Security Returns Research Articles

Financial Constraints, Stock Returns and R&D in Indian Stock Market

When the accessibility of external finance prohibits a firm from taking the optimum decision related to investment, that firm is called financially constrained. By applying the methodology of Kaplan and Zingales (1997) and Lamont et al. (2001), the current study has created a construct to gauge the level of financial constraints (FC) of the companies which emanate from quantitative information. The study explores whether FC factor is present in the Indian stock market and explores whether the security returns of those firms that are financially constrained move in tandem. The study also attempts to establish the association between security returns and R&D of financially constrained firms. On a sample of 63 R&D reporting companies of S&P BSE 500, traded over the period March 2008 to February 2019, the study used the Fama–French methodology, fixed effect model and the ordered logistic regression. The study finds that firms that are highly constrained earn more returns than low constrained firms. Second, the security returns of firms that are financially constrained move in tandem because these firms are affected by common shocks. This suggests that the FC factor exists in the Indian stock market. Finally, when R&D interacts with the level of FC, then this interaction effect has a negative effect on returns.

Euclidean (dis)similarity in financial network analysis

Interdependence among financial return series primarily originate from correlation between underlying assets. However, correlation fully describes interdependence only if the financial system behaves linearly and if an assumption of multivariate normal distribution additionally holds true. At the same time, with intrinsic z score normalization, correlation ignores means (expected return) and variances (risk) when calibrating the interdependence. Such oversight raises the significant question of whether security return networks can be realistically modelled and interpreted by market correlations. This paper proposes the Euclidean (dis)similarity metric which enables incorporation of risk and return along with the primary correlation component. We apply this metric to explain the collective behavior of the MSCI world market and compare the results with other correlation networks. Findings show that realized volatility accounts for 71% of the observed topology whereas correlation explains only 29% of market structure. No evidence was found supporting the importance of expected return. Power law exponents and degree distributions reveal that the centrality of hub nodes are considerably higher in the Euclidean as opposed to correlation networks. Accordingly, the importance and influence of central countries (like US and Japan hubs) in the spreading of high volatility is considerably higher than what correlation networks report.

A multiobjective multiperiod mean-semientropy-skewness model for uncertain portfolio selection

Due to the complexity of the financial market, security returns are sometimes expressed by expert estimates rather than historical data. In this paper, we deal with a multiobjective multiperiod portfolio selection problem based on uncertainty theory. We propose a new uncertain multiobjective multiperiod mean-semisentropy-skewness portfolio optimization model, in which uncertain semi-entropy is used to quantify the downside risk. To be more realistic, several constraints are also considered, such as the transaction costs, cardinality, liquidity, budget, and bound constraint. Moreover, a novel hybrid technique, called the MFA-SOS algorithm, which combines the features of the firefly algorithm (FA) and symbiotic organism search algorithm (SOS) is designed to solve the proposed model. Finally, a numerical example is given to illustrate the effectiveness of the proposed approach.

Multiperiod portfolio selection models under uncertain measure and with multiple criteria

This paper explores a multiperiod portfolio optimization problem under uncertain measure involving background risk, liquidity constraints and V-shaped transaction costs. Unlike traditional studies, we establish multiperiod mean-variance portfolio optimization models with multiple criteria in which security returns, background asset returns and turnover rates are assumed to be uncertain variables that can be estimated by experienced experts. When the returns of the securities and background assets follow normal uncertainty distributions, we use the deterministic forms of the multiperiod portfolio optimization model. The uncertain multiperiod portfolio selection models are practical but complicated. Therefore, the models are solved by employing a genetic algorithm. The uncertain multiperiod model with multiple criteria is compared with an uncertain multiperiod model without background risk and an uncertain multiperiod model without liquidity constraint respectively, we discuss how background risk and liquidity affect optimal terminal wealth. Finally, we give two numerical examples to demonstrate the effectiveness of the proposed approach and models.

