Price Trends Research Articles

Exploring the Factors Influencing Chinese Consumers’ Condominium Purchase Decision

This research aims to explore the factors influencing Chinese consumers’ purchase decisions regarding purchasing of condominiums for residential purposes in Thailand, and to understand consumers’ rationale and concerns in relation to these factors. Data were collected through semi-structured in-depth interviews with respondents of Chinese nationality, selected through purposive sampling. The qualitative data were coded and analyzed using the NVIVO program. As a key finding of the study, the author identified 17 key factors comprising (1) location, (2) functionality, (3) expenses, (4) safety, (5) future rental or sale opportunities, (6) scenery and surroundings, (7) facilities, (8) reputation of developer, (9) growth trend of property price, (10) interior decoration, (11) ventilation, (12) number of floors, (13) physical condition of the condominium, (14) type of residential project, (15) construction quality, (16) property management, and (17) design. The study generates a wide range of factors, rational and concerns of this particular market segment due to qualitative nature of the study, which ultimately contributes a more thorough research model for future quantitative study.

Price Trend Forecasting in the Brazilian Stock Market using Discrete-Time Markov Chain

Price Trend Forecasting in the Brazilian Stock Market using Discrete-Time Markov Chain

Modeling and analysis of crypto-backed over-collateralized stable derivatives in DeFi

In decentralized finance (DeFi), stablecoins like DAI are designed to offer a stable value amidst the fluctuating nature of cryptocurrencies. We examine the class of crypto-backed stable derivatives, focusing on mechanisms for price stabilization and exemplified by the well-known stablecoin DAI from MakerDAO. For simplicity, we consider a single-collateral setting. We introduce a belief parameter to the simulation model of DAI in a previous work (DAISIM), reflecting market sentiments about the value and stability of DAI, and show that it better matches the expected behavior when this parameter is set within a particular range of values. Our methods include comparing simulated data with real-world data, focusing on monthly correlations between ETH and DAI prices and scatter plots illustrating the relationship of their price trends over time. We also propose a simple mathematical model of DAI price to explain its stability and dependency on ETH price. Finally, we analyze possible risk factors associated with these stable derivatives to provide valuable insights for stakeholders in the DeFi ecosystem.

A Novel Bézier LSTM Model: A Case Study in Corn Analysis

Accurate prediction of agricultural product prices is instrumental in providing rational guidance for agricultural production planning and the development of the agricultural industry. By constructing an end-to-end agricultural product price prediction model, incorporating a segmented Bézier curve fitting algorithm and Long Short-Term Memory (LSTM) network, this study selects corn futures prices listed on the Dalian Commodity Exchange as the research subject to predict and validate their price trends. Firstly, corn futures prices are fitted using segmented Bézier curves. Subsequently, the fitted price sequence is employed as a feature and input into an LSTM network for training to obtain a price prediction model. Finally, the prediction results of the Bézier curve-based LSTM model are compared and analyzed with traditional LSTM, ARIMA (Autoregressive Integrated Moving Average Model), VMD-LSTM, and SVR (Support Vector Regression) models. The research findings indicate that the proposed Bézier curve-based LSTM model demonstrates significant predictive advantages in corn futures price prediction. Through comparison with traditional models, the effectiveness of this model is affirmed. Consequently, the Bézier curve-based LSTM model proposed in this paper can serve as a crucial reference for agricultural product price prediction, providing effective guidance for agricultural production planning and industry development.

Carbon emission allowance price forecasting for China Guangdong carbon emission exchange via the neural network

Carbon emission allowance price forecasting is a significant issue for policy makers and investors with the world transitioning to green energy and devoting enormous efforts to be more sustainable. This study explores usefulness of the nonlinear autoregressive neural network for this forecasting problem in a dataset of daily closing prices of carbon emission allowances traded in China Guangdong Carbon Emission Exchange during 19 December 2013–20 August 2021. Through examining various model settings across the algorithm, delay, hidden neuron, and data splitting ratio, the model leading to generally accurate and stable performance is reached. Usefulness of the machine learning technique for the price forecasting problem of the carbon emission allowance price is illustrated. Results here might be used on a standalone basis as technical forecasts or combined with fundamental forecasts to form perspectives of price trends and perform policy analysis, which could better assist different stakeholders in understanding energy cost and planning for green transition.

