Stock Return Series Research Articles

Is there a time-varying nexus between stock market liquidity and informational efficiency? – A cross-regional evidence

PurposeThis study aims to measure the degree of volatility comovement between stock market liquidity and informational efficiency across Asia, Europe, North-South America, Africa, and the Pacific Ocean over three decades. In particular, the authors analyze the extent of the time-varying nexus between different aspects of stock market liquidity and multifractal scaling properties of the stock return series across various regions and diversified market conditions. This study further investigates several factors altering the degree of dynamic conditional correlations (DCCs) between the efficiency and liquidity of the domestic stock markets.Design/methodology/approachThe study measures five aspects of stock market liquidity – tightness, depth, breadth, immediacy, and adjusted immediacy. The authors evaluate the multifractal scaling properties of the stock return series to measure the level of stock market efficiency across the regions and diversified market conditions. The study uses the dynamic conditional correlation-multivariate generalized autoregressive conditional heteroscedasticity framework to quantify the degree of volatility comovement between liquidity and efficiency over the period.FindingsThe study finds the presence of stronger volatility comovement between inefficiency and illiquidity due to the price impact characteristics of the stock markets irrespective of different regions and diversified market conditions. The extent of time-variation increased following the shock periods, indicating the significant role of the financial crisis in increasing the volatility comovement between inefficiency and illiquidity. The highest degree of time-varying correlation is observed in the developed stock markets of Northwestern and Northern Europe compared to the regional and emerging counterparts. On the other hand, weak DCCs are observed in the emerging stock markets of Europe.Originality/valueThe output of the present study assists investors in identifying diversification opportunities across the regions. Additionally, the study has significant implications for market regulators, aiding in predicting future troughs and peaks. The prediction, in turn, helps formulate capital market development plans during dynamic economic situations.

Stochastic Calculus and Its Impact On Analyzing Option Pricing

This research study the behaviour of the correction of Black-Scholes portfolios based on historical stock price data. We build a model that simulates one sample path of the stock price stochastic process at discrete time steps and track the correction over a time interval as it relates to the change in stock price over time. We also study the effect that relaxing this assumption has on the self-financing property of the replicating portfolio. We show that as the frequency of the time steps increases, the correction is more likely to be close to 0. We also show that the majority of historical stock return series that studied have caused the replicated portfolio to have a positive correction. We conclude that the Black-Scholes model can be used to find the no-arbitrage rational price for an option, a financial instrument that derives its value from the value of an underlying asset.

STATISTICAL ANALYSIS BY WAVELET LEADERS REVEALS DIFFERENCES IN MULTI-FRACTAL CHARACTERISTICS OF STOCK PRICE AND RETURN SERIES IN TURKISH HIGH FREQUENCY DATA

The price and return time series are two distinct features of any financial asset. Hence, examining the evolution of multiscale characteristics of price and returns sequential data in time domain would be helpful in gaining a better understanding of the dynamical evolution mechanism of the financial asset as a complex system. In fact, this is important to understand their respective dynamics and to design their appropriate predictive models. The main purpose of the current work is to investigate the multiscale fractals of price and return high frequency data in Turkish stock market. In this regard, the wavelet leaders computational method is applied to each high frequency data to reveal its multi-fractal behavior. In particular, the method is applied to a large set of Turkish stocks and statistical results are performed to check for (i) presence of multi-fractals in price and return series and (ii) differences between prices and returns in terms of multi-fractals. Our statistical results show strong evidence that high frequency price and return data exhibit multi-fractal dynamics. In addition, they show evidence of distinct fractal characteristics on different scales between price and return series. Furthermore, our statistical results show evidence of differences in local fluctuation characteristics of price and return time series. Therefore, differences in local characteristics are useful to build specific predictive models for each type of data for better modeling and prediction to generate profits. Besides, we found evidence that both long-range correlations and fat-tail distributions contribute to the multifractality in Turkish stocks. This finding can be attributed to the major role played by international investors in increasing the volatility of Turkish stocks.

