Security Returns Research Articles

Inference and Prediction of Stock Returns using Multilevel Models

Multilevel models are a generalized form of traditional linear regression models and have several benefits relative to traditional OLS regression including the regularization of parameter estimates, the ability to incorporate prior information, better out-of-sample forecasts, desirable inferential properties, and the ability to directly model the time-series/cross-sectional nature of financial security returns. We demonstrate that multilevel models generalize well-known asset pricing regression techniques like Fama-Macbeth and Fama-French regressions. They also have stronger explanatory power than traditional regression techniques, with a substantially lower out of sample mean squared error and a 5% to 10% higher out of sample R2 vs. the comparable model fit with OLS.

The value relevance of environmental, social, and governance disclosure: Evidence from Dow Jones Sustainability World Index listed companies

AbstractThe relationship between a company's sustainability practices and its financial performance has been investigated with different methods and from different theoretical perspectives. This study aims to answer the following questions: (a) Do investors react to the publication of sustainability reports on company websites? (b) Has the market reaction to the publication of the sustainability report increased in the last few years? In this study, 170 report disclosures were considered from 55 listed companies from all over the world in the period from 2009 to 2016. To analyze the impact of the report publications on the security returns, 33 different event windows were analyzed. Results show two significant event windows and an increasing level of significance in the reports released after 2013.

A Non-Normal Principal Components Model for Security Returns

We introduce a principal components model for securities' returns. The components are non-normal, exhibiting significant skewness and kurtosis. The model can explain a large proportion of the variance of the securities' returns with only one or two components. Third and higher-order components individually contribute so little that they can be considered to be noise terms.

A Novel Hybrid ICA-FA Algorithm for Multiperiod Uncertain Portfolio Optimization Model Based on Multiple Criteria

This paper deals with a multiperiod portfolio selection problem in an uncertain investment environment, in which the returns of securities are assumed to be uncertain variables and determined by experts’ subjective evaluation. Based on uncertain theory, we present a novel multiperiod multiobjective mean–variance–skewness model by considering multiple realistic investment constraints such as transaction cost, bounds on holdings, cardinality, etc. For the proposed solution, we first apply a weighted max–min fuzzy goal programming approach to convert the proposed multiobjective programming model into a single-objective one. After that, we design a novel hybrid of an imperialist competitive algorithm (ICA) and a firefly algorithm (FA), termed ICA–FA, to solve it. Finally, we provide a numerical example to demonstrate the effectiveness of the proposed model and corresponding algorithm.

Artificial Bee Colony Algorithm Based on Novel Mechanism for Fuzzy Portfolio Selection

Although the introduction of fuzzy theory into a portfolio selection model can help improve the model's practicality, it would increase the difficulty of solving the model. To tackle the issue, this paper proposes a novel mechanism based artificial bee colony algorithm (ABC) consisting of two new proposed learning strategies—direction learning and elite learning. The direction learning strategy has a great potential to guide the search toward the promising areas. The elite learning strategy can gradually pick up the convergence rate without loss of the population diversity. The cooperation of the two approaches forms a mechanism, complementing each other to improve the performance of the algorithms. The proposed mechanism, named LL-mechanism, is introduced into three ABC variants-ABC, gbest-guided ABC (GABC), and CABC, generating LL-ABC, LL-GABC, and LL-CABC, respectively. The experimental results demonstrate the superior performance of the LL-mechanism and LL-CABC outperforms other methods in terms of solution quality, convergence rate, robustness, and numerical stability. Finally, the proposed LL-CABC approach is employed to solve the portfolio selection with fuzzy security return. The experiments on two portfolio selection models illustrate that LL-CABC is effective and promising for a fuzzy portfolio selection.

The Endogeneity of Trading Volume in Stock and Bond Returns: An Instrumental Variable Approach

AbstractThis paper investigates the joint determination of trading volume and returns. Our approach follows from the argument that trading activity depends on security returns, thus resulting in a reverse causality from returns to trading activity. Using exogenous instruments for security trading activity, we estimate a system of two‐stage simultaneous equations to better model the return‐volume relationship. Our results confirm that returns and trading volume are determined simultaneously in both stock and corporate bond markets and that conclusions about the direction and significance of causality between volume and returns can be reversed once one corrects for the endogeneity of volume.

