Universe Of Stocks Research Articles

Do Socially Responsible Index Investors Incur an Opportunity Cost?

AbstractWe construct index‐tracking portfolios using integer programming and then compare the tracking errors and performances of portfolios formed from an unrestricted and socially screened stock universe. We find that one can construct a portfolio of socially responsible stocks that deliver market performance. Thus, the exclusion of a set of stocks from consideration does not exhaust the existence of efficient index‐tracking portfolios, especially when the exclusionary screen is for nonfinancial reasons. Our results are robust to various specifications in constructing the portfolio, for example, number of stocks included in the portfolio and weighting schemes, and robust to alternative tracking error measurement; we show that the difference induced from conducting socially responsible screen is never statistically significant.

Exploiting Option Information in the Equity Market

Public option market information contains exploitable information for equity investors for an investable universe of liquid large-cap stocks. Strategies based on several option measures predict returns and alphas on the underlying stock. Transaction costs are an important factor given the high turnover of these strategies, but significant net alphas can be obtained when using a simple approach that reduces transaction costs. These findings suggest that information diffuses gradually from the option market into the underlying stock market.

Low Risk Stocks Outperform within All Observable Markets of the World

This article provides global evidence supporting the Low Volatility Anomaly: that low risk stocks consistently provide higher returns than high risk stocks. This study covers 33 different markets during the time period from 1990-2011. (Two previous studies by Haugen & Heins (1972) and Haugen & Baker (1991) show the same negative payoff to risk in time periods 1926-1970 and 1970-1990.) The procedure for our study is intentionally simple, transparent and easily replicable. Our samples include non-survivors. We look at an international universe of stocks beginning with the first month of 1990 until December 2011; we compute the volatility of total return for each company in each country over the previous 24 months. Stocks in each country are ranked by volatility and formed into deciles. In the total universe and in each individual country low risk stocks outperform, the relationship with respect to Sharpe ratios is even more impressive. We believe this anomaly is caused primarily by agency issues, namely the compensation structures and internal stock selection processes at asset management firms which lead institutional investors on average to hold more volatile stocks. The article also addresses the implications for how corporate finance managers make capital investment decision in light of this evidence. The evidence presented here dethrones both CAPM and the Efficient Market Hypothesis.

Mean-ETL Portfolio Selection under Maximum Weight and Turnover Constraints Based on Fundamental Security Factors

In this article, we model stock returns using fundamental data and minimizing average value at risk (AVaR) and multiperiod portfolio selection with weight and turnover constraints. Equity returns are decomposed into returns explained by fundamental and nonfundamental factors. While the former are found to be independent, the latter are found to be highly dependent among various stocks. Then, we construct models to forecast returns using several ARMA–GARCH models with different innovation distributions and simulate scenarios of future returns. Based on these scenarios, we examine various approaches of portfolio optimization. By comparing actual portfolios based on real data, we find that 1) the ARMA–GARCH model with classical tempered stable distribution provides a superior prediction of equity prices than the normal and Student’s t -distribution and 2) AVaR provides a better risk measure than variance. We also see how portfolio performance changes under weight and turnover constraints and suggest that it is effective to reduce the stock universe and trade large-capitalization securities.

The Sustainability of Mean-Variance and Mean-Tracking Error Efficient Portfolios

In recent years, the market share of socially responsible investment funds has rapidly increased. This has sparked interest of academics and practitioners for the impact of single stock sustainability screening on portfolio performance. Besides eliminating non sustainable assets from the investment universe, a socially responsible investor also cares about the average level of portfolio sustainability. We provide a characterization of the sustainability of mean-variance and mean-tracking error efficient portfolios and derive the impact on performance of imposing a constraint on the portfolio sustainability. Over the period 2003-2010, we find that for the universe of US stocks belonging to the MSCI World index, the theoretically linear relationship between the portfolio return and sustainability of the efficient portfolios is not significant. Furthermore, we estimate the increase in variance (for a given target return) and the loss in return (for a given target variance) due to a sustainability constraint and find that the performance loss is economically small.

Another look at trading costs and short-term reversal profits

Several studies report that abnormal returns associated with short-term reversal investment strategies diminish once trading costs are taken into account. We show that the impact of trading costs on the strategies’ profitability can largely be attributed to excessively trading in small cap stocks. Limiting the stock universe to large cap stocks significantly reduces trading costs. Applying a more sophisticated portfolio construction algorithm to lower turnover reduces trading costs even further. Our finding that reversal strategies generate 30–50 basis points per week net of trading costs poses a serious challenge to standard rational asset pricing models. Our findings also have important implications for the understanding and practical implementation of reversal strategies.