Unit Root Tests of Airline’s Stock Returns Considering Alliances

The purpose of this study is to make a unit root analysis of the stock return time series of 18 air carrier companies due to alliance membership in this paper because of the importance of the unit roots in the detection of time-related matters. Five important unit root tests, which can be classified as Fourier or classical utilized. According to the findings, there is no unit root in the stock return series regardless of airline alliances but there are differences on a regional basis depending on the test and test power, specific Asian airlines stocks’ return series show linearity. These results will shed more light on the efficient market hypothesis on aviation management in the next research.

Can Artificial Neural Networks Be Used to Predict Bitcoin Data?

Financial markets are complex, evolving dynamic systems. Due to their irregularity, financial time series forecasting is regarded as a rather challenging task. In recent years, artificial neural network applications in finance for such tasks as pattern recognition, classification, and time series forecasting have dramatically increased. The objective of this paper is to present this versatile framework and attempt to use it to predict the stock return series of four public-listed companies on the New York Stock Exchange. Our findings coincide with those of Burton Malkiel in his book, A Random Walk Down Wall Street; no conclusive evidence is found that our proposed models can predict the stock return series better than that of a random walk.

Do financial markets predict macroeconomic performance? US evidence from risk‐based measures

AbstractFinancial markets are expected to predict macroeconomic conditions as movement in the former depends upon expectations of future performance for the latter. However, existing evidence is mixed. We argue that this arises because the stock return and term structure series typically used in studies, fail to sufficiently capture investor risk preferences. For US data, we use the variance risk premium (VRP) and default yield (DFY) to better capture such a risk measure and demonstrate that these variables exhibit greater evidence of predictive power for key macroeconomic series. In addition to VRP and DFY, we include further variables that may also capture market risk. Given similar dynamics between different risk measures and the potential for multicollinearity in estimation, we consider variable combinations. Using results obtained through predictive regressions, out‐of‐sample forecasting and a probit model designed to capture periods of expansion and contraction, we show that these combination variables can predict future movements in macroeconomic conditions as well as results using individual variables. Of key interest, combinations that include the VRP and DFY are preferred across all macro‐series.

Twitter‐based uncertainty and stock market returns: Evidence from G7 countries

AbstractThe aim of this study is to investigate the impact of Twitter‐based economic uncertainty (TEU) and Twitter‐based market uncertainty (TMU) on G7 stock returns in the challenging year in which the COVID‐19 pandemic began (2020) under different stock market conditions (bearish, normal, and bullish). To this aim, this study applies novel quantile‐based approaches, namely Quantile autoregression unit root test, Quantile‐on‐quantile approach, and Quantile Granger‐causality test covering the period from 01 January 2020 to 15 September 2020. Main findings of the study are (1) G7 stock return series are stationary for all quantiles of the conditional distributions with minor exceptions meaning that shocks have temporary effects on stock returns of G7 markets. (2) The impact of Twitter‐based uncertainty strongly depends on the market condition, whether it is bullish or bearish for all G7 markets. A heterogeneous association exists between variables caused by different market conditions. (3) A bi‐directional causal association exists between stock returns‐TEU and stock returns‐TMU. This result confirms the existence of feedback hypothesis between G7 stock returns and TEU, TMU, respectively. This study provides important policy implications and recommendations for policy makers and investors on the nexus between Twitter‐based uncertainties and stock returns.

Nonlinear asset pricing in Chinese stock market: A deep learning approach

The redesign of asset pricing models failed to integrate the frequent financial phenomenon that stock markets exhibit a non-linear long- and short-term memory structure. The difficulty lies in developing a nonlinear pricing structure capable of depicting the memory influence of the pricing variable. This paper presents a Long- and Short-Term Memory Neural Network Model (LSTM) to capture the non-linear pricing structure among five elements in the Chinese stock market, including market portfolio return, market capitalisation, book-to-market ratio, earnings factor, and investment factor. The long–short-term memory structure implies that the autocorrelation function of the stock return series decays slowly and has a long-term characteristic. The LSTM model surpasses the standard Fama–French five-factor model in terms of out-of-sample goodness-of-fit and long–short strategy performance. The empirical findings indicate that the LSTM nonlinear model properly represents the nonlinear relationships between the five components.

