Price Behavior Research Articles

Neural networks for estimating Macro Asset Pricing model in football clubs

SummaryThe recent crisis caused by COVID‐19 directly affected consumption habits and the stability sof financial markets. In particular, the football industry has been hit hard by this pandemic and therefore has more volatile stock prices. Given this new scenario, further research is needed to accurately estimate the value of the shares of football clubs. In this paper, we estimate an asset pricing model in football clubs with different compositions of risk nature using non‐linear techniques of artificial neural networks. Usually, asset pricing models have been estimated with linear methods such as ordinary least squares. Our results show a precision higher than 90% for all the estimated models, which far exceeds those shown by linear methods in the previous literature. We find that the residual represents about 40% of the variance of the price‐dividend ratio. Long‐term risks follow in importance, and above all, the habit component and its behaviour in the face of changes. The importance of the residual component exists due to a low correlation between the asset price and consumer behaviour, but to a much lesser extent than that shown in previous studies. The estimation carried out with artificial neural networks, both the Deep Learning methods and especially the Quantum Neural Network, opens up new possibilities to estimate more efficiently the pricing of financial assets in the football industry.

Determinants of Cryptocurrency: An Analysis of Volatility and Risk-Return Trade-Off

As an investor, volatility plays an important role in decision making. It is defined as the rate at which a security’s price increases or decreases, i.e., shows pricing behavior during a definite span of time. A high volatility will lead to high risk. Thus, it becomes critical to determine the volatility and the risk-return trade-off among investments. This paper tries to document the volatility and risk-return trade-off of four prominent crypto-currencies (Bitcoin, Ethereum, Binance and Ripple), based on market-capitalization. For analysis, closing prices of cryptocurrencies has been accumulated through secondary method for 365 days, starting from 1st March 2022 and ending on 28th February 2023. Standard Deviation and Kurtosis, used together for volatility and risk assessment, documented that Bitcoin has the highest volatility and risk associated with expected returns. Regression, for assessing the impact of volatility in BTC price on others, derived that ETH has a strong, but not very strong, bivariate relationship with BTC, among all the pairs. Durbin Watson (DW) test concluded that there was no auto-correlation in the prices of crypto-currencies, i.e., previous day’s price does not play significant role in today’s price. For risk-return trade-off, Coefficient of Variation (CoV) has been applied. It determined that Ethereum has the highest ratio indicating its non-suitability to a conservative investor because of having the lowest returns as compared to risks involved; while Binance has the lowest Coefficient of Variation (CoV) depicting lower risk and maximum return among all.

Do Bubbles in the Bitcoin Market Impact Stock Markets? Evidence From 10 Major Stock Markets

In this paper, we examine the effect of explosive behaviors in the Bitcoin market on the top 10 largest stock markets of developed and emerging countries. The daily dataset, including the Dow Jones Industrial Index (DJIA), Nasdaq (NSQ), Shanghai Composite Index (SSE), Nikkei 225 (N225), Hang Seng Index (HSI), Shenzhen Composite Index (SZSE), Euronext Amsterdam Index (AEX), London Stock Exchange (LSE), Toronto Stock Exchange (TSX), and Bombay Stock Exchange (BSE), spans July 21, 2010, to December 9, 2022. We first investigate the existence of explosive price behaviors using the bubble detection test of Phillips and Shi and the results provide evidence of multiple bubble episodes, coinciding with the monetary policy actions of the FED and ECB. Then, we address the question of whether the explosive behaviors detected affect the variance of equity returns by employing a GARCH model. The impact is negative, albeit its magnitude and significance vary among stock indices.

Housing prices during the COVID-19 pandemic: insights from Sweden

The COVID-19 pandemic triggered a major global economic recession, to which policymakers around the world responded with massive fiscal and monetary support. While housing prices generally fall during economic downturns, they have risen during the pandemic in all OECD countries. A number of factors may have contributed, including expansionary monetary policy, the lifting of some macro-prudential constraints and a shift in housing preferences. This paper uses monthly data to examine the behaviour of real house and flat prices during the pandemic in Sweden, at the national level and in the three biggest cities. While a model containing usual determinants of housing prices tracked price developments well before the pandemic, it underestimates house prices and generally overestimates flat prices in the pandemic period. This suggests a preference shift from flats towards houses, which is consistent with findings from the recent literature on other countries. The introduction of mortgage amortisation requirements in 2016 and 2018 is estimated to have lowered housing prices. However, their lifting during the pandemic seems to have had a relatively minor effect on housing prices.

