Price Behavior Research Articles

Prediction of distributions of unit prices for real estate properties on the basis of the characteristics of PSI-processes

Real estate market price forecasting is always in the focus of interests of scientists-economists, market analysts, market participants (sellers and buyers), marketing services of building complex enterprises, analysts working for banks and insurance companies and investors. Under present day conditions, the price behavior of properties on real estate markets takes especially important meaning subject to the influence of such factors as changes in the structure of household incomes, changes in mortgage rates and their availability, dynamic changes in the macroeconomic and other external socio-economic and political type factors. However, unlike the financial and securities markets, the real estate market is always characterized by a delayed reaction to external perturbations, often up to half a year, which allows us to hope for an appropriate construction of forecasts, at least in time for the delayed reaction. Traditional autoregressive forecasting methods are characterized by rapidly increasing forecast variance, because they assume a factor of stochastic volatility. This paper proposes a model and method of forecast construction based on stochastic processes of the “Poisson random index” having a short time for reaching a stationary stable variance. The model is based on the “principle of replacements” of current prices with new ones. We analyze in detail an example of the application of the “principle of replacements” for construction of price forecasts on secondary residential real estate in St. Petersburg which is based on data of four-year observations of offer prices.

The Solution for Unstable Bubble Assets: Reflection from an Islamic Finance Perspective

In the fast-paced world of technology, there exist popular investment instruments that have no intrinsic value (non-asset-based). As a result, their prices are highly volatile and prone to bubbles. However, from an Islamic perspective, investment vehicles must have a fundamental value (asset-based) to ensure price stability. This research aims to compare the behaviour of asset prices between non-asset-based and asset-based types by utilizing a system dynamics model. Furthermore, this study investigates the impact of two types of market sentiment: relatively stable good market and very good market sentiment, follows by very bad market sentiment. The findings of this study suggest that non-asset-based asset prices tend to bubble in response to good market sentiment and then rapidly decline when significant bad market sentiment arises. In contrast, asset-based assets tend to have more stable prices because investors have benchmarks and do not rely solely on intuition. This research highlights the importance of the fundamental value or asset base in investment instruments, as required by Islamic finance. However, asset-based assets can also be prone to high volatility when irrational investors dominate, and speculation prevails. In such cases, government intervention or regulation may be necessary to mitigate risks and stabilise the market.

Dynamic price competition market for retailers in the context of consumer learning behavior and supplier competition: Machine learning-enhanced agent-based modeling and simulation

This study analyzes the impact of consumer learning behavior and supplier price competition on retailer price competition in a complex adaptive system. Using machine Learning-enhanced agent-based modeling and simulation, the study applies fuzzy logic and genetic algorithms to model price decisions, and reinforcement learning and swarm intelligence to model consumer behavior. Simulations reveal that different learning behaviors result in different retailer competition patterns, and that supplier price competition affects the strength of retailer price competition. Simulation results demonstrate that consumer learning behavior influences retailer competition, with self-learning consumers leading to higher-priced partnerships, and collective-learning consumers leading to a shift in price competition among retailers. In contrast, perfect rationality consumers result in low-price competition and the lowest average margin and profit. Additionally, the competitive price behavior of suppliers impacts retailers' price competition patterns, with supplier price competition reducing retailer price competition in the perfect rationality consumer market and enhancing it in the self-learning and collective-learning consumer markets, leading to lower average prices and profits for retailers. This study presents a simulated market for price competition among suppliers, retailers, and consumers that can be expanded by subsequent scholars to test related hypotheses.

Innovative polygon trend analyses to detect trends for domestic and export onion prices

ABSTRACT Analyzing trends can provide insights into price behavior to predict future change. The primary objective was to examine prospective changes in monthly price over the entire period, between 2008 and 2021 using parametric and non-parametric techniques. Four domestic markets were chosen based on the maximum arrival of onions (Allium cepa L.) in Maharashtra and the Indian export market. Some markets fit the linear regression model; the errors do not conform to parametric assumptions, requiring application of distribution-free techniques. Using the mean of prices, Innovative Polygon Trend Analyses (IPTA) was applied. The IPTA plots for all domestic markets indicated upward trends in almost all months with regular polygons, with the exception of the export market. Plots indicated peak arrival months in markets exhibiting a narrower polygon structure, while lean arrival months showed a broader polygon. When results of trend analysis based on other tests were compared to the IPTA technique, there was good agreement across all approaches. The export market exhibited the least variability in prices, and the average monthly price ranges from 127.90 to 334.56 USD∙Mt−1. Post-monsoon and harvest months exhibited the highest average prices. Based on comparisons of trend analysis techniques, the IPTA methodology places a strong emphasis on identifying trends and offers insight through a visual examination of trend patterns.

