Efficient Market Research Articles

Analysis of low-carbon technology transfer strategies based on a quadrilateral evolutionary game

Promoting low-carbon technology transfer is crucial for reducing global greenhouse gas emissions, combating climate change, and fostering sustainable development. Based on the “efficient market” and “active government” concepts, a “government-intermediary-receiver-sender” game model was constructed, identifying evolutionary stable strategies and key factors affecting strategy selection. It was found that (1) receiver and sender needs and government policy incentives increase intermediaries' willingness to participate, reducing input costs and shifting government support from incentives to compensatory regulations; (2) optimal strategies emerge when incentives for low-carbon technology senders, receivers, and intermediaries, and penalties for serious defaults by senders and receivers exceed a threshold, fostering high participation willingness and leading to government implementation of compensatory regulations; (3) differentiated penalties are necessary to avoid a sender-receiver prisoner's dilemma but only ensure one side's willingness to participate, while government incentive-based regulation can overcome negative constraints and promote active participation from both sender and receiver. Compensatory regulations encourage sustained technology transfer costs and reduce incentive costs, with optimization focusing on lowering government publicity costs and increasing economic benefits for intermediaries.

Pengaruh Perubahan Kurs terhadap Return Saham Sektor Perbankan pada Masa Covid 19

The aim of this research is to determine the influence of Covid 19 which has an impact on the depreciation of the rupiah against the US dollar which then has an impact on stock prices. The case study in this research is the share price of the banking sector in the Indonesia Stock Exchange from March 1 to August 31, 2020. The population taken in this research is the banking sector listed on the Indonesian Stock Exchange. The sampling method used purposive sampling so that the number of samples obtained was 4 companies which are state-owned banking companies. Data collection is carried out by documenting data that has been published by the Indonesian Stock Exchange. Investors' reactions to information on the depreciation of the rupiah against the US dollar can be seen from the abnormal stock returns 7 days before and 7 days after information on the depreciation of the rupiah against the US dollar. The method used is the Event Study method with One Sample T-Test and Paired Test data analysis techniques.The conclusion from the results of research and data analysis from 4 state-owned banking companies listed on the Indonesia Stock Exchange during the window period is that it was found that there was no difference in Average Abnormal Return before the depreciation of the rupiah against the United States dollar (event) and after the depreciation of the rupiah against the United States dollar (event). And the paired t-test analysis shows that stock prices before the event do not affect stock prices after the event. This result shows that this market is a semi-strong market according to the Efficiency Market Hypothesis theory.

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.

OVER REAKSI PASAR TERHADAP HARGA SAHAM PERUSAHAAN MANUFAKTUR DI BURSA EFEK JAKARTA

Overreaction phenomenon is one of the most complexity problem to make an efficient stock market. This research investigates are go public manufacturer company has an overreaction indicate, with year 2003 until 2005 scop. The methode that utilize threathen to Rahmawati and Tri (2005), calculation based on Jogiyanto (2000) formula. The finding of the research indicates there is overreaction that shows by loser portofolio to first rate winner portofolio. The comparison average cummulative abnormal return (ACAR) between loser portofolio and winner portofolio showing there’s significant difference. The research is also shown that winner portofolio is more overreacted than loser portofolio. The grouping of winner portofolio in three groups (A, B, C portofolio) and loser portofolio (D, E, F portofolio) according Rahmawati and Tri 2005), produce that Cumulative abnormal return (CAR) from loser portofolio not showing overreaction effect, only in winner portofolio showing overreaction effect.

An Efficient Numerical Scheme for a Time-Fractional Black–Scholes Partial Differential Equation Derived from the Fractal Market Hypothesis

Since the early 1970s, the study of Black–Scholes (BS) partial differential equations (PDEs) under the Efficient Market Hypothesis (EMH) has been a subject of active research in financial engineering. It has now become obvious, even to casual observers, that the classical BS models derived under the EMH framework fail to account for a number of realistic price evolutions in real-time market data. An alternative approach to the EMH framework is the Fractal Market Hypothesis (FMH), which proposes better and clearer explanations of market behaviours during unfavourable market conditions. The FMH involves non-local derivatives and integral operators, as well as fractional stochastic processes, which provide better tools for explaining the dynamics of evolving market anomalies, something that classical BS models may fail to explain. In this work, using the FMH, we derive a time-fractional Black–Scholes partial differential equation (tfBS-PDE) and then transform it into a heat equation, which allows for ease of implementing a high-order numerical scheme for solving it. Furthermore, the stability and convergence properties of the numerical scheme are discussed, and overall techniques are applied to pricing European put option problems.

