Price Movements Research Articles

Commodity prices and international Inflation, 1851–1913

This paper uses annual data to study the impact of commodity prices on consumer prices in 15 economies from 1851 to 1913. We calculate a simple measure of the common component of commodity prices which co-moves with the international business cycle and Granger causes consumer price inflation. Commodity prices are significant in standard inflation equations estimated by OLS in 14 of 15 economies. Estimating these equations using real shipping costs as an instrument suggests that commodity price movements associated with shifts in demand arising from international business cycles have a particularly large impact on inflation.

Factors affecting Stock Price Behaviour of Commercial Banks in Nepal Stock Exchange

This research investigates how factors such as Dividends Per Share (DPS), Earnings Per Share (EPS), Price-Earnings Ratio (PER), and Net Worth Per Share influence the Market Price Per Share of commercial banks in Nepal. The study analyzed the panel data from four commercial banks, consisting of 40 observations, to evaluate the correlation and influence of factors affecting stock price movements. In this study, secondary panel data covering ten years (2070/2071-2079/2080) has been used. Based on the results of this investigation, there is a significant positive correlation between the Market Price Per Share and the Dividends Per Share, Price-Earnings Ratio, and Net Worth Per Share but a negligible effect of Market Price Per Share and Earnings Per Share of commercial banks.

Deciphering crypto interconnections using a multi-analytical approach

Blockchain technology has primarily underpinned cryptocurrencies that are used either as speculative investment vehicles or for transaction facilitation. There has been a keen interest in understanding the dynamics of interconnectedness and conditional correlations among cryptocurrency prices. While most studies have primarily focused on Bitcoin or the top few cryptocurrencies, this study adopts a comprehensive, multi-analytical approach, incorporating other smaller cryptos that appeal to small and medium investors. Pearson correlational analysis explores the interconnectedness among cryptos and investigates co-movement in crypto prices through their returns, volatility, volume traded, and the CCi30 index returns. Principal Component Analysis (PCA) is used to identify highly correlated clusters, summarizing cross-sectional information based on covariance within the predictors. The predictive regression model of Granger Causality test is applied as a vector autoregression (VAR) forecasting method to examine Granger causality of price movements within the clusters identified. The findings from the correlational matrices of returns and volatilities show no difference in behaviours between larger and smaller cap ones, whereas correlations in trading volumes indicate high correlations in large market-caps. Smaller market-cap cryptos exhibit stronger correlations in volatilities than the larger market cap ones. Two highly correlated clusters emerged from the PCA analysis, with Binance Coin (BNB) and Ripple (XRP) exhibiting greater influence than Bitcoin (BTC) and Tether (USDT) in the second cluster. The findings will enable cryptocurrency users and investors to grasp price mechanisms better, offering valuable insights to improve their decision-making abilities.

What Drives US Import Price Inflation?

Inflation has risen sharply in many countries since the COVID-19 outbreak, and economists have debated the underlying causes. In this paper, we examine the drivers of the global import price inflation, which peaked at approximately 11 percent a year. We find that a common global component closely tracks movements in aggregate US import prices until late 2022. Afterward, idiosyncratic US demand shocks started to dominate.

An adaptive dual-level reinforcement learning approach for optimal trade execution

The purpose of this research is to devise a tactic that can closely track the daily cumulative volume-weighted average price (VWAP) using reinforcement learning while minimizing the deviation from the VWAP. Previous studies often choose a relatively short trading horizon to implement their models, making it difficult to accurately track the daily cumulative VWAP since the stock price movement is often insignificant within the short trading horizon. On the other hand, training reinforcement learning models directly over a longer, daily horizon is burdensome due to extensive sequence length. Hence, there is a need for a method that can divide the long daily horizon into smaller, more manageable segments. We propose a method that leverages the U-shaped pattern of intraday stock trade volumes and uses Proximal Policy Optimization (PPO) as the learning algorithm. Our method follows a dual-level approach: a Transformer model that captures the overall (global) distribution of daily volumes in a U-shape, and a LSTM model that handles the distribution of orders within smaller (local) time intervals. The results from our experiments suggest that this dual-level architecture improves cumulative VWAP tracking accuracy compared to previous reinforcement learning approaches. The key finding is that explicitly accounting for the U-shaped intraday volume pattern leads to better performance in approximating the cumulative daily VWAP. This has implications for developing trading strategies that need to efficiently track VWAP over a full trading day.