Price Behaviour and Market Integration Amongst the Major Potato Markets in India

Variation in potato output over time causes wide price fluctuations, subjecting its producers to a high-risk situation. Using a monthly wholesale price of potato from January 2012 to December 2020, the current study examined the price behaviour of major potato-producing and consuming markets. The study employed correlation analysis, the Johansen Co-integration Test, the Vector Error Correction Model, and Granger Causality Test. As determined by the seasonality index, farmers received higher-than-average prices between June and December. Johansen's co-integration result demonstrated that all the selected markets were well integrated in the long run. The Patna market had the fastest adjustment rate (80 per cent), followed by the Mumbai market. Most states had uni-directional price transmission, with Patna being the only state to establish a bidirectional flow of potato prices with the Kolkata market. The Delhi market was discovered to be the key market influencing the prices of all other markets.

Digital Consumerism among Nepalese College Students: A Study of Online Shopping Behavior

Online shopping is a form of electronic commerce that allows consumers to purchase products or services directly from sellers over the Internet using a web browser. The main objective of this study is to explore the relationship between price, convenience, security, and online shopping behavior of students in Kathmandu. The research adopted the analytical research design. Four hundred students from different colleges in Kathmandu Valley were selected for this study. The convenience sampling method was used while selecting the respondents. The questionnaire survey was developed to collect primary data. The study adopted cross tab, independent t-test, and correlation to analyze the data. Convenience is the most preferable factor when selecting a product in online shopping. Price, convenience, and security are positively correlated with online shopping behavior. As per the result, the price highly correlates with online shopping behavior. Hence, online shopping companies must maintain the product's standard price to meet consumers' expectations. Online shopping companies also consider improving websites with solid security mechanisms for the convenience of online shopping to build up the company's image. Likewise, the firm should consider the preferable factors of online shopping for a better budget allocation for promoting their online business. Future researchers can study the online shopping behavior of the general public with a larger sample size.

Hedging Interest Rate Options with Reinforcement Learning: an investigation of a heavy-tailed distribution

Purpose: The study intends to model an interest rate index option using a heavy-tailed distribution. The goal is to calculate the interest rate path-dependent option prices that are consistent with market data and to develop a reinforcement learning strategy to discretely hedge the position considering transaction costs. Methodology: This paper presents a mathematical framework to calculate the price of interest rate path-dependent options. The research adapted a Fourier cosine series formula to employ the characteristic function of the present value of the forward index, which is modeled as a variance-gamma process and uses deep Q-learning to hedge such options. Findings: There is market evidence that the implied volatility curve is not flat. The study demonstrated that the variance-gamma process generates an increasing volatility smile, which is consistent with market observations. Additionally, hedging results show that the path-dependent options generated from the variance-gamma process can be efficiently hedged with advanced Q-learning techniques. Research limitations/implications: The study comprised only the variance-gamma process. Other probability distributions, such as the Normal Inverse Gaussian model, should be investigated. Practical implications: This study reveals which type of probability distribution should be present in a pricing engine to be consistent with implied volatilities. The approach provided here can assist managers in evaluating and comprehending market pricing behavior as well as achieving discrete hedging with costs. Originality: The paper addressed the merging of a fast pricing method for the interest rate options with a heavy-tailed distribution and the discrete interest rate derivatives hedging with reinforcement learning.

Technical Note—Risk Sensitivity and Firm Power: Price Competition with Mean2-Variance Profit Objective Under Multinomial Logit Demand

Risk Sensitivity and Firm Power: Price Competition with Mean2-Variance Profit Objective Under Multinomial Logit Demand Firms with varying degrees of risk sensitivity perceive the same stochastic profit prospect differently. It is important to understand the equilibrium pricing behaviors under risk aversion and their implications on firms’ survival and market health. The authors identify a power index, which is the ratio of product attractiveness to risk sensitivity, and show that the set of profitable firms is power index ordered. Firms with a favorable power index value earn a positive profit in a price competition, and others are driven to zero profit. Interestingly, although high risk aversion handicaps a firm, this paper shows that moderate risk aversion may give a firm an advantage over an otherwise equivalent competitor that is less risk sensitive. The authors also establish that in an equilibrium with market entry and exit, the power index is generalized to the ratio of product attractiveness and an entry cost-adjusted risk sensitivity measure.