Market Arrival and Price Behaviour of Potato in Tripura, India

"The study is undertaken in Tripura on a macro framework based on secondary data. There are eight districts in Tripura and potato is cultivated in all districts. Two districts viz., West Tripura and South Tripura districts are selected purposively based on higher and lower production. Accordingly, an effort is made in this study to examine the behaviour of arrivals and prices of potato in Tripura. Production of potato and the seasonality in production are the two important factors affecting the price. It observes that the area of potato has increased at the rate of 3.14 percent and 2.04 percent per annum in West and South Tripura districts respectively. Similarly,

Numerical solution of [formula omitted]-Hilfer fractional Black–Scholes equations via space–time spectral collocation method

Trivially, the time-fractional Black–Scholes (FBS) equation is utilized to describe the behavior of the option pricing in financial markets. This work is intended as an attempt to introduce the ψ-Hilfer fractional Black–Scholes (ψ-HFBS) equation. First, we concentrate on demonstrating the existence of the solution to the ψ-HFBS equations. Second, a numerical scheme is presented for solving the equation given the appropriate initial and boundary conditions. The approximate solutions are considered as linear combinations of the Lagrange functions with unidentified coefficients. By collocating the considered equation together with the boundary and initial conditions at Chebyshev-Gauss-Lobato (CGL) points, it will be converted to a system of linear algebraic equations. Next, we have proved the convergence of the approach. Finally, some test problems are given in order to indicate the suggested method.

Multi-layer energy management of smart integrated-energy microgrid systems considering generation and demand-side flexibility

This paper proposes a stochastic framework for the operation scheduling of integrated renewable-based energy microgrid systems. The proposed model presents comprehensive scheduling that simultaneously considers total generation costs, generation flexibility, and demand-side flexibility. This operation management approach is modeled as the tri-layer framework. At the first layer, the microgrid system attempts to minimize daily operation costs considering the probabilistic behavior of renewable generation, signal prices, and loads. The desalination unit and water tank storage have been incorporated into the proposed structure to supply potable water for the system. The second layer reschedules the obtained management of the first layer to increase the thermal flexibility and electrical flexibility of local generation resources. To this end, the integrated energy system tries to maximize the spinning reserve of the local energy resources in the second layer. The last layer is responsible to increase demand-side flexibility. In this layer, a hybrid max–min and min–max approach is developed to uniform the load profile by demand-side management programs. The proposed framework is applied to the general structure of energy systems and the day-ahead results demonstrate that the electrical generating flexibility index and thermal generating flexibility index are improved by 22.98% and 34.64% in the proposed model.

Ibovespa’s response to the behavior of oil and ore prices during the international crisis caused by COVID-19

The systemic risk caused by COVID-19 affected all sectors of the economy, thus showing the vulnerability of some sectors in comparison to others. In this context, the supply shock experienced by the iron ore sector has drawn attention and resulted in a price increase. Linked to this, and in a negative way, oil prices fell due, among other factors, to the price war between producing countries.In this sense, this study analyses the volatility of the Brazilian stock market indicator in relation to the prices of the aforementioned products and the price of the dollar. The results show the importance of the price formation in these markets for the variation of the indicator. The appreciation of Brent oil and iron ore prices on the Dalian Commodity Exchange (DCE), in China, caused the Ibovespa indicator to move in the same direction. In addition, in statistical terms, the study highlights the great importance of the exchange rate as a determinant in the variation of the indicator and, consequently, affecting the intention to invest.