Strategic transparency under authoritarian environmentalism: information disclosure and the role of environmental NGOs in China’s national emission trading scheme

ABSTRACT Transparency in carbon markets is considered critical to ensure market effectiveness and public accountability. Low levels of transparency and lack of participation of non-governmental actors are persistent criticisms of Chinese (pilot) emission trading systems. Building on debates around authoritarian environmentalism and China's role therein, this article analyses transparency, manifested as information disclosure, in China's national carbon emission trading scheme (C-ETS) – the largest carbon emission market worldwide. Using expert interviews and content analysis, the article outlines practices of C-ETS information disclosure and discusses its characteristics in the context of China’s environmental governance. Key findings include discrepancies both between central and provincial governments related to information disclosure, and between different provincial governments. And while most industry actors lack interest and capacity to engage in C-ETS disclosure, environmental non-governmental organizations (ENGOs), by contrast, are significantly more involved in the policy process and information disclosure, often as ‘eco-elites’. Overall, this reveals that international and domestic ENGOs retain high-level channels to influence policy design and implementation. Yet, ENGO input is not pursued as a public good valuable for its own sake, but sought strategically as a means to the larger goal of efficient market functioning. This type of ‘strategic transparency,’ as we call it, suggests that China’s environmental governance model – regularly considered to be a key example of authoritarian environmentalism – instead represents an evolving mixture of liberal and illiberal tactics.

Travel Demand Models for Visitors to Pasar Baru Banggai, Banggai Laut Regency, Indonesia

Pasar Baru Banggai in Banggai Laut Regency is a crucial economic and social center for the local community. This article aims to analyze the factors influencing the travel demand of people to this market. Various aspects such as income, travel distance, travel time, product completeness, and market area were analyzed in depth. The methodology used includes field surveys, participatory observation, and secondary data analysis from relevant sources. The research sample was conducted using stratified sampling with strata based on visit frequency (daily, weekly, and monthly visitors) and potential visitors to the market. The total sample size was set at 100 visitors, which is a combination of all samples from each stratum as the final sample in this study. The research results show that income, travel distance, travel time, completeness of goods sold, and market area are the main factors attracting people to visit Pasar Baru Banggai, following the mathematical model Y = 0.858 + 0.182(X1) + 0.183(X2) + 0.112(X3) + 0.162( X7) + 0.145(X8). Additionally, this market also provides quality local products that meet the needs of the local community, further strengthening its position as the regional economic center. Based on this analysis, several recommendations are proposed to enhance the attractiveness and function of Pasar Baru Banggai, including improving supporting infrastructure, promoting local products, empowering traders through training and support, and implementing efficient market management. With these measures, it is hoped that Pasar Baru Banggai can continue to grow and provide greater benefits to the people of Banggai Laut Regency, as well as contribute to the improvement of the region's economic and social welfare.

Impact of Monetary Policy on Stock Market Volatility

Purpose: The general objective of the study was to analyze the impact of monetary policy on stock market volatility. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the impact of monetary policy on stock market volatility. Preliminary empirical review revealed that monetary policy exerts significant influence on stock market volatility through various channels such as interest rate adjustments, quantitative easing, and forward guidance. It highlighted that the timing and clarity of policy announcements play a crucial role in shaping market reactions, with unexpected shifts often leading to heightened volatility. Global interconnectedness amplified these effects, underscoring the need for coordinated policy responses. Behavioral factors also contributed, as investor sentiment and market psychology could magnify volatility independently of economic fundamentals. Understanding these dynamics is crucial for effective policy design and market stability amidst evolving financial landscapes. Unique Contribution to Theory, Practice and Policy: Efficient Market Hypothesis (EMH, Rational Expectations Theory and Keynesian Economics may be used to anchor future studies on monetary policy on stock market volatility. Recommendations from the study included integrating behavioral finance models into economic frameworks to enhance predictive accuracy. It advocated for clearer and more consistent central bank communications to align market expectations with policy actions, reducing uncertainty-driven volatility. Collaborative efforts among central banks were emphasized to manage international spillovers and enhance global financial stability. Adaptive policy frameworks were proposed to respond flexibly to changing market conditions while promoting regulatory measures to safeguard against excessive risk-taking. These recommendations aimed to foster a stable financial environment supportive of sustainable economic growth and market confidence.

