Individual Stocks Research Articles

Irregularities in forward-looking volatility

We investigate the behavior of 30-day forward-looking volatility derived from options on market indices, commodities and individual active stocks using a wide range of seasonal and calendar anomalies documented empirically in the finance literature. Our findings indicate the consistent and systematic absence of irregularities in the majority of the examined effects because, in general, the volatility indices remain unaffected by seasonal and calendar effects. However, we detect robust and significant weekday seasonality, within-the-month, turn-of-the-month, pre-holiday, and intra-quarter effects. For investors interested in buying or selling volatility products, our findings may be useful in timing their transactions.

Student‐t stochastic volatility model with composite likelihood EM‐algorithm

A new robust stochastic volatility (SV) model having Student‐ marginals is proposed. Our process is defined through a linear normal regression model driven by a latent gamma process that controls temporal dependence. This gamma process is strategically chosen to enable us to find an explicit expression for the pairwise joint density function of the Student‐t response process. With this at hand, we propose a composite likelihood (CL) based inference for our model, which can be straightforwardly implemented with a low computational cost. This is a remarkable feature of our Student‐t process over existing SV models in the literature that involve computationally heavy algorithms for estimating parameters. Aiming at a precise estimation of the parameters related to the latent process, we propose a CL expectation–maximization algorithm and discuss a bootstrap approach to obtain standard errors. The finite‐sample performance of our CL methods is assessed through Monte Carlo simulations. The methodology is motivated by an empirical application in the financial market. We analyze the relationship, across multiple time periods, between various US sector Exchange‐Traded Funds returns and individual companies' stock price returns based on our novel Student‐t model. This relationship is further utilized in selecting optimal financial portfolios. Generalizations of the Student‐t SV model are also proposed.

Stock Market Synchronization and Stock Volatility: The Case of an Emerging Market

The purpose of this paper is to study the effect of stock market synchronization on the volatility of its component assets. For this objective, we calculate the stock market's synchronization using the Minimum Spanning Tree Length (MSTL) network analysis method. Then, we implement forecasting tests in and out the sample to assess the forecasting power on the stock market's synchronization to predict the individual stock realized volatility. Additionally, we test a VAR and a forecast error variance decomposition analysis to study Granger causality's presence on volatility. Our results show that synchronization within a market exists and changes over time. Our main results show that an increase in synchronization causes an increase in financial assets' realized volatility in the following month. Our results made it possible to study financial markets' synchronization and take a systemic risk approach to improve investment management. Our main idea was that the stock markets' synchronization positively correlates with financial assets' volatility. The greater the synchronization, the greater the volatility in the following period. This study offers a new approach to study the stock market volatility.

Analysis of Equity Disputes in Listed Companies With Dispersed Ownership Structure and Protection of Small and Medium Shareholders' Interests.

This paper selected Vanke as the case to study the governance problems of Vanke and the protection of the interests of small and medium shareholders under the situation of equity disputes. At the same time, the study further explored the advantages and disadvantages of the dispersed ownership structure, the long-term impact on the company’s development and the choice of the involved corporate governance methods under the current Chinese capital market conditions. This paper adopted the event research method and selected the period from June 2015 to June 2017 (24 months) as the observation period to analyze the market performance impact of Vanke in the equity disputes. At the same time, this paper also measured Vanke’s individual stock rate of return (Rit) and market rate of return (Rmt), and calculated Vanke’s normal rate of return [E(Ri,t)], abnormal rate of return (ARi,t), and cumulative abnormal rate of return (CARi) during different event windows ([−3,10]). Vanke’s shareholding was too dispersed and the stock price had been sluggish for a long time, which had greatly reduced the acquisition difficulty and cost of Baoneng, thus triggering the “barbarian invasion” of Baoneng. In the struggle for control, whether it was Vanke’s anti-takeover measures or China Resources, Baoneng, and Evergrande’s competition for equity, their actions had harmed the interests of small and medium shareholders. The market supervision department was too lenient to supervise and punish the interests of small and medium shareholders, and opportunism made behaviors that infringe on the interests of others more reckless. However, small and medium shareholders cannot actively participate in the company’s management decision-making to safeguard their legitimate rights and interests, which intensifies the violations of all parties in the equity disputes, thus forming a vicious circle. Therefore, the protection of the interests of small and medium shareholders required the joint efforts and consciousness of regulators, small and medium shareholders, and acquirers.

Constructing Equity Investment Strategies Using Analyst Reports and Regime Switching Models.

