Price Data Research Articles

Cost–benefit analysis as a decision tool for effective conservation planning—The case of the Nyerere Selous‐Udzungwa wildlife corridor in Tanzania

AbstractCost benefit analysis (CBA) is used in many fields to ensure efficient allocation of scarce resources but is rarely applied in conservation. By using a common metric to evaluate projects in complex social‐ecological systems, CBA can help to maximize the impact of conservation funding. It can also help to interrogate issues of distributional equity, plan for effective community‐based conservation, and inform future research and strategic priorities. We demonstrate this using the example of the Nyerere Selous‐Udzungwa Wildlife Corridor in Tanzania, a wildlife corridor aiming to restore connectivity between two protected areas to enhance African elephant (Loxodonta africana) conservation outcomes and mitigate human–elephant conflict. We combine novel data on elephant crop depredation with crop price and productivity data, elephant valuation methods, and ecosystem service values to analyze corridor costs and benefits from contrasting global and local community perspectives. From the global perspective, we find benefits to outweigh costs by at least 4.6:1. From the communities' perspective, we find that while substantial benefits should secure buy‐in to the project, continued subsidy will be required to cover the costs. Our findings support generalized recommendations for application of CBA across diverse systems and conservation priorities.

Evaluating Temporal Price Behaviour of Rice in Bangladesh

Aims: The agriculture sector in Bangladesh is predominantly centered on rice production and it is the staple food. High prices of rice can affect the consumers adversely. This study aimed to analyze the annual price dynamics of rice in Bangladesh. Study Design: The study was based on secondary time series data of prices of different category of rice at wholesale and retail level. Both nominal and deflated prices were sued to analyze for achieving the objectives of the study. Place and Duration of Study: Secondary time series data of price for different category of rice from country as a whole from 1991 to 2021 were used to assess its annual price behavior. Methodology: Descriptive statistics were employed to assess the annual price fluctuations, while linear regression analysis was used to evaluate the annual price trends. Results: The analysis revealed significant variations in the annual wholesale and retail prices of rice (both fine and coarse). Notably, coarse rice exhibited greater price fluctuations compared to fine rice in both wholesale and retail markets. Price fluctuation at retail level for fine rice is greater than the wholesale level but in case of coarse rice, price fluctuation at retail level is lower than wholesale level. The study further found that the wholesale price trend for fine rice increased more sharply than that of coarse rice, a pattern that was also reflected in retail prices. Conclusion: The study indicates that over the study period, the price of fine rice experienced a more pronounced fluctuate and increase compared to coarse rice.

“Superstitious” Investors

Abstract We reconsider the excess volatility puzzle through the lens of a model in which agents believe they can predict dividend growth when in fact they cannot. Besides excess volatility in the time series, the model explains the value premium, and the explanatory power of the value factor. In support of the model, we show that analysts’ earnings forecasts align with market valuation and that analysts are far more optimistic about growth stocks than they are about value stocks. Using both survey and price data, we show that the same mechanism can explain the excess returns earned by investing in high-interest rate currencies. JEL G12, G15, G41

Learning A-Share Stock Recommendation from Stock Graph and Historical Price Simultaneously

The Chinese stock market, marked by rapid growth and significant volatility, presents unique challenges for investors and analysts. A-share stocks, traded on the Shanghai and Shenzhen exchanges, are crucial to China’s financial system and offer opportunities for both domestic and international investors. Accurate stock recommendation tools are vital for informed decision making, especially given the ongoing regulatory changes and economic reforms in China. Current stock recommendation methods often fall short, as they typically fail to capture the complex inter-company relationships and rely heavily on financial reports, neglecting the potential of unlabeled data and historical price trends. In response, we propose a novel approach that combines graph-based structures with historical price data to develop self-learned stock embeddings for A-share recommendations. Our method leverages self-supervised learning, bypassing the need for human-generated labels and autonomously uncovering latent relationships and patterns within the data. This dual-input strategy enhances the understanding of market dynamics, leading to more accurate stock predictions. Our contributions include a novel framework for label-free stock recommendations with modeling stock connections and pricing information, and empirical evidence demonstrating the robustness and adaptability of our approach in the volatile Chinese stock market.