The Limits to Arbitrage Revisited: The Accrual and Asset Growth Anomalies

Using idiosyncratic volatility as a proxy for arbitrage costs, the authors found that the highly publicized accrual and asset growth anomalies exist because of high barriers to arbitrage, occurring predominantly in the universe of stocks with higher arbitrage risks. Therefore, investors who seek to profit from the accrual and asset growth anomalies must bear greater uncertainty in outcomes than was previously understood.

Enhancing the Profitability of Earnings Momentum Strategies: The Role of Price Momentum, Information Diffusion and Earnings Uncertainty

Recent literature indicates that stock characteristics proxying for behavioral biases reinforce the earnings momentum effect. Using data from the investable German HDAX, we analyze whether returns of earnings momentum strategies can be enhanced in a way that not only survives common risk adjustments but also maintains profitability after trading costs. For our liquid stock universe, we find that the rate of information diffusion has the strongest impact. A bivariate sort on earnings momentum and market capitalization yields gross Carhart alphas of up to 22% per year. The abnormal returns are largely robust to reasonable levels of trading costs.

Another Look at Trading Costs and Short-Term Reversal Profits

Several studies report that abnormal returns associated with short-term reversal investment strategies diminish once transaction costs are taken into account. We show that the impact of transaction costs on the strategies’ profitability can largely be attributed to excessively trading in small cap stocks. Limiting the stock universe to large cap stocks significantly reduces trading costs. Applying a more sophisticated portfolio construction algorithm to lower turnover reduces trading costs even further. Our finding that reversal strategies generate 30 to 50 basis points per week net of transaction costs poses a serious challenge to standard rational asset pricing models. Our findings also have important implications for the understanding and practical implementation of reversal strategies.

The Association between Abnormal Birth History and Growth in Children with CKD

Poor linear growth is a well described complication of chronic kidney disease (CKD). This study evaluated whether abnormal birth history defined by low birth weight (LBW; <2500 g), prematurity (gestational age <36 weeks), small for gestational age (SGA; birth weight <10th percentile for gestational age), or intensive care unit (ICU) at birth were risk factors for poor growth outcomes in children with CKD. Growth outcomes were quantified by age-sex-specific height and weight z-scores during 1393 visits from 426 participants of the Chronic Kidney Disease in Children Study, an observational cohort of children with CKD. Median baseline GFR was 42.9 ml/min per 1.73 m(2), 21% had a glomerular diagnosis, and 52% had CKD for ≥ 90% of their lifetime. A high prevalence of LBW (17%), SGA (14%), prematurity (12%), and ICU after delivery (40%) was observed. Multivariate analyses demonstrated a negative effect of LBW (-0.43 ± 0.14; P < 0.01 for height and -0.37 ± 0.16; P = 0.02 for weight) and of SGA (-0.29 ± 0.16; P = 0.07 for height and -0.41 ± 0.19; P = 0.03 for weight) on current height and weight. In children with glomerular versus nonglomerular diagnoses, the effect of SGA (-1.08 versus -0.18; P = 0.029) on attained weight was more pronounced in children with a glomerular diagnosis. LBW and SGA are novel risk factors for short stature and lower weight percentiles in children with mild to moderate CKD independent of kidney function.

Structural and Market Efficacy Implications of the Short-Sell Ban in Europe

This article seeks to estimate the impact of the ban imposed on short selling by various market regulators across Europe in late 2008, at the peak of the financial crisis. The authors select the STOXX 600 constituents as their universes and do an event study of the ban period, in contrast with the preand post ban-period for the same universe of stocks. In particular, the article reports what impact the ban had, if any, on the efficiency of the market (abnormal returns, volatility, etc.) and market microstructure variables (spread, shape of the order book, etc.), using tick data from Reuters. The authors find a deleterious effect on both sets of variables during the ban, even after controlling for the general crisis like market environment (using STOXX 600 stocks not under the purview of the ban). <b>TOPICS:</b>Exchanges/markets/clearinghouses, volatility measures

