Price Behavior Research Articles

Oil-price forecasting based on ARIMA, exponential smoothing, and autoregressive neural network models

Oil prices have an impact on the global economy. Demand shocks, such as those caused by the recent COVID-19 epidemic, or supply shocks caused by production reductions by major global producers, result in sudden price changes. Oil industry has been collecting price data since the late 19th century. A time series of this magnitude allows for consistent analysis of price behavior forecasts following demand or supply shocks. Financial time series are sensitive to exogenous shocks. From this perspective, this work presents a comparative analysis of predictive crude oil prices scenarios, obtained from a historical series of average annual prices. Two approaches were used: first, a combination of classic strategies based on exponential smoothing, and ARIMA models. Second, an autoregressive neural network model. Both approaches are complementary when used for long-term forecasting of oil prices and show good response to volatile data. Therefore, we are able to present an alternative data analysis, in a field where there is a great amount of relevant historical series, using probabilistic and non-linear models in order to observe predictions and make more effective decisions.

An Evolutionary Game-Based Regulatory Path for Algorithmic Price Discrimination in E-Commerce Platforms

With the advent of big data, the swift advancement of diverse algorithmic technologies has enhanced the transaction efficiency of the e-commerce business. Nevertheless, it is crucial to acknowledge that e-commerce platforms might employ algorithmic technology to enforce differential pricing for various consumers with the aim of maximizing profits, thus infringing upon the lawful rights and interests of consumers. This paper focuses on the algorithmic price discrimination commonly observed on e-commerce platforms. To effectively regulate this behavior, the paper utilizes evolutionary game theory (EGT) to analyze the strategies employed by e-commerce platforms, consumers, and market regulators to achieve stability. This research employs a real-life situation and utilizes parametric simulation to visualize and analyze the process and outcomes of the three-party evolutionary game. The results demonstrate the credibility and feasibility of the article’s findings. Based on our research, we have reached the following findings: During the process of evolution, the strategic decisions made by the game participants from the three parties will mutually impact each other, and various elements exert varying degrees of influence on the strategic choices made by the game participants from each party. Collaborative governance can enable consumers and market regulators to regulate the discriminatory pricing behavior of e-commerce platforms effectively. This article offers valuable insights into the governance of violations in the e-commerce sector based on robust data and model research.

The Share Price Behavior of the IPO Surrounding the Expiration of the Lockup Period. Evidence from IPOs with No Analyst Positive Recommendations

This study investigates the share price performance of the IPOs surrounding the expiration of the lockup period in the Malaysian stock market. The study explores whether the IPOs without analysts’ positive recommendations give a significant positive share price performance in situations of the negative effect of the expiration of the lockup period. The IPOs sample of 281 companies was taken from the period of January 2000 to December 2014. The study applied event study methodology to investigate the performance of the IPO share price. The study found that the IPOs without analysts’ positive recommendations gave an insignificant positive share price performance in the event window of (-10, +10) surrounding the expiration of the lockup period. Whilst, the longer event window (-30, +30) shows a significant positive share price performance surrounding the expiration of the lock-up period

Exploring Price Patterns of Vegetables with Recurrence Quantification Analysis

This study investigates the time-series behavior of vegetable prices in the Central Market of Thessaloniki, Greece, using Recurrence Plot (RP) analysis and Recurrence Quantification Analysis (RQA), which considers non-linearities and does not necessitate stationarity of time series. The period of study was 1999–2016 for practical and research reasons. In the present work, we focus on vegetables available throughout the year, exploring the dynamics and interrelationships between their prices to avoid missing data. The study applies RP visual inspection classification, a clustering based on RQA parameters, and a classification based on the RQA analysis graphs with epochs for the first time. The aim of the paper was to investigate the grouping of products based on their price dynamical behavior. The results show that the formed groups present similarities related to their use as dishes and their way of cultivation, which apparently affect the price dynamics. The results offer insights into market behaviors, helping to inform better management strategies and policymaking and offer a possibility to predict variability of prices. This information can interest government policies in various directions, such as what products to develop for greater stability, identity for fluctuating prices, etc. In future work, a larger dataset including missing data could be included, as well as a machine-learning algorithm to classify the products based on the RQA with epochs graphs.