Atlantic Trade and Conflict in Europe

Abstract We use over 200 years of conflict and wheat price data to provide the first quantitative evidence that Atlantic trade reduced conflict in Europe. While other determinants of intra-European conflict during the Early to Late Modern period have been proposed, the role of Atlantic trade has not been previously explored due to a lack of historical trade data. We overcome this constraint by using wheat prices to calculate time-varying measures of price pass-through between Europe and the New World, which we use as a proxy for Atlantic trade. To identify the causal effects of Atlantic trade, we exploit exogenous changes in wind patterns and tropical cyclone activity over the Atlantic Ocean to instrument trade. Our results suggest that a one standard deviation increase in Atlantic trade resulted in a 0.157 standard deviation decrease in intra-European conflict onset. We also find empirical support for Atlantic trade increasing real wages and reducing military sizes in Europe, which is consistent with the idea that the possibility of forgone Atlantic trade acted as a deterrent to conflict.

COMPARATIVE ANALYSIS OF REGRESSION MODELS FOR STOCK PRICE PREDICTION: LINEAR, SUPPORT VECTOR, POLYNOMIAL, AND LASSO

This paper investigates the performance of various regression-based machine learning techniques for short-term stock price prediction. Four regression algorithms – linear regression, support vector regression (SVR), polynomial regression and LASSO regression – were applied to Apple Inc’s historical price data for two-years ending on October 1st, 2024, to predict the next day’s closing stock price. Technical indicators such as moving average, relative strength index (RSI) and lag features were included in the regressions to improve prediction performance. Evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) were employed to assess the models’ performance. Results show that linear regression and LASSO regression were the most performant models, reaching R² values of more than 0.95, with minimal error values. While SVR yielded the poorest results when accounting for all metrics. Overall, the study highlights the predictive power of simpler regression models over more complex ones in stock price predictions and offers recommendations for model selection.

Applications of machine learning in quantitative trading

Abstract. This paper also addresses quantitative trading where machine learning techniques are applied. Quantitative trading uses historical data and makes future predictions that inform and optimise trading strategies. There are two machine learning techniques used in quantitative trading: supervised and reinforcement learning. In a supervised learning setting, machine learning techniques such as regression analysis, classification models and time series prediction are used to forecast future possible outcomes in the market. Data such as historical prices, volume and financial indicators are used to determine trends that inform and optimise trading decisions. Reinforcement learning is another machine learning technique in quantitative trading where adaptive strategies using a reward system are designed. These strategies then analyse market dynamics and optimise trades through interaction and feedback. The third technique, deep learning, takes neural networks and uses them to process large-scale, complex data. It's particularly useful in quantitative trading as it generates more accurate predictions; for instance, neural networks are capable of learning trading signals from price and volume data and even from news headlines. By enabling computers to analyse large quantities of training data, deep learning improves the predictive accuracy of future price trends. It also helps to generate trading signals that are more robust to market conditions.

CroCoDai : A Stablecoin for Cross-Chain Commerce

Decentralized Finance (DeFi), in which digital assets are exchanged without trusted intermediaries, has grown rapidly in value in recent years. Stablecoins , which are pegged to a non-volatile asset such as the US dollar, are a prominent feature of DeFi as they mitigate the risk associated with price fluctuations. However, existing stablecoin systems are tied to individual blockchain platforms, and trusted parties or complex protocols are needed to exchange stablecoin tokens between blockchains. Our goal is to design a practical stablecoin system for cross-chain commerce, and to do so we must overcome two main challenges. The first is to support a large and growing number of blockchains efficiently. The second is for the stablecoin to be resilient to blockchain platform failures and to price fluctuations that affect its collateral. We present CroCoDai to address these challenges. We demonstrate CroCoDai 's efficiency by comparing the performance of a prototype implementation to related baselines, and its resilience through an empirical analysis of historical token price data.

