Efficient Market Research Articles

STOCK MARKET PRICE PREDICTION USING MACHINE LEARNING TECHNIQUES

Predicting stock market prices is a challenging task in the financial sector, where the Efficient Market Hypothesis (EMH) posits the impossibility of accurate prediction due to the inherent uncertainty and complexity of stock price behaviour. However, introducing Machine Learning algorithms has shown the feasibility of stock market price forecasting. This study employs advanced Machine Learning models that can predict stock price movements with the right level of accuracy if the correct parameter tuning and appropriate predictor models are developed. In this research work, the LSTM model, which is a type of Recurrent Neural Network (RNN), time series forecasting Facebook Prophet algorithm and Random Forest Regressor model have been implemented on 10 Dhaka Stock Market (DSEbd) listed companies and six international giants for predicting the stock and forecasting the future price. The dataset of domestic companies is extracted from the graphical representation of the DSEbd website, and the international companies' dataset is imported from Yahoo Finance. In this experiment, Facebook Prophet demonstrates a long period of forecasting with reasonable accuracy, capturing daily, weekly, and yearly seasonality, including holiday effects for market trend analysis. Remarkably, the LSTM model exhibits significant accuracy, yielding the best results with evaluation metrics, including RMSE (0.35), MAPE (0.50%), and MAE (0.30). The experimental results underscore the efficiency of LSTM for future stock forecasting, observed over 15 days of upcoming market prices. A comparison of the results shows that the LSTM model efficiently forecasts the next day's closing price.

Unmasking market turmoil by decoding stock market dynamics post‐fraud allegations: Evidence from Adani‐Hindenburg case

AbstractThe article investigates how the devastating repercussions of fraud allegations, as outlined in the Hindenburg report, are actively and critically affecting the stock prices of the Adani Group. This investigation, which explores the foundation of the accused companies' financial health and market trust, is crucial. These ramifications go far beyond Adani Group and are highly relevant to potential investors globally, highlighting the urgent need for a thorough comprehension of these dynamics in the context of the global market environment. The journey from stock market crash to market correction is analyzed using the theoretical lens of Risk Aversion theory. At the same time, the Efficient Market Hypothesis (EMH) assumptions are being tested. The study employs a long‐term event study approach to analyze the stock price impact of the Hindenburg report using the Cumulative Abnormal Returns (CAR) methodology. For analysis, a sample of six Adani group companies and relevant stakeholders like creditors, LIC, immediate counterparts of each company, and NIFTY bank indices are considered. The study finds that the stock prices of four out of six Adani Group companies declined significantly after the Hindenburg report, defying the assumptions of the EMH. Risk‐seeking investors' regulatory assurance and investment may have helped in a market correction. The associated banks (lenders to Adani) and Banking indices depicted a price decline on the second and third days, which reversed on the fourth day, indicating a diminished spillover effect. Moreover, no competitor besides Tata Power saw abnormally large gains. This study analyses the event from the lens of Risk aversion and tests the assumption of EMH. The study concludes that these findings have practical implications for investors (risk‐averse and risk‐seeking), policymakers, and researchers.

An intelligent multi-layer, multi-agent MMG framework with amelioration of energy efficiency and future investment outlook, under the deregulated day-ahead and real-time market regime

This paper developed an intelligent multi-agent system (MAS) with a multi-layer framework for multi-microgrids (MMGs) using robust and modern communication patterns for deployed agents to achieve distributed tasks. The MMG paradigm introduces three microgrids (MGs) based on the type of load, working environment, and living habitat: residential, commercial, and industrial. In addition, a day-ahead and real-time model is proposed for day-ahead and real-time signals. Intelligent agents in the multi-layer MAS framework make smart decisions based on multiple algorithms to optimize schedule power and minimize costs, considering demand dispatch and demand response as core components. Maximum renewable energy utilization aims to increase user comfort and reduce greenhouse gas (GHG) emissions. Load agents deployed in each MG ensure maximum efficiency. The proposed framework recommends various tariff rates and tariff adjustment strategies to promote and offer an economic evaluation across the respective indices. To minimize the monopoly of the energy market, an efficient energy market model is developed for the proposed MMG paradigm to maximize the competition by incorporating future and spot-market trading schemes for day-ahead and real-time signals. The comparative analysis indicates optimized results based upon the cost-benefit analysis, cost reduction, power transaction in the market, and maximum utilization of renewable energy resources (RERs).

