Behavior Of Asset Prices Research Articles

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.

Exploring Market Dynamics: A Qualitative Study on Asset Price Behavior, Market Efficiency, and Information Role in Investment Decisions in the Capital Market

Exploring Market Dynamics: A Qualitative Study on Asset Price Behavior, Market Efficiency, and Information Role in Investment Decisions in the Capital Market

Anatomy of sovereign yield behaviour using textual news

While the relationship between the information content of macroeconomic news and the behavior of asset prices has been studied extensively in the finance literature, this study provides a new perspective by examining the impact of textual news on sovereign bond yield spreads in an emerging country. This study used bond market news published in newspapers to develop the sentiment scores using a modified word dictionary to unravel news characteristics. A nonlinear regime-shifting regression model of Markov Regime Shifting (MRS) is used to understand the impact of news on sovereign bond yield spreads. The paper results show that textual news sentiment may explain both steepening and flattening of the yield curve, with monetary and fiscal policy news having the most significant impact on yield spread behaviour. The results hold key implications for policymakers, debt fund managers and other market participants.

Extreme Events Cause Market Efficiency or Inefficiency? Evidence from Precious Metals and Green Stock Indices

Background: Green investments offer a promising market-based approach to tackling global climate change but are susceptible to risk transmission from multiple assets. Purpose: To examine the multifractality between precious metals (Gold, Silver, Platinum) and green stock indices, such as Clean Energy Fuels, Nasdaq Clean Edge Green Energy, S&P Global Clean Energy and WilderHill Clean Energy, over the period from 1 January 2018 to 23 November 2023. The study aims to answer the following question: Did the events of 2020 and 2022 cause efficiency or inefficiency in the markets analysed? Methods: The Detrended Fluctuation Analysis (DFA) econophysics model will be used to answer the research question. Results: During the period of stability, there was a clear division between the markets, with Gold and green energy indices demonstrating market efficiency, while Platinum, Silver and some green indices exhibited price persistence, suggesting opportunities for above-average gains. However, the 2020 global pandemic led to widespread inefficiencies in all the markets analysed, with precious metals and green energy indices showing price persistence, indicating patterns that could lead to arbitrage opportunities. The persistence in returns suggested a tendency for asset prices to continue in the same direction for an extended period, offering opportunities to identify ongoing market trends for investment. In the pre-conflict period, the random walk hypothesis was rejected in all markets, with precious metals exhibiting anti-persistence in returns, while green energy indices showed significant persistence. During the conflict period, precious metals and green energy indices converged towards persistence in returns, except Platinum, which showed anti-persistence. Conclusion: This study provides valuable insights for investors and financial market professionals, highlighting the importance of understanding asset price behaviour patterns in different economic contexts.

The Solution for Unstable Bubble Assets: Reflection from an Islamic Finance Perspective

In the fast-paced world of technology, there exist popular investment instruments that have no intrinsic value (non-asset-based). As a result, their prices are highly volatile and prone to bubbles. However, from an Islamic perspective, investment vehicles must have a fundamental value (asset-based) to ensure price stability. This research aims to compare the behaviour of asset prices between non-asset-based and asset-based types by utilizing a system dynamics model. Furthermore, this study investigates the impact of two types of market sentiment: relatively stable good market and very good market sentiment, follows by very bad market sentiment. The findings of this study suggest that non-asset-based asset prices tend to bubble in response to good market sentiment and then rapidly decline when significant bad market sentiment arises. In contrast, asset-based assets tend to have more stable prices because investors have benchmarks and do not rely solely on intuition. This research highlights the importance of the fundamental value or asset base in investment instruments, as required by Islamic finance. However, asset-based assets can also be prone to high volatility when irrational investors dominate, and speculation prevails. In such cases, government intervention or regulation may be necessary to mitigate risks and stabilise the market.

A Mathematical Model of Financial Bubbles: A Behavioral Approach

In this work, we propose a mathematical model to describe the price trends of unsustainable growth, abrupt collapse, and eventual stabilization characteristic of financial bubbles. The proposed model uses a set of ordinary differential equations to depict the role played by social contagion and herd behavior in the formation of financial bubbles from a behavioral standpoint, in which the market population is divided into neutral, bull (optimistic), bear (pessimistic), and quitter subgroups. The market demand is taken to be a function of both price and bull population, and the market supply is taken to be a function of both price and bear population. In such a manner, the spread of optimism and pessimism controls the supply and demand dynamics of the market and offers a dynamical characterization of the asset price behavior of a financial bubble.

