Market Price Trends Research Articles

Analysis of the Effectiveness of Multi-Armed Bandit Algorithms in Crop Pricing

This study explores the application of multi-armed bandit (MAB) algorithms in dynamic pricing of crops, with a focus on evaluating the adaptive upper confidence bound (asUCB) and Thompson Sampling (TS) algorithms. Through simulation experiments on historical data, the study analyzed the performance of these algorithms in fitting actual market price trends and responding to future price fluctuations. The results indicate that the asUCB algorithm performed particularly well in both the training dataset and simulation tests, demonstrating low mean squared error (MSE) and minimal cumulative regret, reflecting its rapid convergence and stable pricing capabilities. In contrast, although the TS algorithm initially performed slightly less effectively, it demonstrated unique advantages in dealing with market volatility due to its strong adaptability. This study demonstrates the potential application of MAB algorithms in dynamic pricing, providing valuable insights for pricing strategies in the agricultural product market.

Real Estate Insights on Mortgage Rates, Apartment Prices, and Rentals in Czech Republic

This study examines the complex dynamics of the Czech real estate market, focusing on the interaction between mortgage rates and residential property prices, the influence of dwelling size on market price trends, and the impact of regional economic disparities on property values. We find that lower mortgage rates paradoxically raise house prices through increased demand, calling into question the effectiveness of monetary policy in improving housing affordability. This counter-intuitive effect highlights the complex interplay between fiscal policy and market responses. In addition, the analysis reveals a significant shift towards smaller dwellings, increasingly favoured by single-person households, reflecting changing demographic trends. This shift has clear implications for price dynamics and market accessibility in both the sales and rental sectors, suggesting a nuanced response to consumer preferences across different market segments. In addition, the research highlights a strong correlation between regional economic vitality and property prices, underlining the profound influence of the broader economic landscape in shaping property values in different areas. By integrating these critical aspects, the study provides a comprehensive and nuanced understanding of the factors influencing the Czech property market, offering valuable insights for policy makers, property developers and investors seeking to navigate or influence this market effectively.

Research on Establishing Inbound Strategies for Supermarkets Based on Time Series and Multi-Objective Planning Models

This paper focuses on the pricing and replenishment decision problems for vegetable products in supermarkets. Pricing and replenishment decisions need to study the interconnections between categories and individual products, so as to make reasonable sales combinations; it also needs to study the correlation between sales volume and time, and predict the future purchase price and sales volume, through visual analysis and correlation analysis, multiple regression model, reveals the interconnections between categories and individual products, and combines the correlation between sales volume and time to predict the future price and sales volume, and developed a profit-maximizing replenishment pricing strategy. For each vegetable category, a multi-objective planning mathematical model was developed using multiple regression and time series models to determine the optimal total daily replenishment and time-of-day pricing strategies. At the individual item level, the entropy weight TOPSIS model was used to comprehensively evaluate and rank the individual items, and the top 33 individual items were selected as the target for stocking, and a multi-objective model was further established to develop the replenishment quantity and pricing strategy for July 1, 2023 for individual items. By focusing on verifying the impact of market price trends, this paper conducts a time series forecasting analysis by analyzing the relevant sales data of a large superstore in order to comprehensively optimize the superstore's vegetable selling strategy.

Temporal Analysis of Bael guda (Aegle marmelos) Prices in Central India

This research presents a comprehensive analysis of Bael guda prices in Central India over a 12-year period, spanning 2010-2014 and 2015-2021. The study delves into the intricate details of pricing dynamics, regional variations, and temporal fluctuations within the Bael guda market. Quarterly price data was collected from various markets, including regional and district levels, to analyze long-term market patterns and price trends. The research highlights the regional disparities in Bael guda pricing, with Shivpuri consistently emerging as the district with the highest average prices, underscoring the influence of factors such as local availability, demand dynamics, and transportation costs on pricing variations. Additionally, the study offers insights into the stability and variability of prices, with the 2015-2021 period exhibiting greater fluctuations, as indicated by higher standard deviations and coefficient of variation values. Furthermore, the research evolves price growth patterns, with remarkable growth rates in certain markets, such as Mandla, Dhamtari and Sheopur (M.P.). This comprehensive analysis not only enhances our understanding of the economic aspects of the Bael guda trade but also provides valuable insights for market stakeholders and policymakers, facilitating informed decision-making in this niche market.

