Security Returns Research Articles

The “Sixth” Factor—A Social Media Factor Derived Directly from Tweet Sentiments

Institutional investors may have an unclear understanding of the role of social media in asset price determination. Although some of the top quant hedge funds use crowd-based information gleaned from tweets, this relationship may be opaque to the rest of our community. In this article, the authors attempt to clarify the confusion regarding social media sentiment and security return behavior. They show that, surprisingly, Tweet sentiments have significant power in explaining the time-series contemporaneous variation in daily stock returns, even in the presence of well-known equity factors. By examining direct tweet sentiments as provided by StockTwits, the authors claim to have identified a Social Media Factor , the “sixth” factor, and they highlight the distinctions vis-a-vis Fama–French’s factors.

Macroeconomic Determinant Variables In LQ45 Retail and Consumer Goods Securities Performance Index.

The research aimed to examine the changes of macroeconomic variables which affected securities performance in retail and consumer goods industry. Investors who wanted to get return in the short-run and long-run from thwassecurities should concern the economic circumtances too, besides other systematic risks. Those industries produced consumer goods and gave direct impacts to the market. Securities performance was proxied by the return of securities. Macroeconomics variables were proxied by return LQ45 Index, consumer price index (IHK–Indeks Harga Konsumen), inflation rates, Gross Domestic Product (GDP), interest rates (BI Rate), and exchange rates of Indonesian Rupiah to US Dollar. Samples werewere monthly securities trading LQ45 Index of retail and consumer goods firms from January 2014 to July 2016, also the macroeconomic data from Bank of Indonesia. The data taken were assumed during the period of Joko Widodo’s presidency. The research methods were multifactor regressions and correlation.

Reliable Portfolio Selection Problem in Fuzzy Environment: An mλ Measure Based Approach

This paper investigates a fuzzy portfolio selection problem with guaranteed reliability, in which the fuzzy variables are used to capture the uncertain returns of different securities. To effectively handle the fuzziness in a mathematical way, a new expected value operator and variance of fuzzy variables are defined based on the m λ measure that is a linear combination of the possibility measure and necessity measure to balance the pessimism and optimism in the decision-making process. To formulate the reliable portfolio selection problem, we particularly adopt the expected total return and standard variance of the total return to evaluate the reliability of the investment strategies, producing three risk-guaranteed reliable portfolio selection models. To solve the proposed models, an effective genetic algorithm is designed to generate the approximate optimal solution to the considered problem. Finally, the numerical examples are given to show the performance of the proposed models and algorithm.

Properties of the Pukthuanthong-Roll Stochastic Discount Factor

Pukthuanthong and Roll (2016) (PR) construct a clever candidate stochastic discount factor (SDF) that uses a vast collection of individual security returns. I show that the PR approach provides a consistent estimate of a valid SDF when the SDF is an affine function of a finite number of economy wide pervasive factors. I show how to modify the approach in PR to the case where the number of securities varies over time. The PR approach automatically selects all of the pervasive factors in an economy without the need for specifying the number of factors.

Robust multiobjective portfolio optimization: A minimax regret approach

An efficient frontier in the typical portfolio selection problem provides an illustrative way to express the tradeoffs between return and risk. Following the basic ideas of modern portfolio theory as introduced by Markowitz, security returns are usually extracted from past data. Our purpose in this paper is to incorporate future returns scenarios in the investment decision process. For representative points on the efficient frontier, the minimax regret portfolio is calculated, on the basis of the aforementioned scenarios. These points correspond to specific weight combinations. In this way, the areas of the efficient frontier that are more robust than others are identified. The underlying key-contribution is related to the extension of the conventional minimax regret criterion formulation, in multiobjective programming problems. The validity of the approach is verified through an illustrative empirical testing application on the Eurostoxx 50.

A Comparison of GARCH Models for Volatility Estimation of Returns on Securities in Resource Group of Thai Stock Market