Regime-Specific Quant Generative Adversarial Network: A Conditional Generative Adversarial Network for Regime-Specific Deepfakes of Financial Time Series

Simulating financial time series (FTS) data consistent with non-stationary, empirical market behaviour is difficult, but it has valuable applications for financial risk management. A better risk estimation can improve returns on capital and capital efficiency in investment decision making. Challenges to modelling financial risk in market crisis environments are anomalous asset price behaviour and a lack of historical data to learn from. This paper proposes a novel semi-supervised approach for generating regime-specific ‘deep fakes’ of FTS data using generative adversarial networks (GANs). The proposed architecture, a regime-specific Quant GAN (RSQGAN), is a conditional GAN (cGAN) that generates class-conditional synthetic asset return data. Conditional class labels correspond to distinct market regimes that have been detected using a structural breakpoint algorithm to segment FTS into regime classes for simulation. Our RSQGAN approach accurately simulated univariate time series behaviour consistent with specific empirical regimes, outperforming equivalently configured unconditional GANs trained only on crisis regime data. To evaluate the RSQGAN performance for simulating asset return behaviour during crisis environments, we also propose four test metrics that are sensitive to path-dependent behaviour and are also actionable during a crisis environment. Our RSQGAN model design borrows from innovation in the image GAN domain by enabling a user-controlled hyperparameter for adjusting the fit of synthetic data fidelity to real-world data; however, this is at the cost of synthetic data variety. These model features suggest that RSQGAN could be a useful new tool for understanding risk and making investment decisions during a time of market crisis.

Can a firm's hierarchical complexity affect its stock price behavior? Evidence from stock price crash risk

PurposeThis paper examines how a firm's hierarchical complexity, which is determined by the way it organizes its subsidiaries across the hierarchical levels, can impact its stock price crash risk.Design/methodology/approachThe authors employ a measure of hierarchical complexity that captures the depth and breadth of how subsidiaries are organized within a firm. This measure is calculated using information about firms' subsidiaries extracted from the Bureau van Dijk (BvD) database that allows the authors to construct each firm's hierarchical structure. The data sample includes 2,461 USA firms for the period from 2012 to 2017 (11,006 firm-year observations). Univariate tests and panel regression are used for the main analysis. Two-stage-least-squares (2SLS) instrumental variable regression and various other tests are employed for robustness check.FindingsThe results show a positive relationship between hierarchical complexity and stock price crash risk. This relationship is amplified in firms with a greater number of subsidiaries that are hierarchically distanced from the parent company as well as in firms with a greater number of foreign subsidiaries in countries with weaker rule of law.Originality/valueThis paper is the first to investigate the impact hierarchical complexity has on crash risk. The results highlight the role that a firm's organizational structure can have on asset pricing behavior.

Neural networks for estimating Macro Asset Pricing model in football clubs

SummaryThe recent crisis caused by COVID‐19 directly affected consumption habits and the stability sof financial markets. In particular, the football industry has been hit hard by this pandemic and therefore has more volatile stock prices. Given this new scenario, further research is needed to accurately estimate the value of the shares of football clubs. In this paper, we estimate an asset pricing model in football clubs with different compositions of risk nature using non‐linear techniques of artificial neural networks. Usually, asset pricing models have been estimated with linear methods such as ordinary least squares. Our results show a precision higher than 90% for all the estimated models, which far exceeds those shown by linear methods in the previous literature. We find that the residual represents about 40% of the variance of the price‐dividend ratio. Long‐term risks follow in importance, and above all, the habit component and its behaviour in the face of changes. The importance of the residual component exists due to a low correlation between the asset price and consumer behaviour, but to a much lesser extent than that shown in previous studies. The estimation carried out with artificial neural networks, both the Deep Learning methods and especially the Quantum Neural Network, opens up new possibilities to estimate more efficiently the pricing of financial assets in the football industry.

Effects of monetary policy news on financial assets: Evidence from Brazil on a bivariate VAR-GARCH model (2006–17)

The impact of news releases related to the inflation targeting regime on the financial market is analyzed by estimating a bivariate VAR GARCH-BEKK-in-mean model. We use daily data, from January 2006 to May 2017, of stock prices index (IBOVESPA), exchange rate (BRL/USD) and interbank deposit rate (DI360). We developed a positive and negative news index to measure the impact of news releases based on Caporale et al. (2016) and Caporale et al. (2018). Although the literature on the subject is vast, this paper fills relevant gaps in three ways. First, we investigate the bidirectional relationship between monetary policy related news releases and the behavior of asset prices before and after the 2008 crisis in Brazil. Second, we consider the relationship between the second moments of the variables of interest, using the conditional volatility as a proxy for uncertainty. Third, we provide a time series approach to measure the effect of macroeconomic related news releases on financial asset returns. The results indicate there are mean spread effects from news for the exchange rate and the Brazilian stock index: (i) the GARCH-in-mean parameter is statistically significant for positive and the difference of news for the DI360; (ii) monetary policy and external shocks are statiscally significant as expected with exception of the external shocks for the Brazilian stock index; and (iii) there are volatility spillovers and changes of this volatility after the crisis for stock index and DI360.