Predictive Analysts Using Different Algorithm Machine And Deep Learning For Financial Market: Review Paper

This paper presents the development and effectiveness of deep and machine learning techniques in forecasting stock market trends. This paper review focus on key developments from 2020 to 2024 involves the integration of ensemble learning, sentiment analysis from social media, and various predictive algorithms such as LSTM, CNN, and ANN. These techniques improves predication accuracy by efficient processing large data groups and detect nonlinear patterns in stock price trends. in addition, the paper presented background about various machine learning algorithms such as KNN, naive Bayes, linearregression decision trees, SVM, randomforests and deep learning models suchas neural network,LSTM and CNN .the findings of this study to providing valuable insights to researchers and practitioners who aim to improve investment strategies and improve predictive accuracy By looking at the appropriate algorithm

Oil Palm in India: Market Arrivals and Price Movements

Oil palm is native to West Africa. It is one of nine major oils traded in the global edible oil. The study focuses on the trends in oil palm arrivals and market prices in India, particularly in Andhra Pradesh and Telangana states. There was a significant increase in annual oil palm arrivals from in Andhra Pradesh and Telangana, with a percentage change of 92.43% and 84.83% respectively. This increase was statistically significant at a 10% level, R² values are 0.9243 and 0.8483 respectively. A linear trend analysis for prices of oil palm from 2010 to 2022 indicated a consistent rise in oil palm prices in both states, with Andhra Pradesh seeing an annual increase of 737.05 Rupees and Telangana 757.72 Rupees. The percentage change in prices was 59.18% and 66.08% respectively, with R² values of 0.5918 and 0.6608. Positive and significant correlation coefficient is observed in both the states. Seasonal variations in prices were also analyzed, highlighting that oil palm prices were typically drop after harvest it is influenced by peak harvesting periods and imports of crude oil from other countries. In Andhra Pradesh, the seasonal price index for oil palm Fresh Fruit Bunches (FFBs) was highest in May and April months, then decreased to a minimum level due to increase in the oil palm arrivals. Understanding these seasonal patterns is crucial for effective decision-making by farmers, traders, and policymakers.

Predicting cryptocurrency price trends using artificial intelligence methods

This article comprehensively explores multiple aspects of cryptocurrencies and their price forecasting. Firstly, the article introduces the definition of cryptocurrency and its development process on a global scale, especially focusing on the launch of Facebook Libra and China's central bank digital currency, highlighting the importance and influence of digital currency in the global financial market. Subsequently, the article analyzes the advantages of digital currencies over traditional currencies, including improving economic transaction efficiency, reducing transaction costs and enhancing transaction transparency. Finally, through specific experimental analysis, the effectiveness of using the deep learning model CNN-LSTM to predict the price of Bitcoin is demonstrated, and the directions of future research and optimization strategy are proposed. In summary, this paper comprehensively presents the research status and prospects of cryptocurrency and its price prediction field through systematic introduction and analysis.

An LSTM-based optimization algorithm for enhancing quantitative arbitrage trading.

Arbitrage trading is a common quantitative trading strategy that leverages the long-term cointegration relationships between multiple related assets to conduct spread trading for profit. Specifically, when the cointegration relationship between two or more related series holds, it utilizes the stability and mean-reverting characteristics of their cointegration relationship for spread trading. However, in real quantitative trading, determining the cointegration relationship based on the Engle-Granger two-step method imposes stringent conditions for the cointegration to hold, which can easily be disrupted by price fluctuations or trend characteristics presented by the linear combination, leading to the failure of the arbitrage strategy and significant losses. To address this issue, this article proposes an optimized strategy based on long-short-term memory (LSTM), termed Dynamic-LSTM Arb (DLA), which can classify the trend movements of linear combinations between multiple assets. It assists the Engle-Granger two-step method in determining cointegration relationships when clear upward or downward non-stationary trend characteristics emerge, avoiding frequent strategy switches that lead to losses and the invalidation of arbitrage strategies due to obvious trend characteristics. Additionally, in mean-reversion arbitrage trading, to determine the optimal trading boundary, we have designed an optimized algorithm that dynamically updates the trading boundaries. Training results indicate that our proposed optimization model can successfully filter out unprofitable trades. Through trading tests on a backtesting platform, a theoretical return of 23% was achieved over a 10-day futures trading period at a 1-min level, significantly outperforming the benchmark strategy and the returns of the CSI 300 Index during the same period.

