Mispricing Research Articles

Periodicity, Elliott waves, and fractals in the NFT market

Non-fungible tokens (NFTs) are unique digital assets that exist on a blockchain and have provided new revenue streams for creators. This research investigates NFT market inefficiencies to identify claimed cyclic behavior and cryptocurrency influences on NFT prices. The research found that while linear models are not useful in modeling NFT price series, models that extract periodic behavior can provide explanations and predictions of price behavior. The investigation of autocycles in cryptocurrency and NFT markets did not support the existence of Elliott Wave behavior in any of these blockchain enabled assets. Rather NFT price behavior is strongly tied to the underlying asset and its community of fans. These fans commit to periodic bouts of idiosyncratic trading which cools for a while, and then restarts. The research found no evidence supporting whole market effects across the full price series of individual NFTs. The research strongly supports prior findings that the offsetting movements significantly influence NFT prices and trading volume in Bitcoin and Ether. The research found NFT markets exhibit characteristics resembling a social media platform rather than more traditional asset markets like stock exchanges. It found that traditional linear econometric models cannot predict or explain NFT price series, only that NFT price and volume were weakly correlated. Fractal models consistent with Elliott wave theory do explain some of NFT price behavior, but are not consistent or stable over time. This research confirmed prior research findings that Bitcoin and Ether price movements are correlated with general NFT price and volume series in periods of between 24 and 48 h, with significant numbers of trades into and out of cryptocurrencies at 2 and 8 h.

Trade policy and environmental quality in Africa: Aggregation issues and policy deductions

The significance of the impact of trade on sustainable development cannot be overstated. Even though there is a large amount of literature examining the trade - environment connection, most of the studies that have been done so have primarily examined variables like trade openness and foreign direct investment, ignoring the impact of particular trade policy tools, like tariffs. Moreover, there is a pertinent concern about aggregation bias in aggregate estimations, which could lead to inaccurate assessments of the overall connections and potentially misguide policymakers. In contrast to the prevailing trend, this study delves into the influence of trade policy, specifically tariffs, on the environmental degradation in 20 African nations. The investigation also addresses the issue of aggregation bias, aiming to provide a comprehensive understanding of the trade policy-environment link. The panel long-run estimates, employing the PMG estimator, reveal a significantly negative influence of tariffs on ecological footprints. This suggests that, in this context, tariffs do not create market inefficiency but rather contribute to strengthening environmental quality in the African sub-region. The findings underscore the importance of considering specific trade policy instruments in environmental policymaking. Policymakers in the region are presented with an opportunity to strategically utilize tariffs as a tool to address environmental degradation issues. The study further highlights that the relationship is negative at the panel level but positive at some of the individual country-level, indicating the presence of aggregation bias in estimating the trade policy-environmental dilapidation association on a panel scale. As aggregation bias may lead to misguided environmental policies, policymakers are prevailed on to carefully consider both panel and individual country-particular level when formulating trade and environmental strategies.

Review of the book Behavioral finance and wealth management: Market anomalies, investors’ behavior and the role of financial advisors

The book “Behavioral Finance and Wealth Management: Market Anomalies, Investors’ Behavior and the Role of Financial Advisors” presents an examination of the intricate relationship between human behavior and financial decision-making, with particular regard to wealth management. By dissecting biases, market anomalies, and investors’ behaviors, the book provides valuable insights for investors, financial advisors, and anyone interested in the intersection of behavioral finance and wealth management. The book is an open-eye opportunity for many financial advisors and, more in general, professionals in the wealth management sector

Short-Term Versus Long-Term Portfolio Management Strategies and the Selection of Securities

