Efficient Market Research Articles

Has Real Time Spot Electricity Market in India Impacted Day-Ahead Spot Electricity Market?

Introduction of Real-Time Market (RTM) on June 1st, 2020, marked pivotal advancement in electricity trading in India featuring bi-hourly auctions and power transmission based on temporal blocks. Adherence to operational protocols outlined in Procedure for Scheduling Collective Transactions in RTM along with statutory directives like CERC Power Market Regulations 2010 ensures efficient market operation. Notable features include 15-minute contract trading, clandestine bidding and authorization from SLDC. Day-Ahead Market (DAM) facilitates electricity exchange through double-sided closed auctions ensuring compliance with regulatory protocols. In this study we explore whether Real Time Spot Electricity Market in India has impacted Day-Ahead Spot Electricity Market. Findings of the study provide crucial insights for market participants, stakeholders, open access consumers and electric utilities is India.

Accurate and efficient stock market index prediction: an integrated approach based on VMD-SNNs

The stock market index typically mirrors the financial market's performance. Hence, accurate prediction of stock market index trends is essential for investors aiming to mitigate financial risk and enhance future investment returns. Traditional statistical approaches often struggle with the non-linear nature of stock market index data, leading to potential inaccuracies in long-term predictions. To address this issue, we introduce the TCN-LSTM-SNN (TLSNN) model, a hybrid framework that integrates Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) for robust feature extraction, within a highly efficient Spiking Neural Network (SNN) architecture. Additionally, we employ the Subtraction-Average-Based Optimizer (SABO) to refine the Variational Mode Decomposition (VMD) technique, thereby separating the periodic and trend components of stock indices, reducing noise interference, and establishing a decomposition ensemble framework to bolster the model's resilience. The experimental results show that the VMD-TLSNN hybrid model suggested in this study surpasses other individual benchmark models and their hybrid models in prediction accuracy. Additionally, it demonstrates notably lower energy consumption compared to other hybrid models.

Comparative Analysis of ETF Arbitrage Strategy in Efficient and Inefficient Markets

This paper analyzes the different performance of an ETF arbitrage strategy in efficient and inefficient markets, the markets of the United States and Russia. It would initiate from the ETFs and their importance to the global financial market. Then, the paper introduced what the arbitrage strategy is, and how it works. Then, by comparing the key factors, it classifies efficient market, the U.S. market, and the inefficient market, the Russian market. In the paper, the S&P 500 ETF represents the U.S. market, and the VanEck Russia ETF (RSX) represents the Russian Market. The paper analyzes the performance in these two markets from 2016 to 2021 and ignores the transaction costs. The paper concluded that the ETF arbitrage strategy performed better in the inefficient market (Russia) compared to the efficient market (U.S.). Limitations are acknowledged in the paper, such as the effect of transaction costs, which would reduce the actual profits and returns, and that the historical market data might not strong enough in predicting the future performance of the market. These would lead to future research and further analysis to enhance the overall effectiveness of the project and make it aligns more closely with the real-world conditions.

The Application of Asset Pricing Models in the Capital Market

With the development of China's financial and capital markets, more and more investors will choose to use different asset pricing models to analyze the effectiveness of their investments. Under the background of efficient markets hypothesis (EMH), this paper will use Fama-French three-factor model, GARCH family model and Heston model to analyze the validity and feasibility of these three commonly used models in Chinese market. Through empirical analysis, descriptive statistics and other research methods, it is found that all three models are effective but have certain limitations. It is found that the Fama-French three-factor model is too dependent on the market. The establishment of the GRACH family model is too complicated due to too many influencing factors in the market; and the time lag of the Heston model. All three models require investors to include more model analysis to get the optimal investment strategy. Therefore, this paper draws the definition of behavioral finance at the end of the paper, according to which investors' decisions can be further analyzed.

Competition policy and enterprise competitiveness: Catalyst or barrier?