Original Research Article Analyzing Price Behaviour and Constraints in Marketing of Small Onion in Tamil Nadu, India

This study examines market trends and seasonal patterns in small onion Markets in Tamil Nadu, while also investigating challenges faced by various stakeholders in the onion supply chain. The various analyses have made use of secondary data that has been gathered from different sources. The NHRDF office in Coimbatore, NHB website and AGMARKNET website are the sources of the monthly data on wholesale market prices and arrivals of small onions f markets of Dindigul, Chennai, Coimbatore and Idukki from 2014 to 2023. Primary data also obtained to fulfill the one of the research objectives by surveying the sample of respondents. Two districts were selected purposefully: Perambalur and Trichy districts, based on the top onion producing districts in Tamil Nadu. The sample size constitutes 180 respondents (80 farmers, 40 retailers, 20 wholesalers and 40 consumers) were randomly interviewed with help of pre structured interview schedule. Trend analysis revealed increasing price trends across all markets, with Idukki experiencing the highest rate of increase. Seasonal index analysis indicated that prices in Tamil Nadu markets are influenced by arrivals, with lower prices during peak arrival periods., while Idukki market in Kerala prices less influenced by arrivals. Farmers struggle primarily with high labor costs (78%) and high cost of fertilizers pesticides and machineries (68.7%), while consumers face problems with price fluctuations (68.8%) and quality concerns (68.6%), retailers face problems with lack of storage facilities (76.27%) and absence of facilities for grading (64.85%), and wholesalers struggle with financial risks (79.2%). The study's findings will aid policymakers to focus on improving market infrastructure, regulating intermediaries, and supporting farmers in managing costs. Leveraging existing labor schemes could help alleviate workforce shortages. Furthermore, customers would gain from initiatives to maintain price stability and raise quality standards.

Corporate earnings, dividend payments, and stock price behavior of manufacturing firms listed in Nigeria

The purpose of the study is to investigate the effects of corporate earnings and dividend payments on the stock price behaviour of manufacturing firms in Nigeria between 2013 and 2022. The study adopted an ex-post facto research design, and deployed Generalised Method of Moments (sys-GMM) and Granger causality tests for the data analysis. The two-step system GMM is used to control for omitted variables such as bias, unobserved panel heterogeneity or heteroscedasticity, autocorrelation within panels or groups, endogeneity issues, and measurement errors in the data. Findings show that share price (SP) and its predictors yield valuable insights into the factors influencing stock valuation. Dividend per share (DPS) emerges as a significant positive predictor, suggesting a notable impact on share price. Firm size (FSZ) proves to be a statistically significant predictor, indicating that the size of a firm plays a role in influencing stock prices, with larger firms associated with a decrease in share price. Conversely, Earnings per Share (EPS) and Dividend Yield (DY3) do not exhibit statistical significance, suggesting limited predictive power in the model. By way of conclusion, the study provides an understanding of the factors influencing share price behavior within the Nigerian manufacturing sector and the positive impact of past performance, particularly lagged share prices, confirming the persistence of stock market trends. The practical implications of this study are that it underscores the understanding of financial dynamics in emerging economies, particularly in the context of manufacturing firms in Nigeria.

Deep learning-based electricity price forecasting: Findings on price predictability and European electricity markets

Deep learning algorithms have transformed day-ahead electricity price predictions in recent years. This phenomenon has implications for price predictability and perceived risks, which in turn could impact investments. This study aims to unravel the impact of deep learning-based price forecasting (DPF) on price predictability and its correlation with price volatility. This has been done by examining 19 bidding zones across 11 European countries from January 2018 to June 2023. This timeframe captures the unique market dynamics influenced by the COVID-19 pandemic and the Global Energy Crisis. This analysis has been enabled by the development of the Day-ahead European Electricity Price Forecasting Kit (Deepforkit), a forecasting tool developed in-house and open-sourced. Notably, Deepforkit offers a running speed at least 5 times faster than a previously suggested benchmark, enabling large-scale and multi-market analyses such as the one conducted in this study. It is quantitatively proven that if DPF is used, price volatility is just moderately correlated to unpredictability, and using it as a proxy for price unpredictability is not reliable. This is attributed to the capacity of DPFs to adjust their predictions in response to evolving market trends and patterns. This finding has an important implication on the investment risk perception and the cost of capital for new investments in the power sector. Moreover, the predictability behavior of prices is compared and contrasted in 3 markets, namely EPEX SPOT, OMIE, and Nord Pool, and across 11 European countries (Belgium, Denmark, Finland, France, Germany, Netherlands, Norway, Portugal, Sweden, Spain, and Switzerland).

