Price Trends Research Articles

Electricity price forecast in wholesale markets using conformal prediction: Case study in Mexico

AbstractIn recent years, machine and deep learning models have attracted significant attention for electricity price forecast in global wholesale electricity markets. Yet, a predominant focus on point forecast in most parts of literature limits the practical application of these models due to the absence of uncertainty quantification. In this study, we first perform an analysis of the electricity price trends in the Mexican wholesale electricity market to determine the influence of key variables. Using independent component analysis and wavelet coherence analysis, we were able to identify primary determinants influencing locational marginal electricity prices. Subsequently, we applied four different models covering the most important algorithms proposed in the literature for electricity price forecast. Our findings revealed that the most accurate forecasting results were achieved using a deep learning‐based method with a decision tree‐based model trailing closely. Finally, we incorporate conformal prediction for uncertainty quantification by calculating the prediction intervals with a target coverage level of 95%. The conformal prediction intervals provide a more comprehensive view of the possible future scenarios, enhancing economic efficiency, risk management, and decision‐making processes. This is particularly important because of the dynamic nature of electricity markets, where prices are strongly influenced by multiple factors.

Stock Price Prediction Using LSTM

Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, favourite product of people for investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. Designing robust and accurate predictive models for stock price prediction has been an active area of research over a long time. Developing an accurate stock prediction method can help investors in making profitable decisions by reducing the investment risks. Our proposition includes a regression model built on long-and-short-term memory (LSTM) network-based predictive models. These spatial features are then fed into LSTM layers, which capture temporal dependencies and long-range correlations in the time series data. The combination of these two architectures enhances the model's ability to capture both short-term fluctuations and long-term trends in stock prices. This system will provide accurate outcomes in comparison to currently available stock price predictor algorithms. The network is trained and evaluated with various sizes of input data to urge the graphical outcomes. This system will provide accurate outcomes in comparison to currently available stock price predictor algorithms Keywords: Long and Short-Term Memory, Time-Series Data, Recurrent Neural Network.

ARIMA Modeling and Forecasting of National Consumer Price Index in Nepal

This research aims to determine an Auto-Regressive Integrated Moving Average (ARIMA) model and forecasting the National Consumer Price Index (NCPI) in Nepal, using annual data from the fiscal year 1972/73 to 2022/23. The research utilized secondary data collected from the online bulletin published by Nepal Rastra Bank, aiming to enhance understanding of NCPI patterns and contribute to predictive modelling techniques for economic indicators. Adopting the Box-Jenkins technique and using E-Views statistical software, this study identifies ARIMA (1, 2, 8) as the most suitable model for NCPI forecasting. This selection is validated through meticulous diagnostic tests, affirming the appropriateness of the model, as evidenced by residuals exhibiting white noise characteristics. The research findings suggest a rapid increase in the National Consumer Price Index for the coming years. Implications of this research extend to both academic and policy-oriented audiences. A successful ARIMA model development holds significant potential for providing accurate predictions of future price trends in Nepal. This, in turn, can assist policymakers in making informed decisions regarding economic strategies, monetary policies, and inflation control measures. Moreover, the study's outcomes offer a foundational framework for future research endeavours in the domain of economic forecasting and modelling, contributing to the broader field of economic analysis and policy formulation.

The price behavior characteristics of China and Europe carbon emission trading market based on the perspective of time scaling and expected returns.