Analysis of Size Portfolios and Risk Factor Loadings of Asset Pricing Models – Threshold Regression Approach

The objective of the study is to analysis portfolio returns and the performance of asset pricing models in the context of macroeconomic variables. Portfolios have been constructed on the basis of firms’ size; two equally weighted decile portfolios and two value weighed decile portfolios representing the cluster of the largest and smallest firms are included in the analysis along with the market portfolio. Portfolio returns are regressed with the factor loadings of CAPM, three-factor and five-factor Fama and French asset pricing models. The relationship of portfolio returns and asset pricing models is evaluated in the presence of four macroeconomic variables namely interest rate, industrial production, producer price index and exchange rate; each macroeconomic variable is introduced separately in threshold regression to the identify regime shift effect. Analyses are performed on monthly returns of securities listed on PSX during the period from 2000 to 2010. Results of the study have revealed that only the interest rate and exchange rate are found to have a threshold effect on the portfolio returns. According to the results, the threshold effect is frequently captured through three factors Fama French model. Finally, the results also suggest that the threshold effect is only evident with large firms’ portfolios.

Tests of Alternative Asset Pricing Models Using Individual Security Returns and a New Multivariate F-Test

This paper examines relative performance of alternative asset pricing models using individual security returns. The standard multivariate test used in studies comparing the performance of asset pricing models requires the number of stocks to be less than the number of time series observations, which requires grouping stocks into portfolios. This results in a loss of disaggregate stock information. We apply a different statistical test to overcome this problem and to investigate relative performance of alternative asset pricing models using individual security returns instead of portfolio returns. Our findings suggest that a parsimonious six-factor model that includes the momentum and orthogonal value factors outperforms all other models based on a number of measures as well as the average [Formula: see text]-test. Unlike the standard multivariate test, we find that the average [Formula: see text]-test has superior power to discriminate among competing models and does not reject all tested models.

Overflow effect of credit rating announcements on stock exchange based on event study

ABSTRACTThis study is aimed to identify the impact of credit rating announcements on the stock returns in stock markets and for this purpose, four different sectors of Pakistan stock exchange were selected and from each of these four sectors, different business organizations were selected, i.e. total 32 business organizations were selected. The credit rating announcement data were collected for these 32 business organizations belonging to four different sectors. Totally 101 credit rating announcements were selected and the time period for which the credit rating warnings were selected include last three years period, i.e. from 2014 to 2016. The collected data were analysed by calculating abnormal returns for each of the selected security and average abnormal returns, and cumulative average abnormal returns were calculated for four different sectors. Event study methodology was applied, and t-test and t-stats value were calculated and results were analysed on the basis of t-statistics. The results of analysis identified that credit rating announcements have a significant impact on stock prices and investors and other market participants are earning abnormal returns during two-day period after the announcements are made. In addition, these abnormal returns were either negative or positive, depending upon the nature of credit ratings announced. If the credit rating announced was upgraded, investors enjoyed positive abnormal returns while in case when credit rating announcements were downgraded, then investors bear negative abnormal returns. Finally, the findings of the study identified the applicability of random walk hypothesis on the Pakistan Stock Exchange and Pakistan Stock Exchange confirms the efficient market hypothesis with its semi-strong form of efficiency.

Reinventing climate investing: building equity portfolios for climate risk mitigation and adaptation

ABSTRACTInstitutional investors are increasingly concerned with the material financial risks associated with global warming and the impacts of climate change on corporate financial performance and security returns. The challenge remains how to empirically quantify climate risk from an investment perspective and build investable portfolios that address the transition to a low-carbon economy. This study analyzes the available metrics for capturing climate-related investment considerations. We undertake a comprehensive review of the data characteristics for metrics such as carbon intensity, green revenue, and fossil fuel reserves, highlighting their coverage and distributional characteristics. These data characteristics are critical when integrating them into investment strategies. Building on our findings, we propose a framework for building climate strategies within public equities which rests on both mitigating the impact of climate risk today and adapting to climate risk in the future. This ‘mitigation and adaptation’ framework has enough flexibility to build portfolios at different levels of concentration, tracking error, and climate risk exposure. For example, we can build a portfolio which aligns with climate model projections. We illustrate our framework with a portfolio calibrated to align with the most conservative climate model projections, which seek to limit cumulative CO2 emissions to a threshold below the 2°C scenario.

A Study on Impact of Liquidity on Returns of Four Government Bonds in the Context of Indian Bond Market

A well-developed capital market consists of equity and bond market. A sound bond market with a significant role played by the Government bond market segment is considered to be important for an efficient capital market and raising for developmental ventures. Bonds are issued and sold to the public for funds. Bonds are interest bearing debt certificates. This study is conducted in order to analyze the impact of liquidity on return of government securities in the context of Indian bond market.

