Improve Portfolio Performance Research Articles

The predictive ability of stock market factors

PurposeThis paper aims to ask whether a range of stock market factors contain information that is useful to investors by generating a trading rule based on one-step-ahead forecasts from rolling and recursive regressions.Design/methodology/approachUsing USA data across 3,256 firms, the authors estimate stock returns on a range of factors using both fixed-effects panel and individual regressions. The authors use rolling and recursive approaches to generate time-varying coefficients. Subsequently, the authors generate one-step-ahead forecasts for expected returns, simulate a trading strategy and compare its performance with realised returns.FindingsResults from the panel and individual firm regressions show that an extended Fama-French five-factor model that includes momentum, reversal and quality factors outperform other models. Moreover, rolling based regressions outperform recursive ones in forecasting returns.Research limitations/implicationsThe results support notable time-variation in the coefficients on each factor, whilst suggesting that more distant observations, inherent in recursive regressions, do not improve predictive power over more recent observations. Results support the ability of market factors to improve forecast performance over a buy-and-hold strategy.Practical implicationsThe results presented here will be of interest to both academics in understanding the dynamics of expected stock returns and investors who seek to improve portfolio performance through highlighting which factors determine stock return movement.Originality/valueThe authors investigate the ability of risk factors to provide accurate forecasts and thus have economic value to investors. The authors conducted a series of moving and expanding window regressions to trace the dynamic movements of the stock returns average response to explanatory factors. The authors use the time-varying parameters to generate one-step-ahead forecasts of expected returns and simulate a trading strategy.

Group sparse enhanced indexation model with adaptive beta value

Enhanced indexing, which has been used by professional portfolio managers for decades, is a portfolio management strategy that attempts to increase returns by building a portfolio around core, index-like positions and adding tactical tilts toward specific styles or individual stocks. This paper proposes an improved enhanced indexation model by considering the systematic risk, measured by Beta value, and the industry rotation phenomenon. The systematic risk is the risk related to the stock market as a whole and can be reasonably controlled to improve portfolio performance, by actively tracking and forecasting the market trend. Sector rotation refers to the investment strategy of taking money that's invested in one stock market industry and moving it to another, by taking advantage of the historical performances of specific industries during different phases of the cycle. In specific, our model aims to find a small set of industries that is mostly likely to thrive in the anticipated future, which is mathematically realized by dividing stocks into industries and minimizing their norm. To evaluate our strategy, we conducted extensive numerical experiments against some major world indices, e.g. CSI 300, S&P 500, FTSE 100 and Nikkei 225. The experimental result shows that our approach can generate sparse portfolios with excellent out-of-sample excess returns and high robustness after deducting transaction costs.

Portofolio Optimization Through MPT on any Economic Situation on Indonesian Stock Exchange (2010-2020)

Modern portfolio theory told us that investors are tend to optimize the expected return of investment subject to a target of risk perception. In the real world, risks are depend on may factors, such as systematic risk (market risk and macroeconomics risk which can not be controlled to diversification) and non-systematic risk (specific company risk which can not be controlled through diversification). Applying Security Market Line Approach, constrained maximization and considering changes in economic situation between 2010-2020, we found that Modern Portfolio Theory can improve portfolio performance when limited impact is spotted on minimizing systematic risk of portfolio.

Optimizing Alpha through Better Information Workflows

This study demonstrates how investment managers can identify and resolve suboptimal operational workflows that diminish an investment strategy’s attainable alpha on the order of 24-242 basis points (annualized, gross of fees). A portfolio’s potential alpha can be best realized by addressing these portfolio implementation shortfalls through systematic improvements in the data schemas that drive the information exchanges among parties regarding transactions, holdings, and valuations. Using graph modelling techniques, we identify and resolve information workflow inefficiencies that occur both within and external to the investment firm. TOPICS:Portfolio management, portfolio construction, equity portfolio management, fixed-income portfolio management, quantitative methods, statistical methods Key Findings ▪ For portfolio managers, implementation shortfalls stemming from suboptimal information workflows can be systematically mitigated to maximize the expression of alpha inherent in an investment strategy. ▪ For operational managers, re-aligning investment operations workflows to deliver differentiated support across individual strategies can significantly improve portfolio performance. ▪ For data scientists, graph models provide fresh evidence of the quantifiable impacts of data interoperability and information parity on the efficiency of enterprise information flows.