Impact of Range Bar and Ergodic Process on Early Price Trend Detection Using Evidence From USD/CNY Currency

In this paper, range bars and the ergodic theory are combined to investigate early price movements for the USD/CNY currency pair in China from 2015 till 2019. The main findings suggested that early price trend detection may be accomplished within two standard deviations of the mean. During the duration of trading that was restricted to a narrow range, the sample revealed that at least 68 percent of the frequency mean result indicated that range bars solved price trend creation. This paper is subjected to additional analysis in order to acquire a correlation coefficient sample of at least 0.8. This sample revealed that ergodic theory prevented overpriced hedge trends. The goal of this paper is to improve the detection of price trends at an earlier stage than the methodology of trend tracking. Because of this, the authorities need to seriously consider putting early price trend detection models into place in order to boost the liquidity of the local currency.

Evaluating Willingness to Sell Vegetation in the White Volta Basin in Northern Ghana

Concepts of ecosystem services have been developed to make explicit connections between human welfare and ecological sustainability for policy, development, and conservation initiatives. Economic concepts such as the distinction between prices and values, and the acknowledgment of their values are context-specific which may change across space and time. Contingent valuation is a survey-based economic technique for valuing non-market resources, such as vegetation. This method is often used to establish the amount people are willing to be compensated for maintaining the existence of an environmental feature such as a tree, shrub, or grass. The level of importance attached to provisioning services as well as cultural services and cultural heritage differ in the rural communities hence different cash values attached. It is often perceived that rural community members do not put monetary value on vegetation, the study is therefore aimed at establishing monetary value rural communities have value for vegetation. The study was conducted in two irrigated and two unirrigated landscapes consisting of about 54 communities and comprising 240 respondents. Participatory Rural Appraisal tools were used. Random Utility Theory was applied and used for the analysis. The willingness to sell vegetation was significant at a 5% confidence level concerning native, sex, age, education, and household head. Marital status was, however, not significant in all the landscapes. The price trend is observed to be across a landscape, from the catchment to the downstream ecosystem.

Examining Dividend Policy as a Mediating Variable in Stock Price Trends: Evidence from the Indonesian Basic Materials

This research investigates the impact of financial ratios, including profitability, liquidity and solvency, on stock prices, with dividend policy as a mediator, in the Indonesian raw materials sector. The analysis covers 100 samples from 25 of companies listed on the Indonesia Stock Exchange (BEI) from 2019 to 2022. The research aims to explore stock price fluctuation trends and examine the relationship between financial ratios and stock prices, as well as the role of dividends policy in mediating this relationship. The findings show that profitability and liquidity are important factors in determining stock prices, while solvency does not influence stock prices, nor does dividend policy play an important role in mediating the relationship between variables. It is hoped that these findings will provide insight for investors, policy makers and industry stakeholders in navigating Indonesia's dynamic construction materials market.

Multi-Scenario Model Predictive Control for Greenhouse Crop Production Considering Market Price Uncertainty