Do central banks respond to house price movements? A Bayesian DSGE approach

AbstractThis thesis explores whether monetary policy reacts to house price movements, by employing a New Keynesian model with a housing factor and estimated using Bayesian estimation techniques. The primary emphasis of this study is on the United States, with supplementary analyses on central banks in several advanced economies. The principal finding of this thesis reveals that the Federal Reserve responded substantially but episodically to house prices. Furthermore, my investigation indicates that the Reserve Bank of Australia, the Reserve Bank of New Zealand, Bank of England, and the European Central Bank did not incorporate house prices into their policy framework, whereas the Bank of Canada did so episodically. The baseline results remain robust across a range of sensitivity checks.

Ethics Perspective of Quality Accounting Information and Future Certainty: A Pattern of Rationally Perceived Decision

Purpose: This research tested the distinctive capability of detecting misleading information when real earnings have positively stimulated the smooth movement of the market price. Methodology/approach: This causal research used the primary data from the investor community in 2022, which amounted to 3.242 respondents for running SEM and endogeneity testing; FGD has been the fittest validation process for capturing the rationality models. Findings: High consistency of integrity-characteristic played a crucial role in designing a prudent accounting treatment which indicates the ethics code related to highly sustainable business during an uncertain future global economy. Inevitably, a measurement of trust in high earning quality pointed out that this quality measurement is to protect this investment decision in a highly secured, safe area. Practical implications: One centralized platform of financial reporting services should support the attractiveness of obedience and compliance as an incomparable advantage of positive earnings management. High-prospected firms should be assessed by high-quality accounting information; the delisting procedure has been prioritized firmly for low-prospect ones. Originality/value: This research explores the pattern of predictive simulation modeling as the existence of game theory, adapted from simplex-linear programming; this rationality model has strengthened as a fundamental requirement to illustrate high future certainty.

Stock Market Prediction and Visualisation

Abstract: Stock price forecasting is a widely discussed and significant topic in both financial and academic circles. The stock market is inherently unpredictable, lacking clear rules for estimating or predicting share prices. Various methods, including technical analysis, fundamental analysis, time series analysis, and statistical analysis, have been employed to forecast stock prices. However, none of these methods consistently serve as reliable prediction tools. In this paper, we explore the implementation, prediction, and analysis of stock market prices. Artificial Neural Networks and Machine Learning prove effective for forecasting stock prices, returns, and modeling stock behavior. By conducting statistical analyses, we establish relationships between selected factors and share prices, contributing to more accurate predictions. While the stock market remains inherently uncertain, this paper aims to apply data analysis and prediction concepts to forecast stock prices. In the era of global digitization, stock market prediction has undergone significant technological advancements, transforming traditional trading models. As market capitalization continues to rise, stock trading becomes a focal point for financial investors. Analysts and researchers have developed tools and techniques to predict stock price movements, aiding decision-making. Advanced models leverage non-traditional textual data from social platforms for market prediction. Machine learning approaches, including text data analytics and ensemble methods, have significantly improved prediction accuracy. However, analyzing and predicting stock markets remains challenging due to dynamic, erratic, and chaotic data. This study explores machine learning-based approaches for stock market prediction, emphasizing a generic framework. By critically analyzing findings from the last decade (2011–2021) from digital libraries like ACM and Scopus, we provide insights for emerging researchers to delve into this promising area.

리픽싱 전환사채 발행기업의 회계선택에 관한 사례연구 : 전환권 분류를 중심으로

[Purpose] The purpose of this study is to analyze through actual cases how a company’s accounting choices related to the right to convert refixing convertible bonds subsequently affect its financial statements. [Methodology] Among companies that issued convertible bonds with similar conditions at the same time, the entity classifies convertible bonds into derivative liabilities, capital, and derivative liabilities, respectively, subsequently reclassifies them into capital to confirm from a practical point of view how the accounting treatment of convertible bonds affects financial statements. In particular, considering that the refixing clause is linked to the stock price of the issuing companies, we consider whether the stock price movement was similar when selecting the sample companies to show that it is not an unusual case that appears only in extreme samples. [Findings] If the conversion right is classified as equity, changes in the share price and subsequent accounting for the conversion right do not affect the financial statements, but if it is classified as a liability, it can have a material impact on the statement of comprehensive income, and the impact increases with the size of the convertible bond issued and the change in share price. We also found that the accounting choice for the conversion right can be changed at the discretion of the companies, but it is confirmed that related disclosures is very insufficient. [Implications] The case of this study confirm that accounting for items that are not clearly presented in K－IFRS can cause confusion in the capital market if the accounting treatment is not clearly defined by the relevant authorities. It also suggests that political efforts should be made to ensure that the IASB’s discussion process for revising IAS 32 takes into account the situation in Korea.

