves forecast accuracy for certain stocks. This research contributes to the field of financial analytics by providing a more comprehensive approach to stock price prediction, integrating ML models and data analytics to support informed decision-making in dynamic financial markets.

An adaptive financial trading strategy based on proximal policy optimization and financial signal representation

Trading strategies play a crucial role in financial trading. However, due to the significant amount of noise present in financial signals, traditional trading strategies and those based on price prediction often fail to achieve optimal results in real market conditions. Nowadays, with the pervasive noise in financial signals, developing successful trading strategies to achieve high returns has become one of the most prominent and challenging research areas in modern finance. Therefore, an adaptive financial trading strategy, called proximal policy optimization based on financial signal representation trading strategy (FSRPPO), is proposed. This strategy employs financial signal representation technology (FSR), which combines complete ensemble extreme-point symmetric mode decomposition with adaptive noise (CEEMDAN) and modified rescaled range analysis (MRS), to accurately and robustly represent dynamic market states, enhancing profitability. Additionally, the designed reward function reduces trading frequency to lower costs, while the designed action space increases trading flexibility to reduce risks. The experimental results on real stock data demonstrate the outstanding profitability and good risk avoidance ability of our proposed trading strategy, which means that the proposed model can effectively filter out noise from financial signals, extract valuable information, and provide reliable decision support for investors.

Modeling and Forecasting Crude Oil Prices in Nigeria Using ARIMA: A Time Series Analysis from 2013-2022

Study’s Excerpt/Novelty This study employs the Box-Jenkins ARIMA model to forecast crude oil prices in Nigeria from 2013 to 2022, identifying the ARIMA (1, 1, 1) model as the most suitable based on AIC, BIC, and HQIC criteria. The findings emphasize the model’s adequacy for short-term price forecasting but highlight its limitations, particularly its sensitivity to non-stationary data. The study recommends incorporating macroeconomic variables or hybrid models for more accurate long-term predictions, while urging Nigerian oil sector stakeholders to explore alternatives like Liquefied Natural Gas (LNG) to mitigate revenue losses from declining crude oil sales. Full Abstract This study utilizes Box-Jenkins ARIMA modeling to forecast crude oil prices in Nigeria from 2013 to 2022. The data, sourced from the CBN statistical bulletin, revealed non-stationarity, corrected by first differencing. The ARIMA (1, 1, 1) model was identified as the most suitable based on AIC, BIC, and HQIC criteria, providing significant information on short-term price forecasting and model adequacy. The study concludes that while ARIMA models are effective for short-term forecasting, their limitations, such as sensitivity to non-stationary data, suggest that future research should incorporate macroeconomic variables or hybrid models for improved accuracy. It was recommended that stakeholders in the Nigerian oil sector should explore other avenues in the sector, such as Liquefied Natural Gas(LNG), in order to absorb the decrease in revenue as a result of the decline in revenue from crude oil prices and sales.

Prediction of stock prices with automated reinforced learning algorithms

AbstractPredicting stock price movements remains a major challenge in time series analysis. Despite extensive research on various machine learning techniques, few models have consistently achieved success in automated stock trading. One of the main challenges in stock price forecasting is that the optimal model changes over time due to market dynamics. This paper aims to predict stock prices using automated reinforcement learning algorithms and to analyse their efficiency compared with conventional methods. We automate DQN models and their variants, known for their adaptability, by continuously retraining them using recent data to capture market dynamics. We demonstrate that our dynamic models improve the accuracy of predicting the directions of various DAX stocks from 50.00% to approximately 60.00%, compared with conventional methods. Additionally, we conclude that dynamic models should be updated in response to shifts rather than at fixed intervals.