Does soft information in expert ratings curb information asymmetry? Evidence from crowdfunding and early transaction phases of Initial Coin offerings

An Initial Coin Offering is the Initial Public Offering of the crypto sector. By issuing crypto tokens in exchange for funds, Initial Coin Offering is a popular way to raise capital for products or services typically associated with cryptocurrencies by issuing crypto tokens in exchange for funds. Initial Coin Offering has gained a lot of attention in the past few years; however, its lack of regulation has raised transparency and investor protection concerns from public agencies. This paper examines the role of soft information in third-party expert ratings and how it can mitigate information asymmetry in the financial markets. Expert ratings normally contain two types of information: hard information – a summary of public, numerically-measured information, and soft information – containing expert insights, rumours, and private conversations. Taking advantage of the special dual-rating system of a leading Initial Coin Offering rating platform, we successfully isolate soft information from expert ratings. We find that soft information reduces information asymmetry between issuers and investors, measured by the amount of raised funds in the crowdfunding phase and the underpricing levels in the early transaction phase. We also test the long-term survival rates and market price trends of issued tokens and conclude that soft information also plays a crucial role for investors in predicting the long-term performance of tokens. A heterogeneity analysis further shows that the soft information provided by expert ratings is more influential in countries or regions with low business environment indices.

A Study of Stock Market Predictability Based on Financial Time Series Models

In today’s era of economic globalization and financial integration, the stock market is constantly complex, showing many deviations that cannot be explained by classical financial analysis, but at the same time, some classic financial statistical features have striking similarities. This suggests that although the stock market is intricate, there are universal laws that can be found through data mining to find its underlying operating rules. In this paper, we construct financial time series models such as ARIMA, ARCH, and GARCH to predict the stock market price fluctuations and trends. The ARIMA model is used to fit the linear financial time series, and the GARCH model is used to fit the nonlinear time series residuals. The results show that the integrated tree model based on the idea of weight voting has high accuracy in predicting stock market bulls and bears, with XGBoost prediction accuracy up to 96%, and the neural network model is also very effective, with an accuracy rate of over 90%.

Asian stock markets closing index forecast based on secondary decomposition, multi-factor analysis and attention-based LSTM model

The analysis and prediction of stock markets in Asian is an important issue which can help to promote the integration and globalization of financial cooperation. However, owning to the non-stationary and complexity of the stock market fluctuation, it is challenging to predict the stock price accurately. Especially after the decomposition of the original series, how to solve the problem of pseudo information and filter the exogenous variables is often certain challenging. This paper presents a hybrid model based on secondary decomposition (SD), multi-factor analysis (MFA) and attention-based long short-term memory (ALSTM) to predict the stock market price trends of four major Asian countries. The original stock price series is preprocessed by two decomposition algorithms so as to capture further non-linear feature and better filter the noise. Multi-factor analysis is introduced as a supplement to the original data information. In the prediction stage, attention layer is added in long short-term memory model to increase the weights of effective information. Finally, four datasets about Asian stock markets and nine compared models were used to verify the performance of the proposed model. The empirical analysis results show that compared to the general long short-term memory, our proposed model can obtain higher 30% accuracy at least. The mean average percentage errors of the system were also the lowest among all models mentioned in this paper (0.612%, 0.903%, 0.606% and 0.402% respectively), which proves the effectiveness of the hybrid model.

Pragmatic Socioeconomics: A Way Towards New Findings on Sources of (Housing) Market Instability

ABSTRACT In this paper, we aim to define basic principles of pragmatic socioeconomics that may create a more solid interdisciplinary bridge between sociology and mainstream economics in the study of economic behaviour. The merit of this new concept demonstrates itself in the results of our research on the impact of social norms on home-buying behaviour and the consequences that such behaviour has for the operation of the housing market and housing price trends. That research demonstrates that interdisciplinary economic sociology may significantly enrich contemporary knowledge about housing price volatility – a phenomenon which increasingly determines social and economic stability in the world. Middle-range interdisciplinary research following the concept of pragmatic socioeconomics introduced in this paper is not limited to housing issues and may help fill in gaps in our knowledge of how markets operate in general.