Intraday financial markets’ response to U.S. bank failures

The arrival of an unfavorable event in financial markets, such as the announcement of a bank failure, generally causes reactions from investors, price changes, and higher market volatility. The present paper investigates the impact of three U.S. banks (Silicon Valley Bank, Signature Bank, and First Republic Bank) collapses on financial markets: price behavior and market volatility. We use intraday data to control and lessen the impact of confounding effects, two different event study approaches to check for the robustness of the results, several U.S. indices, and a digital asset to extend the financial markets. Our results suggest that the reactions of financial markets to the failure of these banks are mixed. The returns analyses indicate that the banks’ collapses result in higher market volatilities than positive news in the case of Bitcoin and Dow Jones Equity Real Estate Investment Trust Index. At the same time, the U.S. Dollar Index shows the reverse. The mixed results could be possibly due to the inconsequential market share of these banks in the United States banking sector. The major implication of the findings is that policymakers have to take serious actions, such as examinations of portfolio holdings, to safeguard financial stability and to prevent undesirable financial markets’ volatility.

Response of anchor investors to pre-IPO earnings management: evidence from an emerging market

PurposeThis study aims to examine the reaction of anchor investors (AIs) to pre-IPO earnings management (EM). The authors use the unique detailed bid data from the Indian anchor experiment. The authors also study the reputed AIs’ EM detection ability and pricing behavior in response to pre-IPO EM.Design/methodology/approachThe authors use unique AI bid data for 169 Indian IPO firms. Utilizing the logistic regression and Tobit regression models with industry and year-fixed effects, the authors examine the relationship between various measures of AI participation and proxies of short-term and long-term discretionary accruals.FindingsThe authors document that pre-IPO EM is positively associated with the likelihood of anchor backing but negatively related to the likelihood of reputed anchor backing. The findings indicate that AIs are misled by pre-IPO EM, but reputed AIs are not. The authors also observe that reputed AIs, compared to the non-reputed, pay less than the upper band with increasing EM. The findings are robust to using various AI measures and EM proxies.Practical implicationsThe findings have significant implications for regulators in the implementation of AI concept in non-anchor markets and better implementation of policies in existing anchor settings. Findings can also be relevant for non-institutional investors in the IPO domain.Originality/valueThis is one of the few studies on institutional investors' IPO bidding behavior in response to pre-IPO EM. However, this is the first study to analyze AIs' IPO bidding behavior in response to pre-IPO EM.

Valuation of Commodity-Linked Bond with Stochastic Convenience Yield, Stochastic Volatility, and Credit Risk in an Intensity-Based Model

In this study, we consider an intensity-based model for pricing a commodity-linked bond with credit risk. Recently, the pricing of a commodity-linked bond with credit risk under the structural model has been studied. We extend the result using an intensity-based model, stochastic volatility model, and stochastic convenience yield model. In the intensity-based model, the credit event by the counterparty occurs at the time of first jump in a stochastic Poisson process, in which intensity is modeled as the sum of two CIR prosesses. We assume that the underlying asset follows the stochastic volatility and convenience yield models. Using the measure change technique, we explicitly derive the commodity-linked bond pricing formula in the proposed model. As a result, we provide the explicit solution for the price of the commodity-linked bond with stochastic convenience yield, stochastic volatility, and credit risk as single integrations. In addition, we present several examples to demonstrate the effects of significant parameters on the value of commodity-linked bond using numerical integration. In particular, examples are provided, focusing on the behavior of prices based on effects of recovery rate.

A Critical and Comprehensive Study on Paddy Market Trends, Predictive Models, and Stability for Agricultural Revenue Enhancement