This paper presents a stochastic model predictive control (SMPC) strategy to maximize the economic profit of a greenhouse crop production. The strategy consists of an optimization problem that is solved with a multi-scenario MPC formulation (MS-MPC). It considers the uncertainty of market price by using its historical evolution per year as multiple price scenarios in the cost function. MS-MPC calculates a single set of dates to harvest and sell the crop production that optimizes profits for all the considered scenarios. In addition, MS-MPC determines the optimal temperature references that should be achieved inside the greenhouse for the growth of the crop. A case study for a Mediterranean tomato crop is simulated to analyze the performance of the developed MS-MPC strategy using a hierarchical control architecture with two layers. In the upper layer, MS-MPC calculations are executed following a receding horizon implementation. In the lower layer, regulatory control techniques are applied to reach the optimal temperature references by using natural ventilation and a heating system. Results show that MS-MPC can improve economic profits compared to the use of an average price scenario for the MPC calculations. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper was motivated by the need of greenhouse farmers for strategies that maximize profit considering market prices and crop production dynamics. It is not easy for them to make decisions to achieve such long-term objectives while minimizing economic risks, because market prices are very difficult to predict. As a solution, this work presents a novel control strategy to maximize profits by means of automatic selection of the best possible dates to harvest and sell the crop production. This selection is made thanks to considering the uncertainty of market prices in the control strategy by evaluating different scenarios, which are recorded evolutions of the prices from previous years. Although the strategy was tested in simulation, results suggest that using multiple scenarios of historical prices is better for the optimization of profits than just considering an average yearly trend of prices. The more scenarios are considered, the more protection against possible evolution of market prices is obtained. In future research, this control strategy could be extended to include other uncertainties of factors affecting decision making, such as weather forecasts.

Energy price prediction based on decomposed price dynamics: A parallel neural network approach

The growing uncertainties of the world due to geographic tensions, and weather conditions challenge the traditional method to achieve a reliable price prediction of energy future for both asset pricing and risk management. Following the initial success of deep learning models in energy price prediction, we attempt to establish a better architecture of neural networks to improve the prediction accuracy. We propose a novel Parallel Hybrid Neural Network (PHNN) model that utilizes independent sub-networks to effectively capture the distinct features of various sequences. Empirical results demonstrate that the PHNN model exhibits a significant performance enhancement of 16.68%, 14.09%, and 2.34% over the EMD–LSTM, the informer model, and the single LSTM model, respectively. In particular, the PHNN outperforms the single LSTM, which is trained on the same inputs, by 2.34% overall while by a remarkable 4.11% during event periods. This suggests that the PHNN derives notable benefits from its distinct architecture, particularly during the initial phase of extreme events characterized by significant price trend changes. Additionally, the study explores the potential benefits of incorporating additional event-tracking indicators for energy futures price forecasting. However, the findings suggest that these indicators may not consistently provide effective complementary information.

Heterogenous Rates of Return on Homes: Do The Rich Do Better?

AbstractRecent work on wealth inequality using the capitalization method wherein aggregate wealth totals are distributed proportionately to property income like dividends motivates concern about whether rates of return on assets vary across the income or wealth distribution. Here I use accrued capital gains and imputed rent on homes estimated from the Survey of Consumer Finances to address this issue. Both capital gains and imputed rent form part of total income. I find strong econometric evidence that returns vary directly with wealth level and are considerably higher for very wealthy compared to poorer households. However, I do not find evidence that higher income households receive higher returns once controlling for overall market house price trends. Returns are also strongly related to overall market house price movements, suggesting that timing the market is a key determinant. However, paradoxically, adding housing returns to baseline household wealth reduces overall wealth inequality.

The Construction of Fuzzy Prediction Model of Stock Price Rise and Fall Based on Machine Learning Technology

In recent years, the use of smart data analysis method to predict the stock price is financial technology; important issues in the field of finch. However, there are many technical indicators and human subjective factors will affect the stock price forecast, so we must effectively grasp the important influence indicators to improve the accuracy of stock price forecast. Therefore, this study uses four machine learning algorithms to predict and analyze the stock price fluctuation through the screening process of technical indicators, and then selects the important technical indicators. In addition, due to the uncertainty and fuzziness of the attributes of technical indicators and human subjective judgment, this study uses the fuzzy inference method to construct the fuzzy inference system to predict the rise and fall of stock price, and proposes the prediction method of the range of the rise and fall of stock price. Finally, this paper makes an empirical analysis on the stock price data of three companies. The results show that the accuracy of stock price forecast is more than 82.13%, and the average accuracy of stock price forecast is more than 83%. Therefore, the fuzzy inference prediction system proposed in this study not only has the theoretical basis, but also can effectively predict the trend and range of stock price, which has practical value and contribution to investors.