Forecasting and unveiling the impeded factors of total export of Bangladesh using nonlinear autoregressive distributed lag and machine learning algorithms

Rising global oil prices are a major challenge for an emerging oil-importing nation such as Bangladesh. The majority of prior research on the economic effects of an oil price shock has concentrated on developed countries, with emerging economies receiving comparatively less attention. Bangladesh is vulnerable to price shocks due to its rising oil consumption over the past decade. This study aims to investigate how changes in oil prices would affect Bangladesh's total export earnings and to forecast the overall export volume. This study utilized a nonlinear autoregressive distributed lag (NARDL) approach to account for the asymmetric behavior of oil prices from 1991 to 2021. To assess the accuracy of predictions, the study employed the Prophet forecasting model and the Long Short-Term Memory (LSTM) method. Additionally, the symmetry test revealed a nonlinear relationship between export volume and oil price but a linear relationship between inflation and export volume. According to the NARDL assessment, both positive and negative oil shocks increase export earnings over the long run. The short run summary clarifies that both positive and negative changes in oil prices exert a significant negative effect on exports. Also, Inflation influences export earnings negatively in the short run but positively over the long term. Moreover, using machine learning methods, it was found that the LSTM method outperforms the prophet model in prediction performance with a low root mean square error (RMSE) of 1.88. Also, the analysis revealed policymakers that the export sector requires diversification to reduce its exposure to oil price shocks.

Exploring Market Dynamics: A Qualitative Study on Asset Price Behavior, Market Efficiency, and Information Role in Investment Decisions in the Capital Market

This qualitative study explores market dynamics, asset pricing behavior, market efficiency, and the role of information in investment decisions within the financial markets. The research aims to provide insights into the underlying motivations, perceptions, and experiences of market participants, offering a comprehensive understanding of these complex phenomena. Employing qualitative methods such as semi-structured interviews and textual analysis, data was collected from a diverse range of participants including investors, financial analysts, and market regulators. The study found that market dynamics are influenced by various factors including investor sentiment, economic indicators, regulatory changes, and technological advancements. Behavioral biases among investors, such as herd mentality and overconfidence, challenge traditional theories like the efficient market hypothesis (EMH), indicating the presence of market inefficiencies. Moreover, information plays a central role in shaping investor perceptions and driving market trends, though concerns exist regarding the reliability of information sources in the era of social media and algorithmic trading. The study underscores the importance of investor education, diversified investment strategies, transparency, and regulatory interventions in fostering fair, efficient, and resilient financial markets. Moving forward, addressing these issues will be crucial for informed investment decision-making and advancing our understanding of financial market dynamics.

Determinants influencing online shopping behaviour among 18-30-year-olds in Gauteng, South Africa: an examination of pricing, quality, security, time and information availability

This study explores the determinants influencing online shopping behaviour among 18-30-year-olds in Gauteng, South Africa, focusing on key factors, such as pricing, quality, security, time, and information availability. Grounded in the Technology Acceptance Model (TAM) and Perceived Risk Theory, this research aims to provide a comprehensive understanding of how these factors impact consumer decisions and behaviours. A cross-sectional survey methodology was employed, targeting 1,043 respondents who have shopped online within the past six months in Gauteng. The findings reveal that all five factors significantly influence online shopping behaviour, with information availability and time efficiency showing the strongest correlations. The study also highlights that despite the high perceived convenience and satisfaction with online shopping, security concerns remain a barrier to adoption. The study suggests that online retailers should enhance their security measures, provide detailed product information, and streamline their online processes to improve customer experience and trust. The study developed a conceptual framework to identify the factors influencing online shopping behaviour among 18-30-year-olds in Gauteng. This model includes six constructs: pricing, quality, security, time, information availability, and online shopping behaviour. This framework can be applied in future research. Limitations of the study include its narrow geographic and demographic focus on the 18-30-year-old population in Gauteng, South Africa, which restrict the generalisability of findings to other age groups or regions. Additionally, the cross-sectional design provides a snapshot rather than longitudinal insights into how online shopping behaviours evolve over time.

The impact of intermediate option availability on consumers' choice: The moderating role of price roundness

Given the surge in consumer demand and rapid inflation, businesses have turned their attention to premiumization as a means of market segmentation. Building upon previous studies that highlight the significance of the compromise effect in gaining market share for intermediate options, this research examines the feasibility of revenue management strategies using 'psychological pricing' in hospitality and tourism products. By investigating choice behaviors related to intermediate options, we explore how subtle pricing manipulations can enhance the compromise effect. Through five experiments, we validate our hypotheses. Our findings not only extend the literature on pricing behavior and hospitality concerning rounded pricing but also provide insights on how to strategically adjust psychological pricing to align with real-time market conditions.