China has the world's largest carbon market in terms of greenhouse gas emissions, but its system needs to be improved and enhanced. In comparison, the European carbon market stands as the most mature and well-developed carbon market globally. Carbon trading prices, serving as a barometer for the carbon market, are significantly influenced by investor behavior. Therefore, it is necessary to analyze the characteristics of carbon trading prices in both China and Europe, considering the impact of investor trading intervals and psychological expected returns. This study utilizes the Zipf method to characterize the dynamic behavior of carbon trading prices between China and Europe, conducting a comparative analysis. The results show distinctive asymmetry in the behavior of carbon trading prices in both markets. In the Chinese market, when τ < 277, the absolute deviation da(τ, ε) value gradually changes but consistently indicates a bullish trend. However, when τ ≥ 277, the da(τ, ε) value surges rapidly, reflecting a pronounced bullish sentiment among investors toward carbon trading prices in China. In the European market, within the sample period, regardless of variations in τ and ε, the da(τ, ε) value shows a linear upward trend, indicating a significant overall bullishness in prices. This suggests a higher probability of long-term bullishness in carbon trading prices. Investors' investment time scale (τ) and expected returns (ε) both influence the behavior of carbon trading prices in both China and Europe. Generally, a longer τ implies a higher probability of bullishness. As for ε, higher values lead to more extreme judgments on price movements, resulting in greater distortion in carbon trading prices. Short-term investors (τ<1 month) anticipate extreme fluctuations, exhibiting random behavior when ε < 0.15 and converging rapidly to extreme values of 1 or 0 when ε ≥ 0.15. Long-term investors (τ>quarter) are less biased, expressing a bullish outlook on both Chinese and European carbon prices. With increasing ε, the probability of bullishness either increases or decreases rapidly until reaching the saturation point. Once saturated, there is no further distortion in carbon price behavior. Furthermore, the Chinese carbon market displays a positive trend in carbon trading prices and a higher probability of long-term bullishness. For the European market, lower expected returns contribute to considerable carbon trading price fluctuations, exacerbating risk and uncertainty. The results of this study contribute to understanding the diverse trading behaviors in Chinese and European carbon markets and provide guidance for avoiding extreme volatility in carbon trading prices.

Time-varying and multi-scale analysis of copper price influencing factors based on LASSO and EMD methods

In this paper, we select 75 indicators to conduct a comprehensive analysis of the factors influencing the copper price along six dimensions: inventory, supply, demand, the macroeconomy, finance, and geopolitics. Facing the high-dimensionality problem, we use the least absolute shrinkage and selection operator (LASSO) regression model to select variables to measure the contribution of each category of factors. Furthermore, we identify the time-varying nature of the relationship among factors with rolling windows. Then, we decompose copper prices into different scales of fluctuations by means of empirical mode decomposition (EMD) and investigate the driving factors at each scale. The results show that financial and geopolitical factors have played an important role in copper pricing in recent years. The long-run fluctuation trend of copper prices is mainly determined by fundamental factors, while financial and geopolitical factors have a more direct impact on short-term fluctuations.

Monthly Pork Price Prediction Applying Projection Pursuit Regression: Modeling, Empirical Research, Comparison, and Sustainability Implications

The drastic fluctuations in pork prices directly affect the sustainable development of pig farming, agriculture, and feed processing industries, reducing people’s happiness and sense of gain. Although there have been extensive studies on pork price prediction and early warning in the literature, some problems still need further study. Based on the monthly time series data of pork prices and other 11 influencing prices (variables) such as beef, hog, piglet, etc., in China from January 2000 to November 2023, we have established a project pursuit auto-regression (PPAR) and a hybrid PPAR (H-PPAR) model. The results of the PPAR model study show that the monthly pork prices in the lagged periods one to three have an important impact on the current monthly pork price. The first lagged period has the largest and most positive impact. The second lagged period has the second and a negative impact. We built the H-PPAR model using the 11 independent variables (prices), including the prices of corn, hog, mutton, hen’s egg, and beef in lagged period one, the piglet’s price in lagged period six, and by deleting non-important variables. The results of the H-PPAR model show that the hog price in lagged period one is the most critical factor, and beef price and the other six influencing variables are essential factors. The model’s performance metrics show that the PPAR and H-PPAR models outperform approaches such as support vector regression, error backpropagation neural network, dynamic model average, etc., and possess better suitability, applicability, and reliability. Our results forecast the changing trend of the monthly pork price and provide policy insights for administrators and pig farmers to control and adjust the monthly pork price and further enhance the health and sustainable development of the hog farming industry.

Prediction of Agricultural Commodity Prices using Big Data Framework

The agriculture sector plays a crucial role in the economy of Pakistan, contributing significantly to the Gross Domestic Product (GDP) and the employment rate. However, this sector faces challenges such as climate change, water scarcity, and low productivity, which have a direct impact on agricultural commodity prices. Accurate forecasting of commodity prices is essential for farmers, traders, and policymakers to make informed decisions and improve economic outcomes. This paper explores the use of a big data framework for agricultural commodity price forecasting in Pakistan, using a historical dataset on commodity prices in various Pakistani cities from 2007 to 2022 and Apache Spark to preprocess and clean the data. Based on historical spinach prices in Vehari City, the machine learning models Auto-Regressive Moving Average (ARIMA), Random Forest, and Long-Short-Term Memory (LSTM) were applied to price trends, and their performance was compared using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and squared correlation coefficient (R2). LSTM outperformed ARIMA and Random Forest with a higher R2 value of 0.8 and the lowest MAE of 125.29. Such predictions can help farmers to effectively plan crop cultivation and traders to make well-informed decisions.