As a seminal institutional innovation within the framework of Chinese-style modern competition theory, the question of whether the Fair Competition Review System (FCRS) can harness policy dividends and bolster enterprise competitiveness stands as a pivotal concern for accelerating the emergence of new productive forces and fostering a harmonious integration of an "effective government + efficient market." Leveraging data from China's listed manufacturing enterprises spanning the period from 2010 to 2020, this paper undertakes an empirical examination of the impact of competition policy frameworks on the competitiveness of manufacturing enterprises. The FCRS is treated as a quasi-natural experiment, and a difference-in-differences (DID) methodology is employed for analysis.The findings of this paper are as follows: (1) The implementation of the FCRS has been found to effectively augment the competitiveness of manufacturing enterprises by approximately 1.97 %, and this fundamental conclusion retains its validity after a battery of robustness tests; (2) The FCRS primarily influences enterprise competitiveness by lowering financing costs, enhancing technological innovation, and mitigating over-investment; (3) Heterogeneity analysis reveals that non-high-tech enterprises and those with lower financing constraints can attain more pronounced effects in promoting enterprise competitiveness through competition policies. Additionally, enterprise size and operational risks also exert an influence on the efficacy of competition policies in enhancing competitiveness; (4) Expanded analysis indicates that competition policies optimize the capital structure of enterprises through "debt reduction" and "capacity enhancement," thereby exploring the viability of analyzing the macro factors that impact enterprise "deleveraging" from the lens of competition policies. The research conclusions presented in this paper carry substantial guiding significance for the government in clarifying the preeminent role of market allocation of resources, promoting the establishment of a unified national market, and orchestrating a coherent fair competition system.

Comparing Two Methods for Testing the Efficiency of Sports Betting Markets

Sports betting markets can be considered strongly efficient if expected returns on all possible bets on an event are equal. If this form of efficiency holds, then there is a direct mapping from betting odds into probabilities of outcomes of sporting events. We compare two regression-based methods for testing this form of efficiency that have been used in previous research: One that uses normalized probabilities as the explanatory variable for event outcomes and one that uses the inverse of the decimal odds. We show that the normalized probability method produces good tests of the null hypothesis of strong market efficiency but that the inverse odds method does not, with results biased against finding favorite-longshot bias. We illustrate this finding using large datasets of bets and outcomes for tennis and soccer and also with realistic simulations.

How does enterprise digitalization affect corporate carbon emission in China: A firm-level study

This paper develops a theoretical framework to examine the influence of enterprise digitalization on corporate carbon emissions. Then it empirically measures the extent of digitalization in Chinese enterprises through their share of intermediate inputs to their value added from information and communication technologies. Data from the China Enterprise Tax Surveys and Inter-Country Input-Output Tables from 2007 to 2016 are used for the empirical analysis. It is noted that digitalization in enterprise manufacturing leads to an increase in corporate carbon emissions (CCE). The impact from the service sector, however, is not significant. The exacerbation of CCE is observed primarily among low-leverage enterprises, downstream industries, and firms located in relatively lower-income regions. In accordance with the theoretical model, it is observed that enterprise digitalization intensifies CCE by crowding out investment in environmentally friendly green production and by altering production patterns towards mass production. However, an efficient market mechanism effectively mitigates this exacerbation effect. The findings reveal the misalignment of enterprise digitalization transformation modes with long-term “low carbon” objectives, thereby exposing the need for appropriate policy interventions.

Contribution to the Study of the Impact of IFRS on the Quality of Information and Financial Markets: Which Theories are Used?

The aim of this article is to examine the extent to which theories of financial market effi-ciency, agency and signaling can be used to study and understand financial phenomena, in particular conflicts of interest between managers and shareholders, and investor behavior in financial markets following the transition to International Financial Reporting Standards (IFRS).

Impact of Mergers & Acquisitions Announcements on Stock Prices

This study examines the impact of M&A announcements on stock prices in India, by utilizing event study methodology and a dataset of 15 announcements. It identifies dynamic trends in stock performance before and after theannouncements. The Efficient Market Hypothesis (EMH), which posits that the market incorporates all relevant information, is evident in stock price volatility. The post-COVID-19 market environment has heightened investor caution due to substantial losses. The research, covering M&A announcements from 2014 to 2023, uniquely contributes by analysing pre- and post-COVID investor sentiments. Although substantial results were produced, the analysis of Abnormal Returns (AR) and Cumulative Average Abnormal Returns (CAAR) indicates that M&A announcements did not significantly impact overall stock performance, thereby supporting the EMH. These findings provide valuable insights into market responses to M&A in India, benefiting investors, business strategists, policymakers, and other stakeholders.