The way agricultural commodities' prices behave indicates how prices occasionally fluctuate between markets [1]. Comprehensive analysis provides valuable insights into the dynamics of paddy prices in Gujarat, emphasizing the importance of considering both short-term fluctuations and long-term trends. The findings contribute to a better understanding of market behaviour, aiding stakeholders in informed decision-making within the agricultural sector. In order to provide farmers with the knowledge of how much prices fluctuate over time in commodity markets and so increase their revenue, a ten-year study on the price behaviour of paddy was conducted inside the borders of the state of Gujarat (2009-2019). In order to gather statistics, three paddy markets in the state of Gujarat were specifically chosen based on the paddy arrivals that were highest. The trend diagram of the wholesale prices' yearly index number showed constant price fluctuations across the study period. Cuddy Dell Valle Index (CDI) was calculated to measure the instability in yearly and monthly prices. Based on the investigation, it was determined that paddy displayed a poor stability index within their respective marketplaces. The trend in paddy prices was measured using the linear and quadratic models. For paddy markets, the coefficient of multiple determination (R2) was greater than 70%. Thus, it can be said that a significant portion of the changes in paddy prices over the chosen study period can be attributed to the linear model. In keeping with the quadratic trend, the markets for paddy, or Ahmedabad and Gandhinagar, also showed a positive coefficient of the quadratic term, or T2. Comparing the quadratic and linear model for the markets of paddy the quadratic model performed better in Gandhinagar than the linear model did in Ahmedabad and Surat. Compound rate of increase in annual prices were analysed by exponential model. The rates of growth in the prices of Paddy in Ahmedabad, Surat, and Gandhinagar were 5.80 percent, 5.84 percent, and 4.68 percent, respectively. To separate seasonal variation from the original composite data, a multiplicative model was applied. To arrive at the twelve-month total of 1200 points, these ratios were averaged for each of the twelve months for the whole period. The Ahmedabad market's paddy produced the highest coefficient of seasonal variation (25.84 percent).

A micro-foundation of a simple financial model with finite-time singularity bubble and its agent-based simulation

This paper proposes a mathematical model of financial security prices in continuous time with bubbles in which prices may diverge and crash in finite time. Just before the bubbles burst, prices increase super-exponentially. In addition, a discrete-time excess demand model is proposed to provide a micro-foundation for the continuous-time model. The derived discrete-time security price model has the same characteristics as the continuous-time price model and expresses the finite-time singularity. Furthermore, based on the excess demand model, an agent-based simulation is performed to check the price behavior. As expected, we can confirm that prices can diverge in finite time and increase super-exponentially.

Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices

The growing share of renewables, the retirement of coal generation, and the increasing significance (and price) of carbon emissions continue to reshape electricity market dynamics. Ongoing power sector transformations, along with recurrent energy price shocks, bring new challenges to electricity price prediction and projection. Adept at capturing nonlinear and time-varying dynamics of the power market, machine learning (ML) methods can significantly enhance modeling and projection capabilities. This paper leverages ML modeling to (1) analyze the day-ahead electricity price dynamics and (2) develop future price projections under varying market conditions. First, we offer a methodology for identifying and ranking the factors determining day-ahead power price behavior. Guided by the understanding of power price drivers, we then develop an ensemble ML model for high-resolution (i.e., for each day of the next year) electricity price projection. Hence, the first part of our analysis helps relate fundamental modeling approaches to ML studies, and the second part extends the existing literature, which is limited to high aggregation level (e.g., yearly basis) projections. High-resolution projections provide valuable insights into within-year price dynamics and help answer highly debated questions raised by the growing share of renewables and the current energy market turmoil. We demonstrate our methodology on the German power market, using data on 80 explanatory variables from 2015 to 2021. We identify the most critical power price drivers and develop an ML model to study the within-year impact of high fuel and carbon prices. Our findings indicate that the often-neglected interaction variables may significantly impact price projections and should be included in day-ahead electricity price modeling. We confirm the efficacy of our approach with a forecasting exercise based on day-ahead electricity prices in the first two quarters of 2022.

A Comparison of Predictive Modelling Techniques for Reducing Price Volatality in the Malaysia Sector

Malaysia is one of the most important contributors in fisheries to the economy, as the country's GDP is rose to 12%. Hence, the fisheries price has always been an issue for Malaysian citizens. Recently, planning and decision-making in fisheries have been tough for the fisherman as they need to bear low prices despite the higher fishing cost. At the same time, the retailer receives higher fisheries prices. Thus, the fisheries prices have risen over time and are subjected to price fluctuation. This study aims to forecast the monthly price behaviour of many main varieties of fisheries that are commonly consumed by Malaysian using the method of ARIMA, MLFFN and hybrid ARIMA-MLFFN. The data used in this study is divided by two which in-sample data of 7 years from January 2010 to December 2018, while out-sample data of 2 years from January 2019 to December 2020 regards with the ex-vessel, wholesale and retail price of the fishery (Longtail tuna, Torpedo scad and Shortfin scad). Forecasts were made for the price of fisheries in the future for the next twelve months. According to the findings of this study, we concluded that all of three model: ARIMA, MLFFN and hybrid ARIMA-MLFFN are comparable models since 96% MAPE of out-sample forecast were less than 10%, which is highly accurate forecast.