Updating standardized unit costs from a societal perspective for health economic evaluation

The aim of this work was to update the 2015 unit costs (UC) for the monetary valuation of health-related resource use from a societal perspective for the years 2019 and 2020 in Germany. The update follows the methodology of Bock et al. 2015. Based on the newly established care levels, UC for care degree 1 to 5 are now provided. To account for change in price trends during the Covid-19 pandemic, average growth rates in UC are shown from 2011-2019 and compared to 2019-2020. Updates of UC for the outpatient medical sector, remedies and aids, hospitals, (in)formal care services, and rehabilitation for 2019 and 2020 are provided. The updated UC can be used as reference values for the monetary valuation of individual resource use in health economic evaluations in Germany.

The Non-Seasonal Holt-Winters Method for Forecasting Stock Price Returns of Companies Affected by BDS Action

The non-seasonal Holt-Winters method is one of the methods of smoothing theory. This method can be implemented on time series data that does not have a seasonal component. In this study, this method is used to forecast the stock price returns of companies affected by the Boycott, Divestment, and Sanctions (BDS) action. Forecasting gets very good results that can be seen from the MAPE value of modeling the six stocks affiliated with Israel that continue to carry out Zionism against Palestine is not more than 10%. This method can also accommodate the limitations of existing data while still obtaining good forecasting results. In addition, the use of several transformations of stock price returns in this case is very useful in modeling to obtain appropriate error assumptions. The forecasting results of the model formed as a whole follow the trend in the stock price of each company. To produce good forecasting results using this method, it is recommended to do forecasting in the short term. The forecasting results show that of the six company stocks, almost all of them experienced a decrease in stock price returns. Only one stock of PT Map Boga Adiperkasa Tbk has increased. This also illustrates that the BDS action influences on these companies.

Fluctuations and trends in the prices of red chilies and cayenne peppers in the traditional markets of Makassar City

Chili is a strategic food commodity which is the main seasoning for food in Indonesia. The chili commodity has high price fluctuations because production and supply are highly dependent on the risk of climate change. This research aims to analyse fluctuations, trends and comparisons of prices for large red chilies, curly red chilies and red cayenne peppers in the Makassar City Traditional Market. The data type is monthly time series secondary data for January 2020 to May 2023. Data analysis is coefficient variation, trend linear analysis, and t test. The results of this research show that the prices of large red chilies and curly red chilies have moderate fluctuations, while the prices of red cayenne peppers have high fluctuations. The price trend for large red chilies increased by IDR 144.24 per kilogram, curly red chilies IDR 200.99 per kilogram, and cayenne pepper IDR 362.42 per kilogram per month. The price of cayenne pepper has proven to be significantly higher than the price of large red chilies and the price of curly red chilies. The prices of red chilies and cayenne peppers at Pabaeng-baeng Market and Daya Market do not have a significant difference.

Comparative analysis of machine learning techniques for cryptocurrency price prediction

The emergence of cryptocurrencies has revolutionized the concept of digital currencies and attracted significant attention from financial markets. Predicting the price dynamics of cryptocurrencies is crucial but challenging due to their highly volatile and non-linear nature. This study compares the performance of various models in predicting cryptocurrency prices using three datasets: Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models analyzed include Moving Average (MA), Logistic Regression (LR), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM). The objective is to uncover underlying patterns in cryptocurrency price movements and identify the most accurate and reliable approach for predicting future prices. Through the analysis, it could be observed that MA, LR, and ARIMA models struggle to capture the actual trend accurately. In contrast, LSTM and CNN-LSTM models demonstrate strong fit to the actual price trend, with CNN-LSTM exhibiting a higher level of granularity in its predictions. Results suggest that deep learning architectures, particularly CNN-LSTM, show promise in capturing the complex dynamics of cryptocurrency prices. These findings contribute to the development of improved methodologies for cryptocurrency price prediction.