The objectives of this study are to estimate volatility of returns on securities in ResourceGroup of Thai Stock Market using basic specifications of ARCH/GARCH/EGARCH model, and to measure skewness and kurtosis of the error term distribution from models selection.A test of ARCH Effect is performed in order to examine whether all data fitt with the ARCH family, and then ARCH (1, 1), GARCH (1,1) and EGARCH (1,1) are developed. A conditional variance equation from ARCH (1,1) is 2 0 1 1 2 t = + t− σ α α ε , and fromGARCH (1,1) is in a form 2 1 1 2 0 1 1 2 σ t = α +α at− + β σ t− and for EGARCH (1,1) is 1 1 1 2 1 1 2 ln( ) ln( ) − − − − = + − + + t t t t t t σ ε γ σ ε σ ω β σ α . The Akaike Criterion (AIC) and Schwarz criterion (SC) statisticsare used in model selection for non-nested alternatives-smaller values of the AIC and SC are preferred. For testing normality, a test statistic is Jarque-Bera and if the residual are normally distributed, the histogram should be bell-shaped and the Jarque-Bera statistic should not be significant, and if the null hypothesis is rejected, then innovation would be introduced through student’s t distribution. After all these steps, skewness and kurtosis are calculated in the study. Results of the study showed that 6 out of 30 securities data fitted with ARCH (1,1) model with insignificance statistical results which indicated that there must be a reconsideration of applying higher order of ARCHinstead of ARCH (1,1) model. There were 5 securities fitted with GARCH (1,1) model, with the same pattern, i.e., lower value for the coefficient of the ARCH term and higher value for the GARCH term which lead to a high fluctuations in GARCH. This finding indicated that the asset return was expectedly large in either the upward or the downward direction, and the estimation results indicated that GARCH (1,1) is appropriated to explain volatility cluster and estimate value of risk on security return. There were 19 securities that data esimation indicated that EGARCH (1,1) model was an appropriate form of variance equation, and showed that all volatility estimation were about 50% or about at moderated level. The skewness and Kurtosis of all residuals distributions showed that all skewness values were greater than zero (skewed to the right) and all Kurtosis values were greater than 3 (Leptokurtic distribution). This means that for each security, the daily price was concentrated on the left side of the mean value, and there were some extreme values to the right side. For the kurtosis, the distribution had a thicker tail which meaned that there was a high probability that the set of data would gain such extreme values

Does the US. macroeconomic news make the south african stock market riskier?

The relationship between macroeconomic risk and security returns has been central to financial economics. A well-known concept in capital asset pricing theory is that only systematic risk factors affect security prices. Macroeconomic news announcements are among the most important systematic risk factors for financial markets because the state of the economy is a major source of non-diversifiable risk. The effects of US macroeconomic news on South African stock markets has received no attention in the domestic or international literature. To fill this gap, this article analyzes the impact of US macroeconomic announcement surprises on the volatility of the South African equity market by employing the asymmetric GJR-GARCH model that allows for both positive and negative surprises about inflation and unemployment rate announcements in the U.S. The study uses daily Johannesburg All Share Index (JALSI) returns from 31 May 1994 to 8 March 2016; and inflation and unemployment rates news surprises about the U.S., where the data on market expectations are obtained from the Money Market Survey, and the Bloomberg Survey expectations. This paper finds that shocks to volatility are persistent and asymmetric. While bad news about US inflation does not affect the volatility of South African stock returns, good news tends to increase the volatility. The South African stock market becomes riskier with an unexpected increase in the US unemployment rate and less risky with an unexpected decrease in the US unemployment rate, with the latter effect being stronger than the former. The unexpected decreases in inflation and increases in unemployment raises stock market volatility in South Africa, which in turn, would imply that financial conditions in South Africa would deteriorate and affect the real economy negatively. This is an important finding for portfolio allocation and asset pricing, as well as for policy makers. The stock market volatility is an important component in measuring the financial conditions scenario for South Africa, and that worsening of the financial conditions negatively affect the real economy, which in turn, requires monetary authorities to pursue expansionary monetary policies. Given this, policy makers need to be aware such a scenario and need to respond with expansionary monetary policies to boost the real economy.

The Dual Index Model That Astutely Augurs Stock Prices Using Sectoral Indices An Empirical Evaluation of Securities That Are Not Constituents of India's Premier Stock Exchange Index Namely BSE-Sensex

The concept of Single index model for pricing of assets had been widely used as a simple tool for forecasting returns of individual securities in tune with the movements of a general market index. The Capital Asset Pricing Model, a footing based on the fact that the alpha component and the residual risk tends toward zero as the number of securities are increased, reduces the single-index model equation to the market return multiplied by the risky portfolio's beta. The fundamental analysis and technical analysis have been the pillar stones on which asset pricing was built. However, no attempts have been made so far to connect technicals to fundamentals of a security. With single index model and multi factor model at the two extremes, an intermediate model, that augments the technicals of a stock to its fundamentals, is the critical idea postulated, to predict future prices. The model referred to Dual Index Model, empirically tests the security returns of two securities that are not constituents of general market index of India's premier stock exchange, the Bombay Stock Exchange, namely BSE-Sensex The dual index model, proposed in this paper, attempts to augment fundamental factors affecting security prices, such as company, industry and economy factors into the technical framework of regressing excess return on individual security with the fundamentals as regressors. While the company factors are decomposed into the expected excess return of the individual stock due to firm-specific factors that are commonly denoted by its alpha coefficient (α) or intercept or predictor constant, the industry factors and economic factors represented by excess returns on sectoral index and market index respectively are assumed to additional predictors in the multi regression, where the excess returns on security returns is the dependent variable. The effectiveness of the dual index model in precisely predicting returns, in the case of securities that are not constituents of the market index, is brought under the scanner and it has been conclusively evidenced that when sectoral index and market index are not highly correlated the dual index model is much superior to single index model in forecasting returns, be it forecast for a short period or long.