CRYPTOCURRENCY PRICE PREDICTION USING LSTM

Cryptocurrency, a digital form of currency, has emerged as a prominent asset class with unique characteristics such as decentralization, security, and global accessibility. As the adoption of cryptocurrencies continues to grow, the need for accurate price prediction using machine learning (ML) algorithms becomes crucial for various stakeholders in the financial ecosystem. This paper presents a comprehensive approach to cryptocurrency price prediction, focusing on the following key aspects: The data for this study is collected from the Binance platform, a leading cryptocurrency exchange known for its extensive market data and liquidity. The dataset includes historical price data across different time frames, including 1-hour, 4-hour, and daily intervals. Prior to analysis, the collected data undergoes thorough preprocessing steps to ensure data quality and consistency. This process includes handling missing values, removing outliers, and standardizing data formats for further analysis. Long Short-Term machine learning model algorithm is employed for price prediction. This model is chosen for its ability to capture complex patterns and dynamics in cryptocurrency price movements. The prediction phase involves training and testing the ML model using the preprocessed data. Performance evaluation metrics such as R-squared, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean absolute percentage error (MAPE) are utilized to assess the accuracy and robustness of the prediction model across different time frames. Accurate cryptocurrency price prediction is essential for various stakeholders, including investors, traders, businesses, and regulators. It facilitates informed decision- making, risk management, market analysis, trading strategy development, business planning, and regulatory compliance in the dynamic cryptocurrency market. By addressing these key elements, this study aims to contribute to the advancement of cryptocurrency price prediction methodologies using ML techniques, thereby enhancing decision-making processes and fostering a more efficient and transparent digital asset market ecosystem.

Interpreting the modern history of finance theory from Henri Poincaré’s perspective

Henri Poincaré is well known for his work in mathematics, physics, and the philosophy of science. From 1900 onwards, Poincaré had the opportunity to become a forerunner of the Efficient Market Hypothesis (EMH), a concept that was formally developed 65 years later, but he never seised it. We show that Poincaré’s view on randomness was incompatible with the EMH framework. Although he did not work directly on financial topics, his work has helped to understand the limits of random walks in asset pricing; a way to reconcile the mathematician with the field of finance.

Gender identity, salience of information, and tacit coordination: Gender differences in response to strategic uncertainty

We use a lab experiment to explore whether gender composition and gender identity salience influence team coordination. Identity in the experiment is induced using gender-specific and neutral avatars. In contrast with many previous studies, we do not find the presence of in-group favoritism, irrespective of whether gender identity or random avatars define the group. In addition, behavior remains unchanged when the gender of the counterpart is revealed. However, females are found to cooperate significantly more when gender information is disclosed, implying an elevation in the salience of gender identity induces the females to choose based on social expectations. This research adds to the discourse on gender dynamics in decision-making and suggests that gender identity plays a role in economic choices, innovating traditional views on diversity in teamwork. Our research sheds light on the intricate dynamics of gender composition in team settings, particularly under conditions of risk and uncertainty. These findings have the potential to inform both organizational practices and public policy, thereby contributing to a more equitable and efficient labor market.