Enhancing Portfolios with Global Equity Managers

As progressive investors gravitate away from style-box investing and embrace both unconstrained managers and core–satellite portfolios, the employment of global equity is a natural evolution. Global managers run portfolios unconstrained by country, seeking opportunities across the globe. Implementing talented global managers in a portfolio has several advantages: 1) global flexibility allows the pursuit of unconstrained alpha; 2) country of domicile has become a less important driver of stock performance; 3) the non-U.S. stock universe increasingly dwarfs that of the U.S.; and 4) global managers have outperformed a blend of U.S. and international managers. Sourcing best-of-breed global equity managers is not easy, and many strategies are not integrated into their investment process, but simply a U.S. strategy combined with an international strategy. The ability to go anywhere can also result in high tracking error or high volatility. Higher correlations and an increased prevalence of unconstrained investment strategies means that the investment industry is on the cusp of a major shift in how portfolios are managed. The last major shift began in 1992, when the earlier segmentations among “growth,” “growth and income,” and “aggressive growth” were slowly replaced by the Morningstar style-box matrix model (large/mid/small and value/blend/growth). This transition only took a few years, as the consultant/advisor community accepted these neat categories fairly easily. The next secular change will likely take three to five years to be fully complete, but it will likely involve variations on global equity categories. <b>TOPICS:</b>Portfolio construction, global, performance measurement, manager selection

Reinganum's trading strategies revisited

PurposeThe purpose of this paper is to reexamine the value, momentum and size factors employed in the trading strategy originally proposed by Reinganum. Also to enhance our understanding of the impact of these factors over time; to evaluate the effectiveness of these factors; to develop new strategies through a framework presented by Chan et al.; and to investigate the possibility of limiting the number of filters to allow for a larger universe of eligible stocks without hurting performance for both long and short strategies.Design/methodology/approachUsing data from 1970 to 1983, Reinganum developed a profitable trading strategy based on four (or nine) variables to select stocks. First this paper shows why it is increasingly difficult to implement his original trading strategy, then tests his strategy on 23 additional years of data through 2006, and compares it to similar strategies that incorporate straightforward modifications to his filters. The analysis is further extended to a long/short trading strategy similar to that of Chan et al.FindingsUsing the Capital Asset Pricing Model, Fama‐French's three‐factor model and Carhart's four‐factor model to evaluate returns following portfolio formation, significant and consistent alphas and portfolio sizes were found.Research limitations/implicationsIt is concluded that strategies that mix value, momentum and size filter rules can be developed to produce consistently negative or non‐positive (vs solely positive) alphas.Originality/valueThe paper shows that it is possible to form profitable short‐only and long/short strategies and to increase the number of eligible stocks in the sample despite our modifications to Reinganum's variables. By creating strategies based on mixed filter rules to produce either consistently positive (or negative) risk‐adjusted returns, it can be concluded that slightly different versions of a given filter rule – often much simpler versions – not only enhance return performance but also increase its effectiveness in terms of portfolio size.

Sin Stock Returns

In this article, the authors examine the issue of how social values affect economic values. Based on a small subset of the stock universe that has been generally associated with sin-seeking activities, such as alcohol consumption, adult services, gaming, tobacco, weapons, and biotech alterations, the authors find that a sin portfolio produced an annual return of 19% over the study period, unambiguously outperforming common benchmarks in terms of both magnitude and frequency. Several likely reasons for the positive excess returns in sin stocks are identified. The authors argue that trustees or fiduciaries who develop institutional investment policy statements should fully understand the economic consequences of screening out stocks of companies that produce a product inconsistent with their value systems. In addition, institutional investors should question if the cost to uphold common social standards is worthwhile.

The Scale-Invariant Brownian Motion Equation and the Lognormal Cascade in the Stock Market

A continuous-time scale-invariant Brownian motion (SIBM) stochastic equation is developed to investigate the dynamics of the stock market. The equation is used to solve the fat tail distribution of the stock universe and the DJIA time series. It is also used to model the volatility clustering in the DJIA time series. The equation is transformed from the Langevin equation into a fractal expression involving an infinite array of random walk. It predicts an elegant way of generating the skew form of the lognormal cascade distribution (Kolmogorov and Mandelbrot), which describes the static log-return distribution in the financial market as well as the velocity distribution in Largrangian turbulence. The higher order randomness (HORN) hypothesis is introduced as the stochastic source of the cascade distribution. A leakage term from HORN is introduced to model the covariance between large volatility and large negative return. A volatility model based on two SIBM processes is built to model the volatility autocorrelation. The volatility half-times of 20 days and 300 days are extracted from the DJIA data. The model generates the static log-return distributions from 10 days to 320 days that match the DJIA data satisfactorily. It also predicts an alternative interpretation of the volatility smile/skew observed in the options market. The relation between the SIBM model and the multifractal random walk model is examined, which yields a simplified SIBM model that could be quite useful in finance.

The rise and fall of single-letter ticker symbols

A single-letter stock ticker symbol is a limited resource – only 26 possibilities are available in a stock universe of over 475,000 possible one-, two-, three- or four-letter ticker symbols. These symbols were first allocated based on trading volume therefore some of the most important companies at the time were initially placed into this group. This paper examines the history of this group of stocks and documents a decline in the importance of these firms due to a natural turnover in commercial leadership and no established mechanism to remove the single-letter designation from firms that lost their prominence.