Implementation of solar system for electricity generation for rural farmers: A review

Solar energy offers a promising renewable alternative to traditional fossil fuel-based electricity generation for powering agricultural activities in remote rural areas. Several studies have demonstrated the technical and economic feasibility of photovoltaic, solar thermal, and hybrid solar systems for various on-farm applications such as water pumping, crop drying, greenhouse heating. These systems provide clean energy for irrigation, milling, cooling, and mechanical operations to improve productivity. When integrated with battery storage, solar also enables electrification and lighting in off-grid farms. The upfront capital cost of solar installations has been reducing significantly, and various incentive programs have enhanced the affordability for smallholder farmers. However, adoption of solar energy in the agriculture sector still faces certain challenges. Lack of adequate financing options and initial higher costs compared to conventional fuels limit widespread deployment. Technical skills are required for installation, operation and maintenance of these systems. Seasonal variations and uncertainty of solar resources necessitate proper system sizing and integration with demand patterns. Policy support through subsidies, tax benefits and financing schemes can help address these barriers. With the declining price trends and increasing reliability of solar technologies, the potential for energy access and economic gains from solar power in rural agriculture appears promising.

Agricultural policy and crop location: Long‐run output and spatial climate risk consequences

AbstractChoices by farmers—notably what crop to grow, where—are not only influenced by spatially sensitive environmental attributes but also economic factors that respond to changes in government policies. In South Africa, the policy stance toward agriculture swung toward an extended period of support spanning the middle of the 20th century. Subsequently, the agricultural support policies were eliminated in the post‐Apartheid period beginning in the 1990s. Using a purpose‐built, spatially explicit data set for South African agriculture spanning the period 1918–2015, we show these structural shifts in agricultural policy regimes concord with major shifts in national corn price trends and variability, and the area planted to corn (accounting for half the country's cropped area). More subtly, and much less studied, we reveal that these switching policy regimes also aligned with changes in the location of crop production, with pronounced consequences for crop output and climate risk. At its peak, policy‐aligned crop movement in South Africa reduced corn output by between 7.9% and 15.3%, and placed production in areas with reduced and riskier rainfall patterns. Upon removal of the policy distortions, the decline in total corn area continued, and the crop largely reverted to its predistorted, less climate‐risky geographical locations. The geographical sensitivities of the agricultural policy–production–climate risk nexus we reveal suggest these locational aspects deserve more concerted analytical and policy design attention, especially in light of the longer run, spatially sensitive production and food security risk implications of the changing climate realities facing agriculture the world over.

INFLATION VS INFLATION EXPECTATIONS IN 2020–2021 – THE IMPACT OF THE COVID-19 PANDEMIC

Aim: During the COVID-19 pandemic in 2020–2021, there was a significant increase in inflation and inflation expectations due to the government policies of the United States (US) and the European Union (EU), including the Euro Area (EA). With that being said, the evolving situation within these three po­litical-economic areas highlights the importance of understanding the relationship between inflation and inflation expectations, as perceived by both consumers and professional forecasters. The purpose of this article is to analyze the changes in these phenomena, using descriptive survey’ data analysis and a hybrid model of inflation expectations. Methods: The research methods refer to the descriptive survey data anal­ysis that provides evidence on the long-term courses of inflation in the US, the EU, and the EA, as well as the consumers’ and professional forecasters’ inflation expectations within the period 1999–2019. A hybrid model of inflation expectations has been used to estimate this data during the period of 2021. The study correlates and compares the findings of both types of research. Results: The results demonstrate that dur­ing the pandemic, the evidence supports the occurrence of periodic medium-high inflation in the studied economic areas. Inflation expectations in the US and the EU were periodically weakly anchored, although they were stabilized in the EA. Conclusions: The research, conducted by analyzing price trends and their expected trajectories over various timeframes, revealed consistent patterns. These patterns highlighted similarities in anticipated price changes before and during the pandemic.

Decentralized Storage Cryptocurrencies: An Innovative Network-Based Model for Identifying Effective Entities and Forecasting Future Price Trends

Decentralized Storage Cryptocurrencies: An Innovative Network-Based Model for Identifying Effective Entities and Forecasting Future Price Trends