Purpose: This study examines the effectiveness of short-term versus long-term portfolio management strategies and the selection of securities. It aims to provide insights into how different strategies impact investment performance, considering risk tolerance and financial goals. Research Design and Methodology: The study synthesizes findings from academic journals and empirical research using a qualitative literature review approach. The methodology involves thematic analysis to identify key themes, patterns, and insights related to portfolio management strategies and security selection. Findings and Discussion: The research highlights that short-term strategies like momentum trading can capitalize on transient market inefficiencies but entail higher transaction costs and volatility. Conversely, long-term strategies, such as value investing, focus on fundamental analysis and offer more stable returns over time. The integration of ESG criteria into security selection is shown to enhance portfolio performance and align investments with sustainability objectives. Behavioral biases and technological advancements also significantly influence portfolio management decisions. Implications: The study underscores the importance of balancing short-term and long-term strategies based on investor risk tolerance and financial goals. Financial practitioners can leverage these insights to design diversified portfolios and offer tailored advice. Future research should explore the dynamic interplay between these strategies and the impact of technological and regulatory changes on portfolio management. Integrating ESG considerations is crucial for sustainable investing and aligning with evolving market dynamics.

Behavioral Finance and Stock Market Anomalies: Exploring Psychological Factors Influencing Investment Decisions

Behavioural finance questions the rational decision-making process that is based on the information and economic realities of traditional finance. This study investigates the impact of emotions and irrationality on financial decision-making. The increase in study on stock market anomalies leads to an investigation of the psychological factors that influence irrational investor behaviour and market inefficiencies. This research challenges the principles of rational finance theories by examining the significant impact of behavioural finance on stock market anomalies. The study demonstrates the impact of biases, emotions, and cognitive errors on human behaviour, highlighting specific cognitive faults such as anchoring bias, overconfidence, and loss aversion. These flaws lead to distorted decision-making and contribute to market anomalies The study of investing social dynamics explores the phenomenon of herd mentality and the unreasonable increases in asset values. In addition to questioning established financial theories, behavioural finance has the potential to assist investors, politicians, and market regulators. The study aims to construct intricate models that accurately depict financial decision-making by examining psychological variables that give rise to market oddities. The incorporation of behavioural finance into investment decision-making has transformative consequences for investors and financial professionals. Investors acquire the ability to make more astute decisions, spread out risks, and manage market instability by using emotional intelligence. Behavioural data may assist financial professionals in enhancing client-advisor relationships, refining communication methods, and increasing competitiveness in an evolving financial landscape. Behavioural finance provides a constantly evolving understanding influenced by biases and emotions, which poses a challenge to financial practices.

Market Minds: Understanding Behavioral Finance Concepts

The topic of behavioural finance is challenging the traditional notion of rational decision-making in the ever-changing financial markets. This paradigm shift examines the interaction between psychology, cognitive biases, and financial decisions, offering insights into market anomalies and behaviours that contradict conventional economic theory. Behavioural finance is an interdisciplinary field that combines psychology, cognitive science, economics, and finance. Its purpose is to connect economic theory with actual financial behaviours seen in the real world.

Spillover effects between fossil energy and green markets: Evidence from informational inefficiency

This paper builds an indicator of the market inefficiency degree by quantifying the extent of the market deviation from an efficient status. From a novel perspective of informational inefficiency, we further model the dynamic spillover effects between fossil energy and green markets by using a time-varying parameter vector autoregressive (TVP-VAR) model. Our findings indicate that the market inefficiency degree and its spillovers exhibit evident time-varying dynamics and are sensitive to extreme events. The international crude oil benchmarks serve as the most significant information transmitter and receiver in the spillover network of market inefficiency. Within green-related markets, carbon market demonstrates the strongest correlation with the fossil energy markets. Additional analyses involving consideration of economic policy uncertainty reassure the robustness of our findings.