Stock Price Prediction Using Sentiment Analysis

Abstract: This research discusses the ever-changing nature of the stock market by using models such as LSTM and ARIMA to forecast stock prices. It recognizes the volatility and unpredictability of financial markets recognizing the need for robust tools that can accurately predict. Sentiment analysis is used for enhancing the accuracy of stock price prediction, drawing information from various sources like news resources and social media platforms that reflect public sentiments and opinions. We aim to analyze a thorough aspect of factors influencing stock prices by combining these sentiments with the models. The LSTM model is used to study long-term dependencies in stock price trends and ARIMA provides information into the time series components. Combining sentiment analysis helps us to scan the emotional tone around a particular stock and contributes valuable data to the prediction process. The integration between the machine learning models and sentiment analysis offers an extensive approach to predicting stock prices considering both past trends and current public sentiment. This study adds to the work in progress to develop more accurate and adaptive tools for exploring the difficulties of the stock market.

인공지능 기반 농작물 성숙도 체크와 농산물 시장가격 변동을 고려한 출하시기 결정시스템 연구

This study aims to develop an integrated agricultural distribution network management system to improve the quality, profit, and decision-making efficiency of agricultural products. We adopt two key techniques: crop maturity detection based on the YOLOX target detection algorithm and market price prediction based on the Prophet model. By training the target detection model, it was possible to accurately identify crops of various maturity stages, thereby optimizing the shipment timing. At the same time, by collecting historical market price data and predicting prices using the Prophet model, we provided reliable price trend information to shipping decision makers. According to the results of the study, it was found that the performance of the model considering the holiday factor was significantly superior to that of the model that did not, proving that the effect of the holiday on the price was strong. The system provides strong tools and decision support to farmers and agricultural distribution managers, helping them make smart decisions during various seasons and holidays. In addition, it is possible to optimize the distribution network of agricultural products and improve the quality and profit of agricultural products.

Improving Stock Price Prediction Accuracy with StacBi LSTM

This research aimed to enhance stock price prediction accuracy using the Stacked Bidirectional Long Short-Term Memory (StacBi LSTM) model. The study addressed the challenge of capturing long-term dependencies and temporal patterns inherent in stock price data. The research objectives were to evaluate the model's performance across different input sequence lengths and identify the optimal length for prediction. Leveraging a dataset from the Indonesian Stock Exchange, the model's predictions were evaluated using key metrics such as RMSE, MAE, MAPE, and R2. Results indicated that the StacBi LSTM model excelled in capturing stock price trends and demonstrated strengths over traditional methods. The optimal input sequence length was identified, balancing computational efficiency and prediction accuracy. This research contributes valuable insights into improving stock price prediction techniques and offers practical implications for traders and investors. Future research directions encompass hybrid models and integrating external factors to enhance predictive capabilities further.

The prediction of Apple stock price based on linear regression model and random forest model

In the financial market, due to various factors, the stock price fluctuation is universal. Therefore, the directional prediction of stock market price based on technical analysis is very important in stock investment. This paper conducted a regression analysis and forecasted the future trends in the close price of Apple stock through recent five years between 2018 and 2023. For the purpose of this specific study, this paper did descriptive statistical analysis of the dataset, and made graphs and analyses of regression and predictions relied on the techniques of the Linear Regression Model and Random Forest Model. Based on the three indices: MSE, RMSE, and MAE, the paper compared the advantages and disadvantages of the two machine learning methods. The result of the experiments indicated that the regression generated through employment of the Linear Regression Model outperforms the result of the Random Forest Model, leading to the conclusion that Linear Regression Model is a more effective method to forecast in this dataset.

How to Become Rich Using Quantitative Trading?

Bitcoin has become a very common investment product, also known as 'digital gold' because of its global discussion and rising prices. This paper develops a model that can only rely on these data to predict future price fluctuations and plan trading decisions. The trading decision planning model consists of three sub-models that are mutually undertaken. For model 1, in order to judge the price trend reliably, this paper uses the Long Short-Term Memory (LSTM) neural network, which is sensitive to the time series, to learn all the time series price data up to the trading day, and to predict the future price data of them respectively, which is provided to another sub-models for subsequent work. For Model 2, after obtaining the follow-up price data, all the technical indicators needed are calculated in this model, and then the moving average convergence divergence (MACD) rule is used to determine whether the transaction is carried out for the first time, and then the predicted price trend is used for the second judgment. For model 3, the model begins to make decision- making planning. This model mainly solves the bi-objective programming problem with the goal of maximizing returns and minimizing risks. In the range of controllable risks, the model will give the optimal solution of maximizing returns. Finally, using the model, the sensitivity of the model to transaction cost is studied by changing the transaction commission, and it is found that the final investment income is more sensitive to the transaction commission of bitcoin.