Uncertain Portfolio Selection with Background Risk and Liquidity Constraint

This paper discusses an uncertain portfolio selection problem with consideration of background risk and asset liquidity. In addition, the transaction costs are also considered. The security returns, background asset return, and asset liquidity are estimated by experienced experts instead of historical data. Regarding them as uncertain variables, a mean-risk model with background risk, liquidity, and transaction costs is proposed for portfolio selection and the crisp forms of the model are provided when security returns obey different uncertainty distributions. Moreover, for better understanding of the impact of background risk and liquidity on portfolio selection, some important theorems are proved. Finally, numerical experiments are presented to illustrate the modeling idea.

Assesing Performance of Moving Average Investment Timing Strategy Over the UK Stock Market

Predicting stock returns, despite being complicated, has always been of interest to investors, financial analysts, academicians and policy makers. The finance theory, broadly, follows two approaches, namely fundamental and technical analyzes, in order to forecast future prices. Fundamental analysis uses variables related to intrinsic properties of the stock to estimate price, or value. In contrast, technical analysis utilizes historical data on prices (or other information) to drive signals about future prices. Despite its practical usage, profitability of technical analysis remains questionable. While prior studies apply technical analysis, especially moving average, on US stocks, this study follows the methodology adopted by Han, Yang and Zhou (2013) to assess the performance of moving average technical analysis on UK stock market. The study uses daily data on UK-DS Market-PRICE INDEX, 1-Month T-Bill rate, and all stocks listed on London Stock Exchange, United Kingdom (UK). Data has been downloaded from Thomson Reuters's DataStream over a period of more than sixteen years from December 31, 1999 to February 29, 2016. Data on risk free rate and market index do not have any missing values. However, stock prices do include missing values; we replace all of them by not available (NA). We apply moving average investment timing strategy to five quantile portfolios sorted by volatility and compare their performance with respective buy-and-hold portfolios. We also asses risk adjusted returns of quantile portfolios using CAPM. For robustness check, we test the strategy for alternative lag lengths, random switching, and breakeven transactions costs. The results suggest that MA strategy substantially outperforms buy-and-hold strategy in UK stock market by producing significantly higher average returns and risk adjusted returns, lower standard deviations, higher Sharpe ratios, and reasonable success ratios. Results are quite robust to most of the lag lengths and quantiles. However, random switching strategy does not produce significant results in this case. We test MA strategy for various lag lengths; the strategy based on 10-day lag length performs the best. This indicates that signals generated from closer history provide better proxy for future trading. Finally, although breakeven transaction costs are considerably larger than actual transaction costs in UK, other variables measuring trading behaviour under MA strategy provide mixed results when seen in relation to volatility. The Uk stock market is weak from the efficiency empirically proved through this study, then the investors should be able to exploit predictable patterns of share returns in order to earn excess returns on a regular basis. There is reasonable degree of predictability in their security returns.

A Reconsideration of the Equity Premium Puzzle

This paper develops a tractable asset pricing framework based on an Arrow Debreu economy with heterogeneous agents. The model re-casts a number of well known asset pricing relationships in terms of four underlying variables. The financial relationships studied here are the risk free rate, the market risk premium, a call option, the expected returns of individual securities, and the dividend growth model. The theoretical formulations are used to measure preference parameters in three different ways. The most precise estimates yield a value of relative risk aversion of 1.27, and a time preference discount rate of 3.82% per year.