Deep Learning, Predictability, and Optimal Portfolio Returns

We study dynamic portfolio choice of a long-horizon investor who uses deep learning methods to predict equity returns when forming optimal portfolios. Our results show statistically and economically significant benefits from using deep learning to form optimal portfolios through certainty equivalent returns and Sharpe ratios. Return predictability via deep learning also generates substantially improved portfolio performance across different subsamples, particularly during recessionary periods. These gains are robust to including transaction costs, short-selling and borrowing constraints.

Can Robust Optimization Offer Improved Portfolio Performance? An Empirical Study of Indian market

The emergence of robust optimization has been driven primarily by the necessity to address the demerits of the Markowitz model. There has been a noteworthy debate regarding consideration of robust approaches as superior or at par with the Markowitz model, in terms of portfolio performance. In order to address this skepticism, we perform empirical analysis of three robust optimization models, namely the ones based on box, ellipsoidal and separable uncertainty sets. We conclude that robust approaches can be considered as a viable alternative to the Markowitz model, not only in simulated data but also in a real market setup, involving the Indian indices of S&P BSE 30 and S&P BSE 100. Finally, we offer qualitative and quantitative justification regarding the practical usefulness of robust optimization approaches from the point of view of number of stocks, sample size and types of data.

Diversification Benefits, Where Art Thou?

Following the global financial crisis, a portfolio concentrated in US large cap equity and aggregate fixed income has provided higher returns than diversified portfolios through 2019. Such a prolonged experience causes investors to question the benefits of diversification. This leads us to use a longer history of data across 15 asset classes to understand the historical benefits of diversifying a portfolio with international equity, real assets, and below investment grade fixed income. Our results portray the frequency and magnitude of risk-adjusted return improvement coming from different diversifying asset classes over five-year holding periods. We find that certain asset classes, such as below investment grade fixed income, regularly improve risk-adjusted return of the portfolio, while other asset classes like commodities improve risk-adjusted returns less frequently. Further, we observe that some asset classes do not deliver meaningful risk-adjusted return improvements in the presence of other asset classes. Our conclusion is that investors should continue to build diversified portfolios, but in doing so they should consider that some asset classes more consistently improved risk-adjusted returns than others. TOPICS:Wealth management, retirement, fixed-income portfolio management Key Findings • Certain asset classes such as below investment grade fixed income have provided consistent diversification benefits through time, while other asset classes such as commodities (despite its low correlation to other asset classes) have less frequently improved portfolio performance. • Some asset classes have not delivered meaningful portfolio diversification benefits in the presence of other asset classes during the periods we examined. • We observe cyclicality in the improvement of portfolio returns from holding a more diversified portfolio. The periodicity appears longer than the US business cycle. Moreover, the time series of relative price-to-earnings ratios of US and international equity appears to move countercyclically with the outperformance of diversified portfolios.

Improving portfolio performance of renewable energy stocks using robust portfolio approach: Evidence from China

As the largest coal and the second largest crude oil consumer in the world, China has urgent task to develop its renewable energy industry quickly. By now, there are over 80 renewable energy companies listed in China’s stock exchanges. Thus to manage the market risk and achieve better investment returns of renewable energy stocks in China is of great value for policy makers and investors. The aim of this paper is to introduce an innovative portfolio allocation approach, robust portfolio, to improve portfolio performance of renewable energy stocks in China by considering the parameter uncertainty in the process of portfolio optimization. Furthermore, to make the conclusions more robust, we classify the stock market into three commonly recognized statuses: bull market, bear market and steady market, respectively, and compare the performances of robust portfolio method with traditional Markowitz approach. The empirical results indicate that the robust portfolio method can produce better performance of the renewable energy stock portfolio than Markowitz approach in various market statuses with much more flexibility in handling the problem of parameter uncertainty. This paper provides an alternative but very effective strategy other than Markowitz method for the portfolio allocation of renewable energy stocks in China.

Dissecting Shareholder Yield

High dividend yield stocks do not reliably earn above-average risk-adjusted returns. More complete measures of shareholder yield, which account for net share repurchases, perform better. We explore the use of net-debt paydown as a way to further enhance shareholder yield. The addition of net-debt paydown enhances risk-adjusted returns and creates a shareholder yield metric that is more robust over time. We also explore the technique of separating yield metrics by payout percentage as a way to enhance return predictability. We find some evidence that using payout percentage within a yield category can systematically improve portfolio performance.