Research on the Correlation between the Movement of the Dollar and the Price of Gold

Abstract. Gold acts as a hedge to protect investors' assets. The U.S. dollar is a global currency and has an important place in international trade. Oil, on the other hand, is a non-renewable resource and an important international resource with unstable prices. This study used price data for the U.S. dollar, gold and oil from 2000 to 2023 to analyze the movement of gold, U.S. dollar and oil prices. The experiment uses a regression model to determine the effect of the dollar on the price of oil and gold. The study found a significant negative correlation between the U.S. dollar and the price of gold and an insignificant negative correlation with oil between 2000 and 2014. Between 2015 and 2023, there is a change in U.S. monetary policy, which leads to a weakening of the negative price correlation between gold and the U.S. dollar, but U.S. dollar still with the negative correlation with oil remaining weak. These results suggest that buying gold is the best way to protect your assets in times of financial crisis or market instability, but the US dollar and oil can change in price due to macroeconomic and political factors. To some extent these data provide a reference value for investment in the coming year.

Connectedness between green bonds, clean energy markets and carbon quota prices: Time and frequency dynamics

In this paper, we investigate the time and frequency dynamics of connectedness among green assets such as green bonds, clean energy markets, and carbon prices. Using daily price data, we explore return spillovers across these green financial markets by applying the novel framework on time and frequency dynamics proposed by Baruník and Krehlík (2018). This allows us to identify the direction of spillovers among our variables, and decompose the connectedness to differentiate between short-term and long-term return spillovers. Our results indicate that green bonds and carbon prices act as net receivers of shocks, but mainly in the short-term. We also observe a low level of connectedness among our clean energy markets across both low and high frequency bands, even during times of economic or political crisis. Additionally, there are periods in which connectedness between the clean energy assets is driven by the long-term. In periods of economic and political stability, carbon prices may also provide an interesting diversifying tool for short-term investors. Our results should be of interest for investors and portfolio managers who focus on green financial markets, by strengthening the notion that green financial markets can offer diversification opportunities, for both short-term and long-term investors. Policy makers could also benefit from our insights on conectedness in their work on short-term and long-term climate policies. This paper is the first to use this framework to investigate systematic risks within green financial markets.

Jaringan Saraf Tiruan Backpropagation untuk Prediksi Harga Bahan Pangan di Wilayah Kabupaten Deli Serdang

Foodstuffs are raw materials in the form of agricultural, vegetable and animal products that are used by the food processing industry to produce a food product. Food ingredients consist of plant foods and animal foods. Food is the most basic need for human resources in a country. Food prices sometimes experience erratic increases or decreases. The aim of this research is to determine the results of food price predictions in the Deli Serdang Regency area using the Backpropagation algorithm. The data used in this research is food price data from 2020 to 2023 which comes from the official National Food Ingredients website. This research uses the Backpropagation algorithm artificial neural network method which uses several architectural models and the results of this test will produce the best accuracy values. The test results show that the best architecture for research on implementing the backpropagation algorithm in predicting food prices in Deli Serdang Regency is 2-10-1 with an accuracy of 87.5% and the 2-3-8-1 architecture with an accuracy of 87.5%.

A Novel Deep Learning Model for Stock Prediction Integrating Historical Prices, Core Volatility Trends and Financial News

Accurate stock price forecasting is crucial for investors and analysts aiming to make informed decisions and mitigate risks in financial markets. While deep learning models have proven effective in identifying complex patterns in quantitative data, they often struggle to account for non-quantitative factors such as corporate events, market sentiment, and regulatory changes that can cause unpredictable stock price movements. To address this challenge, an ensemble model is introduced that integrates historical price data, financial news, and stock fundamentals to predict next-day stock prices. The model combines one-dimensional Convolutional Neural Networks (1D CNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs), applied independently to each data stream and concurrently as a unified ensemble, to capture both short-term and long-term market behaviors. NVIDIA (NVDA) stock was selected for testing due to its high volatility and rapid price fluctuations, presenting a challenging case for predictive modeling. Using data spanning from April 2014 to March 2024, the model achieved a coefficient of determination (R²) of 0.983 and a mean absolute error (MAE) of $12.72. These results suggest that the ensemble approach effectively captures both quantitative and qualitative factors, making it a promising tool for improving stock price prediction in volatile market conditions.