Copper cross-market volatility transition based on a coupled hidden Markov model and the complex network method

Copper, as a strategic resource, has attracted widespread attention due to its price changes. Copper's futures prices and spot prices tend to be highly correlated in different countries. The copper market price change should consider the relationship between market trends of different countries, especially in the current situation where economic globalization and financial integration are inevitable. The volatility transition between datasets of different countries presents the characteristics of nonlinearity, interaction, dynamics and heterogeneity. To explore the cross-market transition of copper price fluctuations, this paper proposes a hybrid method of the coupled hidden Markov model (CHMM) and complex network. The coupled hidden Markov model (CHMM) is used to identify the transition characteristics of the market price trends, and the complex network method is used to analyze the evolutionary characteristics of the market state transition. We take the copper futures and spot market prices between the U.S., China, and the UK between January 2003 and December 2019 as the object. The results show that the copper futures market's state transition has a clustering effect, the price rise is easy to maintain, and the decline is rapid and easy to transfer. The relationship between daily markets is more volatile, and weekly and monthly are more stable. The British copper futures market has shown advantages in terms of transition ability, assimilation ability, media ability and transfer structure, while the influence of China's copper futures market is relatively weak. In addition, during the financial crisis, the UK spot market played an important role in the frequent declining transition. The nonlinear measurement model and conclusion established in this paper provide a new framework and analysis tool for explaining the volatility transition of the copper market.

Markets, mis‐direction and motives: A factual analysis of hoarding and speculation in southern Murray–Darling Basin water markets

There is a long history of concern about speculation in water markets, and potential adverse impacts on agricultural water users. However, applications of traditional analysis into price increase drivers in water markets are challenging due to data limitations. To address this, we use speculation theory, most notably Hirshleifer’s four fundamentals for speculative trade, to examine whether speculation or hoarding price increase drivers are evident in Australia’s largest water market, the southern Murray–Darling Basin. While speculation is likely, and with good reason given the gains possible, we find no evidence of hoarding behaviour in market price or volume trends. It is more plausible that agricultural sectors – notably horticultural users – have driven price increases given their requirements to access water at any cost during periods of low supply. These findings conform to theoretical expectations and help to inform badly needed insights into water market fundamentals required for future analysis. Overall, calls for stricter market controls on non‐agricultural water users are not supported by our findings. However, greater transparency in water market and broker activity, arising from substantial improvements in the underlying data and trade regulations associated with water markets, would be an ideal outcome from any public police extension.

Price analyses on direct-acting antiviral drugs of hepatitis C

The oral direct-acting antiviral drug (DAA) offers tremendous opportunities to eliminate hepatitis C. We analyzed the market trends, price trends and global treatment progress of DAA drugs by reviewing the guidelines recommended by the World Health Organization for the treatment of hepatitis C, and discusses the current accessibility of DAA treatment and its influencing factors. The results show that WHO recommends the use of DAA for treatment of hepatitis C, but the price of DAA is expensive. Although some countries have taken measures to lower prices and improve accessibility, the advancement of DAA treatment has been unsatisfactory and coverage is still not high. In addition to price, factors such as strategy, health system, accessibility and service provision need to be considered, and public health methods should be adopted to promote DAA treatment to achieve the elimination of hepatitis C.

Evolutionarily stable strategy analysis of Quantitative investment strategy

Based on Chinese Stock Index Future, this study focuses on two types of market price changes, trends and shake. How investors’ strategies evolve to influence the market from trend to shake or from shake to trend to obtain the “evolutionary stable strategy” (ESS) of the market is conducted. The results show that trend strategies are more favourable in most market environments, and find a single strategy cannot survive in the market, and trading frequency should be appropriately reduced to reduce transaction costs, so as to survive in the market for a long time.