Testing of Market Efficiency of BSE GREENEX: An Empirical Analysis

According to the United Nations Principle for Responsible Investment, an investment in a non-ESG compliance company has 28% higher risk per annum as compared to an ESG compliance company. Such company has a positive relationship with the financial performance and the investors cannot outperform these stocks as the information about sustainability compliance is readily available in the market. This concept is of the Efficient Market Hypothesis. Employing a quantitative research design, this study considered 17 companies of BSE Green Index that has considerable ESG scores and extracted their daily, weekly and monthly data to examine and understand its stock return movement and to test whether it follows the Efficient Market Hypothesis. This study makes use of the various statistical tools such as the Run Test, Auto-correlation Function and the Unit Root Test. Summarising the results of run test, Auto-correlation Function and two-unit root test i.e., Augmented Dickey Fuller test and Philips-Perron, it statistically confirmed the findings that the daily, weekly and monthly stock return series of the 17 stocks of BSE Green Index do not obey the ‘Random Walk Hypothesis’ which is the key findings of the study and hence rejects all the three hypotheses of the study. Backing the empirical findings of this study, it would be rational to state that the concept of the Efficient Market Hypothesis does not hold good in the BSE Green Index of the Indian Stock Market and this finding would be very imperative to the investors concerned about their investment decisions with regard to sustainability practices.

Investigation of Fractal Market Hypothesis in Emerging Markets: Evidence from the MINT Stock Markets

This study aims to investigate the market efficiency of emerging stock markets, namely the Mexico, Indonesia, Nigeria, and Turkey (MINT) stock markets based on the Fractal Market Hypothesis. For this purpose, the ARFIMA and ARFIMA-FIGARCH type models are used to analyze the MINT stock return series. In this study, the dataset encompasses the daily frequency data of the MINT stock market indices from January 12, 2018, to January 12, 2022. The empirical findings show that long memory is reported for the MINT stock returns. The long memory in the returns implies that the MINT stock prices follow a predictable behavior that is consistent with the Fractal Market Hypothesis. The long memory in the volatility implies that the uncertainty or risk is an important factor in the formation of price movements in the MINT stock prices. Moreover, the MINT stock prices consist of the effect of shocks and news that occurred in the recent past. Thus, this study contributes to investors, academics, and market regulatory authorities. Besides, as far as we know, the current literature on the analysis of the fractal market hypothesis to explore the efficiency of the MINT stock markets has not been found.

Comovement across BRICS and the US Stock Markets: A Multitime Scale Wavelet Analysis

During the past two decades, financial markets across the globe have experienced sporadic waves of crashes. Such waves raise concerns about the vulnerability of global financial markets and the transmission mechanisms of shocks beyond borders. The current study examines the co-movement of stock markets in BRICS (Brazil, Russia, India, China and South Africa) countries and the United States of America (US). It unfolds their exposure to contagion effects during the major financial crises, which have flared up since 2000. Daily close price indices of selected stock markets were used in this endeavour. These data spanned from 5 January 2000 to 10 March 2021. A wavelet decomposition on stock return series was performed on these data to determine the multihorizon nature of comovement (pure contagion or interdependence) and the dynamics of market integration. It emerges that before the 2006-US-housing-bubble and after the 2011/13-EU-sovereign-debt crises, some shocks caused pure contagion. Such transmission generated short-term shocks. Most of the earlier shocks, particularly the US subprime and the EU Sovereign Debt crises, were spread via interdependence. Trade linkages and economic integration improvements enhanced such interdependence. In addition, when analysing the episodes of market integration, it arises that, in general, the short- and long-term integration strengthened and deepened comovement among equity markets. From the portfolio diversification and risk management perspectives, these results indicate that the market in China provided lucrative grounds for short-run investors from the other countries covered in the current study. These results can be helpful for investors interested in portfolio diversification in the BRICS region. They might also help policymakers in the region mitigate the exposure to external shocks of markets.

Predicting GDP growth with stock and bond markets: Do they contain different information?