The Relationship Between Social Attention and the Risk of Stock Price Collapse of Enterprises: Perspective from Baidu Tieba

With the rapid progress of the world economy, the problem of stock price collapse has become a major threat to the stable development of stock markets in various industries. Based on Baidu Tieba, this paper collects the stock price data of each listed company in the CSMAR database as the dependent variable NCSKEW_Mdeq, the total posts, total users and average repost number as the independent variables. Use Stata to conduct regression analysis on the two. As a result, it was found a significant negative link between Total Posts and Total Users and the dependent variable NCSKEW_Mdeq and there was no statistically significant link between Avg Repost Num and the dependent variable. This study provides a brand-new perspective for reducing the risk of stock price collapse in enterprises, aiming to explore and analyze the key factors that affect the stability of corporate stock prices, and thus provide clear direction and strong theoretical support for future research on protecting the interests of shareholders.

A survey of availability and affordability of polypills for primary and secondary prevention of cardiovascular diseases in selected countries

Abstract Background/Introduction The recent inclusion of polypills—fixed-dose combinations of two or more antihypertensive drugs and statin with or without aspirin—in the World Health Organization’s Essential Medicines List (EML) reiterates the potential of this approach to improve global treatment coverage for primary or secondary prevention of cardiovascular diseases (CVDs). Although there exists extensive evidence on efficacy, safety, and acceptability of polypills, there is no research to-date assessing the "real world" availability and affordability of polypills globally. Purpose We conducted a cross-sectional survey of public and private sector facilities in 13 countries from around the world to evaluate the availability and affordability of polypills. Methods In the surveyed countries, we first ascertained whether any polypill was authorized for marketing and/or included in the national EMLs and clinical guidelines. The facility survey employed a modified WHO/Health Action International methodology. In each country, we collected retail and price data for polypills from at least one public sector facility and three private pharmacies using convenience sampling. Polypills were considered unaffordable in a given country if the lowest paid worker spends more than a days’ wage to purchase monthly supply. Results Polypills were approved for marketing in four of the 13 surveyed countries: Spain, India, Mauritius, and Argentina. None of these countries included polypills in national guidelines, formularies, or EMLs. Further, none of the surveyed public pharmacies in any of the four countries stocked any polypills. Among the surveyed private sector facilities, availability ranged from 0% in India (out-of-stock on the survey day but listed for sale) to 100% in Argentina and Spain. In the private sector, we identified seven unique polypill combinations, marketed by eight different companies. Although not in stock, India had a wider range of various combinations (n=5). Many combinations (63%) found in this survey were affordable in the local context, but these were limited to India and Spain. A lowest paid government worker would spend between 0.2 (India)–2.8 (Mauritius) days’ wages to pay the price for one month’s supply of the polypills. Polypills were likely to be affordable in countries that locally manufactured them. Further, two-pill combinations (one antihypertensive and a statin) were stocked by private pharmacies in Argentina, Bangladesh, Cameroon, India, Nepal, and Spain, with affordability ranging from 0.2 (India)–7.1 (Cameroon) days’ wages. Conclusion Low availability and affordability of polypills in both public and private sectors suggest that the implementation and overall uptake of polypills remains poor globally. Context-specific multi-disciplinary health system research is required to understand factors affecting polypill implementation and to design and evaluate appropriate implementation strategies.

Optimizing Bitcoin Price Prediction with LSTM: A Comprehensive Study on Feature Engineering and the April 2024 Halving Impact

This research aims to develop a Bitcoin price prediction model using machine learning techniques, with a specific focus on Long Short-Term Memory (LSTM) neural networks. The Bitcoin market is characterized by unique features such as high volatility and the influence of various external factors, which differ significantly from traditional financial markets. As such, precise feature engineering is crucial for accurately modelling Bitcoin prices. Utilizing historical Bitcoin price data from 2014 to 2023, this study extensively evaluates LSTM models. The results indicate that LSTM models provide highly accurate predictions, with a Mean Squared Error (MSE) of 0.0001798 and a Mean Absolute Error (MAE) of 0.0101322. These results demonstrate that LSTM effectively captures the complex and dynamic patterns of Bitcoin prices, outperforming other methods. The findings have significant implications for financial market analysis, especially within the rapidly evolving domain of crypto assets. By leveraging machine learning methodologies, this research enhances understanding of the complexities of the crypto market and offers potential strategies for smarter investment decisions. The success of the LSTM model in improving Bitcoin price prediction accuracy underscores its importance in navigating the volatile and dynamic nature of the crypto market. Overall, this study highlights the substantial potential of machine learning approaches, particularly LSTM models, in analyzing and predicting crypto market behavior. It contributes to the growing academic discourse on the application of advanced technologies in finance and can stimulate further discussions on how machine learning can address challenges and opportunities in the crypto market.