The Prosumer

In recent years, the concept of the prosumer has garnered extensive attention across various fields, including marketing, energy consumption, and innovation research. This attention is driven by the significant role prosumers play in developing more efficient, sustainable, and health-conscious market systems, propelled by advancements in social and technological domains. Broadly defined, a prosumer is an individual who acts as both a producer and a consumer. Originally coined by Toffler in the 1980s, the term describes individuals who blur the lines between producers and consumers by engaging in the creation of value for their own use or for others. Prosumers are seen as external partners who participate in co-creation processes with organizations, contributing to innovative outcomes and the production of the products and services they consume. The concept of the prosumer, individuals who simultaneously act as producers and consumers, has gained significant attention across various sectors. This entry explores the evolving role and impact of prosumers on sustainability, innovation, and market dynamics. A comprehensive literature review and empirical analysis were conducted to understand prosumer behavior and contributions. The findings reveal that the prosumers’ roles range from significantly enhancing sustainability by generating renewable energy to promoting personalized education via teacher–prosumer models. They also drive technological advancements in fields such as 3D printing and cryptocurrency. The study concludes that prosumers have the potential to foster a more resilient and inclusive economy, although challenges such as regulatory barriers and technological dependencies must be addressed to fully leverage their contributions.

Adaptive Market Efficiency Hypothesis in ASEAN Stock Markets: A Variance Ratio Analysis Across Market Capitalization Segments

This study examines the efficiency of the ASEAN stock market and investigates the dynamic adaptability of this efficiency over time. Utilizing advanced methodologies, including the Multiple Variance Ratio (MV) and Wild Bootstrapped Variance Ratio (WBVR) tests, in conjunction with the Rolling Window technique, the research assesses market efficiency across different periods from March 2009 to March 2024. The analysis is conducted using daily price data, segmented into the overall market and three sub-groups based on market capitalization, to explore the potential impact of company size on market efficiency. The findings indicate that market efficiency is not static; it fluctuates, particularly during periods of economic crises or significant events, when all market segments exhibit inefficiency, deviating from the Random Walk theory. This suggests that during such periods, stock prices become more predictable, contrary to the expectations of an efficient market. Additionally, the study finds that changes in market efficiency are consistent across different company sizes, suggesting that market or company size does not significantly influence efficiency.

Marketing Outcomes and Shareholder Value: A Review and Research Agenda

One of the most fundamental management questions is how market-based assets affect shareholder value. To provide an answer, we review the extensive literature on marketing outcomes and shareholder value using the Theory, Context, Characteristics, and Methodology (TCCM) framework. The analysis reveals that researchers mainly rely on the efficient market hypothesis, signaling theory, and market-based arguments to theorize the relationship between customer-based market assets and shareholder value. Researchers predominantly collect secondary data and employ event study, persistence methods, and instrumental variables approach to show that marketing actions (such as advertising and brand building) and customer-based market assets (such as customer satisfaction, customer loyalty, and brand equity) positively influence shareholder value. After outlining the findings, future research directions on the relationship between market assets and shareholder value are explored.

“Green Companies” and Financial Performance: The Green Premium

We examine the relationship between Corporate Environmental Responsibility and Corporate Financial Performance for the S&P500 firms over a period of fifteen years. We test the effect of Jensen's alpha, stock returns, return-on-asset, size, sales, and profit on Corporate Environmental Responsibility by building a CAPM model of risk-adjusted excess returns under efficient-market hypothesis and introduce the “Green Premium,” the cost in stock return stockholders have to incur for their company's “greenness”. Although, “green” practices are positively related to sales and profit, the results suggest that market value of the company is not increased and that a significant negative relationship exists between “greenness” and stock's performance.

An Entropic Analysis of Efficiency in the West Texas Intermediate Crude Oil Futures Market

For the last 50 years or so, the efficient market hypothesis (EMH) has been the central pillar of economic thought and the building block of portfolio theory. However, the validity of the EMH still remains controversial, and the methods of testing for market efficiency have been criticised. This study utilises entropy analysis combined with generalised autoregressive conditional heteroscedasticity (GARCH) regressions to provide extensive empirical evidence on informational efficiency. This study develops an innovative method for studying market efficiency by investigating the complexity of information saturated in the market. To this end, we have used daily West Texas Intermediate (WTI) crude oil futures contracts from June 1983 to February 2016, giving a total data set of 282,427 observations. The results of the Jensen–Shannon informational criteria indicate that the informational efficiency measure has never reached the maximum value of 1 which indicates weak-form efficiency. Our study has further found that the informational efficiency measure tends to decrease consistently during recessionary periods and to increase in subsequent periods. We have also found that the entropy measure tends to increase over time, supporting the adaptive market hypothesis over the EMH.