Agricultural Commodities in the Context of the Russia-Ukraine War: Evidence from Corn, Wheat, Barley, and Sunflower Oil

The Russian invasion of Ukraine on 24 February 2022 accelerated agricultural commodity prices and raised food insecurities worldwide. Ukraine and Russia are the leading global suppliers of wheat, corn, barley and sunflower oil. For this purpose, we investigated the relationship among these four agricultural commodities and, at the same time, predicted their future performance. The series covers the period from 1 January 1990 to 1 August 2022, based on monthly frequencies. The VAR impulse response function, variance decomposition, Granger Causality Test and vector error correction model were used to analyze relationships between variables. The results indicate that corn prices are an integral part of price changes in wheat, barley and sunflower oil. Wheat prices are also essential but with a weaker influence than that of corn. The additional purpose of this study was to forecast their price changes ten months ahead. The Vector Autoregressive (VAR) and Vector Error Correction (VECM) fanchart estimates an average price decline in corn, wheat, barley and sunflower oil in the range of 10%. From a policy perspective, the findings provide reliable signals for countries exposed to food insecurities and inflationary risk. Recognizing the limitations that predictions maintain, the results provide modest signals for relevant agencies, international regulatory authorities, retailers and low-income countries. Moreover, stakeholders can become informed about their price behavior and the causal relationship they hold with each other.

Modeling Price and Variance Jump Clustering Using the Marked Hawkes Process

Abstract We examine the clustering behavior of price and variance jumps using high-frequency data, modeled as a marked Hawkes process (MHP) embedded in a bivariate jump-diffusion model with intraday periodic effects. We find that the jumps of both individual stocks and a broad index exhibit self-exciting behavior. The three dimensions of the model, namely positive price jumps, negative price jumps, and variance jumps, impact one another in an asymmetric fashion. We estimate model parameters using Bayesian inference by Markov Chain Monte Carlo, and find that the inclusion of the jump parameters improves the fit of the model. When we quantify the jump intensity and study the characteristics of jump clusters, we find that in high-frequency settings, jump clustering can last between 2.5 and 6 hours on average. We also find that the MHP generally outperforms other models in terms of reproducing two cluster-related characteristics found in the actual data.

Seasonality Analysis of Onion with Special Reference to Wholesale Markets of Gujarat

The study was conducted about the wholesale markets of Gujarat state with a study period of seventeen years for onion crops (January 2004 to December 2020). These data were collected from various portals such as NHRDF, Agmarknet, and NHB. The data collected were subjected to statistical analysis using different software such as R and E-views. Twelve monthly moving average was used to analyze the seasonal behaviour in arrivals and prices of onions. Seasonal indices of onion explained onion arrivals were higher in the first half of the year but relatively declined during the second half of the year in the Mahuva, Ahmedabad and Gondal markets. The variation between the lowest and highest intra-year price rise aligned between the lowest (11.79 per cent) in the Mahuva market to the highest (71.08 per cent) in the Gondal market for onion. The maximum value of the coefficient of variation (CV) for onion was witnessed in Gondal (92.94 per cent) market, followed by Ahmedabad (82.78 per cent) and Mahuva (82.16 per cent). Proper transmission of price signals from one market to another assist in price stabilization and foster healthy competition. It would also aid in defending the interests of producer sellers.

Price Behaviour of Coconut and Coconut Products in Markets of Kerala: An Economic Analysis

The present study examined the price behaviour of coconut and coconut products, namely copra and coconut oil, in Kerala in the pre- and post-liberalisation periods. The overall price of all the products showed an increasing trend and variability over the years. The seasonal and cyclical nature of coconut production was evident in both periods, with coconut prices showing pronounced irregular variations compared to coconut oil and copra. The study found that the intra-annual volatilities in the prices of copra and coconut increased, while it decreased for coconut oil. Also, after the trade reforms of 1995, there was an overall decrease in the instability of prices. Price instability can be effectively managed as well as reduced, not only by increasing the production of coconut but also by diversifying it into different products. The provision and use of timely market intelligence would aid the farmers in overcoming the implications of price fluctuations in coconut.