Pengaruh Pengungkapan Corporate Social Responsibility, Profitabilitas, Leverage, dan Ukuran Perusahaan terhadap Nilai Perusahaan

The aim of this research is to obtain empirical evidence on how corporate social responsibility (CSR) disclosure, profitability, leverage, and firm size influence firm value. Firm value is an indicator that shows company performance through stock price movements. This study was conducted on SRI-KEHATI indexed companies during the 2020-2022 period. The data collection method uses non-participant observation. The sampling method uses non-probability sampling with purposive technique. The collected data was analyzed using multiple linear regression. The research results show that CSR disclosure has no influence on firm value, profitability and leverage have a positive influence on firm value, and firm size has a negative influence on firm value

Macroeconomic Environment and Stock Price Movement in Nigeria: An Evaluation of Fama’s Model of Efficient Market

The study examined the link between macroeconomic environmental dynamics and stock price movements in Nigeria using Fama’s model of efficient market approach. The macroeconomic climate posed issues for Nigerian stock markets. The buying power of local currency occasionally impacted unfavourable changes in the majority of macroeconomic variables. In this study, an ex post facto research design was used. The study looked at certain macroeconomic variables and the evolution of Nigerian stock prices between 1985 and 2021. To test the hypothesis, quantile regression estimates of the Fama-French three-factor coefficient were derived. The findings showed that the interest rate and stock price movement have a negative and negligible association. Additionally, a strong and positive correlation was observed between the exchange rate and changes in stock prices. It was recommended that in order to promote stock market activity and, by extension, enhance stock prices in Nigeria, a suitable monetary policy and a sufficient amount of money supply must be implemented. Also, well-diversified portfolios that mirror the entire market portfolio should be recommended to investors to protect themselves against stock price volatility. This would serve as a guide to protect them against shocks resulting from the macroeconomic environment.

Nexus between carbon, stock, and energy markets in New Zealand: An analysis of causal domains

In the context of Industry 4.0 and the urgent need for a decarbonization transition, the Emissions Trading Scheme (ETS) emerges as a vital and cost-effective tool for promoting global decarbonization. While the connections between the ETS, stock, and the energy markets are theoretically strong, the nature of their causality has been a subject of debate in existing research, especially in New Zealand (NZ). Therefore, we quantitatively explore the potential dynamic linkages within the “Carbon-Stock-Energy” system using more advanced techniques, such as nonparametric Granger causality-in-quantile and cross-quantilogram techniques. Our findings attest to a bidirectional causality between the ETS and the markets examined in the short run. The foreseeability of all lag lengths is, moreover, positive in a bullish market context. Thus, price movements in the NZ ETS could contribute to the volatility of price variations in the stock and the energy markets. Fundamentally, the outcomes of the freshly devised regime-dependent portfolio strategy call for strategic investors to switch their portfolios adaptively between short-term and long-term positions in the ETS. In short, the outcomes may give a deeper glimpse into the complex interconnections between the ETS, stock, and the energy markets in NZ.

Stock Market Prediction Using Machine Learning Techniques

The prediction of stock market prices holds significant importance within the contemporary economic landscape. Consequently, there has been a notable surge in scholarly interest directed towards exploring novel avenues for enhancing stock market prediction capabilities. Recent research endeavors have illuminated the potential predictive value inherent in various data streams, including historical stock data and user-generated content sourced from platforms such as Twitter and web news. These investigations have revealed a discernible relationship between social mood, as reflected in online discourse, and future stock price movements. However, prior studies have often overlooked the incorporation of such sentiment-derived information, thus presenting an information gap. In the present study, we address this gap by proposing an effective methodology for the integration and analysis of multiple information sources to facilitate more accurate stock price predictions. Leveraging Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) models, we conduct a comprehensive analysis of disparate data sources. Furthermore, we employ an ensemble approach, incorporating Weighted Average and Differential Evolution techniques, to refine the predictive accuracy of our models. Our findings demonstrate the efficacy of the proposed methodology in generating highly accurate stock price predictions across varying future time horizons, including one-day, seven-day, 15-day, and 30-day intervals. These predictions offer valuable insights for investors seeking to make informed decisions regarding their investment strategies and enable companies to gauge their anticipated performance within the stock market landscape.