Financial Revolution through Agent-based Artificial Simulation Computational Models for Predicting Market Behavior

The fundamental theory of the Efficient market hypothesis (EMH), which states that market participants are rational, has received a lot of criticism. The complexity of behavior in the capital market is still a black box, especially when psychological biases influence aggressively on decision-making amid uncertainty. Experimental research on finance and capital markets in the form of AI using machine learning seeks to predict the results of more complex interactions. This multidisciplinary approach offers efforts to explain social phenomena from the micro level to macro descriptions which are built artificially through the computational world. The processing modeling approach is preferred because it includes the complexes that emerge from the behavior and interactions of individuals in the real world. Agent Based Model (ABM) is an AI approach in the form of computational simulation that performs a bottom-up approach by combining irrational–rational agent interactions through networks in microenvironments. Using the ABM approach through Netlogo computing, this study proves that AI can be used to analyze investor behavior in the capital market. Keywords: Agent Based Model, artificial intelligence, investor behavior

The influence of emotional intelligence and behavioural biases on mutual fund churning frequency: Evidence from India

Behavioural finance invalidates the rationalistic assumptions of the efficient market hypothesis by proposing a realistic explanation for overreaction and underreaction. These phenomena are caused by investors making financial decisions based on their emotions without realizing them. This study attempts to establish the effect of investors' emotional intelligence on behavioural biases, namely, herding, overconfidence bias, and disposition effects, and its consequences for the churning frequency of mutual fund portfolios. This quantitative cross-sectional study was undertaken to collect data from 499 mutual fund investors using a self-administered questionnaire. We found that the disposition effect has a negative impact and that overconfidence bias positively affects the churning frequency of mutual fund portfolios. Furthermore, emotional intelligence, particularly its subconstructs, affects herding bias and overconfidence bias, ultimately impacting the churning frequency of investors. An investor with a higher level of self-motivation is likely to have a disposition effect and herding bias. Investors with disposition impact may be encouraged to reorganize their portfolio if there are any schemes that have been providing negative returns for more than two years or that are not projected to offer significant returns in the future. By understanding how they stack up on each emotional intelligence metric, investors and financial advisors may focus on avoiding the biases that could jeopardize long-term portfolio returns.

The Impact of Investor Expectation on the Financial Decision-Making

Unlike traditional finance, behavioral finance challenges the traditional financial theory of investor rationality, emphasizing that investors are not absolutely rational in real life. This perspective provides new insights for understanding and predicting financial markets. This study examines core concepts in behavioral finance, such as loss aversion, market anomalies, momentum effects, reversal effects, endowment effects, and framing effects, and analyzes how they influence investors' financial decisions. These concepts highlight the significant role of human psychology and behavior in shaping financial decisions, thus aiding investors in making more rational choices in complex financial markets. Loss aversion, for instance, suggests that investors are more sensitive to losses than to gains, leading to suboptimal decision-making. Market anomalies, such as price bubbles and crashes, indicate that markets are not always efficient. Momentum effects suggest that past stock performance can predict future trends, while reversal effects point to the tendency of extreme stock performances to revert to the mean. The endowment effect reveals that individuals value their own possessions more highly than similar items they do not own. Lastly, framing effects demonstrate how the presentation of information can influence decision-making. Future research can delve deeper into how the framing effect can be harnessed to improve the design and marketing strategies of financial products, thereby enhancing the efficiency and effectiveness of investment decisions. This exploration could lead to more sophisticated financial products and better-informed investors, ultimately contributing to a more stable and efficient financial market.

Chaos, overfitting and equilibrium: To what extent can machine learning beat the financial market?

In this paper, we applied 10 technical analysis indicators to predict stock price movement directions using support vector machines, investigating the effects of hyperparameter variations on the out-of-sample classification performance and the profitability of the resulting trading strategies. We collected daily data between January 1st, 2018, and March 31st, 2023 for the 30 firms that compose the Dow Jones Industrial Average (DJIA). Our results indicated that the out-of-sample accuracy converged to 50%, while a small percentage (13.63% for the pre-COVID period and 23.16% for the post-COVID period) of the hyperparameter combinations yielded gains above the buy-and-hold strategy; on the other hand, no clear patterns about the best-performing hyperparameter combinations emerged, as the behavior of the out-of-sample performance was found to exhibit high sensitive dependence to the hyperparameters settings in comparison to its in-sample counterpart. The outcomes of our empirical analysis are consistent with both classic results in the finance literature (such as the Efficient Market Hypothesis) and empirical setbacks commonly seen in machine learning experiments, notably the occurrence of overfitting under the incorporation of high-dimensional non-linear interactions.