Portfolio rebalancing under uncertainty using meta-heuristic algorithm

In this paper, we solve portfolio rebalancing problem when security returns are represented by uncertain variables considering transaction costs. The performance of the proposed model is studied using constant-proportion portfolio insurance (CPPI) as rebalancing strategy. Numerical results showed that uncertain parameters and different belief degrees will produce different efficient frontiers, and affect the performance of the proposed model. Moreover, CPPI strategy performs as an insurance mechanism and limits downside risk in bear markets while it allows potential benefit in bull markets. Finally, using a globally optimisation solver and genetic algorithm (GA) for solving the model, we concluded that the problem size is an important factor in solving portfolio rebalancing problem with uncertain parameters and to gain better results, it is recommended to use a meta-heuristic algorithm rather than a global solver.

Portfolio rebalancing under uncertainty using meta-heuristic algorithm

In this paper, we solve portfolio rebalancing problem when security returns are represented by uncertain variables considering transaction costs. The performance of the proposed model is studied using constant-proportion portfolio insurance (CPPI) as rebalancing strategy. Numerical results showed that uncertain parameters and different belief degrees will produce different efficient frontiers, and affect the performance of the proposed model. Moreover, CPPI strategy performs as an insurance mechanism and limits downside risk in bear markets while it allows potential benefit in bull markets. Finally, using a globally optimization solver and genetic algorithm (GA) for solving the model, we concluded that the problem size is an important factor in solving portfolio rebalancing problem with uncertain parameters and to gain better results, it is recommended to use a meta-heuristic algorithm rather than a global solver.

Linear Beta Pricing with Inefficient Benchmarks in a Given Factor Structure

We show the equivalence between the zero-beta version of a multi-factor arbitrage pricing model and a linear pricing model utilizing undiversified inefficient benchmarks in a given factor structure. The resulting linear model is a two-beta model, with one beta related to the inefficient benchmark and another adjusting for its inefficiency. This linear model shows that there are only two distinctive and computable sources of risk, affecting security expected returns, despite the existence of several risk factors. In a short empirical example we demonstrate that the model can be employed to provide guidance and allow researchers to test for the validity of their selection of the underlying risk factors driving variations in security returns.

Estimation of Weak Factor Models

This paper investigates estimation of sparsity-induced weak factor (sWF) models, with large cross-sectional and time-series dimensions (N and T, respectively). It assumes that the kth largest eigenvalue of a data covariance matrix grows proportionally to N^alpha_k with unknown exponents 0 < alpha_k <= 1 for k=1,...,r. Employing the same rotation of the principal components (PC) estimator, the growth rate alpha_k is linked to the degree of sparsity of kth factor loadings. This is much weaker than the typical assumption on the recent factor models, in which all the r largest eigenvalues diverge proportionally to N. We apply the method of sparse orthogonal factor regression (SOFAR) by Uematsu et al (2018) to estimate the sWF models and derive the estimation error bound. Importantly, our method also yields consistent estimation of alpha_k. A finite sample experiment shows that the performance of the new estimator uniformly dominates that of the PC estimator. We apply our method to forecasting bond yields and the results demonstrate that our method outperforms that based on the PC. We also analyze S&P500 firm security returns and find that the first factor is consistently near strong while the others are weak.

Secure return home service based on IOT biometric and web application

Secure return home service based on IOT biometric and web application

A Multi-Objective Portfolio Selection Model With Fuzzy Value-at-Risk Ratio

Considering nonstatistical uncertainties and/or insufficient historical data in security return forecasts, fuzzy set theory has been applied in the past decades to build portfolio selection models. Meanwhile, various risk measurements such as variance, entropy, and Value-at-Risk have been proposed in fuzzy environments to evaluate investment risks from different perspectives. Sharpe ratio, also known as the reward-to-variability ratio, which measures the risk premium per unit of the nonsystematic risk (asset deviation), has received great attention in modern portfolio theory. In this study, the Sharpe ratio in fuzzy environments is introduced, whereafter, a fuzzy Value-at-Risk ratio is proposed. Compared with Sharpe ratio, Value-at-Risk ratio is an index with dimensional knowledge that reflects the risk premium per unit of the systematic risk (the greatest loss under a given confidence level). On the basis of the two ratios, a multi-objective model is built to evaluate their joint impact on portfolio selection. Then, the proposed model is solved by a fuzzy simulation based multi-objective particle swarm optimization algorithm, where the global best of each iteration is determined by an improved dominance times based method. Finally, the algorithm superiority is justified via comparing with existing solvers on benchmark problems, and the model effectiveness is exemplified by using three case studies on portfolio selection.

