Common Stock Portfolios Research Articles

Investing in US Timberland Companies

Are common stocks issued by timberland companies a good investment? Portfolios of large US timberland corporations are compared to simultaneous investments in a diversified US common stock index. Over a 20-year sample period it turns out that the US timberland corporations, on average, perform about as well as the highly diversified US stock market index. It is surprising that the timberland companies do not outperform the stock market indexes because, in order to encourage tree planting, the US Congress has almost completely exempted timberland companies from paying federal income taxes. Furthermore, it is scientifically impossible to assess the value of the large amounts of photosynthesis that the timberland companies produce. As a result of these two ambiguities, it is difficult to state decisively that the timberland companies are better investments than a diversified portfolio of common stocks. However, valuing timberland companies is more practical than endeavoring to value the trees directly.

The income bond puzzle

The income bond puzzle

The Covid-19 Pandemic and its Impact on the Yields of Sharia Stock Business Portfolio in Indonesia

Purpose: This research aims to examine the impact of the COVID-19 pandemic on the performance of stock portfolios in Indonesia, particularly the sharia stock portfolios and common stocks in response to information sentiment due to the COVID-19 pandemic on the Indonesian stock exchange. Theoretical framework: Research by Bash & Alsaifi (2019) and Shanaev & Ghimire (2019) examined the relationship between stock returns through the stock index on political activity or its connection with environmental exploitation (Alsaifi et al., 2020; Guo et al., 2020). The relationship between stock price changes with sports (Buhagiar et al., 2018) and disasters (Kowalewski & Śpiewanowski, 2020). Design/methodology/approach: The data used in this study were 564 stocks, consisting of 356 Islamic stocks and 208 common shares provided by the Osiris database. The model testing used panel data regression with a time lag approach, where information on the number of positive cases, deaths, and recoveries due to COVID-19 each week affected the performance of the stock portfolio on that day. Findings: The testing of the performance of sharia and conventional stock portfolios showed that there is no significant difference in response to the COVID-19 pandemic information, but statistical statistics on Islamic stocks have a tendency to be more resistant to pressure from selling by investors in the Indonesian capital market compared to common stock portfolios, this is proven by the sharia stock correlation coefficient shown to be positive, on the other hand, the stock regression coefficient shows the opposite direction. Research, Practical & Social implications: This research is expected to be a recommendation material that has benefits for the development of Islamic economics, primarily can be implemented by sharia-based banks in Indonesia and other countries. Originality/value: This research focuses on studying the performance of stock portfolios in Indonesia. This research responds to the impact of the Covid-19 pandemic on the performance of stock portfolios, which is focused on Islamic stock portfolios.

Stock Prediction Using Machine Learning Algorithms

In the recent times, the stock markets have emerged as one of the top investment destinations for individual and retail investors due to the lure of huge profits that are possible with stock investments compared to more traditional and conservative forms of investments such as bank deposits, real estate and gold. The stock markets unlike other forms of investment are highly dynamic due to the various variables involved in stock price determination and are complex to understand for a common investor. Individual and small-time investors have to generate a portfolio of common stocks to reduce the overall risk and generate reasonable returns on their investment. This phenomenon has given way too many individual and retail investors incurring huge losses because their decisions are based on speculation and not on sound technical grounds. While there are financial advisory firms and online tools where individual investors can get professional stock investment advice, the reliability of such investment advice in the recent past has been inconsistent and not meeting the rigor of quantitative and rational stock selection process. Many of such stock analysts and the tools mostly rely on short term technical indicators and are biased by the speculation in the market leading to huge variances in their predictions and leading to huge losses for individual investors. While the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques is widely adopted in the financial domain, integration of AI/ML techniques with fundamental variables and long-term value investing is a lacking in this domain. Some of the stock portfolio tools available in the market use AI/ML techniques but are mostly built using technical indicators which makes them only suitable for general trend predictions, intraday trading and not suitable for long term value investing due to wide variances and reliability issues. The availability of a Financial Decision Support System which can help stock investors with reliable and accurate information for selecting stocks and creating an automated portfolio with detailed quantitative analysis is lacking. A Financial Decision Support System (DSS) that can establish a relationship between the fundamental financial variables and the stock prices that can VII automatically create a portfolio of premium stocks shall be of great utility to the individual investment community. As part of this thesis, the researcher has designed and developed a Financial Decision Support System (DSS) for selecting stocks and automatically creating portfolios with minimal inputs from the individual investors. The Financial DSS is based on a System Architecture combining the advantages of Artificial Intelligence (AI), Machine learning (ML) and Mathematical models. The design and development of the Financial DSS is based on the philosophy to combine various independent models and not rely on a single stock price model to increase the accuracy and reliability of the stock selections and increase the overall Return on Investment (ROI) of the stock portfolio. The Machine learning models are used to establish the relationship between fundamental financial variables and the price of the stock, a mathematical model is developed to calculate the intrinsic value of the stock taking in to account the full lifecycle of the stock which involves various phases and a comprehensive model to analyze the financial health of the stocks. The AI/ML stock models are independently trained using historical financial data and integrated with the overall Financial DSS. Finally, the Financial DSS tool with a graphical user interface is built integrating all the three models which shall be able to run on a general-purpose desktop or laptop. To reliably validate the Financial DSS, it has been subjected to wide variety of stocks in terms of market capitalization and industry segments. The Financial DSS is validated for its short term and long-term Return on Investment (ROI) using both historical and current real-time financial data. The researcher has reported that the accuracy of the AI/ML stock price models is greater than 90% and the overall ROI of the stock portfolios created by the Financial DSS is 61% for long term investments and 11.74% for short term investments. This system has the potential to help millions of individual investors who can make their financial decisions on stocks using this system for a fraction of cost paid to corporate financial consultants and value eventually may contribute to a more efficient financial system.