Research on the Amplification Effect of Trading Volume on Mispricing in the Chinese A-share Market

With the continuous growth of the economy, the residents' demand for preserving and appreciating family wealth has gradually become prominent. The stock market has become the primary choice for investors pursuing asset appreciation. Investors expect to achieve higher returns through scientific methods, and factor models, as well as mispricing, have become the focus of attention, aiming to obtain excess returns through market anomalies. However, with technological advances, the complexity of the market is increasing, making it increasingly difficult to find excess returns through pricing models. As trading volume is the most fundamental indicator in the market, containing many effective market insights and capturing investor disagreements, it is necessary to delve into the role of trading volume in the impact of mispricing on expected returns. This research selects all A-share stocks from 2000 to 2022, excluding the Sci-Tech Innovation Board, measuring mispricing through the anomaly in pricing models. Then, by independently double-sorting mispricing and trading volume into 5x5 portfolios, constructing market-weighted investment portfolios, and holding for one month, we observe the predictive ability of mispricing for future returns at different levels of trading volume. The results reveal that in stocks with high trading volume, the predictive ability of mispricing is stronger. Specifically, in stocks with high trading volume, the difference in future returns between the undervalued stock portfolio and the overvalued stock portfolio is greater than the difference in returns in low trading volume stocks. In other words, trading volume plays a moderating role in the impact of mispricing on expected returns, enhancing the predictive ability of mispricing on returns.

Multi-factor Stock Forecasting Based on Regression and Machine Learning Models

In some stock predictions, shallow learning is better than deep learning. With this in mind, this study will adopt a machine learning model and a regression model to predict Chinese stocks to help the stock timing strategy. Different from the previous usage of plenty of indicators as stock features for prediction, this study selected specified multi-factor data and compared the prediction effects of foreign multi-factor data models and China's multi-factor data models to verify. It is found that the prediction effect of the Chinese new salience multi-factor data model which has a better ability to explain market anomalies is better. However, factor models with higher correlations have poor results. Compared with randomly selecting factors or selected factors weight by model, choosing a specific factor model in advance can not only improve calculation efficiency, but also improve prediction accuracy. In addition, compared with the regression model, the prediction results of machine learning also have better performance results.

Forecasting number of corner kicks taken in association football using compound Poisson distribution

This article presents a holistic compound Poisson regression model framework to forecast number of corner kicks taken in association football. Corner kick taken events are often decisive in the match outcome and inherently arrive in batch with serial clustering pattern. Providing parameter estimates with intuitive interpretation, a class of compound Poisson regression including a Bayesian implementation of geometric-Poisson distribution are introduced. With a varying shape parameter, the corner counts serial correlation between matches is handled naturally within the Bayesian model. In this study, information elicited from cross-market betting odds was used to improve the model predictability. Margin application methods to adjust market inefficiency in raw odds are also discussed.

Deep reinforcement learning applied to statistical arbitrage investment strategy on cryptomarket

In the complex and dynamic nature of financial markets where increasingly sophisticated investment strategies are required, deep reinforcement learning (DRL) has proven successful in generating real-time investment strategies, outperforming classical models. Alongside this, statistical arbitrage strategies exploit temporary market inefficiencies to generate returns. In this regard, a novel DRL-based arbitrage method has been developed.This paper presents a unified framework that combines classical statistical arbitrage theory with DRL framework to generate investment strategies. The framework addresses the challenges of identifying similar asset portfolios, extracting signals indicating temporary price deviations, and determining optimal trading rules given market conditions. The proposed methodology involves constructing arbitrage portfolios based on cointegration relationships, removing signals from price series and portfolios, and using a DRL agent to make optimal decisions within a fixed time horizon.The empirical analysis focuses on the cryptocurrency market, known for its volatility and risk. Results demonstrate that DRL agents can generate strategies with positive returns in out-of-sample periods 79.52% to 112.82% with no transaction cost, outperforming market benchmark Bitcoin 32.51% return, the best performing over the period Litecoin with 57.11% return and the worst performing Solana with a 35.70% loss. Moreover, these strategies effectively reduce risk, achieving higher risk-adjusted returns on individual assets. The strategies maintain positive returns when considering transaction costs, with the DRL agent outperforming the standard arbitrage strategy. The best-performing strategy is based on a Deep Q-Network (DQN) agent with a return of 18.39%, annualized volatility of 12.22%, and an annualized Sharpe ratio of 2.43. At the same time, Bitcoin holds an annualized volatility of 44.13% and a Sharpe ratio of 1.08.Their randomness and coherence are studied to verify the robustness of the agents’ decisions. The actions generated by the agents are not random but based on well-founded policies, which align with the obtained results.