Comparative analysis of the explanatory power of the CAPM and the Fama-French three-factor model on cross-sectional variation of returns on stocks trading on the Official market of the Macedonian Stock exchange

The amount of literature on factors that explain the cross-sectional variation in average returns is vast, however, the majority of these papers attempt to explain the variation of returns in developed and emerging markets. In that sense, the literature lacks sufficient evidence regarding the variation of returns of frontier markets. The Republic of North Macedonia is considered to be a frontier market and in this paper we aim to empirically test the ability of the Capital Asset Pricing Model and the Fama-French Three Factor Model in explaining the cross-sectional variations of stock returns of securities trading on the Macedonian Stock Exchange. The empirical study is based on monthly returns from January 2011 to April 2021. Additionally, we use annual data obtained from the financial statements of the analysed companies included in this study. Using OLS time series regression we find that both models have limited explanatory power of the cross-sectional variation in expected returns on the Macedonian Stock Exchange. The study shows that only the size factor exhibits some limited explanatory power regarding stock returns. Based on the comparative analysis the Fama-French Three-Factor Model describes the variation of returns on the MSE much better than the Capital Asset Pricing Model.

Empirical test of capital asset pricing model on securities return of listed firms in Nigeria

This paper applied the capital-asset pricing model (CAPM) to determine stock returns of listed firms in the Nigeria Stock Exchange (NSE). For the purpose of investigation, annual data on stock price of twenty six (26) listed firms, Treasury bill a measure of risk- free rate and all share indexes a proxy for market returns were extracted while beta value was computed for the period 2010 to 2016 upon which the model was analyzed. Finding indicates that the CAPM generated a very high return among the firms given the influence of the beta coefficient. The study concludes that higher market risk measured by beta, is associated with higher expected returns. It is therefore recommended that managers of firms in other sectors in Nigeria need to constantly use this model to price security return with a view to guiding investors at investing in securities based on risk preference behavior and also to enable them maximize wealth from a basket of portfolio.

Sampling James R. Thompson's inspired nonparametric portfolio approaches

AbstractAsset or security returns are an example of phenomena whose distributions still cannot be convincingly modeled in a parametric framework. James R. (Jim) Thompson (1938–2017) used a variety of nonparametric approaches to develop workable investing solutions in such an environment. We review his ground breaking exploration of the veracity of the capital asset pricing model (CAPM), and several nonparametric approaches to portfolio formulation including the Simugram™, variants of his Max‐Median rule, and Tukey weightings.This article is categorized under: Applications of Computational Statistics > Computational Finance Statistical and Graphical Methods of Data Analysis > Transformations Statistical and Graphical Methods of Data Analysis > Modeling Methods and Algorithms

Is (Systematic) Value Investing Dead?

Value investing involves buying securities that appear cheap relative to some fundamental anchor. For equity investors, that anchor is typically a measure of intrinsic value linked to financial statements. Recently, much has been written about the death of value investing. Although undoubtedly many approaches to value investing have suffered recently, the authors find the suggestion that value investing is dead to be premature. Both from a theoretical and empirical perspective, expectations of fundamental information have been and continue to be an important driver of security returns. The authors also address critiques leveled at value investing and find them generally lacking in substance. TOPICSAnalysis of individual factors/risk premia, factor-based models, fundamental equity analysis Key Findings ▪ The authors address a series of possible (ex post) rationalizations of the recent underperformance of value strategies. They find little empirical evidence or theoretical foundation to support the criticisms. ▪ Building on prior academic research, the authors find strong evidence that fundamental information is relevant for stock prices. ▪ However, the authors find that there are periods in which stock prices become less connected to fundamental information; when value underperforms significantly, it is primarily attributable to a widening gap between prices and fundamentals.

Do Contented Customers Make Shareholders Wealthy? - Implications of Intangibles for Security Pricing

We explore the relation between customer satisfaction and security returns. Firms with high customer satisfaction levels earn significant abnormal returns. This result is robust to variations of model specification and test methodology. Additional tests do not reveal evidence of systematic mispricing. Our results rather suggest that there are, consistent with the model of Eisfeldt and Papanikolaou (2013), sources of risk not covered by standard risk factors. We identify firm characteristics, such as the Hoberg et al. (2014) product market fluidity measure, and macro variables, such as patenting activity and aggregate R&D spending, that are related to these sources of risk.

Back to Basis: A Universal Return Predictor Across Asset Classes

This paper shows analytically that the basis between spot and futures contracts contains information about future returns of securities across the asset classes of commodities, equity indices, fixed income and foreign exchange. The bases in commodities are positively correlated with a leading indicator of the business cycle whereas the bases in the financial assets are negatively related to the short-term rate. The return predictability of the basis can be captured with a simple multi-asset long-short strategy which produces an out-of-sample Sharpe ratio of 0.5 and an alpha of 2.5%-4.5% per annum with respect to commonly used asset pricing models. Specifically, the analysis includes five Fama-French Factors, a bond index and futures risk premia of multi-asset momentum, value, time-series momentum, and four asset-specific carry factors. The strategy performance is counter-cyclical and robust to transaction costs.