Seasonal Behaviour of Arrivals and Prices of Major Crops in Krishi Upaj Mandis of Chhattisgarh, India

Chhattisgarh state is famous for its forest wealth and mineral resources and there is abundant production of crops. According to total area of crops, Paddy (77.47 percent) and Chickpea (5.43 percent) are major crops of Chhattisgarh state. Arrivals and price data (Year 2014-15 to 2023-24) of major crops were collected from website of Chhattisgarh state Mandi Board. The study was observed that the peak arrivals & price of paddy were started in the same months November to January in both market (KUM, Bemetra & KUM, Bhatapara) except peak price season starts in August to October with narrow difference in KUM, Bhatapara. In case of Chickpea, the peak arrivals and price started in the month of March to May & September to November, respectively. The Price stability of both crops (paddy & Chickpea) was higher in KUM, Bhatapara as compared to KUM, Bemetra. The less price fluctuation of paddy as compared to chickpea in Chhattisgarh state due to purchasing directly to farmers by the Government with Minimum Support Price (MSP) and also provided the Bonus. The study was suggested that the economic development of farmers in state, the government should purchase agricultural products other than paddy and cooperate in setting up dal mills & processing units. Necessary steps should be taken to increase of marketable surplus and development of community level threshing area. The study was importance in knowing the market behaviour and price fluctuation during the peak season and the major reason behind. The studies also throw light on the aspect how to enrich the economic condition of the farmers.

A hybrid load forecasting system based on data augmentation and ensemble learning under limited feature availability

Accurate power load forecasting is an important part of power system operation planning, it can ensure the stable operation of power systems and improve the efficiency of energy utilization. The power load is affected by many factors including temperature, season, population density, and so on, however due to privacy protection and other reasons, it is difficult to obtain some characteristic information that affects the load. The lack of characteristic data will reduce the accuracy of load forecasting and the generalization ability. To solve it, a new hybrid load forecasting framework is proposed, which is composed of two subsystems: a data preprocessing system and a high-precision forecasting system. Based on the load sequence itself, subsystem 1 obtains the trend data and denoising data by variational mode decomposition method, obtains the indicator variable for the weekend according to the one-hot encoding, and also introduces the electricity price data, thus obtaining the 4-dimensional extended data. Subsystem 2 constructs a hybrid prediction model by synthesizing various models, including deep learning and machine learning models, to forecast the expanded data. Finally, the multi-objective JAYA algorithm based on tent chaotic mapping and cross-perturbation strategy is used to ensemble the prediction results of the sub-models. To verify the superiority of the proposed forecasting framework, we conducted experiments using four sets of load data from New South Wales, Australia. The experimental results show that the average absolute percentage error of the hybrid framework on the four data sets are MAPEMarch=0.8070, MAPEJune=0.8296, MAPESeptember=0.7238 and MAPEDecember=0.7709, which are significantly lower than other models and provide a basis for power system scheduling management.