Service Quality, Price and Consumer Switching Behaviour in The Nigerian Telecommunication Industry, Moderating Effect of Value Added Services on the Relationship

The main objective of this study is to examine the Determinants of consumer switching behaviour in Nigerian telecommunication industry, the moderating role of value added services. Data were collected from the students of tertiary institutions in Borno state, North eastern Nigeria using a cross-sectional study design. The study adopted Kriejcie and Morgan (1970) rule of thumb and arrived at 382 respondents and questionnaires were distributed and collected through the personally-administered method. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to test the study hypothesis. This study finds that Value Added Services have significant influence on consumer switching behaviour, while Service Quality and Price have no significant influence on consumer switching behaviour, it also finds that value added services does not moderate the relationship between service quality, price and consumer switching behaviour. The results of this study provide important insights to managers, mobile operators and researchers to further understand the factors that influence consumers to switch from one service provider to another in the Nigerian telecommunication industry. Mobile operators should concentrate on factors like value added services so as to attract more customers to their network.

The ethanol market in Brazil: price behavior and regional differences

AbstractThe conditions facing the Brazilian ethanol market have created a need to improve the industry's understanding of price formation, and to analyze the effects of mandatory policies and the interaction between Brazil's ethanol, agricultural, and fuel markets. In this context, this study sought to analyze the behavior of ethanol production and consumption at the national and regional levels in Brazil from 2012 to 2016; hence, the supply and demand functions are estimated at national and at regional levels using the two‐stage least squares method. Our findings indicated that gasoline and anhydrous ethanol blended gasoline were substitutes for hydrous ethanol on the demand side in Brazil. At the national level, the estimates also showed that the income elasticity of hydrous ethanol characterizes it as a normal good. At the regional level, the regions with the highest production and consumption (southeastern and central‐western Brazil) responded to price variations less elastically than the other regions (southern, northeastern, and northern Brazil).

Economic scheduling of virtual power plant in day-ahead and real-time markets considering uncertainties in electrical parameters

The virtual power plant (VPP) concept, which offers energy efficiency, benefits significantly from the tremendous potential of integrating distributed energy resources (DERs). This work proposes optimal economic dispatch of virtual power plant (VPP), and the problem is formulated in a novel way considering the potential of energy storage systems (ESS) of electric vehicles (EVs) and data center (DC) in a data center facility test model using optimized energy management system (EMS). The problem is evaluated for maximizing the revenue for VPP considering market prices and the uncertainties in the distributed energy resources. To this purpose, a techno-economic analysis of the proposed test system of VPP is evaluated using mixed-integer linear programming (MILP). Renewable generation, electricity price, load demand, and real-time EV behavior uncertainty analysis are forecasted using Long Short-Term Memory (LSTM) along with DC battery degradation analysis which constitutes the optimization model. The results showed that the VPP EMS can perform optimal power scheduling with the day ahead and real-time electricity price scenarios. Moreover, further analysis including battery degradation cost of DC UPS ESS proved the capability of the proposed framework by up to 2064.4 $/h revenue for day-ahead price and 562 $/h revenue for the real-time price compared to revenue without battery degradation cost. A simulation scenario is also carried out where the constant load demand and the battery storage powers are varied between 50% and 150%. It can be concluded that the 500 kW and 750 kW ESS (100% and 150%) are better choices as compared to 250 kW (50%) during increased load demand for investors due to their higher revenue. Hence it is concluded that this research highlights the importance of revenue benefits from deploying VPP with the collaborative operation of DERs.

Predict Gold Yields with ARIMA and LSTM

The volatility of the gold price has a profound effect on many financial activities around the world. Developing a reliable forecasting model can provide insight into the volatility, behavior and dynamics of the gold price and can ultimately provide the opportunity to make significant profits. Common forecasting models fall into two categories: classical linear models and deep learning models using neural networks. In this paper, the ARIMA model and the LSTM model are selected as representatives of the two types mentioned above to forecast the gold return on May 1, 2020.The experimental results show that the 90% confidence interval of ARIMA includes the actual gold return, but there is a substantial discrepancy between the point forecast and the gold return rate itself. And the prediction accuracy of the LSTM model depends heavily on the data size of the training set. Finally, by comparing the forecasting results with the actual results, this paper analyzes the shortcomings of each model and proposes improvement directions for future gold return rate forecasting research.