Portfolio management with background risk under uncertain mean-variance utility

This paper studies comparative static effects in a portfolio selection problem when the investor has mean-variance preferences. Since the security market is complex, there exists the situation where security returns are given by experts’ estimates when they cannot be reflected by historical data. This paper discusses the problem in such a situation. Based on uncertainty theory, the paper first establishes an uncertain mean-variance utility model, in which security returns and background asset returns are uncertain variables and subject to normal uncertainty distributions. Then, the effects of changes in mean and standard deviation of uncertain background asset on capital allocation are discussed. Furthermore, the influence of initial proportion in background asset on portfolio investment decisions is analyzed when investors have quadratic mean-variance utility function. Finally, the economic analysis illustration of investment strategy is presented.

Capital Asset Pricing Model (CAPM) and the Douala Stock Exchange

This study examines if the Capital Asset Pricing Model (CAPM) can be applied to the Douala Stock Exchange. The study utilized monthly stock returns from the three companies listed on the Douala Stock Exchange (DSX), for the period 30th April 2009 to 31st August 2017. Ordinary Least Square regression analysis was adopted for the study to examine if individual stocks can predict a better stock beta. The Black, Jensen, and Scholes (1972) CAPM version were also examined in this study to assess the validity of the zero beta estimate. The result of the individual estimates could not establish the validity of the CAPM theory. Further analysis showed that the Beta for the three assets combined portfolio was not statistically significant. However, when two securities were combined into a single asset portfolio, the portfolio bêta was statistically significant. The significant result of the two asset portfolio confirms that Beta was a linear function of security returns in the DSX market. The study concludes that there will be a need for the government of Cameroun to liberalize the DSX market and allow more firms to be quoted on the floor of the exchange. This decision will allow for the deepening of the DSX market, enhance the liquidity level of the market, and enable investors to reap adequate returns from their investment through holding a portfolio of assets.

Elliptic entropy of uncertain random variables with application to portfolio selection

This paper investigates an elliptic entropy of uncertain random variables and its application in the area of portfolio selection. We first define the elliptic entropy to characterize the uncertainty of uncertain random variables and give some mathematical properties of the elliptic entropy. Then we derive a computational formula to calculate the elliptic entropy of function of uncertain random variables. Furthermore, we use the elliptic entropy to characterize the risk of investment and establish a mean-entropy portfolio selection model, in which the future security returns are described by uncertain random variables. Based on the chance theory, the equivalent form of the mean–entropy model is derived. To show the performance of the mean–entropy portfolio selection model, several numerical experiments are presented. We also numerically compare the mean–entropy model with the mean–variance model, the equi-weighted portfolio model, and the most diversified portfolio model by using three kinds of diversification indices. Numerical results show that the mean-entropy model outperforms the mean–variance model in selecting diversified portfolios no matter of using which diversification index.

Uncertain portfolio selection with mental accounts

Since the security market is so complex, in real life, there are situations where the future security returns cannot be reflected by the past data and are given by experts' estimations according to their knowledge and judgement rather than by historical data. This paper discusses a portfolio selection problem in such an uncertain environment. In the paper, in order to reflect different attitudes towards risk that vary by goal in one portfolio investment, we apply mental account to the investment. Using uncertainty theory, we propose a new mean–variance uncertain portfolio selection model with mental accounts. Furthermore, we discuss the shape of the mean–standard deviation efficient frontier of the subportfolios of each mental account when security returns are normal uncertain variables and further give the condition where the optimal aggregate portfolio is on the mean–standard deviation efficient frontier. In addition, we compare the optimal portfolio with mental accounts with that without mental accounts. Finally, a numerical example is given as an illustration.

A minimax regret approach for robust multi-objective portfolio selection problems with ellipsoidal uncertainty sets

Since security return cannot be accurately estimated using past data, in this paper it is assumed to take values in a given ellipsoidal uncertainty set. This paper aims to discuss a robust multi-objective portfolio selection problem based on the minimax regret criterion under an ellipsoidal uncertainty sets, in which the two objective functions are the portfolio return to be maximized and the mean absolute deviation as a risk measure to be minimized. The robust counterpart formulation for the proposed model is firstly presented, then an algorithm based on the relaxation procedure is designed to solve the robust counterpart formulation with second-order cone constraints and infinite constraints. Finally, a practical example based on real market data is presented to illustrate the effectiveness of the proposed model and the algorithm. Compared with the traditional robust portfolio model based on minimax robustness, the robust minimax regret optimal solutions proposed in this paper have better performance on several evaluation criteria.