A Stock Prediction Method Based on Multidimensional and Multilevel Feature Dynamic Fusion

Stock price prediction has long been a topic of interest in academia and the financial industry. Numerous factors influence stock prices, such as a company’s performance, industry development, national policies, and other macroeconomic factors. These factors are challenging to quantify, making predicting stock price movements difficult. This paper presents a novel deep neural network framework that leverages the dynamic fusion of multi-dimensional and multi-level features for stock price prediction, which means we utilize fundamental trading data and technical indicators as multi-dimensional data and local and global multi-level information. Firstly, the model dynamically assigns weights to multi-dimensional features of stocks to capture the impact of each feature on stock prices. Next, it applies the Fourier transform to the global features to capture the long-term trends of the global environment and dynamically fuses these with local and global features of the stocks to capture the overall market environment’s impact on individual stocks. Finally, temporal features are captured using an attention layer and an RNN-based model, which incorporates historical price data to forecast future prices. Experiments on stocks from various industries within the Chinese CSI 300 index reveal that the proposed model outperforms traditional methods and other deep learning approaches in terms of stock price prediction. This paper proposes a method that facilitates the dynamic integration of multi-dimensional and multi-level features in an efficient manner and experimental results show that it improves the accuracy of stock price predictions.

Revisiting the dynamics of major cryptocurrencies

Purpose – This study aims to reassess the dynamics of major cryptocurrencies sur-rounding recent economic and geopolitical events. By employing wavelet analysis and quantile regression methods, it seeks to understand the behavior of cryptocurrencies before, during, and after the COVID-19 pandemic. Research methodology – This research employs the Least Asymmetric Daubechies (LA8) wavelet function to decompose log-returns of major cryptocurrencies into various frequency scales. Additionally, it utilizes wavelet coherence and quantile-on-quantile regression techniques to analyze daily price data spanning from July 2017 to May 2024. Findings – The findings reveal a strong long-term association among cryptocurrencies, with a decline in medium-term correlations. Bitcoin exhibits synchronization with major cryptocurrencies, excluding Tether, while BTC-ETH and BTC-BNB display a rapid, interconnected behavior alongside their fundamental links. Moreover, empirical evidence indicates Bitcoin’s heterogeneous nexus with other alternatives, showcasing greater sensitivity to positive extremes over negative ones. Research limitations – The study’s scope is delimited by the selected time frame (July 2017 to May 2024) for data analysis, potentially limiting insights into longer-term trends. Additionally, the reliance on specific methodologies like wavelet analysis might introduce constraints in capturing the entirety of cryptocurrency dynamics, leaving room for alternative interpretations or unexplored aspects. Practical implications – Results suggest that understanding the varying correlations among major cryptocurrencies during different market phases could aid investors and policymakers in devising more nuanced strategies. Recognizing the sensitivity of Bitcoin’s connections with alternatives to market trends could inform risk management approaches, particularly in navigating extreme market conditions. Originality/Value – The originality of this study lies in its comprehensive examination of cryptocurrency dynamics across varying time scales, utilizing wavelet analysis and quantile regression techniques. The findings offer valuable insights into the complex interconnections among cryptocurrencies, especially in terms of their sensitivity to different market conditions, providing a nuanced perspective for investors, analysts, and policymakers navigating the crypto landscape.

Sustainability Implications of Commodity Price Shocks and Commodity Dependence in Selected Sub-Saharan Countries

Sub-Saharan economies often rely heavily on a narrow range of commodities, making them particularly vulnerable to price fluctuations in global markets. This volatility predisposes these countries to economic instability, threatening short-term growth and long-term development goals. As a result, this study examines the sustainability implications of commodity price volatility and commodity dependence for 31 Sub-Saharan African countries from 2000 to 2023. Eleven agricultural commodity-dependent countries, six energy commodity-dependent countries, and fourteen mineral and metal ore-dependent countries were chosen. This study uses balanced annual panel data from World Development Indicators, World Bank Commodity Price Data, and Federal Reserve Bank Data. The data were analyzed using the VECM, and this study’s findings were threefold and unanimous for all three categories of commodities (agricultural, energy and mineral, and metal ore). First, commodity dependence is positively related to economic growth, suggesting that higher commodity prices benefit the economy in the long run. Second, commodity price volatility is negatively related to economic growth, indicating adverse impacts on economic stability in the long run. Third, commodity dependence is positively related to commodity price volatility in the long run. By analyzing the interconnectedness of these factors, this study underscores the need for diversified economic policies and sustainable practices to reduce vulnerability and promote sustainable development in the region. The findings highlight the critical role of strategic resource management and policy interventions in achieving economic stability and ensuring the well-being of future generations.