Exploring the Nordic numbers: an analysis of price clustering in Scandinavian stocks

PurposeThis paper investigates share price clustering and its determinants across Nasdaq Stockholm, Copenhagen, Helsinki, and Iceland.Design/methodology/approachThis paper investigates share price clustering and its determinants across Nasdaq Stockholm, Copenhagen, Helsinki, and Iceland. Univariate analysis confirms widespread clustering, notably favouring closing prices ending in zero. Multivariate analysis explores the impact of firm size, price level, volatility, and turnover on clustering.FindingsUnivariate analysis confirms widespread clustering, notably favouring closing prices ending in zero. Multivariate analysis explores the impact of firm size, price level, volatility, and turnover on clustering. Results reveal pervasive clustering, strengthening with higher prices and turnover but weakening with larger trade volumes, firm size, and smaller tick sizes. These empirical findings support the theoretical expectations of price negotiation and resolution hypotheses.Practical implicationsThe observed clustering presents an opportunity for investors to potentially capitalize on this market anomaly and achieve supra-normal returns.Originality/valuePrice clustering, the phenomenon where certain price levels are traded more frequently, challenges the efficient market hypothesis and has been extensively studied in financial markets. However, the Scandinavian stock markets, particularly those in the Nasdaq Nordic Exchange, remain unexplored in this context.

Cryptocurrency and Its Role in Portfolio Diversification

Purpose: This study sought to explore cryptocurrency and its role in portfolio diversification. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to cryptocurrency and its role in portfolio diversification. Preliminary empirical review revealed that incorporating cryptocurrencies into investment portfolios offered promising diversification benefits due to their low correlation with traditional assets, despite their high volatility and regulatory uncertainties. It highlighted the significant risk management challenges posed by cryptocurrencies' extreme price fluctuations and the evolving regulatory landscape. The study emphasized the importance of careful, limited allocation to cryptocurrencies, robust risk management practices, and continuous market monitoring. Ultimately, it suggested that cryptocurrencies could enhance portfolio performance when strategically used alongside traditional diversification methods. Unique Contribution to Theory, Practice and Policy: The Modern Portfolio Theory, Efficient Market Hypothesis and Behavioural Finance Theory may be used to anchor future studies on portfolio diversification. The study recommended a cautious yet strategic inclusion of cryptocurrencies in investment portfolios to enhance diversification, emphasizing the importance of ongoing research, robust risk management, and proactive monitoring due to their high volatility and regulatory uncertainties. It called for clear and consistent regulatory frameworks to protect investors while fostering market growth, and highlighted the need for collaboration between academia, industry, and regulatory bodies to improve financial literacy and market stability. These recommendations aimed to contribute to theoretical, practical, and policy aspects of cryptocurrency investments.

Behavioral Finance: Investor Psychology and Market Outcomes

Purpose: The general objective of the study was to explore how psychological factors and cognitive biases influence investor decisions and market dynamics. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to investor psychology and market outcomes. Preliminary empirical review revealed that psychological factors such as cognitive biases and investor sentiment significantly impacted financial decision-making and market dynamics, leading to anomalies like market bubbles and volatility. It highlighted the need for integrating behavioral insights into financial models to better predict and understand market behavior, emphasizing the importance of financial literacy in mitigating adverse effects of these biases. Unique Contribution to Theory, Practice and Policy: The Prospect Theory, Herding Theory and Efficient Market Hypothesis (EMH) may be used to anchor future studies on behavioural finance. The study recommended further development of behavioral finance models to incorporate psychological factors, and for financial institutions to integrate behavioral insights into their services to guide better investment decisions. It also advised policymakers to design regulations enhancing market transparency and protecting investors from biases, while promoting financial literacy and implementing measures to monitor and mitigate systemic risks from irrational behavior.

A bootstrap dynamic multivariate panel Granger causality analysis to examine the relationship between the COVID-19, Delta and Omicron pandemic era and the maritime shipping freight industry

This work applies a bootstrap multivariate panel Granger causality test to examine the causal relationship between the coronavirus disease 2019, Delta and Omicron pandemic eras and the maritime shipping freight industry across 15 Chinese marine transportation freight stocks through the data from Jannuary 23, 2020 to December 6, 2022. After accounting for both dependency and heterogeneity across these industries, we find support for six of the 15 maritime shipping freight enterprises which display a unidirectional Granger relationship between stock returns and pandemic-related shocks and five of them which demonstrate a bidirectional association. On the other hand, the stock values of three firms in this cohort Granger “lead” the epidemic, suggesting that the adjustment of expectations to external shocks and the efficient market hypothesis could elucidate apparent causal connections. The empirical findings of this work provide important policy implications for the maritime shipping freight industry.