Vacillating Behavior of TOM Effect and Adaptive Market Hypothesis: A Firm Level Evidence from Emerging Stock Market of Pakistan

Through the current study we amplify the available literature on AMH (Adaptive Market Hypothesis) and calendar anomalies because this is the first study of its nature which links TOM effect with AMH which allows the behavior of conventional TOM-effect to swing over time. To fulfill the drive, study investigates daily mean return from PSX of Pakistan using data of 107 firms individually over a longer period of time ranging 1996-2015. To discover the time variation in the levels of predictability of TOM returns, study uses four different sub-samples covering identical length of observations of five years each to investigate how TOM effect has performed over time. There are few studies in the literature investigating TOM effect at firm level and very rare studies examining TOM effect through (AMH), so the current study may be of importance and interest to finance researcher, academicians and practitioners alike. To elucidate the volatility and its varying nature, the study applies GARCH (1,1) regression model which enables for time-variation in volatility of security returns. Kruskal-Wallis test-statistic is used to handle non normality in the equity return series. We find that with the passage of time performance of TOM effect evolves, consistent and aligned with the assertion of AMH. Finally, this study exhibits that behavior of TOM effect is well elucidated by Adaptive Market Hypothesis (AMH) than conventional Efficient Market Hypothesis (EMH). The results may be used for better decision making for investors and the article complements studies on market efficiency and TOM effect in developing and developed countries.

Time domain and frequency domain Granger causality networks: Application to China’s financial institutions

We propose Granger causality networks in the time domain and frequency domain to investigate the interconnectedness of Chinese financial institutions based on the daily returns of banks, securities, and insurers during 2011–2018. We find that the system-level interconnectedness mainly concentrates on the medium-high frequency, but individual-level interconnectedness varies across different frequencies. Dynamically, the system-level interconnectedness is consistent in the time domain and frequency domain, while this consistency in the individual-level interconnectedness does not hold, but both of them are affected by macroeconomic situations and financial events. During 2015–2016 and 2018, the system-level interconnectedness increased significantly and was at a high level.

The Minimum Variance Portfolio, the Tangency Portfolio, and the Associated Matrix Algebra

The matrix algebra associated with finding minimum variance portfolio weights and tangency portfolio weights is greatly simplified by using an Excel presentation. A further simplification of the tangency portfolio weights process is also presented using excess returns for the risky securities. The lesson drawn from this presentation is readily performed online by sharing or recording an Excel screen with students.

Validity of the Arbitrage Pricing Theory in Turkey’s economy: An empirical analysis in the context of global economic crisis

In this study, validity of the Arbitrage Pricing Theory in Turkey's economy is investigated with two separate analyzes for the period of 2002-2008 and 2008-2019. In the study, gold prices, time deposit interest rates, exchange rate, Central Bank Leading Indicators Index and VIX fear index data which are considered to affect the returns of securities (Stock Exchange) are used. Stationarity of the series is tested by ADF unit root test and the series are determined to be stationary at different levels. The cointegration of the series in the model is examined by Bounds Testing method and it is seen that the series are cointegrated. The regression analysis is performed by ARDL method and it is determined that gold is the substitute of BIST 100 just in the short term between 2002 and 2008, while foreign exchange is substitute of it both in the long and short term in the two periods. Moreover, the time deposit interest rates are substitutes of BIST 100 in the long term between 2002 and 2008. It is also found that the time deposit interest rates during the period of 2008-2019, similar to economic conjuncture in the country in the 2002-2019 periods, have a supportive effect on BIST 100 index in the long term. It is observed that the increases in financial fear level (VIX) negatively influence BIST 100 index returns in the post-2008 period. Error correction mechanisms of the models operate.

Dynamic relation between global Islamic and conventional sectoral stock and bonds indexes

This paper examines the relationship between 10 Global sectoral conventional and Islamic assets. For each sector, a conventional, an Islamic stock index and a bond are retained. The analyzed relations are done by taking into account diverse investment horizons by using MODWT and GARCH-DCC-type models. Our results indicate that adding bond indexes into a portfolio composed with conventional stock or Islamic stock is efficient. As for the correlations between conventional and Islamic sectoral indexes, they depend on the sector. Relations between returns of securities are quite similar to the relations between high-frequency part of these series and are very volatile at low frequency.