Delving into the Exchange-Traded Funds (ETFs) Market: Understanding Market Efficiency

Exchange-traded funds (ETFs) are the most popular products in the financial sector today. There is extensive literature on the multifractal analysis of some stock markets, but not about the multifractal behaviour of the ETF market. This study examines the efficiency of stock index ETFs worldwide from an Efficient Market Hypothesis (EMH) perspective, using the ETFs: Ishares Msci World ETF (URTH), Ishares Russell 1000 ETF (IWB), SPDR S&P 500 ETF TRUST (SPY), Ishares Global Clean En. ETF (ICLN), Ishares USD Green Bond ETF (BGRN), from 1 January 2021 to 24 May 2024. It analyses a pre-conflict and a geopolitical conflict to uncover distinct patterns of behaviour reflecting significant changes in market conditions. Before the conflict, the Ishares MSCI World, Ishares Russell 1000, SPDR S&P 500 and Ishares USD Green Bond ETFs showed signs of anti-persistence in returns, indicating a lack of strong relationship or predictability between short-term price movements. The Ishares Global Clean Energy ETF did not reject the random walk hypothesis, suggesting that returns follow a pattern closer to random, where market prices already efficiently reflect all available information. During the conflict, there was a transition in the ETFs' behaviour patterns, as evidenced by the increases in slope values for Ishares MSCI World, Ishares Russell 1000, SPDR S&P 500, Ishares Global Clean Energy and Ishares USD Green Bond. Thus, the possible transition from anti-persistence to long-term memories in ETF returns during the conflict. For portfolio managers, these findings highlight the need to continually adapt investment strategies to manage risks better and take advantage of opportunities in a dynamic and complex investment environment.

DISAPPEARING THE DAY OF THE WEEK EFFECT ON RETURN TO INVESTORS: NOVEL EVIDENCE FROM INDIAN STOCK MARKET

The efficiency in emerging markets is becoming more important as the trend of investment in these markets is accelerating nowadays. The level of market efficiency influences an investor's investment strategy because of its improper valuation. The improper valuation of securities may lead to abnormal gains for the investor. On the other hand, abnormal gain in a perfectly efficient market is impossible as the share price absorbs all the information available in the public domain. However, efficiency in its finest sense is a distant reality. Many researchers have pointed out different levels of efficiencies, including the presence of calendar anomalies. The purpose of the study is to capture the effect of the day of the week on the stock return and volatility in India. Daily time series data of Sensex and Nifty were collected from the two prominent exchanges of India, i.e. Bombay Stock Exchange and National Stock Exchange websites, for a period of four years. After validating stationarity with ADF and Phillip-Perron tests, the study used GARCH, EGARCH and TGARCH models to capture day impacts on stock return and volatility. The study reveals that the Tuesday effect persisted in the Indian stock market during the study period. Moreover, the TGARCH model was found to be the most suitable model for describing volatility behaviour in the Indian stock market. As other days in the week were less significant, the study's findings suggest that the gradual disappearance of day affects the Indian capital market. Another study finding is the significance of the leverage effect, which implies the increased role of bad news over good news while capturing the day-of-the-week effect. Considering the above facts, the present study's findings contribute to a deeper understanding of stock market dynamics and the challenge of the day-of-the-week effect on market efficiency.

A STUDY OF THE DAY OF THE WEEK EFFECT IN BANK NIFTY AND NIFTY50

Seasonality and seasonal variations are in any commodity market and India is also privy to it. Seasonality is an anomaly. Day of the week effect is an anomaly, which this paper tries to unravel. Many researchers have investigated the effects of seasonal fluctuations such as, , the day-of-the-week, day-of-the-month, and month-of-the-year effects. Historical evidence suggests that these anomalies are prevalent in the market and if properly studied investors can take advantage of the market and try to beat the Efficient Market Hypothesis (EMH). This paper attempts to understand the effect of day-of-the-week on the Indian stock market.