Justifying Mean-Variance Portfolio Selection when Asset Returns Are Skewed

We show that, in the presence of a risk-free asset, the return distribution of every portfolio is determined by its mean and variance if and only if asset returns follow a specific skew-elliptical distribution. Thus, contrary to common belief among academics and practitioners, skewed returns do not allow a rejection of mean-variance analysis. Our work differs from Chamberlain's [Chamberlain G (1983) A characterization of the distributions that imply mean-variance utility functions. J. Econom. Theory 29(1):185–201.] by focusing on the returns of portfolios, where the weights over the risk-free asset and the risky assets sum to unity. Furthermore, it extends Meyer's [Meyer J, Rasche RH (1992) Sufficient conditions for expected utility to imply mean-standard deviation rankings: Empirical evidence concerning the location and scale condition. Econom. J. (London) 102(410):91–106.] by introducing elliptical noise into their generalized location-scale framework. To emphasize the relevance of our skew-elliptical model, we additionally provide empirical evidence that it cannot be rejected for the returns of typical portfolios of common stocks or popular alternative investments.This paper was accepted by Kay Giesecke, finance.

Examining the Skewness and Kurtosis of Value vs. Glamour Portfolios

We analyze the relationships between higher moments (i.e., skewness and kurtosis) of common stock portfolio returns in the context of how they reflect the behavioral traits or investment style of market participants. Normally, any participant in the stock market is classified as an investor. However, Benjamin Graham, the acknowledged father of value investing, defines investment as an operation that upon thorough analysis promises safety of principal and satisfactory returns. Anything other than that is speculative. We classify investors, or more specifically value investors, as those who make their investment decisions in a manner consistent with Benjamin Graham’s definition. In contrast to Benjamin Graham’s definition, traditional finance theory labels as “investor” anyone who is making choices among financial assets. In this paper, we make a distinction between those who are behaving as investors as defined by Benjamin Graham and those who are making common stock investment decisions that are more representative of gambling behavior. We hypothesize that the common stock portfolio returns of value investors will be more negatively skewed, or at least lower than that of gamblers. Similarly, we hypothesize that the kurtosis of portfolio returns of value investors will be greater than that of gamblers. Using stock returns data from NYSE from 1999 to 2013, we created two portfolios based on price-to-book ratio: one we call quasi value (lowest price-to-book) and the other glamour portfolios (highest price-to-book). We then compared the higher moments of both sets of portfolios. The results show that contrary to what we hypothesized, the returns of glamour portfolios had lower skewness than the quasi value portfolios. However, as hypothesized, the kurtosis of the quasi value portfolios were higher than those of the glamour portfolios. We interpret that to mean that without a full value investing analysis, the extent of diversification of a portfolio is the primary determinant of its skewness.

Who Are the Bulls and Bears in Global Lodging Markets?