MF-DAT: a stock trend prediction of the double-graph attention network based on multisource information fusion

Stock forecasting research, which aims to predict the future price movement of stocks, has been the focus of investors and scholars. This is important for practical applications related to human-centric computing and information sciences. Previous research has generally only considered market information other than the relationship between stocks, and it is challenging to learn a better representation of stock characteristics by considering the relationship between stocks. In the existing methods of combining market information with stock relationship modeling, most of them use predefined industry relationships to construct stock relationship diagrams, which inevitably ignores the potential interactions between stocks, especially the hidden relationships between stock groups. To this end, a new dual-graph attention model (MF-DAT) based on multisource information fusion is designed. Specifically, first, multiple features are fused by the LMF module, then the long-term and short-term state characteristics of stocks are learned through the first layer of the graph attention layer, and finally the node representation of the stock relationship network constructed by the mining stock cluster structure through community detection is updated. Our model takes into account both stock time-series information and potential relationships between stocks. Experiments on the S &P 500 and NASDAQ datasets show that our MF-DAT has better performance than the 8 SOTA methods that are now more popular.

Statistical Analysis on Turmeric Production and Price Movement in Tamil Nadu

Since ancient times, agriculture has been one of the chief factors in the advancement of many nations. Also, it has been a huge contributor to the GDP (Gross Domestic Product). But the farmers and the small-scale retailers are still unaware of the demand and supply of the commodities in mandis. Ultimately, they are compelled to use the repetitive pattern of crops. Through knowledge of the intra-mandi prices for seasonal crops is necessary to gain the desired profit. Price trends in several Tamilnadu markets were investigated for Turmeric in order to forecast price volatility and problems that produce mandi-level projections, enhancing its usefulness. Given that medicinal crops have various medical and culinary uses, boosting their productivity could increase farmers' profits in comparison to typical pattern crops. So, the aim of this study is to evaluate the optimum preferred crop conditions for Turmeric with respect to profit. A data from the past decade was screened to observe the variation in prices of the crops along with production analysis. Also, it was validated statistically by various statistical measures like Standard Deviation, Mean and Coefficient of Variation. We examined intra-mandi prices and found that prices fluctuate over the whole peak season, with notable fluctuations in modal prices among several mandis. To demonstrate the variations in pricing reported for certain mandis on specific days, we've given a few instances. A low standard deviation implies that the commodities price is clustered around the mean, whereas a large standard deviation shows that the commodities price is more dispersed. Price volatility is the discrepancy between what is anticipated and what occurs. Keywords: Agriculture, Turmeric, Marginal farmers, Price Movement

Extreme weather, climate risk, and the lead–lag role of carbon

This study employs the thermal optimal path method to establish a framework for dynamic nonlinear connections between Chinese carbon and foreign exchange markets. Subsequently, it examines the effects of extreme weather events on the lead–lag role played by carbon. The empirical results indicate that China's carbon market typically lags behind its currency exchange market. Compared to the Hubei carbon market, the Guangdong carbon market experiences synchronized price movements between carbon and foreign exchange due to high pricing efficiency. Furthermore, shocks from extreme weather events can attract public attention to the carbon market and cause the typical lead–lag structure to reverse, whereupon the carbon market leads the foreign exchange market under such shocks, especially during heat waves. Our findings have implications for investors aiming for positive cumulative returns on hedging portfolios and policymakers wishing to bolster the financial market's ability to withstand exogenous shocks.

Crude oil price volatility—An unintended consequence of carbon pricing: Evidence from transfer entropy and wavelet-partial wavelet coherence analyses

The urgent need to achieve the COP-27 targets has become evident due to the more frequent and severe climate-induced disasters and their socioeconomic consequences. The adverse effects of climate change highlight the need for an immediate shift away from fossil fuels without compromising economic development. Internalization of the negative externality in market transactions through the imposition of carbon pricing is widely touted as the most economically efficient means of solving this problem. This study, however, argues that crude oil price volatility could be an unintended consequence of carbon pricing. To this end, information flows between the European Union Emissions Trading Scheme and crude oil price volatility are examined through transfer entropy and wavelet-partial wavelet coherence analyses. Daily data from January 1, 2014 to July 1, 2023 are analyzed. The transfer entropy results show that information on carbon pricing reduces uncertainty about crude oil price volatility and vice versa, indicating that carbon pricing would be quite informative in building models to predict crude oil price volatility. The wavelet-partial coherence analyses reveal that the surge in carbon prices experienced in the late 2010s induced crude oil volatility, whereas the crude oil price volatility triggered by the COVID-19 pandemic forced carbon prices down. This study therefore identifies carbon price movements as a legitimate fear for policymakers, as it is a new source of volatility in conventional energy markets. Caution should thus be the watchword regarding optimal carbon pricing. Aiming to rapidly attain the full optimal carbon price is not recommended. Rapid changes in carbon prices will have strong redistributive implications across economies.