This study demonstrates whether analysts' sentiments toward individual stocks are useful for stock investment strategies. This is achieved by using natural language processing to create a polarity index from textual information in analyst reports. In this study, we performed time series forecasting for the created polarity index using deep learning, and clustered the forecasted values by volatility using a regime switching model. In addition, we constructed a portfolio from stock data and rebalanced it at each change point of the regime. Consequently, the investment strategy proposed in this study outperforms the benchmark portfolio in terms of returns. This suggests that the polarity index is useful for constructing stock investment strategies.

How Have ETFs Changed Market Macro Efficiency and Risk Structure?

In the late 1990s, Samuelson offered a dictum suggesting that the equity market, although micro efficient, is also macro inefficient. That is, at the aggregate level, the market does not accurately reflect the available information. We further explore this dictum in the context of a market that has evolved to provide larger options for trading across the whole spectrum of investors. We argue that the macro efficiency of the stock market has been increasing over the past 20 years. We show that the development and adoption of exchange-traded funds (ETFs) have played a major role in the transmission of information to the market, resulting in an increase in market macro efficiency. Our approach is based on the R-squared of the market model explaining US stock and sector returns. First, we propose these R-squared values as a proxy for market macro efficiency. Then, we use regression analyses at the individual stock and sector levels to observe the relationship between the rise in ETFs and market macro efficiency. Finally, we use extrafinancial data related to the awareness and adoption of ETFs to confirm their contribution.

Genetic identification of Chinese shrimp Fenneropenaeus chinensis post-release in Jinzhou Bay: Implications for management of stock enhancement

The Chinese shrimp (Fenneropenaeus chinensis), one of the important fishery resources in North China, has sharply declined. Artificial propagation and release of F. chinensis have been developed in recent decades in order to supplement wild stocks and improve economic profit. For investigation of the effects of stock enhancement in Jinzhou Bay, we traced this program by sampling 574 female shrimp after spawning and recaptured 293 individuals post-release. Among recaptured individuals, 21 were identified as being hatchery-raised via mtDNA control region and six microsatellite markers identification, accounting for 7.2%, indicating that the hatchery-released juvenile shrimps could survive in the natural environment and contribute to F. chinensis population replenishment in the wild. To address genetic impacts from stock enhancement, we compared the genetic variations between spawners used for enhancement release and local recaptured individuals. High genetic diversities were maintained in F. chinensis stocks, but local recaptured individual stock seemed to exhibit lower genetic diversity. Deviation from Hardy–Weinberg equilibrium and positive values in the inbreeding coefficient in all microsatellite loci and stocks indicated that homogenization and inbreeding may have occurred. More appropriate strategies to restore F. chinensis populations should be discussed for future fishery management.

Quantifying impact and response in markets using information filtering networks

We present a novel methodology to quantify the ‘impact’ of and ‘response’ to market shocks. We apply shocks to a group of stocks in a part of the market, and we quantify the effects in terms of average losses on another part of the market using a sparse probabilistic elliptical model for the multivariate return distribution of the whole market. Sparsity is introduced with an L 0-norm regularization, which forces to zero some elements of the inverse covariance according to a dependency structure inferred from an information filtering network. Our study concerns the FTSE 100 and 250 markets and analyzes impact and response to shocks both applied to and received from individual stocks and group of stocks. We observe that the shock pattern is related to the structure of the network associated with the sparse structure of the inverse covariance of stock log-returns. Central sectors appear more likely to be affected by shocks, and stocks with a large level of underlying diversification have a larger impact on the rest of the market when experiencing shocks. By analyzing the system during times of crisis and comparative market calmness, we observe changes in the shock patterns with a convergent behavior in times of crisis.

Reference point formation: Does the market whisper in the background?

AbstractWell‐established studies in behavioral finance confirm that investors are highly influenced by subjective reference points when making economic decisions. Although the literature examines reference point formation for individual stocks based on observed price sequences, the stocks are often treated without considering market context. We extend this literature by incorporating market price information as background to the behavior experiment, and we consider both prospect theory and anticipation/disappointment‐based utility functions in a combined framework to investigate the impact of market context on reference point formation. Our article provides new evidence and insights for investment practitioners and behavioral researchers.