This article looks at the values of market-based equities (common stocks) based on the relative investment valuation, analyzed with several earnings multiples in global lodging markets in 2014. To be specific, we compare the value of various common stock portfolios sorted for four geographical regions with those relative value investment metrics so that we can make a solid judgment about bullish and bearish markets demonstrating investors’ confidence or stagnancy, anomalies in prices indicating under and overvalued stocks, and outperforming lodging portfolios in global markets. Common stock prices in portfolios are standardized, utilizing earnings parameters such as Current Price/Earnings Ratio (PE) and its variants (Trailing and Forward PE), Price/Earnings to Growth (PEG), and The Market Value of Debt and Equity free of Cash-to-EBITDA (Value/EBITDA). Linking the relative value with earnings proxies and the stock portfolios, which are extracted in a homogeneous industry but are sorted from heterogeneous r...

Spin glasses and nonlinear constraints in portfolio optimization

We discuss the portfolio optimization problem with the obligatory deposits constraint. Recently it has been shown that as a consequence of this nonlinear constraint, the solution consists of an exponentially large number of optimal portfolios, completely different from each other, and extremely sensitive to any changes in the input parameters of the problem, making the concept of rational decision making questionable. Here we reformulate the problem using a quadratic obligatory deposits constraint, and we show that from the physics point of view, finding an optimal portfolio amounts to calculating the mean-field magnetizations of a random Ising model with the constraint of a constant magnetization norm. We show that the model reduces to an eigenproblem, with 2N solutions, where N is the number of assets defining the portfolio. Also, in order to illustrate our results, we present a detailed numerical example of a portfolio of several risky common stocks traded on the Nasdaq Market.

Redefining Risk: Propositions to Motivate a Re-Examination of the Standard Risk vs. Return Relationship in Common Stock and Bond Portfolio Management

Modern Portfolio Theory and Asset Pricing Models have major limitations as systems for modeling investor portfolio choices and prices of financial assets. Financial market participants are not a uniform group of rational investors. Instead, they are an amalgamation of heterogeneous traders with varied and sometimes very divergent goals. In fact, not every financial market participant is an investor. Even the investors in the market operate in a manner such that a lot of decisions do not line up with rationality. Thus the current paradigm of using Modern Portfolio Theory to represent the activities of market participants as rational investors leads to predictions that do not fit what is observed in financial markets. First, we define investment and an investor from a Value Investing perspective according to Benjamin Graham. We present a number of propositions based on critiques of Modern Portfolio Theory that are becoming common in the literature to focus discussion on ways to view investment operations from a value investing perspective as propounded by Benjamin Graham. These propositions deal mainly with how investors perceive and handle risk in their portfolio management decisions. We conclude that we have the tools and methodology to develop portfolio theory that incorporates time and investor behavior that is different from the homogeneous group of ultra-rational decision makers on whom the current models are based.

The market performance of franchise stock portfolios

PurposeThis paper's aim is to examine the risk‐adjusted market performance of an overall franchise and three sub‐sector franchise common stock portfolios from 1990 through 2008.Design/methodology/approachFour sets of franchise sector portfolios are constructed, their returns are calculated, and their performances relative to three market benchmarks are evaluated using the Sharpe ratio and Jensen's α.FindingsThe all franchise portfolio significantly outperformed the three market benchmarks. Among the sector portfolios, the services and restaurant portfolios also outperformed the market benchmarks, but not the lodging portfolio. Results support the theoretical hypothesis that franchising may provide superior advantages to investors and point to a possible “franchising anomaly”. Investors consider franchise firms to be less risky than the average publicly traded firms and therefore require a lower rate of return.Practical implicationsThe results of the study suggest that in the past, franchise managers may have paid a much higher cost of capital than warranted by their firms' risk characteristics. Study results also have positive implications for franchise firms' access to capital and for evaluating franchise managers' compensation arrangements. Investors should consider allocating a portion of their investible funds to franchise stocks. Many lodging firms may not have taken full advantage of the benefits of franchising to reduce their financial risks. Restaurant firms may further improve their financial performance by selling their riskier units.Originality/valueThis is the first comprehensive study of the risk‐adjusted market performance of franchise firms over an extended period of time covering a variety of economic conditions that also analyzes the risk‐adjusted performance of the main business subcategories in franchising.