Trading behavior by foreign and domestic investors

Purpose- Momentum, a well-known anomaly in financial markets, has garnered considerable attention from both academicians and market participants due to its implications for the market efficiency hypothesis and investment plans of investors. While momentum trading can yield excess returns, it is not a possible strategy for all investors because if one segment of investors employs a momentum trading strategy to profit from their trades, it requires another type of investor to take the opposite position to clear the market. Methodology- I investigate the momentum trading behavior of investors in different segments of Borsa İstanbul. Specifically, I focus on the momentum trading behavior among local and foreign investors and private and institutional investors. Findings- The results show that foreign investors’ stock holdings are positively associated with past returns inferring momentum trading while domestic investors are contrarians. Further analysis of domestic investors’ investing strategies reveals that the contrarian trading of domestic investors is mainly driven by individual investors. Conclusion- The results show that foreign investors’ stock holdings are positively associated with past returns inferring momentum trading while domestic investors are contrarians. Also, the contrarian trading of domestic investors is mainly driven by indvidual investors. Keywords: Momentum trading, investor behavior, foreign investor, domestic investor, panel data JEL Codes: G40, G11, G14

REVISITING WEAK-FORM EFFICIENCY OF MAJOR SECTORS OF MALAYSIA DURING THE COVID-19 PERIOD

There are plenty of literature on the efficient market hypothesis (EMH) especially for a stock market, but relatively fewer studies for across sectors. We look into the weak-form informational efficiency for twelve sectors in the Malaysia’s stock exchange, i.e. Consumer Products & Services (KLCSU), Construction (KLCON), Energy (KLENG), Financial Services (KLFIN), Health Care (KLHEAL), Industrial Products & Services (KLPRO), Property (KLPRP), Plantation (KLPLN), Transportation & Logistics (KLTRAN), Telecommunications & Media (KLTEL), Utilities (KLUTL), and Technology (KLTEC) during the outbreak of COVID-19 pandemic. We use QTEST (without trend) and TQTEST (with trend) which incorporate two structural breaks to carry out the analysis. Data are collected from 1/1/2020 through 31/3/2022. These indices show non-linearity and structural breaks in data. The findings of our study suggest mixed results in regard to informational efficiency when breaks are taken. Four indices, i.e. KLCON, KLFIN, KLPRP, and KLUTL are found to be inefficient while the remaining eight sectors are found to be efficient. Investors can predict the movements of stocks in the inefficient indices and are able to gain abnormal returns from these stocks. Policymakers may encourage investors to have more trades; so that to make the market more efficient by gradually eliminating the opportunities on earning abnormal returns.

Market Behaviors and Economic Crisis: Insights from Financial Economics

The research abstract encapsulates a comprehensive exploration of market behaviors during economic crises within financial economics. The study aims to elucidate the intricate dynamics of market behaviors amidst crises and their implications for theory and practice in financial economics. Employing a mixed-methods approach, the research integrates theoretical frameworks with empirical evidence to offer insights into the complexities of market dynamics. The study design thoroughly examines theoretical constructs and empirical investigations, drawing upon established frameworks such as the Efficient Market Hypothesis and behavioral finance theories. The research unravels the interplay between rational and irrational factors driving market outcomes during crises through empirical studies, including analysis of historical market data and examination of investor behavior. The findings underscore the need for a nuanced understanding of market behaviors, emphasizing the integration of rational and behavioral perspectives in financial economics theory and practice. The study's implications extend to policymakers, investors, and market participants, providing actionable insights to navigate turbulent economic environments effectively and foster stability in global financial markets.