Year-round genetic monitoring of mixed-stock fishery of Atlantic cod (Gadus morhua); implications for management

Abstract Mixed-stock fisheries pose a challenge for fisheries management recommendations and management, as mixed stocks can lead to unintentional over- or under-exploitation of stocks, with both short- and long-term biological and economic consequences. This study demonstrates practical application and implementation of year-round high-resolution genetic stock monitoring in management of mixed Atlantic cod (Gadus morhua) stocks in western Greenland. Using a panel of 96 high resolution gene-associated SNPs, we identified the stock of origin for four cod stocks in the two major inshore mixed-stock fisheries. Contribution of individual stocks was quantified to identify if fjord sections, times of year, and fish size, would allow a “clean fishery” with a minimal contribution of the depleted West Greenland Offshore (WGO) population. High level of mixing was identified with 50% of inshore catches originating from the inshore stock, 20% from the East Greenland/Iceland offshore stock, and 30% from the WGO stock. Although some spatiotemporal variation of stock proportions was identified, a practice allowing exclusively fishing a specific stock was not possible. Accordingly, we demonstrate the need to allocate catches at the biological stock level and show that application of high-resolution genetics is a reliable and necessary tool.

Aggregate recruiting intensity and cross-sectional stock returns

This study investigates the effects of aggregate recruiting intensity (ARI) on cross-sectional stock returns. The procyclicality of ARI implies that the risk inherent in ARI should be positively priced and stocks whose returns are more sensitive to ARI risk should provide higher expected returns. When forming decile portfolios sorted by individual stocks’ estimated beta to ARI risk, we find that the highest-minus-lowest ARI beta portfolio earns a positive mean return. Furthermore, the link between the ARI beta and stock returns is stronger among firms in traditional industries or with less organization capital, because these firms face more flexibly adjustable labor.

Global Building Morphology Indicators

Characterising and analysing urban morphology is a continuous task in urban data science, environmental analyses, and many other domains. As the availability and quality of data on them have been increasing, buildings have gained more attention. However, tools and data facilitating large-scale studies, together with an interdisciplinary consensus on metrics, remain scarce and often inadequate. We present Global Building Morphology Indicators (GBMI) — a three-pronged contribution addressing such shortcomings: (i) a comprehensive list of hundreds of building form multi-scale measures derived through a systematic literature review; (ii) a methodology and tool for the computation of these metrics in a database suited for big data and comparative studies, and release the code freely and open-source; and (iii) we carry out the computations using high performance computing, generating a public repository with data quantifying the form of selected urban areas around the world, and demonstrate their value with novel analyses comparing morphological parameters across cities. GBMI introduces a formalised, structured, modular, and extensible method to compute, manage, and disseminate urban indicators at a large scale and high resolution, while the precomputed dataset facilitates comparative studies. The theory and implementation traverse multiple scales: at the building level, both individual and contextual ones based on encircling buildings by multiple buffers, and aggregations at several hierarchical administrative levels and at multiple grids. Our open dataset, comprising billions of records on a growing scope of urban areas worldwide, is the most comprehensive instance of morphological data parametrising the individual building stock, supporting studies in urban analytics and a range of disciplines.

COVID-19 pandemic and liquidity commonality

This paper shows how the US, UK, Germany and China are financially connected through their stock market liquidity in the COVID-19 pandemic. Using high frequency data on transaction costs, we identify a decrease in stock market liquidity and an increase in liquidity commonality amongst these countries after the World Health Organisation (WHO) declared the global pandemic. Furthermore, there is increased transmission of liquidity shocks from the country with higher COVID new cases and COVID-related death cases, indicating that markets are more connected with increased outbreak severity. Our results suggest that COVID-19 intensifies liquidity risk and worsens the vulnerability of individual stock market's liquidity to aggregate liquidity shocks in financial markets.

Forecasting Value at Risk and expected shortfall using a model with a dynamic omega ratio

A joint model for the Value at Risk (VaR) and expected shortfall (ES) can be estimated using a joint scoring function. Previous work has modelled the ES as the product of the VaR and a constant factor. However, this implies the same dynamics for the ES and the VaR. We propose a time-varying multiplicative factor. The ES has been expressed as the product of an expectile and a constant factor that depends on the expectile level. We rewrite this as the product of a quantile and a function of a time-varying expectile level. The expectile level is a function of the Omega ratio, which is the ratio of the expected gain to the expected loss. This leads us to model the ES as the product of the VaR and a factor that is a function of a time-varying Omega ratio. We provide empirical support using stock indices and individual stocks.

Factor Momentum and the Momentum Factor

ABSTRACTMomentum in individual stock returns relates to momentum in factor returns. Most factors are positively autocorrelated: the average factor earns a monthly return of six basis points following a year of losses and 51 basis points following a positive year. We find that factor momentum concentrates in factors that explain more of the cross section of returns and that it is not incidental to individual stock momentum: momentum‐neutral factors display more momentum. Momentum found in high‐eigenvalue principal component factors subsumes most forms of individual stock momentum. Our results suggest that momentum is not a distinct risk factor—it times other factors.