What Determines China's Grain Imports and Self‐sufficiency? The Role of Rising Domestic Costs and Varying World Market Prices

AbstractChina's grain sectors have faced unprecedented challenges in recent years as the ever‐increasing and historically high level of grain output has failed to reduce grain imports. On the contrary, high grain imports and high domestic stock have accompanied historically high domestic output, a situation dubbed the “triple high” phenomenon in current policy discussion. This paper explores the role of widening domestic–world market price gaps in determining the triple high phenomenon. Unlike earlier studies that relied on production capacities, this paper argues that domestic production and demand (hence imports) are functions of domestic and world market prices and proposes an analytical framework to explicitly capture such price gaps under restricted trade linkages in general equilibrium. Following this approach, a set of price scenarios for the 2011–2020 period are constructed and simulated in a computable general equilibrium model. Results from the core scenarios, in which recent domestic and world market price trends are assumed to continue, suggest that further widening price gaps would substantially increase grain imports and reduce domestic output (by 60 million tons) and self‐sufficiency ratios from base levels. In the alternative scenarios with larger (smaller) price gaps, we find higher (lower) imports and larger (smaller) decreases in domestic output and self‐sufficiency ratios. Such results provide important policy implications as China's agricultural policy undergoes significant adjustment.

Market price trends of Latin American and Caribbean sea cucumbers inform fisheries management

Market price trends of seafood can inform fishery management measures and strengthen the bargaining power of fishers. The four-sided sea cucumber Isostichopus badionotus and donkey dung sea cucumber Holothuria mexicana are heavily exploited in small-scale fisheries in the Gulf of Mexico and Caribbean for export to China. We recorded prices and sizes of the dried sea cucumbers from 41 lots in 28 shops in Hong Kong and Guangzhou, China. Market value ranged 132–358 US$ kg−1 for I. badionotus, and 16–209 US$ kg−1 for H. mexicana. The relationship between product length and price per kg was weak for both species, revealing large variability in the marketplace. Price per individual increased linearly with product length for H. mexicana, and increased nonlinearly for I. badionotus indicating that large specimens were disproportionately more valuable than small ones. Thus, the economic performance of fisheries, especially those for I. badionotus, could be optimised by strictly enforcing large minimum size limits. High market value identifies I. badionotus as a species of conservation concern. A large difference between reported prices received by fishers and market retail prices suggests substantial scope for upgrading value chains of small-scale export fisheries.

Using a cycle reverting price process in modeling metal mining project profitability

PurposeThe purpose of this paper is to demonstrate how managerial estimates of long-term market price trends can be included into investment analysis of metal mining. The inclusion of subjective market information with a new cycle reverting price process is proposed.Design/methodology/approachSubjective managerial estimates are included into stochastic metal price modeling by defining separately the following parameters of each price cycle phase: approximated length, approximated long-term price level and volatility. An net present value-based investment analysis model is applied together with Monte Carlo simulation.FindingsIt is plausible to combine managerial estimates about metal price trends and cycles with stochastic modeling for shorter term and to include the information into investment analysis. The results show that the difference between the proposed process and the commonly used mean reverting process is remarkable in terms of decision-making implications.Originality/valueThe proposed method allows the inclusion of more relevant information into the metal price modeling used in mining investment analysis. Results suggest that the cyclical nature of metal prices affects project value of metal mining projects, and it should be considered when making irreversible investment decisions. The proposed method can be generalized for any cyclical processes.