A Quantitative ABCD Analysis of Factors Driving Share Price Volatility in the Indian Pharmaceutical Sector

Purpose: This research employs an innovative ABCD (Advantages, Benefits, Constraints, and Disadvantages) analysis framework to intricately explore and evaluate the diverse factors influencing share price volatility in the Indian Pharmaceutical Sector. Delving into both individual and systemic characteristics, the study quantitatively assesses the effectiveness of various elements impacting share prices. Each component related to share price movements is meticulously assigned a score or weightage, providing a nuanced understanding of their respective contributions. Design: The researcher utilized focused group interviews to collect participants’ viewpoints on the factors influencing share price dynamics in the Indian pharmaceutical industry. Opinions were obtained through designated measurements, and the collected data underwent subsequent quantitative analysis. Findings: In the specific context of the present study, which focuses on the quantitative ABCD analysis of factors driving share price fluctuations in the Indian pharma industry, the research findings underscore the notable impact of advantageous factors on volatility. The outcome of the ABCD analysis substantiates the effectiveness of the concepts/ideas related to Factors Driving Share Price Volatility in the Indian Pharmaceutical Sector. Originality: This research introduces a novel application of the ABCD analytical paradigm to investigate factors influencing variations in the pricing dynamics of shares in the Indian pharmaceutical industry. While ABCD analysis is prevalent in diverse areas, this study pioneers a unique exploration within the pharmaceutical domain. By identifying and evaluating the crucial elements influencing share prices, this work contributes to the originality of studies in the field, presenting a distinctive approach to understanding the dynamics of the Indian pharmaceutical market. Type of Paper: Empirical Analysis.

The effect of the COVID-19 pandemic on TOM in the housing market

Purpose The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations. Design/methodology/approach Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opted to use Poisson regression as an alternative approach. Additionally, recognizing the possibility of overdispersion in the count data, we also explored the application of the negative binomial model as a means to address this concern, if present. Findings This study’s findings offer an insightful perspective on the housing market’s resilience in the face of COVID-19 external shock, aligning with previous research outcomes. Although TOM showed a decrease of around 10 days with standard regression and 27% with Poisson regression during the COVID-19 pandemic, it is noteworthy that this reduction lacked statistical significance in both models. As such, the impact of COVID-19 on TOM, and consequently on the housing market, appears less dramatic than initially anticipated. Originality/value This research deepens our understanding of the complex lead–lag relationships between key factors, ultimately facilitating an early indication of housing price movements. It extends the existing literature by scrutinizing the impact of the COVID-19 pandemic on the TOM. From a pragmatic viewpoint, this research carries valuable implications for real estate professionals and policymakers. It equips them with the tools to assess the prevailing conditions of the real estate market and to prepare for potential shifts in market dynamics. Specifically, both investors and policymakers are urged to remain vigilant in monitoring changes in the inventory of houses for sale. This vigilant approach can serve as an early warning system for upcoming market changes, helping stakeholders make well-informed decisions.

Deep learning-based spatial-temporal graph neural networks for price movement classification in crude oil and precious metal markets

In this study, we adapt three spatial-temporal graph neural network models to the unique characteristics of crude oil, gold, and silver markets for forecasting purposes. It aims to be the first to (i) explore the potential of spatial-temporal graph neural networks family for price forecasting of these markets, (ii) examine the role of attention mechanism in improving forecasting accuracy, and (iii) integrate various sources of predictors for better performance. Specifically, we present three distinct models: Multivariate Time Series Graph Neural Networks with Temporal Attention and Learnable Adjacency matrix (MTGNN-TAttLA), Spatial Attention Graph with Temporal Convolutional Networks (SAG-TCN), and Attention-based Spatial-Temporal Graph Convolutional Networks (ASTGCN), to capture the intricate interplay of spatial and temporal dependencies within crude oil and precious metals markets. Moreover, the effectiveness of the attention mechanism in improving models' accuracies is shown. Our empirical results reveal remarkable prediction accuracy, with all three models outperforming conventional deep learning methods such as Temporal Convolutional Networks (TCN), long short-term memory networks (LSTM) and convolutional neural networks (CNN). The MTGNN-TAttLA model, enriched with a temporal attention mechanism, exhibits exceptional performance in predicting the direction of price movement in the WTI, Brent, and silver markets, while ASTGCN is the best-performing model for the gold market. Additionally, we observed that incorporating technical indicators from the crude oil and precious metal markets into the graph structure has improved the classification accuracy of spatial-temporal graph neural networks.