An Application of Portfolio Mean-Variance and Semi-Variance Optimization Techniques: A Case of Fiji

In this paper, we apply the Markowitz portfolio optimization technique based on mean-variance and semi-variance as measures of risk on stocks listed on the South Pacific Stock Exchange, Fiji. We document key market characteristics and consider monthly returns data from SEP-2019 to FEB-2022 (T = 30) of 17/19 listed companies on the stock exchange to construct various portfolios like 1/N (naïve), maximum return, and market and minimum-variance with and without short-selling constraints. Additionally, we compute each stock’s beta using the market capitalization-weighted stock price index data. We note that well-diversified portfolios (market portfolio and minimum-variance portfolio) with short-selling constraints have relatively higher expected returns with lower risk. Moreover, well-diversified portfolios perform better than the naïve and maximum portfolios in terms of risk. Moreover, we find that both the mean-variance and the semi-variance measures of risk yields a unique market portfolio in terms of expected returns, although the latter has a lower standard deviation and a higher Sharpe ratio. However, for the minimum-variance portfolios and market portfolios without short selling, we find relatively higher returns and risks using the mean-variance than the semi-variance approach. The low beta of individual stock indicates the low sensitivity of its price to the movement of the market index. The study is an initial attempt to provide potential investors with some practical strategies and tools in developing a diversified portfolio. Since not all the portfolios based on mean-variance and the semi-variance analyses are unique, additional methods of investment analysis and portfolio construction are recommended. Subsequently, for investment decisions, our analysis can be complemented with additional measures of risk and an in-depth financial statement/company performance analysis.

Investor sentiment, credit rating, and stock returns

We confront the distress risk puzzle. Conventionally, stocks with high distress risk should yield higher returns. However, this notion is found to be empirically inaccurate. We develop a stock-level investor sentiment measure and find that behaviors of individual investors affect the future excess returns of stocks despite the presence of distress risk. Our findings suggest that net buying by individual investors enhances our understanding of the negative relationship between credit ratings and future stock returns. To do so, we develop a cross-sectional measure of the investor sentiment for each individual stock at each month.

PENGARUH LOAN TO DEPOSIT RATION DAN RETURN ON ASSET TERHADAP DIVIDEN PERUSAHAAN PERBANKAN PADA BURSA EFEK INDONESIA PERIODE 2016-2020.

The research was conductedto determine the effect Loan of Deposit Ration, Return On Asset to individual banking firm Indonesin stock exchange (IDX) priod 2016-2020. This research uses quantitative statistical data of banking companies, literature, financial report published by banks or the internet that have ben audited by banking companies on the Indonesia Stock Exchange (IDX), in order to obtain an overview of the bank. The result of this study indicate that the Loan to Deposit Ration, Return On Assets and devidends are partially respectively. Loan to Deposit Ration has insignificant and negative effect on devidends. This can be explained by the Loan to Deposit Ratio t value of 1.889 with a propability level of 0.064 which is smaller tahn 0.05. Return on assets (ROA) has asignificant and positive effect on devidends. This can explained by the t count value of 1.889 with a probability level of 0.0064 grater than 0.05. Loan to Deposit Ration (LDR) and Return On Assets (ROA) simultaneously on devidends have a moderate effect of 0.501. The amount of contribution Requare or the coefficientof determinarion is 0.251, which means that the Loan to Deposit Ratio (LDR) and Return On Assets (ROA) variable contribute 25.1% to devidends. While the remaining 74.9 %is influenced by other variables

Climate events and return comovement

We show that individual stock returns comove more with market returns when there are climate disasters such as hurricanes and floods. Comovement increases in the month of and the month following the disaster before declining back to normal levels. The disaster impact is stronger in recessions and crisis periods but is evident in all periods. The increased return correlation stems more from an increase in covariance than an increase in stock or market standard deviation. Moreover, we show climate events have a greater impact on comovement in stocks with greater sensitivity to their local economy and higher information asymmetry.

Financial contagion effects of major crises in African stock markets

This study examines financial contagion effects in African stock markets during major crises over the period 2005 to 2020. We investigate contagion effects in individual stock markets and from a regional perspective using dynamic conditional correlations during the global financial crisis, European debt crisis, Brexit, and COVID-19. The empirical evidence confirms contagion effects in some individual markets. However, significant evidence of contagion is found only during the global financial crisis from the regional perspective. Our findings suggest that the regional impacts of crises differ due to the nature of those crises. We also find financial contagion increases in the country-level risk, market capitalization and export to GDP and decreases in corruption.