Introduction to Corporate Finance: A Practical Guide for Postgraduate and Research Students

The current position of the business can be verified through ratios analysis. According to this analysis managers, creditors, and investors can receive valuable information about the effectiveness and efficiency of the operations. Managers by using especially profitability, operating, and asset utilization ratios can maintain a fairly accurate perception of the financial health of their business. Different types of ratios can help them as they are indicator of well goals are being achieved. When actual results can not meet the standard set, then, ratios indicate where the problem may be. There are different types of ratios. Liquidity ratios are current and quick asset ratios. Profitability and operating ratios are return on asset, gross profit margin and net profit margin. Gearing ratios are divided into debt to equity ratio and number of times interest earned. There are different shareholder investment ratios such as return on shareholders, earnings per share, price earnings ratio, dividend yield and dividend covers. Under asset utilization ratios, we have the net asset turnover, debtor collection period, stockholding period and creditor payment period. Investment appraisal is a process which assists mangers to take decisions and evaluate the future success of a project in terms of profitability, suitability, and compatibility with company objectives. The value of money changes with time. Money received today has a different value from money received in the future. the investors before making any investment must take into consideration that different types of securities will have different kinds of risks. The several sources of risk can be classified as: interest rate risk, market risk, inflation risk, business risk, exchange rate risk, financial risk, country risk, and liquidity risk. No investment will be made unless the expected rate of return is high enough to compensate the investor for taking extra risks. In general, it is believed that the higher the perceived risk associated with an investment opportunity, the higher should be its expected return to persuade an investor to accept the investment opportunity. Investors are risk averse, namely that they want to reduce their risk through a diversified balanced portfolio. This leads us to modern portfolio theory, which was developed by Markowitz (1959). Arbitrage pricing theory, APT, is a more general approach to asset pricing because it allows for the possibility that many factors can be used to explain security returns. The assets of a company are financed by either debt or equity. After calculating the cost of capital for equity and debt, then, we combine them in a form of weighted average to calculate the final weighted average cost of capital. The reason of measuring the weighted average cost of capital is to find how much interest the company or the government has to pay for every Euro it borrows. Factors that affect the cost of capital are the capital structure of the company, the dividend policy, the investment policy, the level of interest rates and the tax rates. Managing the working capital of the business is very important step that will prevent the shareholders from bankruptcy. It is very important to control short and long – term cash in relation to short and long – term liabilities. The relationship between fixed and variable costs is very important to determine the amount of leverage that the company will use. Leverage is determined by the profit changes due to changes in sales. The proportion of costs in the capital structure of a company determines the level of leverage that the company will use. The valuation of a derivative product is determined by changes of the main asset. They are risky products and due to the leverage effect they provide high positive or negative returns. Business valuation is an important measure in case that the shareholders seek short- term liquidity to finance their debt. The most common measures are dividend yield, price earnings ratio and net asset value.

ANALISIS PEMBAGIAN DEVIDEN PADA PERUSAHAAN YANG TERCATAT DI BEI

The purpose of this study was to examine the role and return of investment securities in the form of dividends to maximize investor expectations. This study has a causality design with hypothesis testing. Data sources are secondary from the financial statements of issuers that distribute dividends during the years 2008-2012. Sampling using purposive sampling technique. The unit of analysisis the firm. The method of analysis is using multiple regression. The results showed that simultaneous ROE, PER, and WCTT influence DPR. Partially PER positively affects ROE, while WCTT and ROE has no effect to DPR. The study's findings showed PER dominant influence DPR, so that accounting information has value of relevance of accounting with investment decisions. The result shows that the accounting information has a function as a signal. Keyword : DPR, ROE, PER, WCTT, Debt

Extractive Institutions? Investor Returns to Indian Railway Companies in the Age of High Imperialism

Did colonial policies in India deliver excessive returns to British investors? We answer this question using annual data on Indian securities trading on the London Stock Exchange. We present new series on market capitalization, capital gains, dividend yields, and total returns of railway securities from 1879 to 1929. The average annual total return on the largest and most important Indian railway securities was 3.7%. These returns were not excessive by any financial standard. Indeed, they were lower than the return on railway securities for other regions like North America, Latin America, and Asia. We also undertake an event study analysis to assess if Indian railways significantly benefited British investors. When the Government of India purchased large positions in the private railway companies between 1880 and 1910, there were opportunities for profit-making. However, we find no evidence of abnormal investor returns in the years leading to the purchase of railway companies. Broadly our findings call into question the extractive nature of colonial railway policy.