AbstractThis paper examines the ability of bond and stock markets to predict subsequent GDP growth over a range of horizons for 12 international countries. The results, using linear, probit, time‐ and regime‐varying in‐sample regressions and out‐of‐sample forecasting, confirm the view that both financial markets exhibit predictive power for future output growth. Moreover, there is notable variation within the strength of the predictive relation, for example, predictive power increases during the financial crisis period. Results suggest that while the term structure arguably exhibits stronger predictive power, both series contain distinct predictive information. Notably, predictive power emanating from the stock return series appears stronger over shorter (up to four‐quarter) time horizons, while the term structure series exhibits more consistent predictive power over a range of horizons. Considering different regimes, we observe that the bond market exhibits greater predictive power for a flatter yield curve and lower stock prices relative to fundamentals, while the stock market exhibits greater predictive power for a steeper yield curve and higher relative stock prices. This suggests that the two financial markets exhibit different information content for future output growth. This view is further supported by forecast results whereby a model that includes both financial series outperforms a model that only includes one. Forecast encompassing tests further support the view that stock returns contain additional information over that presented by the term structure alone.

Time-Varying Conditional Co-movement between Inter-Sector Stock Returns: Evidence from Ghana Stock Market

Investors generally exhibit home bias with regards to their investment destinations. To diversify their portfolio, such investors invest in different sectors within the domestic economy. However, such behaviour could be counter-productive in periods of increased co-movement of assets returns. In this paper, we examine the inter-sector stock return co-movement among the major sectors of the Ghanaian economy with the view to shedding some light on the nature of assets return correlations and its implications for portfolio diversification. A sample of 332 weekly observations of stock returns of five major sectors within the Ghanaian economy is used to undertake the analysis. Dynamic Conditional Correlation - Generalized Autoregressive Conditional Heteroscedasticity (DCC-GARCH) techniques are applied to the weekly stock return series from January 2010 to June 2017. The DCC-GARCH model was estimated with correlation targeting and asymmetric DCC. We find dynamic conditional correlation among stock returns of all the sectors, implying that the correlation between the sector returns is time-varying. This result challenges the assumption of constant correlation among stock returns of different sectors in the domestic markets. We also find that the conditional correlation between returns of the various sectors ranges from 0.234 to 0.998, which indicates medium to very high interdependence among the stock returns. Based on the result of this study, we propose that fund managers and investors should not limit their diversification strategies to inter-sector investments since in periods of uncertainty, the ability of the investor to enjoy diversification benefits is seriously undermined.

THE IMPACT OF OUTLIERS ON STOCK AND BOND RETURNS

ABSTRACT We examine the impact of large positive and negative returns for bond and stock portfolios. We use Ibbotson Associates' Large-Capitalization Stock and Long-Term Corporate Bond portfolio return series. Previous studies have shown that a stock portfolio's returns are highly impacted by the few extremely large and small returns in the series. We confirm this result for the updated large-capitalization stock return dataset. We extend previous findings by showing that extreme values impact bond returns in a similar manner. We also show the impact of extreme returns on ten to forty year investment periods. Keywords bond and stock returns, return statistics, return outliers

Linkages between global crude oil market volatility and financial market by complexity synchronization

In this paper, we investigate the linkages between global crude oil market volatilities and financial markets and the degree of synchrony of crude oil markets and stock markets. A nonlinear cross-analysis of bivariate data method called CRP is applied to study the probability distribution of occurrence of similar states and the time span of occurrence of synchronization dynamics for crude oil return series and stock return series. Further, a new composite multiscale complexity invariance distance is introduced to measure the similarity of complexity between crude oil markets and stock markets. The results of this study show that there is a synchronization in the crude oil markets and stock markets, and those two systems have similar complexity from composite multiscale perspective.

Modeling local trends with regime shifting models with time-varying probabilities

In this paper we show that persistence and switching of trends are phenomena that appear in most long-lived stock return series. We model stock returns using a family of models based on hidden Markov models with duration-dependent transition probabilities. Trends are correlated so that aggregates such as indexes exhibit the same persistence and switching behavior as single stocks themselves. Hidden Markov models can thus explain medium-term momentum.