Mandatory IFRS Adoption and the U.S. Home Bias

SYNOPSISThis paper examines whether mandatory IFRS adoption at the country level lowers U.S. investors' propensity to overweight domestic stocks in their common stock portfolios (generally referred to as home bias). We find that, on average, U.S. home bias decreases for countries that mandate IFRS adoption, after controlling for country-fixed effects. We also find that the reduction in the U.S. home bias after the mandatory adoption of IFRS is greater for countries with larger differences between IFRS and their domestic accounting standards, for countries with a stricter rule of law and a common law legal origin, and in countries with greater incentives to report high-quality financial information. Overall, our results indicate that a common set of global accounting standards matters for portfolio holdings of U.S. investors and that U.S. investors regard the enforcement of standards to be a key factor in making investments outside the U.S.Data Availability: Data are publicly available.

Ownership Control And Small Firm Anomalities

Tests using the Capital Asset Pricing Model show that during the 1981 through 1983 period, a portfolio of common stocks composed of owner-controlled firms significantly outperformed a group of manager-controlled firms, even after adjusting for systematic risk. Small capitalization stocks were also shown to have generated large abnormal returns compared to large firm stock during this period. Interestingly, the statistical significance of both the ownership control and small firm effects was not reduced when the other effect was controller. The implications of these results for investors are that large excess returns may be earned by tilting stock portfolios towards owner-controlled, small capitalization firms.

Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation

Long-Term Equity Anticipation Security or LEAPS is a call option introduced as a more conservative security that can replicate a common stock position. This study’s objective is to examine the effect of applying the strategy of “Buying In-the-Money LEAPS Calls vs. Purchasing Stocks” proposed by CBOE on the performance of traders in terms of risk and return trade-off and the risk-adjusted performance in practice, using a sample of 54 common stocks listed on NYSE and NASDAQ and 54 LEAPS calls on the same underlying stocks listed on CBOE during 2008-2010. The results indicate that LEAPS calls are not a preferred financial instrument to replace common stocks for risk-averse traders. When the stock market experiences a progressive downturn trend, the portfolios of LEAPS calls provide much higher negative returns, significant loss and poor performance as well as higher levels of volatility relative to the portfolios of common stocks. The results of this study also suggest that risk-seeking traders, who can tolerate the higher level of risk in compensation for higher returns, choose the portfolio of LEAPS calls with high Book-To-Market (BTM) ratio assets. This portfolio is less volatile relative to the portfolio of LEAPS with low BTM ratio and provides higher rates of return in comparison to the portfolios of common stocks in favorable market conditions.

Mandatory IFRS Adoption and the U.S. Home Bias

This paper examines whether mandatory IFRS adoption at the country level lowers U.S. investors’ propensity to overweight domestic stocks in their common stock portfolios (generally referred to as home bias). We find that, on average, U.S. home bias decreases for countries that mandate IFRS adoption, after controlling for country-fixed effects. We also find that the reduction in the U.S. home bias after the mandatory adoption of IFRS is greater for countries with larger differences between IFRS and their domestic accounting standards, for countries with a stricter rule of law and a common law legal origin, and in countries with greater incentives to report high-quality financial information. Overall, our results indicate that a common set of global accounting standards matter for portfolio holdings of U.S. investors and that U.S investors regard the enforcement of standards to be a key factor in making investments outside the U.S.

The Role of Realised Volatility in the Athens Stock Exchange

Using a newly developed dataset of daily, value-weighted market returns we construct and analyze the monthly realized volatility of the Athens Stock Exchange (A.S.E.) from 1985 to 2003. Our analysis focuses on the distributional and time series properties of the realized volatility series and on assessing the connection between realized volatility and returns through a multi-factor asset pricing model. In particular, we find strong evidence on the existence of a volatility feedback effect and a leverage effect, and on the existence of asymmetries between lagged returns and volatility. Furthermore, we examine the cross-sectional distribution of unconditional loadings on the realized risk factor(s) for different sets of characteristics-sorted common stock portfolios. We find that realized risk is a significantly priced factor in A.S.E. and its high explanatory power for the cross-section of portfolio average returns is independent of any return variation related to the market (CAPM) or size and book-to-market (Fama-French) factors. We discuss our findings in the context of the recent literature on realized volatility and feedback effects, as well as the literature on the pricing power of realized risk.