Do Options Belong in the Portfolios of Individual Investors?

The use of options by individual investors has grown dramatically in recent years. The authors evaluate several popular options strategies, including portfolio insurance, life cycle investing, buy-write, and single-stock call-buying, from the perspective of an individual investor. The authors suggest that Expected Utility is the most appropriate metric for such evaluation, as it accounts for both return <i>and</i> risk, and naturally handles non-linear payoffs. They assess the different options strategies under a range of assumptions of asset price behavior, investor risk appetite, and option market pricing relative to fair value. They find that for a representative investor the benefit of adding options to the portfolio is at best quite small, and most of this improvement also can be achieved through periodic portfolio rebalancing. However, the benefits of options can be greater for several special investor categories and in certain market environments. The authors also identify several popular uses of options that are likely to be quite harmful to investor welfare.

The Role of Beliefs in Asset Prices: Evidence from Exchange Rates

A long-standing challenge in the asset pricing literature is to understand why asset prices sometimes underreact and sometimes overreact to news. We seek to address this challenge in the context of currency markets. We construct a model of exchange rate determination disciplined by survey data, where short-lived investors each (1) receive noisy private signals about the future path of interest rate differentials between the US and other countries and (2) overestimate the persistence of interest rate differentials. The model is able to qualitatively and quantitatively match patterns of underreaction and overreaction of exchange rates in response to news. The model also matches the failure of uncovered interest rate parity (UIP), capturing the return predictability of interest rate differentials for the returns to borrowing in USD and lending in foreign currency, as well as the fact that this return predictability is declining in the maturity of bonds used to borrow and lend. Finally, we use the model to help understand the reversal of the failure of UIP in recent years, the role of higher-order uncertainty, and the persistence of subjective beliefs. Our results highlight the important role that investors' beliefs may play in asset price behavior.

Feedback Trading and Bubbles

The paper develops a model of bubbles that can be taken to the data and explain the behavior of asset prices and their statistics. We depart from the rational expectations framework and assume that investors are only boundedly rational. They observe the price process, but do not fully understand how its volatility and expected returns are determined in equilibrium. Investors learn about the market by looking at past prices. When they observe unexpectedly high returns, they infer that the asset must currently have a high Sharpe ratio, and therefore, allocate a higher share of their wealth to the asset, further increasing the asset price. The interaction of this feedback effect with investors' wealth effect determines the price dynamics and evolution of investors' beliefs in the model. We fit the model to cryptocurrency markets and show that it can successfully explain many empirical facts in these markets.

Preferred Habitat Investors in the UK Government Bond Market

Most tests of preferred habitat theory are indirect; they infer the existence of preferred habitat behaviour in financial markets by examining the behaviour of asset prices. We instead identify preferred habitat behaviour directly from whether investors show a preference towards a particular duration habitat. We do so by making use of a newly available and highly granular data set on the UK government bond (gilt) market, which allows us to examine investors’ gilt transactions and their daily stock of gilt holdings during 2016 and 2017. Using cluster analysis, we find that investors can be classified into distinct groups, some of which more closely display the behavioural properties that theory associates with preferred habitat investors. We find that these groups of investors are less sensitive to price movements than other investor groups and include institutional investors, like life insurers and pension funds, which are typically associated with preferred habitat behaviour. Evidence from the Bank of England’s QE4 purchase programme during August 2016 to March 2017 suggests that these investor groups sold relatively more of their gilt holdings to the Bank than other groups of investors.

U.S. unconventional monetary policy and risk tolerance in major currency markets

This paper studies the effects of U.S. unconventional monetary policy announcements on the implied volatility of three major currency pairs, Dollar/Euro, Dollar/British Pound and Dollar/Yen by using panel data analysis along with several model specifications and robustness tests. Monetary policy announcements not only have an effect on the realized behavior of asset prices, but also influence market participants’ expectations regarding future volatility. Our empirical findings show that Federal Reserve’s unconventional monetary policy announcements significantly reduce the market expectations about future realized volatility of exchange rates, suggesting that lax monetary policy leads to elevated risk-tolerance in currency markets. Furthermore, our findings indicate that market participants’ expectations respond differently to the different rounds of U.S. quantitative easing.