Unveiling the Art and Science of Investment and Financing Decision Making

Purpose: This study explores the intricate dynamics of investment and financing decision-making, emphasizing the integration of qualitative judgment and quantitative analysis. The research investigates the foundational theories, empirical findings, and practical implications that shape these decisions, highlighting their significance in financial management and economic activities. Research Design and Methodology: The research employs a comprehensive literature review methodology, systematically analyzing existing scholarly works to identify themes, patterns, and relationships within the literature. This approach involves identifying relevant sources through extensive search strategies, critically evaluating the quality and relevance of the selected literature and synthesizing key findings and theoretical insights. Findings and Discussion: The study reveals that a complex interplay of theoretical frameworks and empirical evidence drives investment and financing decisions. The Modigliani-Miller theorem and the efficient market hypothesis provide foundational insights, while behavioral finance highlights cognitive biases that influence decision-making. Empirical findings emphasize the role of firm-specific characteristics and market conditions in shaping capital structure choices and investment behavior. Practical considerations, including managerial overconfidence and asymmetric information, further complicate these decisions. Implications: The insights from this research are valuable for practitioners, policymakers, and researchers. Understanding the multifaceted nature of investment and financing decisions can enhance financial management practices, inform regulatory frameworks, and guide future research directions. Integrating theoretical insights with empirical evidence and practical considerations enables stakeholders to navigate the complexities of financial markets, fostering informed decision-making and promoting sustainable economic growth.

Integration of European legislative practices in risk management and solvency in the domestic insurance industry

The insurance industry of Ukraine is on the verge of significant changes associated with the integration processes into the European legal framework. It has been identified that the adaptation of domestic insurance legislation to EU Directives requires insurers to form solvency capital, taking into account the minimum capital requirements, and implement a risk-oriented approach in managing their activities. It is noted that there is a need for a gradual implementation of the legislative norms of the European Union presented in the Solvency II Directive into the domestic legislation. This is because the Ukrainian insurance sector is in a turbulent state, primarily associated with military risks, and the key uncertainty currently lies in the liabilities of insurers. The paper employs methods such as analysis, comparison, logical inference formation, and generalization based on the materials of studies by domestic and foreign scholars, as well as legislative norms. These methods are used to investigate the main legislative requirements for risk and capital management in insurance companies. Differences in the sizes of the minimum required capital for domestic and foreign companies are identified, and recommendations are provided to reduce the review periods of its absolute regulatory values defined by Ukrainian legislation, taking into account new risks. The necessity of gradually introducing European legislative innovations is substantiated, considering the peculiarities of the domestic insurance market. This is because the provisions of the Solvency II Directive impose heightened requirements on the capitalization of insurers to cover risks. A conclusion is drawn that the implementation of a risk-oriented approach will not only allow for a prompt response to identified risks but also ensure commitments to clients and enhance the financial stability of insurance companies in Ukraine. The transformation of Ukrainian insurance legislation into the European legislative field provides many positive aspects for domestic insurers to conduct their activities on an efficient, risk-free, fair and stable insurance market.

ESG and the performance of energy and utility portfolios: evidence from Australia

PurposeThis study aims to establish the effect of environmental, social and governance (ESG) practices on Australian energy and utility investment performance.Design/methodology/approachConventional and ESG-rated portfolios are constructed using monthly returns and ESG scores of S&P/ASX 300 listed energy and utility firms from 2014 to 2022. Portfolio performance is estimated using a four-factor regression model, controlling for any economic shocks associated with the COVID-19 pandemic.FindingsThe findings show that the lower the ESG score associated with the overall ESG and environmental portfolios, the greater the performance compared to the market (but not the conventional and other ESG portfolios). High ESG scores do not appear to influence the performance of the energy and utility portfolios, which contrasts expectations that the uptake of ESG should deliver superior risk-return outcomes for investors. The findings also indicate that a contrarian investment approach may be a reasonable performance indicator for high-rated ESG portfolios. ESG practices did not impact portfolio performance during the COVID-19 pandemic.Originality/valueThis research has contributed to the literature by offering ESG investment insights for policymakers, regulators, fund managers and investors. Consistent with the agency perspective on ESG practices and efficient market hypothesis, the evidence implies that, regardless of ESG scores (either high or low), investors should consider investing passively in diversified energy and utility portfolios or low-cost index fund equivalents.