To tip or not to tip? Explaining tipping behavior in restaurants with service-inclusive pricing

Tipping behavior is a vital way for waiting staff to enhance their wages, and for managers to monitor guest satisfaction. Despite its importance, there is not yet an established consensus on reasons why people tip. Our lack of understanding about tipping behavior is exacerbated by a strong reliance on studies conducted in countries that have a system of voluntary tipping (e.g., the United States). The study aims therefore at expanding our understanding of tipping behavior beyond voluntary tipping countries and more specifically explaining tipping behavior under service-inclusive pricing. Data obtained from 1458 guests in five European countries show that income and payment method are the strongest predictors of customers’ decision to tip, whereas bill size is the most robust predictor of tip amount. Results advance knowledge by suggesting that social norm theory plays a major role to understand tipping behavior in service-inclusive pricing.

Market Structure and Market Behavior Analysis of Anti-Monopoly of Platform Economy

Monopoly and anti-monopoly regulation in the field of platform economy has gradually become a hot and difficult issue of global concern. Based on the competitive monopoly market structure of the platform economy, algorithmic price discrimination, algorithmic price collusion, exclusive monopoly agreements and prohibitions, and other price and nonprice market behaviors, this paper conducts a case study on the relationship between the special market structure, market behavior, and anti-monopoly regulation in the field of the platform economy. The study finds that the market behavior of the Platform economy is relatively independent of the market structure. The market structure of digital platforms is effective, realizing the transformation of the Marshall Conflict. The focus of anti-monopoly regulation of the Platform economy should be on market behavior.

An Empirical Analysis of Forecasting Bitcoin and Gold Price Using ARIMA Model

The autoregressive integrated moving average (ARIMA) model is a widely used technique for capturing past dependencies and trends in order to generate future predictions. This study presents a comparative analysis of the ARIMA models forecasting capabilities as applied to gold and Bitcoin prices. The methodology employed consists of obtaining historical price data, implementing machine learning techniques, fitting the ARIMA model, then validating its predictive ability using multiple error metrics. Our results indicated that the optimal ARIMA parameters for Bitcoin and gold are different, which emphasizes their different price behaviors. Additionally, the study examined implications for policy, including issues such as prices for CPUs and GPUs, the role of market dynamics, as well as the possibility for price manipulation, which is of special relevance for cryptocurrencies that exist outside the mainstream. The study also suggests potential directions for future research, such as applying advanced machine-learning techniques and adopting cross-validation. This research offers important insights regarding Bitcoin and gold price dynamics and demonstrates the applicability of the ARIMA model for financial forecasting while demonstrating the necessity of further investigation into more refined predictive models.

The Ultra-short-, Short- and Short-medium- Term Impact of Covid-19 Pandemic on Bitcoin Prices

The global outbreak of the COVID-19 pandemic has led to significant disruptions in financial markets, including the virtual currency market. This study examines the impact of the pandemic on Bitcoin prices across ultra-short, short, and short-medium time scales. Using ARIMA modeling, this paper analyze the dynamic behavior of Bitcoin prices in response to the effects of the pandemic. This findings illustrate distinct patterns of behavior in different time frames. First of all, from the ultra-short term perspective, after the local outbreak of the epidemic in China, compared with the control group constructed by the model, the price of Bitcoin has a relatively obvious upward trend during this period. In the short term, the outbreak of the global epidemic has led to a setback in investor confidence in all kinds of financial assets, and Bitcoin is no exception, and it is also facing a sharp decline. In the short to medium term, Bitcoin has acted as a safe haven asset in the context of the global pandemic of Covid-19. Understanding these intricate dynamics provides valuable insights into Bitcoin's multifaceted role during times of global uncertainty.

Dynamic Competition, Price Frictions and Institution Building: the CMA in 2022–2023

This article discusses three projects of economic significance that the Competition and Markets Authority (CMA) completed in the last year: First, we discuss the dynamic competition concerns that were at the heart of the CMA’s analysis of the Meta/GIPHY merger; this was the first case of a competition authority’s blocking an acquisition by one of the ‘Big Tech’ firms. Second, our work on the road fuels market study examined pricing behaviour in an important consumer-facing market and found that competition had weakened because historic price leaders have been taking a less aggressive approach to pricing, Third, we established a new research centre—the Microeconomics Unit—to bring economic expertise on competition, innovation, and productivity closer to regulatory and policy decisions.