An endogenous evolution mechanism model of asset prices based on time-varying risk aversion coefficient

In the traditional heterogeneous agent model, investors are assumed to be risk averse, and the wealth expected utility function maximization principle is used to form the optimal asset quantity demand. In such models, the risk aversion coefficient of investors is often assumed to be constant. This paper considers that the risk aversion coefficient of investors is time-varying and changes with the change of wealth, and establishes an endogenous evolutionary mechanism model formed by fundamental analysts, technical analysts, and market makers. Compared with the fixed risk aversion coefficient model, this paper analyzes the investor’s behavior, the interaction between investor behaviors, and the influence of different types of investors on the stability of the market. At the same time, we test asset price and asset behavior and conclude that investor behavior affects the stability of the system model. The numerical simulation of the corresponding stochastic model shows that the model can simulate the basic characteristics of financial time series, such as the partial peak and thick tail of asset return series, and the long memory of fluctuations.

Individual and cluster demand response in retail electricity trading with end-users in differentiated oligopoly market: A game-theoretical approach

The characterization of the competitive interdependence among power consumers in power trading system contributes to pursuing efficient power supply and suppressing peak–valley load differences. Even though Stackelberg–Nash game approach has been applied to retail electricity trading with a single utility company and a group of end-users under competitive interdependence, the extension to multi-cluster retail electricity markets is not focused yet and the best strategies of end-users are still decoupled to each other. To bridge this gap this paper investigates the price-based demand response problem in multi-cluster retail electricity markets to coordinate the energy consumption behavior of the end-users and the retail price behavior of the utility companies. Adopting the structure of hierarchical game theory, the cluster retail price is controlled by the utility company to affect the energy consumption demand of end-users within the corresponding cluster and hence increase the income of both the utility companies and the end-users. In order to describe the coupled competition relationship among the bottom end-users in the system, the Cournot competition model of the differentiated oligopoly market is constructed by taking energy consumption as the individual strategy of end-users. The existence and uniqueness of Nash equilibrium and Stackelberg Nash equilibrium in such a hierarchical game system are proved, and a seeking algorithm is proposed to seek the Stackelberg Nash equilibrium, which is regarded as the extension from the existing algorithm for multi-cluster setup. This work gives an insight to theoretically check whether the utility companies need to redesign the contract with the power generator to ask for larger maximum supply.

Perbandingan Ekspor Kopi Indonesia Pada Tahun 2021 Dan 2022

This study aims to gain a deeper understanding of the comparison of the amount of capacity (kg) of Indonesian coffee exports between 2021 and Indonesian coffee exports in 2022. The data used in this study were gathered through library research, which allowed us to collect data from literature related to the object of our research, such as documents or archives containing data on coffee production, coffee plantation area, total capacity (kg) of Indonesian coffee exports, and the behavior of Indonesian coffee prices. The data sources were kindly provided by the Central Bureau of Statistics (BPS) and the Center for Agricultural Data and Information Systems of the Secretariat General, Ministry of Agriculture. In this research, we employed two data analysis techniques: the Shapiro-Wilk normality test and the Wilcoxon test. The results of these tests indicate a comparison between Indonesian coffee exports in 2021 and 2022. This comparison is characterized by a decrease and also an increase in mean rank and sum of rank.

Dividend Payments and Share Price Behaviour of Selected Manufacturing Firms in Nigeria

Dividend Payments and Share Price Behaviour of Selected Manufacturing Firms in Nigeria

On measuring climate risks using attention search and testing the clean energy-climate hypothesis

ABSTRACT We measure physical and regulatory climate risks as innovations in several attention to climate change indexes that we construct using search volume data in Google Trends. The intention is to use the constructed risk indexes to test the empirical validity of the clean energy-climate hypothesis, which posits that clean energy prices fall (rise) following a drop (rise) in attention to climate risks. We test the empirical validity of this hypothesis at the macro (market level) using regime switching models and at the micro (firm level) using time-series regressions across clean energy sub-sectors. The macro analysis reveals that our hypothesis is only valid for climate pledges and physical climate risks. When attention to climate pledges drops or when physical climate risks are low, investors become reluctant to hold clean energy assets and vice-versa. Climate policy and climate solution risks have no impact on the nonlinear dynamic behaviour of clean energy prices. These results mean that investors and the public believe that climate policies lack credibility and climate solutions are not effective. The firm-level analysis confirms the findings of the macro analysis and reveals the heterogeneity of climate risks across clean energy sub-sectors.