Multivariate simulation‐based forecasting for intraday power markets: Modeling cross‐product price effects

AbstractIntraday electricity markets play an increasingly important role in balancing the intermittent generation of renewable energy resources, which creates a need for accurate probabilistic price forecasts. However, research to date has focused on univariate approaches, while in many European intraday electricity markets all delivery periods are traded in parallel. Thus, the dependency structure between different traded products and the corresponding cross‐product effects cannot be ignored. We aim to fill this gap in the literature by using copulas to model the high‐dimensional intraday price return vector. We model the marginal distribution as a zero‐inflated Johnson's distribution with location, scale, and shape parameters that depend on market and fundamental data. The dependence structure is modeled using copulas, accounting for the particular market structure of the intraday electricity market, such as overlapping but independent trading sessions for different delivery days and allowing the dependence parameter to be time‐varying. We validate our approach in a simulation study for the German intraday electricity market and find that modeling the dependence structure improves the forecasting performance. Additionally, we shed light on the impact of the single intraday coupling on the trading activity and price distribution and interpret our results in light of the market efficiency hypothesis. The approach is directly applicable to other European electricity markets.

Charting by machines

We test the efficient market hypothesis by using machine learning to forecast stock returns from historical performance. These forecasts strongly predict the cross-section of future stock returns. The predictive power holds in most subperiods and is strong among the largest 500 stocks. The forecasting function has important nonlinearities and interactions, is remarkably stable through time, and captures effects distinct from momentum, reversal, and extant technical signals. These findings question the efficient market hypothesis and indicate that technical analysis and charting have merit. We also demonstrate that machine learning models that perform well in optimization continue to perform well out-of-sample.

Let me sleep on it: sleep and investor reactions to earnings surprises

We explore if sleep deprivation affects how investors react to relevant news. Using the transition to Daylight Saving Time (DST) in spring as a disruption to sleeping patterns, we show that investors underreact to a firm’s earnings surprise in the days after the transition to DST. Further, an earnings surprise in the days after the transition to DST is associated with a positive drift in the post-announcement period. Our findings are consistent with sleep-deprived investors mispricing and subsequently revisiting relevant information. Overall, our results highlight the importance of investors’ cognitive ability for efficient market pricing.

EMPIRICAL TESTING OF FAMA-FRENCH ASSET PRICING MODEL IN INDONESIA STOCK EXCHANGE DURING COVID-19 PANDEMIC

The volatility of the Indonesian Stock Exchange (BEI) increased significantly during the Covid-19 pandemic period. In this period return predictability and price volatility in the stock index experienced a single structural break. There is concern among investors and academics that the asset pricing approach model that has been empirically accepted so far is unable to explain the return or excess return of an asset or investment during the Covid-19 pandemic period. This research tests the significance of the size (market capitalization), profitability, value (book-to-market), investment, and market risk premium (Rm-Rf) factors on the excess return of stock portfolios on the Indonesian Stock Exchange during the Covid-19 pandemic period. Existing studies show that the Covid-19 pandemic has affected investor sentiment, causing investors to panic and be pessimistic about their investments. In addition, there were deviations from the efficient market hypothesis during several pandemic periods in several countries so that stock prices did not fully reflect the available information. After testing, it was found that the factors size (market capitalization), profitability, value (book-to-market), investment, and market risk premium (Rm-Rf) did not have a significant influence on the excess return of stock portfolios on the Indonesia Stock Exchange during the period Covid-19 pandemic.

Empirical Evidence on Testing the Efficient Market Hypothesis in Egypt: Case of Currency Devaluation

Empirical Evidence on Testing the Efficient Market Hypothesis in Egypt: Case of Currency Devaluation

Penyelesaian Sengketa di dalam Hukum Pasar Modal

The capital market is an organized financial system that brings together shareholders and investors who had a certain period, directly or through intermediaries. The capital market sector is one of the financial services sectors that has a significant role in national development. Nowadays, lots of criticisms of capital market dispute resolution caused by many factors related to law enforcement, so efforts to create an orderly and efficient capital market have not been optimally achieve. In writing this researches the method used refers to normative juridical, which contains many juridical reviews from several sources and references to the object under study. In writing this article, a juridical approach used to get conclusions and keywords that are very precise from the object of studies in this article.