Research on Gold Price Changes and Factors Under the Background of Fed Interest Rate Hike

With the change of international form, the gold market is changing rapidly. In 2021, the U.S. stagflation was serious, so in 2022, the Federal Reserve adopted a series of interest rate hikes, coupled with the impact of the conflict between Russia and Ukraine. As a safe haven product, the price of gold fluctuated greatly in the short term. As an important commodity, gold is crucial to judge its price change trend. This paper mainly analyzes the U.S. monetary policy from 2022 to 2023. By studying the reasons and background of the promulgation of monetary policy, it judges the causes of the Federal Reserve's interest rate increase, and judges the background of joint inflation and regional conflict according to the international form in 2022. Combined with the change trend of gold price, it makes a joint analysis, and gets the impact trend of the Federal Reserve's interest rate increase on gold price from 2022 to 2023. Finally, it comes to the conclusion that the increase of interest rate will lead to the decline of gold price. In the early stage of interest rate increase and a large number of interest rate increases, the gold price is more affected by the increase of interest rate, so it is suitable to short gold at this time, while in the late stage of interest rate increase and the slowdown of interest rate increase policy, the gold price is more affected by other factors, so it is suitable to long gold at this time.

External Event Crises on The Stock Market: Taking Japan’s Sea Discharge Incident as An Example

On August 22, 2023, the Japanese government announced that it would start discharging nuclear wastewater into the sea from August 24. This decision was widely questioned and opposed by the international community and caused strong concerns in Japan. This article aims to take the Japanese nuclear sewage discharge incident into the sea as an example to explore the impact of social crisis on Japan's domestic stock market. The main research method of this article is based on stock return analysis, using the ARIMA model to predict the Japanese stock price trend after excluding the impact of this event, and then comparing it with real data. The results of this study provide evidence that the predicted stock prices are close to the actual stock prices, indicating that as of mid-September, there was no short-term negative impact on the Japanese stock market due to the event, but from a long-term perspective, the predictions remain It is uncertain whether stock prices in Japan's financial markets will fall because of this incident. The main conclusion drawn from this study is that external events may not have a large impact on stock prices in the short term. Different from previous literature, this study uses ARIMA analysis on daily data and weekly data to obtain the relationship between them. Recommendations include organizing a team of experts; hiring a legal team; establishing a complete procedure to handle social media information, and having a dedicated public relations team may be helpful.

Prediction for Some Tech Stock Prices of U.S. Stock Market based on ARIMA Model

Predicting the future price of stocks is a common practice in the financial industry. Individuals and organizations engage in stock price forecasting for investment decision-making, risk management, portfolio optimization and asset allocation. However, the predicting stock prices is challenging due to the complexity of financial markets, the influence of numerous variables, and the presence of random and unpredictable events, which all make it harder to predict. This paper uses predicted future stock price trends for technology companies in The United States based on ARIMA and breaks it down using APPLE as a specific example for a more in-depth discussion of ARIMA. The raw data is collected from 2001/5/11 to 2021/4/22. Then it predicted the stock prices for the next two weeks (working days) by ARIMA and made a fitted trend curve, and finally compared it with the real data from 2021/4/23 to 2021/5/12 to see if ARIMA's predictions were accurate. The result shows that prediction model is in the form of ARIMA (1,1,1), and the prediction of APPL from 2021/4/23 to 2021/5/12 is trending down, which matches the real trend.

Trend Prediction Analysis of Shanghai Composite Index Based on LSTM Neural Network

The prediction of stock indices, particularly the Shanghai Composite Index (SCI), is crucial for understanding the health and direction of China's financial market in the post-pandemic era. Given the challenges of traditional forecasting methods due to the unpredictable nature of stock prices, this study explores the application of the Long Short-Term Memory (LSTM) neural network, complemented by Monte Carlo simulations and regularization with the daily trading data from September 21 2020, to August 13, 2023, to forecast the SCI's price trends for Q4 2023. The findings suggest a trajectory characterized by an initial decline, succeeded by a steady upward trend throughout the entirety of the quarter. Notably, the results have adeptly encapsulated both the lingering effects of the pandemic and the long-term rising trend of the Shanghai Composite Index (SCI). This research accentuates the potential of advanced neural network models in deciphering complex stock market behaviors, offering a groundbreaking perspective for market participants in navigating future investment decisions.