The Dual Index Model - Empirical Proof of an Astute Model that Augurs Stock Prices across Assorted Sectors

The power of Sectoral indices computed and published by stock exchanges, in the pricing of asset had been overlooked and existing literature on sectoral indices had been limited to influence of macroeconomic factors on them. The Single index Model (1964) postulates the market index to represent all the macroeconomic uncertainties. The Fama and French Three Factor Model (1993) is an asset pricing model that expands on the Capital Asset Pricing Model (CAPM) by adding size and value factors to the market risk factor in CAPM. With single index model and multi factor model at the two extremes, an intermediate model, that augments the technicals of a stock to its fundamentals, is the critical idea postulated here, to predict future prices. The model referred to Dual Index Model, empirically tests the security returns of all thirty securities that are constituents of India’s premier stock exchange index, i.e. The Bombay Stock Exchange’ (BSE) BSE- Sensex and its Sectoral Indices. The multiple regression models developed augments technical analysis to fundamentals of a stock by incorporating the company, industry and economy factors as intercept and slopes by assuming that industry factors are represented by excess returns on sectoral indices while economy factors are reflected in excess returns of the general market index. The validated multiple regression models either Ordinary least squares or Auto Regressive Conditional Heteroskedasticity, has been used to forecast returns and predict prices of an out of sample period that follows the sample period. The experimental results on the predictive potential of the postulated model is a conclusive evidence of high degree of precision in forecasts. The use of multi variate analysis with the postulated model can be effectively applied to any securities listed on the stock exchange irrespective of the fact whether it is included in the computation of market index or not.

Multi-period portfolio selection with dynamic risk/expected-return level under fuzzy random uncertainty

In this study, we discuss multi-period portfolio selection problems when security returns are described as fuzzy random variables. The main concern of this work is to apply dynamic risk tolerance and expected return levels in mathematical modeling; i.e., these two indices of each period are influenced by the investment result of the previous period as well as human risk attitudes instead of static values over the entire investment horizon. Essentially, this assumption is based on the reality that investors tend to update targets when their wealth changes. In addition, fuzzy random variables are employed here to incorporate historical data with expert knowledge when estimating security future returns. Based on the above considerations, two multi-period portfolio selection models are built in light of the different risk attitudes. We then provide property analysis on complicated nonlinear optimization problems and derive several equivalents of the models, which can be solved by the existing dynamic programming. In general situations, a fuzzy random simulation-based particle swarm optimization algorithm is developed to search for approximate optima. The performance of this research is exemplified by a real market data-based case study in which the superiority of the dynamic strategy is demonstrated by a comparison with conventional approaches.

An efficient dynamic model for solving a portfolio selection with uncertain chance constraint models

This paper presents a neural network model for solving maximization programming model with chance constraint, in which the security returns are uncertain variables. The main idea is to replace the portfolio selection models when the uncertain returns are chosen as some special cases such as linear uncertain variables, trapezoidal uncertain variables and normal uncertain variables, with a linear programming (LP) problem. According to the saddle point theorem, optimization theory, convex analysis theory, Lyapunov stability theory and LaSalle invariance principle, the equilibrium point of the proposed neural network is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the portfolio selection problem with uncertain returns. Two illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.

Do actively managed funds perform better than index funds? A test in the Canadian market

Actuality of the study: Mutual funds are a favourite investment product among many investors. They provide a simple means of diversification, especially for those with smaller amounts of capital, and the popularity of mutual funds has increased with the success of the marketing efforts behind them.
 Purpose: This study evaluates the performance of actively managed and index mutual funds within the Canadian equities market.
 Findings: As index investing has increased in popularity, and other markets have become more connected and open, there is a need for research on equity mutual funds in countries outside the US.
 Originality / Value: The majority of previous research on index funds and actively managed mutual funds is focused on the US market and related indexes such as the S&P 500.
 Practical implications: This study suggests that, on average, active funds in Canada fail to beat their benchmarks net (but not gross) of the common fee or management expense ratio. Surprisingly, this research finds no positive relationship between higher fees and better gross performance. Actively managed funds also have poorer performance over the long term. This study finds that investors would be better off purchasing low cost index funds as they provide a more secure return.
 Future research: This study endorses research on other markets with inclusion of additional variables in order to explain gross performance and secure returns.

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

This paper investigates the joint determination of two dimensions of a security: trading volume and return. In much of the existing literature, volume is modeled as being exogenously related to security returns. Our analysis evaluates the extent to which trading activity also depends on security returns, thus resulting in a reverse causality from returns to trading activity. Using instruments for security trading activity, we compare approaches using ordinary least squares (OLS), two-stage least squares (2SLS), and generalized method of moment (GMM) estimators. Using both stock and bond data, our results indicate that returns and trading volume are determined simultaneously. We show that estimates of the magnitude, direction, and significance of causality between volume and returns can be substantially changed once one corrects for the endogeneity of volume.