NAVIGATING FINANCIAL DECISIONS: A COMPREHENSIVE REVIEW OF THEORIES, BEHAVIOURS, AND INNOVATIONS

This literature review synthesizes seminal and contemporary research on financial decision-making, emphasizing theoretical frameworks and empirical findings. Fama's (1970) Efficient Market Hypothesis sets the foundation for understanding market behavior, while Kahneman and Tversky's (1979) Prospect Theory introduces psychological influences, highlighting biases such as loss aversion (Tversky & Kahneman, 1991). Jensen and Meckling (1976) provide insights into agency theory, exploring the implications of managerial behavior on firm performance. The influence of gender on investment strategies is examined by Barber and Odean (2001), illustrating the role of overconfidence. Additionally, the impact of social interactions on financial choices is explored by Bursztyn et al. (2014). Financial literacy, addressed by Lusardi and Mitchell (2014), underscores the importance of informed decision-making in personal finance. The review also highlights the integration of behavioral insights in corporate finance, particularly in capital structure decisions (Myers, 1984) and risk management practices (Hull, 2012). Overall, the collected works illustrate a rich tapestry of factors influencing financial decisions, blending economic theory with behavioral insights, and emphasizing the necessity for ongoing research in this dynamic field. KEYWORDS: Financial Decision-Making, Behavioral Finance, Prospect Theory, Market Efficiency, Agency Theory, Overconfidence, Financial Literacy, Risk Management, Capital Structure and Social Interactions.

Information aggregation with heterogeneous traders

The efficient market hypothesis predicts that asset prices reflect all available information. Recent experimental work found the rational expectation model to outperform the prior information model in contingent claim markets when traders hold homogeneous values, despite the no trade equilibrium. However, recent experiments have also demonstrated the inability of contingent claim markets to successfully aggregate information when traders hold highly differentiated asset values. These prior findings beg the question of whether homogeneous values are a necessary condition for efficient market outcomes in contingent claim markets. The experiments reported in this paper show that homogeneous values are not a necessary condition for information aggregation.

The Sustainable Development Model of China’s Tourism-Based Poverty Alleviation Industry: Analysis of the Configuration of an Active Government, an Efficient Market and a Caring Society

Tourism-based poverty alleviation strategies have played an important role in China’s efforts to eliminate poverty. In the post-poverty-alleviation era, the mechanism by which the government, market and society jointly promote the sustainable development of the tourism-based poverty alleviation industry requires further research. Based on data from 33 tourism-based poverty alleviation demonstration counties in China, this paper uses the fsQCA method to explore the role positioning and function of the three major entities of government, market and society. The results show that no single condition can promote the long-term development of tourism-based poverty alleviation. Four modes are proposed: government financial support, resource endowment, ecosystem tourism and multisubject coordination. The government provides a diverse and sustainable “haematopoietic” mechanism by focusing on different elements in each path. In addition, public tourism services are crucial to the long-term mechanism of tourism-based poverty alleviation.

Railway cold chain freight demand forecasting with graph neural networks: A novel GraphARMA-GRU model

Accurate demand forecasting is imperative for efficient railway cold chain freight operation planning, resource optimization, and market responsiveness. Given the unique spatiotemporal characteristics and diversity of cold chain demands, the mismatch between capacity and demand has become a bottleneck, constraining the development of railway cold chain freight transportation. To tackle this challenge, we propose a graph neural network model with ARMA graph convolutional layer (ARMA Filter) and gated recurrent units (GRU), namely the GraphARMA-GRU Model, for adaptive and efficient short-term forecasting of railway cold chain freight demand. Our model can effectively capture temporal features, external factors, and the intricate spatiotemporal relationships influencing railway cold chain demands. The ARMA Filter is employed to grasp the spatial connectivity within the railway network, and GRU layers are utilized for refining temporal features. Furthermore, it also integrates external factors and refined temporal features in two graph convolutional layers to better capture multimodal characteristics. The proposed model is validated with real data of railway cold chain freight in China, whose results show an 18% improvement in prediction accuracy compared to the average performance of baseline models. In addition, interpretability methods are introduced to enhance the model’s transparency and promote future development for railway cold chain freight transportation, which may offer deep insights and support critical decisions for a smooth transition from road-based to railway-based cold chain freight transportation.