On the efficiency and its drivers in the cryptocurrency market: the case of Bitcoin and Ethereum

Most previous studies on the market efficiency of cryptocurrencies consider time evolution but do not provide insights into the potential driving factors. This study addresses this limitation by examining the time-varying efficiency of the two largest cryptocurrencies, Bitcoin and Ethereum, and the factors that drive efficiency. It uses daily data from August 7, 2016, to February 15, 2023, the adjusted market inefficiency magnitude (AMIMs) measure, and quantile regression. The results show evidence of time variation in the levels of market (in)efficiency for Bitcoin and Ethereum. Interestingly, the quantile regressions indicate that global financial stress negatively affects the AMIMs measures across all quantiles. Notably, cryptocurrency liquidity positively and significantly affects AMIMs irrespective of the level of (in) efficiency, whereas the positive effect of money flow is significant when the markets of both cryptocurrencies are efficient. Finally, the COVID-19 pandemic positively and significantly affected cryptocurrency market inefficiencies across most quantiles.

Variance of entropy for testing time-varying regimes with an application to meme stocks

Shannon entropy is the most common metric for assessing the degree of randomness of time series in many fields, ranging from physics and finance to medicine and biology. Real-world systems are typically non-stationary, leading to entropy values fluctuating over time. This paper proposes a hypothesis testing procedure to test the null hypothesis of constant Shannon entropy in time series data. The alternative hypothesis is a significant variation in entropy between successive periods. To this end, we derive an unbiased sample entropy variance, accurate up to the order O(n-4)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^{-4})$$\\end{document} with n the sample size. To characterize the variance of the sample entropy, we first provide explicit formulas for the central moments of both binomial and multinomial distributions describing the distribution of the sample entropy. Second, we identify the optimal rolling window length to estimate time-varying Shannon entropy. We optimize this choice using a novel self-consistent criterion based on counting significant entropy variations over time. We corroborate our findings using the novel methodology to assess time-varying regimes of entropy for stock price dynamics by presenting a comparative analysis between meme and IT stocks in 2020 and 2021. We show that low entropy values correspond to periods when profitable trading strategies can be devised starting from the symbolic dynamics used for entropy computation, namely periods of market inefficiency.

The barriers to sustainable risk transfer in the cyber-insurance market

Abstract Efficient risk transfer is an important condition for ensuring the sustainability of a market according to the established economics literature. In an inefficient market, significant financial imbalances may develop and potentially jeopardize the solvency of some market participants. The constantly evolving nature of cyber-threats and lack of public data sharing mean that the economic conditions required for quoted cyber-insurance premiums to be considered efficient are highly unlikely to be met. This paper develops Monte Carlo simulations of an artificial cyber-insurance market and compares the efficient and inefficient outcomes based on the informational setup between the market participants. The existence of diverse loss distributions is justified by the dynamic nature of cyber-threats and the absence of any reliable and centralized incident reporting. It is shown that the limited involvement of reinsurers when loss expectations are not shared leads to increased premiums and lower overall capacity. This suggests that the sustainability of the cyber-insurance market requires both better data sharing and external sources of risk tolerant capital.

The Impact of Cognitive Biases and Emotional Factors on Investor Behavior and Stock Market Anomalies

The Impact of Cognitive Biases and Emotional Factors on Investor Behavior and Stock Market Anomalies

Effectiveness of Momentum Strategies in Cryptocurrency Markets: An Analytical Research for the Period of 2018 to 2023

Investors in financial markets seek to maximize returns and minimize risk. To this end, they seek investment strategies that will enable them to achieve above-average returns. Many strategies are based on stock market anomalies. Momentum is one such strategy. That strategy, involves selecting assets for the portfolio that have recently gained above-average rates of return. The phenomenon, which has been identified in a number of global stock markets, recommends investing in securities that achieved the highest rates of return over a period of 3 to 12 months and selling short the securities with the lowest returns.