Witching days and abnormal profits in the us stock market

This paper examines price effects related to witching days in the US stock market using both weekly and daily data for three major indices, namely the Dow Jones, S&P500 and Nasdaq, over the period 2000–2021. First it analyses whether or not anomalies in price behaviour arise from witching by using various parametric (Student’s t-test, and ANOVA) and non-parametric (Mann-Whitney) tests as well as an event study method and regressions with dummies; then it investigates whether or not any detected anomalies give rise to profit opportunities by applying a trading simulation approach. The results suggest the presence of the anomaly in daily returns on witching days which can be exploited by means of suitably designed trading strategies to earn abnormal profits, especially in the case of the Nasdaq index. Such evidence is inconsistent with the Efficient Market Hypothesis (EMH).

Reward Shaping-Based Actor–Critic Deep Reinforcement Learning for Residential Energy Management

Residential energy consumption continues to climb steadily, requiring intelligent energy management strategies to reduce power system pressures and residential electricity bills. However, it is challenging to design such strategies due to the random nature of electricity pricing, appliance demand, and user behavior. This article presents a novel reward shaping (RS)-based actor–critic deep reinforcement learning (ACDRL) algorithm to manage the residential energy consumption profile with limited information about the uncertain factors. Specifically, the interaction between the energy management center and various residential loads is modeled as a Markov decision process that provides a fundamental mathematical framework to represent the decision-making in situations where outcomes are partially random and partially influenced by the decision-maker control signals, in which the key elements containing the agent, environment, state, action, and reward are carefully designed, and the electricity price is considered as a stochastic variable. An RS-ACDRL algorithm is then developed, incorporating both the actor and critic network and an RS mechanism, to learn the optimal energy consumption schedules. Several case studies involving real-world data are conducted to evaluate the performance of the proposed algorithm. Numerical results demonstrate that the proposed algorithm outperforms state-of-the-art RL methods in terms of learning speed, solution optimality, and cost reduction.

Price behavior of small-cap stocks and momentum: A study using principal component momentum

This study analyzes why the negative momentum effect appears in Asian (China, Japan, Korea) stock markets, contrary to the U.S. market. We use principal component momentum (PMOM), a newly devised momentum measure. The PMOM is constructed by extracting commonalities from traditional momentum measures using principal component analysis. The results show evidence of positive and negative momentum profits in the U.S. and Asian markets, respectively. Negative momentum profits in Asian markets are attributable to the strong performance reversal of small stocks in the loser portfolio. Conversely, the positive momentum profits of the U.S. market are driven by the performance continuity of small stocks in the winner portfolio. The PMOM strategy is significantly more advantageous than traditional momentum strategies, based on the economic and statistical perspectives of momentum profits. These results are robust to changes in empirical designs.

Price Dynamics and Integration in India’s Staple Food Commodities—Evidence from Wholesale and Retail Rice and Wheat Markets

Uncertain price movement in staple food commodities puts agrarian economies at risk if not monitored and managed consistently. Hence, an attempt has been made to analyze the price behavior and integration across major wholesale and retail markets for rice and wheat in India. Monthly data (July 2000 to June 2022) on prices viz. wholesale and retail were sourced from the Food and Agriculture Organization and analyzed using growth rate, instability index, seasonal price index, Bai-Perron’s test for structural breaks, Johansen’s test on cointegration, Granger causality test, and impulse response function. Findings indicated strong evidence of price dynamics in the selected markets in terms of spatial and temporal variation, clear-cut seasonality linking to production, and price divergence between wholesale and retail markets. Johansen’s test indicated a strong cointegration between wholesale and retail prices after accounting for structural breaks, exhibiting unidirectional-, bidirectional- and no causality. Impulse response analysis revealed that the selected wheat and rice markets are efficient in terms of ‘price discovery’ which takes place initially in the wholesale market, and is then transmitted to the retail market. The study advocates decision-making information to the producers, traders, and consumers who are interested in taking advantage of the price movement. It is concluded that strengthening the market intelligence and reducing the distortion in markets will improve the existing overall performance.