Firm Customer Bases

Using consumer transaction data, this paper demonstrates that it is possible to construct accurate pictures of firm revenue, growth, geographic dispersion, and customer base characteristics. We develop two new measures which characterize firms’ customer bases: the rate of churn in a firm’s customer base and a metric of the pairwise similarity between firms’ customer bases. We show that these measures provide important insights into the behavior of both real firm decisions and firm asset prices. Rates of customer churn affect the level and volatility of firm-level investment, markups, and profits. Churn also affects how quickly firms respond to shocks in the value of their growth options (i.e. Tobin’s Q). Moreover, high churn firms tended to face steeper declines in consumer spending during the recent COVID-19 outbreak. Similarity between firms’ customer bases highlights one under-explored type of predictability among stock returns – we demonstrate that significant alpha can be generated using a trading strategy that exploits our index of customer base similarity across firms.

Asset Allocation Strategies: Enhanced by Micro-Blog

The rapid rise of social media communication has touched upon all aspects of our social and commercial life. In particular, the rise of social media as the most preferred way of connecting people on-line has led to new models of information communication amongst the peers. Of these media Twitter has emerged as a particularly strong platform and in the financial domain tweets by market participants are of great interest and value. News in general, and commercial and financial news wires, in particular provide the market sentiment and in turn influence the asset price behaviour in the financial markets. In a comparable way micro-blogs of tweets generate sentiment and has an impact on market behaviour, that is , the price as well as the volatility of stock prices. In our recent research [Sadik et al. (2019a)] we have introduced news sentiment based filters such as News RSI (NRSI) and Derived RSI (DRSI), which restrict the choice of asset universe for trading. In this present study, we have extended the same approach to StockTwit’s data. We use the filter approach of asset selection and restrict the available asset universe. We then apply our daily trading strategy using the Second Order Stochastic Dominance (SSD) as an asset allocation model. Our trading model is instantiated by two time series data, namely, (i) historical market price data and (ii) StockTwits sentiment (scores) data. Instead of NRSI we compute the Micro-blog RSI (MRSI) and using this a DRSI is computed. The resulting combined filter (DRSI) leads to an enhancement of the SSD based trading and asset allocation strategy. Empirical experimental results of constructing portfolios are reported for S&P 500 Index constituents.

Macroeconomic news and market reaction: Surprise indexes meet nowcasting

Market operators monitor a massive flow of macroeconomic news every day and react to the unexpected component of each release. Can we replicate the market’s pricing of macroeconomic news automatically? This paper shows that a “Nowcasting Surprise Index”, constructed by aggregating forecast errors from a nowcasting model using model-based weights, resembles the surprise indexes proposed in the recent literature or constructed by practitioners, which cumulate survey-based forecast errors weighted by using the average effect of news on asset prices. This suggests that market operators and a nowcasting model filter the macroeconomic data flow similarly and confirms the link between news about macroeconomic indicators and asset prices. Moreover, the paper shows that recent cumulated news in macroeconomic data, which carry information about the underlying state of the economy, accounts for a non-negligible part of asset price behaviour.

Detección de caminata aleatoria en precios bursátiles mediante cadenas de Markov: aplicación al Índice de Precios y Cotizaciones de México

The Random Walk (weak form efficient market) hypothesis is of vital importance in economics and finance to explain the behaviour of asset prices. Several authors have examined the validity and conditions under which the hypothesis holds. Most of the techniques and models used, rely on runs and serial correlation tests, however test using Markov chains are rare. Most Markov chains applications perform an stratification of returns defining the structure of the state space. The aim of this research is to detect the presence of random walk in stock market returns using Markov chains. The chain states are defined as the run lengths the process can develop. The concept of cycles is also introduced modelling the process in a more concretely. Conclusions are drawn analysing stationarity of the steady state probability distributions under diverse scenarios. The Mexican stock market daily closing prices index is analysed, covering a 16-year period, finding that the random walk is not present. This result is corroborated applying conventional random walk hypothesis tests.

Limited Participation in the Joint Behavior of Asset Prices and Individual Consumptions

We propose an asset pricing model featuring both limited participation and heterogeneity, in which agents randomly participate in the bond and stock markets according to a probability that depends on their non-financial income. We develop an indirect inference method to estimate our model on individual US consumption (CEX) and financial data. Our estimated model performs very well at jointly replicating the equity premium and the unequal distribution of individual consumptions. As an external validity check, our model accurately predicts the estimated stock market participation cost and its decline over the period 1980-2004, as well as observed financial market participation.