Generalized Spectral Tests for Multivariate Martingale Difference Hypotheses

This study proposes new generalized spectral tests for multivariate martingale difference hypotheses, specifically geared toward high-dimensionality scenarios where the dimension of the time series is comparable or even larger than the sample size in practice. We develop an asymptotic theory and a valid wild bootstrapping procedure for the new test statistics, in which the dimension of the time series is fixed. We demonstrate that a bias-reduced version of the test statistics effectively addresses the high-dimensionality concerns. Comprehensive Monte Carlo simulations reveal that the bias-reduced statistic performs substantially better than its competitors. The application to testing the efficient market hypothesis on the U.S. stock market illustrates the usefulness of our proposal.

The Epidemic of COVID-19 and the Persistence of MINT Stock Prices

This paper analyzes the Efficient Market Hypothesis (EMH) and stock price predictability for the economies of Mexico, Indonesia, Nigeria, and Turkey (MINT) using the fractional integration method. The result negates the Efficient Market Hypothesis for Nigeria and Indonesia. However, there is a slight divergence in the level of shock persistence in the two stock markets. Additionally, the differencing parameter was relatively unstable during the COVID-19 period, suggesting that the pandemic has had an impact on the level of stock price persistence in MINT countries.

Market memory, advance reaction, and retail investor herding

The arrival of new information, in conjunction with investor cognitive biases, has commonly been acknowledged as the main cause of herding behavior. However, whether past market information embedded in market memory influences investor herding has been less explored. We utilize tick-level trading data from the Chinese stock market to examine the impact of the market memory formed of stocks' exceptional performance during their initial trading days on the future herding behavior of retail investors. The empirical results show that the market memory of newly listed stocks is positively correlated with retail investor herding one year later at the appearance of a market-wide upward trend. We also propose an advance reaction channel in which retail investors tend to pay early attention to and preemptively invest in stocks with deep market memory due to their limited attention and the availability heuristic, leading to further herding due to confirmation bias. Our findings show that market information, if not fully incorporated into current stock prices through investor trading, can be partly stored in market memory. When activated by specific market conditions, market memory can be recalled and affect investor behavior and subsequent price dynamics. Our findings provide additional evidence to challenge the efficient market hypothesis (EMH) from the perspective of behavioral finance.

Pengaruh Moving Average dan Transaction Volume pada Return Saham Perbankan Indonesia

This study investigates the impact of Moving Average (MA) Stock prices and Transaction Volume on Stock returns in the Indonesian banking sector, specifically focusing on the LQ45 index from 2020 to 2023. It delves into the concepts of Efficient Market Hypothesis (EMH), Behavioral Finance, and Technical Analysis. EMH posits that Stock prices reflect all available information and are hard to consistently beat, while Behavioral Finance suggests that psychological factors can lead to market inefficiencies exploitable by technical analysis. Employing a quantitative approach and data from the Indonesia Stock Exchange (BEI), the study finds that MA and Transaction Volume positively and significantly influence Stock returns in the banking sector. However, variations exist in individual bank samples due to factors like fundamentals and unique behaviors, which cannot be solely determined from historical Stock prices. Thus, the research underscores the importance of considering additional factors beyond past price trends when analyzing the Stock performance of individual banks.

Pengaruh Moving Average dan Transaction Volume pada Return Saham Perbankan Indonesia

This study investigates the impact of Moving Average (MA) Stock prices and Transaction Volume on Stock returns in the Indonesian banking sector, specifically focusing on the LQ45 index from 2020 to 2023. It delves into the concepts of Efficient Market Hypothesis (EMH), Behavioral Finance, and Technical Analysis. EMH posits that Stock prices reflect all available information and are hard to consistently beat, while Behavioral Finance suggests that psychological factors can lead to market inefficiencies exploitable by technical analysis. Employing a quantitative approach and data from the Indonesia Stock Exchange (BEI), the study finds that MA and Transaction Volume positively and significantly influence Stock returns in the banking sector. However, variations exist in individual bank samples due to factors like fundamentals and unique behaviors, which cannot be solely determined from historical Stock prices. Thus, the research underscores the importance of considering additional factors beyond past price trends when analyzing the Stock performance of individual banks.