MEASURING MARKET RESPONSE TO DIVIDEND ANNOUNCEMENTS OF VALUE AND GROWTH STOCK COMPANIES THROUGH INFORMATION CONTENT

The main objective of this research is to demonstrate the extent of the market's response to dividend announcements for value and growth stock companies through information content and to demonstrate the impact of information content and its reflections on the returns of these stocks through the daily event window, which lasts for 21 days, and to provide information to help investors make rational decisions to eliminate the problem of asymmetry of information between insiders (managers) and investors, because information content sends signals to investors in the financial market that they can interpret either positively or negatively, and whose content indicates that the company's management believes that the shares are traded in the financial market at less or more than their real value. The research relied on a sample consisting of (70) dividend announcements by (27) companies from various sectors listed in the Iraqi Financial Market for the period from 1/1/2016 to 31/12/2023. The sample companies were classified into value stocks and growth stocks based on the book value to market value. If the book value is greater than the market value, they are considered value stocks, and if the opposite is true, they are considered growth stocks. Using a number of financial and statistical methods through the EXCEL and SPSS-26 programs in order to analyze the research variables and test its hypotheses, the research reached a number of conclusions, the most important of which was that the results of the event window study proved that value and growth stocks are strongly related to the content of information sent to the Iraqi stock market through dividend announcements, which contradicts the first and second hypotheses. This was proven through the results of the daily averages of the accumulated abnormal returns of value stock companies, which were higher than the averages of growth stocks through the event window. Based on this, the research came out with a number of recommendations, perhaps the most important of which is that the management of the Iraq Stock Exchange should realize the danger of the market's inefficiency in responding to public information in general and dividend announcements in particular, because this could be exploited by some professionals to formulate trading rules that enable them to achieve abnormal returns at the expense of investors. In the market, causing a disruption in market performance.

Exclusive Portfolio Dealing and Market Inefficiency

Exclusive Portfolio Dealing and Market Inefficiency

Behavioral Finance: Several Key Effects of Investor Decision-Making

This paper examines the field of behavioral finance. First, a review of the behavioral finance literature will highlight the key findings and theories that have emerged in this field. Second, this paper will also discuss the practical applications of behavioral finance, including how it can be applied to improve investment decisions and reduce cognitive biases. Finally, several future research directions that may further advance the field of behavioral finance are analyzed, such as exploring the role of emotions in financial decision-making and examining the impact of social factors on investor behavior, pointing out shortcomings and making extensions to current research in this field. This paper will specifically analyze behavioral finance in terms of loss aversion, market anomalies, framing effect and endowment effect, revealing the psychological bias and cognitive bias of human beings in the process of financial decision-making, and providing new perspectives and methods for understanding the operating mechanism of the financial market and investment strategies.

The Impact of AI on Sustainable Development of The Labor Market

With the emergence of generative AI technologies like ChatGPT, the conversation has shifted increasingly toward the possibility that machines will take over human jobs. The rise of artificial intelligence (AI) poses a double challenge to job security and the labor market organization while also bringing about remarkable efficiencies and introducing fresh job sectors. The complex landscape requires intentional government intervention based on fundamental economic principles. Information asymmetry is emphasized in discussing AI’s incorporation into the labor market, utilizing Joseph Stiglitz’s information theory. Discrepancies in access to information about emerging technologies and the required skills for the AI-driven economy could exacerbate market inefficiencies and result in job loss. Stiglitz’s remarks underscore the crucial need for governments to actively address these information gaps to enhance market efficiency and promote worker well-being. Potential actions could involve promoting partnerships between educational institutions and businesses to align curriculum with evolving labor market demands and mandating that companies reveal the incorporation of AI into their practices to enhance the accuracy of workforce development plans. This paper analyses the role of government in harmonizing AI’s impact on the labor market, AI’s influence on labor market dynamics, and navigating job displacement in the AI era. In addition, the relevant recommendations are proposed.