Rank is all you need: development and analysis of robust causal networks

Financial networks can be constructed to model the intertemporal price dependencies within an asset market, giving rise to a causal network. These networks are traditionally inferred through multivariate predictive modelling. However, the application of these techniques to financial data is highly challenged. The interplay of social and economic factors produces unstable price behaviour that violates most conventional modelling assumptions, limiting the informational content of networks derived from standard inference tools. Despite these limitations, it remains unclear whether the improved accuracy of robustly estimated networks translates into qualitatively unique insight. This study provides an extended analysis of our recently introduced Rank-Vector-Autoregression model, demonstrating its capacity to identify properties that are undetected with standard methodology. We initially validate the superior accuracy of Rank-VAR through a simulation study on processes that contain adversarial abnormalities. When applied to a dataset of 261 cryptocurrencies, our rank network uniquely displays capitalisation-dependent hierarchical ordering, with outgoing influence being positively, and incoming influence negatively correlated to total coin valuation. Applying our method to the squared deviations verifies that even under robust estimation, volatility networks display fundamentally differing dynamics to raw returns, with more connections, clustering, and causal cycles. Our results demonstrate the use of Rank-VAR to identify and verify unique properties in the causal structures of cryptocurrency markets.

Revealing the risk perception of investors using machine learning

Corporate disclosures convey crucial information to financial market participants. While machine learning algorithms are commonly used to extract this information, they often overlook the use of idiosyncratic terminology and industry-specific vocabulary within documents. This study uses an unsupervised machine learning algorithm, the Structural Topic Model, to overcome these issues. Our findings illustrate the link between machine-extracted risk factors discussed in corporate disclosures (10-Ks) and the corresponding pricing behavior by investors, focusing on a previously unexplored US REIT sample from 2005 to 2019. Surprisingly, when disclosed, most risk factors counterintuitively lead to a decrease in return volatility. This resolution of uncertainties surrounding known risk factors or the provision of additional facts about these factors contributes valuable insights to the financial market.

Price Behaviour and Price Forecasting of Tomato in Koyambedu Market, Tamil Nadu

This study aimed to comprehend the price behavior of tomato crops and forecast their prices in the Koyambedu market in Tamil Nadu, one of the major vegetable markets in South India. Secondary data on price of tomatoes were collected from Agricultural Produce Market Committees (APMCs) for a period of 8 years, from 2014 to 2022. Statistical tools such as trend analysis and seasonality indices were computed to understand the movement of prices in the Koyambedu market. Results revealed peak market prices in November and lowest in March, with a positive trendline indicating a gradual price increase over the study years. Comparative analysis between Autoregressive Integrated Moving Average (ARIMA) and Seasonal-ARIMA (SARIMA) assessed price prediction accuracy. The periodogram highlighted significant seasonality, guiding the use of SARIMA to forecast 2023 monthly prices. Predicted prices ranged from 3675.00 Rs/Qtl in November to 1178.76 Rs/Qtl in May.

What drives the price behavior of US sustainable stocks?

PurposeThis paper aims to identify the key drivers of US sustainable stock price movements in both the short and long term, deploying a rich collection of variables corresponding to green finance, investor attention and sentiment, market fear and uncertainty, macroeconomic variables, common market risk factors, commodity markets and the carbon emission market.Design/methodology/approachThe empirical analysis is based on two main methodologies. First, the elastic net penalized regression is utilized to select the factors most influential on the price formation of sustainable stocks. Second, short- and long-run dynamics of the chosen factors are examined using the dynamic simulations of the autoregressive distributed lag (DYNARDL) model.FindingsOf 32 candidate variables, the elastic net chooses US renewable energy, European sustainable stock market, EU ETS emission allowances, public attention to sustainable finance, gold and European renewable energy as the most contributing factors to the price behavior of sustainable stocks. The DYNARDL estimation results reveal that US renewable energy, European sustainable stock market and EU ETS emission allowances are important determinants in the short and long term, while public attention (European renewable energy) tends to affect sustainable stock prices only in the short (long) run.Practical implicationsThe corresponding short- and long-run effects of US renewable energy, EU ETS emission allowances and European sustainable stocks on US sustainable stock prices should induce policymakers to keep the price behavior of these factors under systematic review. The formulation of policy measures could serve to safeguard the sustainable stock market from the price vagaries in these influential markets.Originality/valueRelevant literature often focuses on the reaction of sustainable stocks to mainstream assets and risk proxies, limiting analysis to a few factors and providing an incomplete understanding of the drivers behind sustainable stock prices. More comprehensive research is needed due to the lack of studies on the determinants of sustainable stock prices and the growing global demand for these investments. This paper aims to address this gap by examining the potential explanatory power of 32 candidate factors representing key players in the global economic and financial landscape.