Look-Ahead Benchmark Bias in Portfolio Performance Evaluation

Performance of investment managers is predominantly evaluated against targeted benchmarks, such as stock, bond, or commodity indices. But most professional databases do not retain time series for companies that drop from the database and do not necessarily track changes in the benchmarks over time. Consequently, standard tests of portfolio performance suffer from <i>look-ahead benchmark bias</i>, meaning that a given strategy is naively backtested against the assets constituting the benchmark of reference at the end of the testing period (i.e., now), rather than at the beginning of the period. In this article, the authors report that look-ahead benchmark bias can be surprisingly large in portfolios of common stocks—up to 8% per annum when the S&amp;P 500 Index is the benchmark. Using CRSP data for the running top 500 U.S. capitalizations over the period 1927–2006, the authors demonstrate that look-ahead benchmark bias can account for a gross overestimation in performance metrics, such as the Sharpe ratio, as well as an underestimation of risk measured, for example, by peak-to-valley drawdowns. A general methodology to test investment strategy properties is advanced by the authors in the context of several random strategies having similar investment constraints. <b>TOPICS:</b>Manager selection, analysis of individual factors/risk premia, VAR and use of alternative risk measures of trading risk

Common stock portfolio selection: a multiple criteria decision making methodology and an application to the Athens Stock Exchange

An integrated multiple-criteria methodological framework is proposed to support decisions that concern the selection of common stock portfolios. At the first stage of the methodology, two multiple-criteria methods are employed, within the context of the outranking relations theory, towards the initial appraisal of the stocks that are examined. We then utilize a non-linear optimization model, to generate portfolios that consist of the stocks that are classified as those with the optimal characteristics, during of the first stage. Finally, the portfolios designed at the previous stage are evaluated by using a wide set of well-known sophisticated evaluation criteria. The preferences and experiences of professionals and experts in the field of portfolio management were taken into consideration, through all the stages of the process. The validity of the above methodology is tested through a large scale illustrative application on the stocks that constitute the FTSE-140 index of the Athens Stock Exchange. The advantages of the proposed model against contextual approaches are finally stressed.

Towards a Multiple Criteria Decision Making framework for common stock portfolio selection

An integrated multiple criteria methodological framework is proposed to support decisions that concern the construction and selection of common stock portfolios. The proposed framework combines discrete multiple criteria evaluation methods with a non-linear continuous optimisation model. The preferences and experiences of professionals and experts in the field of portfolio management are strongly taken into consideration, through all the stages of the process. The validity of the above methodology is tested through a large scale illustrative application on the stocks that constitute the FTSE-140 index of the Athens Stock Exchange.

Look-Ahead Benchmark Bias in Portfolio Performance Evaluation

Performance of investment managers are evaluated in comparison with benchmarks, such as financial indices. Due to the operational constraint that most professional databases do not track the change of constitution of benchmark portfolios, standard tests of performance suffer from the look-ahead benchmark bias, when they use the assets constituting the benchmarks of reference at the end of the testing period, rather than at the beginning of the period. Here, we report that the look-ahead benchmark bias can exhibit a surprisingly large amplitude for portfolios of common stocks (up to 8% annum for the S&P500 taken as the benchmark) - while most studies have emphasized related survival biases in performance of mutual and hedge funds for which the biases can be expected to be even larger. We use the CRSP database from 1926 to 2006 and analyze the running top 500 US capitalizations to demonstrate that this bias can account for a gross overestimation of performance metrics such as the Sharpe ratio as well as an underestimation of risk, as measured for instance by peak-to-valley drawdowns. We demonstrate the presence of a significant bias in the estimation of the survival and look-ahead biases studied in the literature. A general methodology to test the properties of investment strategies is advanced in terms of random strategies with similar investment constraints.

International Real Estate Review

The return performance and factor sensitivities of Canadian equity real estate investment trusts (E-REITs) are examined. Today, typical and average Canadian E-REIT IPOs are correctly priced based on first- day and subsequent short-run returns. The overpricing evident earlier in the 1993-96 period for typical and average E-REIT IPOs has corrected. E-REITs are equity investments with about one-half the market risk, and greater ensitivity to interest-rate changes, than the S&amp;P/TSX Composite Index. E-REITs outperformed the S&amp;P/TSX Composite over the 1996-2004 period on a return, risk, and market- and/or risk-adjusted basis. Thus, E-REITs provided material diversification benefits with no sacrifice in return, when added to a common stock portfolio during the studied period.