SELEKSI SAMPEL SEBAGAI PROKSI INDEKS PASAR DALAM PENGUJIAN CAPITAL ASSETS PRICING MODEL DI PASAR INDONESIA

Some test about Capital Asset Pricing Model (CAPM) are in fact, most of them showed that there were in equilibrium between the CAPM theory with the empirical facts. This in equilibrium most can be see in the intercept which is not the same as zero. Some tests are resulting that linear relation between the return level which can be hoped by the risk which is measured with beta, but in the other fact shows that there is still factor or variable beside beta which influence the return of security, those variables are not explained by CAPMThe failure of empirical test for proving the CAPM hyphotesis are caused by the failure of the L""'APM specification itself then the use of market model which maybe is not relevant because of the return fluctuation and the free investment risk The investment process in security is doing evaluation to the investment performance, in security investment which is often done in portofolio form, so it is needed portofolio performance l!valuation. Some tests, empirical CAPM can be concluded that: (1) Security return is in fact in linear way can be related with beta as it is predicted in CAPM test; (2) There is positive relation between beta with historical return, that it means the bigger the beta the bigger the return; (3) The market line security test (SML) is in fact less steeply slope comparing to the theoretical SML, it means that shares with lower beta will have higher return as it is predicted by the Capital Asset Pricing Model, while the shares with higher beta have lower return as it is predicted, this is because of the mistake in the measurement; (4) The intercept criteria which is bigger can be predicted theoretically than the value offree risk return (RJ.Even the test result can not give support from the theory of Capital Asset Pricing Model, this test gives the consistent implication, beta is the linear systematic risk taht is related positively with the historical return.

The value relevance of earnings, cash flows and book values in Egypt

PurposeMotivated by the lack of research on the value relevance of accounting information in the emerging markets of Middle Eastern countries, and the unique institutional and accounting setting in Egypt, this paper aims to investigate the relation between capital market and accounting information in the emerging market of Egypt. Specifically, based on Egyptian data, this study examines the value relevance of earnings, cash flows from operations and book values.Design/methodology/approachTo examine the value relevance of the above accounting measures, this study uses statistical associations between accounting information and capital market values: the association between earnings and annual returns; the association between cash flows and accruals, and annual returns; and the association between earnings and book values of equity, and stock prices.FindingsThe results show that, first, earnings have value relevance. However, earnings changes are significantly more successful than earnings levels in explaining security returns. These results suggest that changes in earnings are largely permanent; hence, earnings follow (close to) a random walk model. Second, contrary to what is stated in the literature, cash flows from operations are not successful in explaining stock returns. This result suggests that cash flows are less important and not value relevant in Egypt compared to the USA or the UK. A possible explanation is that cash flows in Egypt are very volatile (high variance) and not persistent, so the market does not rely on them. Third, individually, both earnings and book values significantly explain stock prices; however, jointly, earnings have incremental explanatory power beyond book values for stock prices whereas book values do not. These results suggest that in Egypt the income statement is much more important than the balance sheet for valuation purposes. Overall, these results are interesting because they do not completely replicate the results from other countries.Practical implicationsThe existence of value relevance for earnings despite the apparent lack of value relevance for cash flows can be interpreted as indicating that accruals are designed to offset and smooth cash flows’ volatility and low value relevance, so that earnings are relatively more persistent and relevant. These results show that earnings potentially are a much more important and informative measure of a firm’s value than cash flows from operations in Egypt. However, we certainly need the cash flows information as an ex-post validation of the prior earnings. Overall, it appears that the investors in Egypt are looking at the accounting data when evaluating the value of the firm, which is a good sign. However, the empirical findings of this paper are discussed.Originality/valueThis study contributes to the limited research on value relevance of accounting information in the emerging market of Egypt.

Prepayment Risk and Expected MBS Returns

We present a simple, linear asset pricing model of the cross section of Mortgage-Backed Security (MBS) returns. We measure prepayment risk and estimate security risk loadings using real data on prepayment forecasts vs. realizations. Estimated loadings are monotonic in securities’ coupons relative to the par coupon, as predicted by the model. Prepayment risks appear to be priced by specialized MBS investors. In particular, we find convincing evidence that prepayment risk prices change sign over time with the sign of a representative MBS investor’s exposure to prepayment risk.

Portfolio selection models based on Cross-entropy of uncertain variables

This paper researches the uncertain portfolio selection problem when security returns are hard to be well reflected by previous data. We take security returns as uncertain variables. Firstly, three new portfolio selection models are presented based on the cross-entropy of uncertain variables. Secon dly, the crisp form of the new model is also provided when security returns are linear uncertain variables. In addition, the gravitation search algorithm and numerical integration are introduced to solve the proposed models. Finally, several numerical examples are presented to illustrate the effectiveness of the algorithm and the application of the mathematical models.