DYNAMIC ECONOMIC RELATIONS AMONG STEEL PRICE INDICES

Bulk shipping is now a globalized industry, with market prices entirely dependent on the world's economic climate. For the bulk shipping market, the steel price index is a crucial standard of reference. In a global industry, the ability to understand the international relationship between changes in information and their effects on economics can help to reduce losses caused by the uncertainty of risk. Because the types of ships and contracts used in bulk shipping are wide-ranging, market participants tend to have difficulty in determining market price changes and trends, and are therefore forced to face greater market uncertainty and volatility. Because steel price is one of the leading indicators used in the bulk shipping market, a clear understanding of steel price index fluctuations can help to reduce the risks associated with bulk shipping. This study uses the vector autoregressive moving-average model as the principle method of analysis to determine how three major economic variables representative of Asian Steel Price Index (ASI), European Steel Price Index (ESI), and North American Steel Price Index (NASI) might influence one another. The results are as follows: (1) ASI is affected by its own positive changes as well as those of NASI, whereas the ESI is only affected by its own moving average. (2) The ASI and NASI are considered as leading indicators. This study aims to provide bulk shipping participants with additional ideas to determine the ideal time to enter the market.

Mineral resources: Geological scarcity, market price trends, and future generations

The extractable ores of the world's geologically scarcest mineral resources (e.g. antimony, molybdenum and zinc) may be exhausted within several decades to a century, if their extraction continues to increase. This paper explores the likelihood that these scarce mineral resources can be conserved in time for future generations without intervening but instead simply relying on the price mechanism of the free market system. First we discuss the role of geological scarcity in the long-term price development of mineral resources. Then, to see whether geological scarcity affects the price of minerals we compare the historical trends in the prices of geologically scarce mineral resources with those of geologically more abundant mineral resources. The results show that in the period 1900–2013 the price mechanism did not result in high prices that provide advance warning of exhaustion of minerals. We therefore argue that if conservation is left to market forces, it is not certain that geologically scarce minerals will be timely, automatically, and sufficiently conserved for future generations. We recommend preparing international policy measures targeted at a price increase of the scarcest mineral resources, in order to accelerate substitution and recycling of these materials and help save the geologically scarcest mineral resources for future generations.

소지역 단위의 주택시장 불안정 진원지 파악에 관한 연구*

Fundamental purpose of this paper is to identify spatial origins of housing market unstability based on the small spatial unit. The hot-spot and cold-spot areas derived from spatial statistical techniques were considered as the origin of market unstability. For this purpose, market price data of Seoul was collected and analyzed using real transaction data. The main findings are follows. First, it was proved that the current statistical unit is too large to identify origin areas of market unstability because it contains different market price trends in the same unit of analysis. Therefore, a smaller unit of statistical area, such as the primary administration unit, is necessary to identify more accurate market trend. Second, through kernel density estimation and hotspot analysis, it was able to find the origins of market unstability in the region where transactions occur intensively. Based on these findings, this paper recommends some policy directions to utilize the market price data more effectively.

Macroeconomic Variables and Real Estate in Italy and in the usa

This paper analyses the influence of the economic system on housing market price trends in the usa and Italy using a Vector Autoregression Model (var). This econometric model comprises the historic series of house prices from 1970 to 2012 and the historic series of certain macroeconomic variables, defined through the empirical findings of similar studies carried out in an international setting. Comparison between the Italian and American models exhibits a clearly different attitude of demand for real estate in the two countries. Whilst the models developed show a similar behaviour for the variable Gross Domestic Product, which has a direct influence on housing prices, they highlight an opposite attitude for the variables ‘inflation’ and ‘stock price index’. The variable interest rate, which contributes to explaining the variability of the Italian housing prices, has no effect in the American model. For both models, the statistical significance of the functional relationships identified and the logical interpretation of the behavioural and institutional relations, arising from economic theory, confirm the reliability of the results.

Lithium-ion Battery Cost Analysis in PV-household Application

Abstract With strongly decreasing prices of battery energy storage systems (BESS) and the stepwise reduction of remuneration for photovoltaic grid feed-in power in Germany, ‘home storage’ battery usage for buffering of surplus PV generation and subsequent self-consumption is a field of growing interest and market activity. In this paper we use a multi-parameter economic model which allows profitability estimation for BESS with sensitivity to both technical and economical parameters, such as battery end-of-life criterion, battery ageing behavior, electricity prices and storage investment costs. Using this model, we derive a clear picture of system profitability with dependence to all major influencing parameters. Applying the baseline ageing characteristic and given the German market price trends, system profitability is expected to be reached in the very near future.