Multifactor Expected-Returns Models and the Performance of Superstock Portfolios in the UK Equity Market

Two empirically testable behavioural finance hypotheses are that (1) 'superstock' portfolios derived from multifactor expected-returns models will have higher than average returns and lower than average risk in terms of statistical and economic significance; and, (2) the expected-returns factor models will demonstrate greater predictive and explanatory power than the risk- and expected-returns-risk factor models of modern finance. Using a dataset comprised of the entire universe of fully-listed stocks in the UK market for the period 1987 to 2002 a multifactor expected-returns model is constructed to estimate security payoffs to factors related to various characteristics such as risk, price level, liquidity, growth potential and previous performance. These payoffs are then used to estimate out-of-sample expected returns and to construct a 'superstock' portfolio. Our analysis suggests that compared to the existing risk-factor models, an 'expected-returns' factor model exhibits increased predictive power of expected returns and has consistently higher than average realised returns with lower than average risk. These results are significant in both statistical and economic terms and corroborate the above behavioural finance hypotheses.

Taiwan multi‐factor model construction: equity market neutral strategies application

Purpose – With the development of the modern portfolio theory and the advancement of information technology, the employment of quantitative approaches to practically measure asset risks and returns, and the construction of portfolios (even dynamic portfolios) has become possible and popular. Therefore, the purpose of this paper is to construct a multi‐factor model for Taiwan stock universe using fundamental technical descriptors and then to apply the equity market neutral investing using multiple‐factor models as a tool.Design/methodology/approach – This study constructs a Taiwan equity multi‐factor model using cross‐sectional fundamental technical approach.Findings – The model involves 28 explanatory factors (including 20 industry factors), and the results of the estimations are satisfactory. The model's explanatory power is 58.6 per cent on average. Furthermore, this multi‐factor model is feasible, modulized, dynamic (i.e. modified over time) and updating.Originality/value – The multi‐factor model, cons...

Stock Selection - an Innovative Application of Genetic Programming Methodology

One of the major challenges in an information-rich financial market is how effectively to derive an optimum investment solution among vast amounts of available information. The most efficacious combination of factors or information signals can be found by evaluating millions of possibilities, which is a task well beyond the scope of manual efforts. Given the limitations of the manual approach, factor combinations are typically linear. However, the linear combination of factors might be too simple to reflect market complexities and thus fully capture the predictive power of the factors. A genetic programming process can easily explore both linear and non-linear formulae. In addition, the ease of evaluation facilitates the consideration of broader factor candidates for a stock selection model. Based upon State Street Global Advisors (SSgA)’s previous research on using genetic programming techniques to develop quantitative investment strategies, we extend our application to develop stock selection mode ls in a large investable stock universe, the S&P 500 index. Two different fitness functions are designed to derive GP models that accommodate different investment objectives. First, we demonstrate that the GP process can generate a stock selection model for a low active risk investment style. Compared to a traditional model, the GP model has significantly enhanced future stock return ranking capability. Second, to suit an active investment style, we also use the GP process to generate a model that identifies the stocks with future returns lying in the fat tails of the return distribution. A portfolio constructed based on this model aims to aggressively generate the highest returns possible compared to an index following portfolio. Our tests show that the stock selection power of the GP models is statistically significant. Historical simulation results indicate that portfolios based on GP models outperform the benchmark and the portfolio based on the traditional model. Further, we demonstrate that GP models are more robust in accommodating various market regimes and have more consistent performance than the traditional model.

Insider Trading and Corporate Governance in Latin America: A Sequential Trade Model Approach

Unlike outside investors, controlling groups have the option to trade on inside information, and can exercise it at the expense of the former. In this paper, a simple theoretical model rationalizes the relationship between corporate governance and insider trading decisions through reputational arguments. We compute probabilities of private information-based trading (PIN) for the universe of liquid stocks from seven Latin American countries, trading both at home and as ADRs, and apply them to address corporate governance questions. We find substantial heterogeneity of PIN within a given institutional environment. Nevertheless, we can identify significant differences in mean PIN across volume ranges, countries, and security types. PIN has an intuitively appealing correlation with some (but not all) of the country-wide investor protection variables used in the literature. PIN is priced in the market: companies with higher PINs fetch lower Tobin's qs. We conclude that the private information-based trading probability proxies for unobservable corporate governance quality as the heterogeneity of firm behavior seems to be recognized by the market and priced accordingly.