Accelerating score-driven time series models

We propose a new class of score-driven time series models that allows for a more flexible weighting of score innovations for the filtering of time varying parameters. The parameter for the score innovation is made time-varying by means of an updating equation that accounts for the autocorrelations of past innovations. We provide the theoretical foundations for this acceleration method by showing optimality in terms of reducing Kullback–Leibler divergence. The empirical relevance of this accelerated score-driven updating method is illustrated in two empirical studies. First, we include acceleration in the generalized autoregressive conditional heteroskedasticity model. We adopt the new model to extract volatility from exchange rates and to analyze daily density forecasts of volatilities from all individual stock return series in the Standard & Poor’s 500 index. Second, we consider a score-driven acceleration for the time-varying mean and use this new model in a forecasting study for US inflation.

Predicting GDP Growth with Stock and Bond Markets: Do They Contain Different Information?

This paper examines the ability of bond and stock markets to predict subsequent GDP growth over a range of horizons for twelve international countries. The results, using linear, probit, time- and regime-varying in-sample regressions and out-of-sample forecasting, confirm the view that both financial markets exhibit predictive power for future output growth. moreover, there is notable variation within the strength of the predictive relation, for example, predictive power increases during the financial crisis period. Results suggest that while the term structure arguably exhibits stronger predictive power, both series contain distinct predictive information. Notably, predictive power emanating from the stock return series appears stronger over shorter (up to four-quarter) time horizons, while the term structure series exhibits more consistent predictive power over a range of horizons. Considering different regimes, we observe that the bond market exhibits greater predictive power for a flatter yield curve and lower stock prices relative to fundamentals, while the stock market exhibits greater predictive power for a steeper yield curve and higher relative stock prices. This suggests that the two financial markets exhibit different information content for future output growth. This view is further supported by forecast results whereby a model that includes both financial series outperforms a model that only includes one. Forecast encompassing tests further support the view that stock returns contain additional information over that presented by the term structure alone.

The chaotic behavior among the oil prices, expectation of investors and stock returns: TAR-TR-GARCH copula and TAR-TR-TGARCH copula

This paper has two aims. The first one is to investigate the existence of chaotic structures in the oil prices, expectations of investors and stock returns by combining the Lyapunov exponent and Kolmogorov entropy, and the second one is to analyze the dependence behavior of oil prices, expectations of investors and stock returns from January 02, 1990, to June 06, 2017. Lyapunov exponents and Kolmogorov entropy determined that the oil price and the stock return series exhibited chaotic behavior. TAR-TR-GARCH and TAR-TR-TGARCH copula methods were applied to study the co-movement among the selected variables. The results showed significant evidence of nonlinear tail dependence between the volatility of the oil prices, the expectations of investors and the stock returns. Further, upper and lower tail dependence and co-movement between the analyzed series could not be rejected. Moreover, the TAR-TR-GARCH and TAR-TR-TGARCH copula methods revealed that the volatility of oil price had crucial effects on the stock returns and on the expectations of investors in the long run.

SAD and stock returns revisited: Nonlinear analysis based on MF-DCCA and Granger test

Using both multifractal detrended fluctuation analysis (MF-DFA) and multifractal detrended cross-correlation analysis (MF-DCCA) methods, this paper examines the effects of seasonal affective disorder (SAD) on stock returns in the Chinese stock market. We first separate stocks into small- and large-cap stocks according to market value, and find that SAD and these two stock-return series are multifractal, based on MF-DFA. Moreover, we employ MF-DCCA and show that the cross-correlation between SAD and stock returns is anti-persistent, and the cross-correlation between SAD and return of small-cap stock is stronger than that between SAD and return of large-cap stock. Furthermore, we find that long-range correlation is the main source of multifractality in the cross-correlations between SAD and small-cap stock, while both long-range correlation and fat tails contribute to the multifractality between SAD and large-cap stock. Finally, utilizing nonlinear Granger Causality test, we find that SAD is the Granger cause of stock returns, not vice versa.