How Knowledge Management Can Capture Untapped Alpha: A Case Study of Informed Rebalancing at the San Bernardino County Employees’ Retirement Association

Asset owners have to deal with the challenge of a potentially low-yielding investment environment for the foreseeable future and the likelihood of a market correction in the nearer term, with Q4 2018 providing a simple reminder of the downside risk of asset allocation. The traditional approach for many asset owners has been to use external managers to achieve their goals and hope that diversification will allow them to navigate difficult markets. A complementary strategy is to use knowledge management to improve returns and governance and capture a typically untapped alpha source in portfolios. Knowledge management in institutional asset management is defined as the explicit and systematic management of knowledge – and its associated processes of creation, organization, diffusion, use and exploitation – in pursuit of improving portfolio performance and managing risks. This paper highlights how the San Bernardino County Employees’ Retirement Association (SBCERA) used Knowledge Management and InvestTech to develop its own internal investment model to dynamically manage asset allocations and has added over $1billion (over 12 years) through its Informed Rebalancing program. In doing so, the portfolio and risks are better managed by the investment team and the program has survived many staff departures demonstrating that knowledge has been institutionalized. This approach is easily replicable by other asset owners, and each fund can develop its own bespoke approach based on their objectives and abilities.

Active Portfolio Management with Conditioning Information

In this article, the authors propose an efficient and practical portfolio management methodology in which portfolio weights are determined by several market observations. Their method is based on the managed portfolio construction technique, which makes it tractable for fund managers. The key points of this work are as follows: (i) to impose constraints on active weights against the prespecified benchmark weights, and (ii) to show empirically that the constraints stabilize and improve portfolio performance by conducting backtests.

Dare to Be Different: How to Commit to Investment Beliefs Through Knowledge Management

Knowledge Management (“KM”), in institutional asset management, is defined as the explicit and systematic management of knowledge – and its associated processes of creation, organization, diffusion, use and exploitation – in pursuit of improving portfolio performance and managing risks. Asset owners exist at the nexus of unique knowledge and data flows from partners such as asset managers, banks, and consultants. Innovative asset owners can crowdsource unique data sets from partners and leverage InvestTech to dynamically manage asset allocations (and risk factors). This paper describes the importance of converting KM into a skill that supports alpha generation, and how most asset owners can replicate this process to develop bespoke approaches based on specific relationships, key risks, and objectives. The paper demonstrates how investors can commit to their investment beliefs using KM (or conversely, imply their investment beliefs from investment choices made) around three major areas: (i) investing style (formulaic or discretionary); (ii) optimization method (for setting the Strategic Asset Allocation, (“SAA”) and Tactical Asset Allocation (“TAA”)); and (iii) rebalancing process (mechanical or informed). The paper extends the analysis to show how effective KM can also help with risk-management, with a particular example of a new approach to risk-management, termed “Agent-Based Risk Management,” and also how KM may lead to different implementations for say a public pension plan versus a captive insurance company.

The Curious Case of the Mid-Cap Premium

Although many mutual fund families and exchanged-traded fund vendors offer mid-cap equity funds, research interest in mid-cap equities has been lacking. This phenomenon may be a result of behavioral patterns: Most equity analysts focus on the largest companies, and the popularity of the Fama–French factors stimulates enthusiasm for small equities. The mid-cap equities, however, may benefit from this lack of interest with good prospective performance. This article examines three popular U.S. mid-cap equity indexes, the Russell Mid-Cap Index, the CRSP Mid-Cap Index, and the S&P 400 Mid-Cap Index, analyzing their compositions, performance statistics, factor exposures, and potential roles in portfolios. The study confirms the existence of the mid-cap premium and finds that exposures to the size and profitability factors may explain this premium. The mid-cap premium may help investors improve portfolio performance without adding significant extra risk, yet investors must fully understand the specific mid-cap index they intend to use to avoid causing distortions to their portfolios.

The information content of the stock and bond return correlation

We believe that the correlation between stock and bond returns carries information for the future values of these return series and economic conditions more widely. The correlation reflects investor perceptions regarding future economic performance, with a declining and negative correlation indicating heightened economic and market risk. Using US data from 1900, we show that the correlation has predictive power for subsequent stock and bond returns and can be used in a market timing strategy to improve portfolio performance. Moreover, the correlation also predicts bear market periods. Further, the correlation contains predictive power for a set of key macroeconomic variables, and has predictive content for contractionary periods. We believe the results in the paper are of interest and relevance to academics, practitioners and policy-makers.

Volatility as an Alternative Asset Class: Does It Improve Portfolio Performance?

We investigate the potential role of Exchange Traded Products (Notes) as vehicles to trade volatility (here proxied by the VIX index) as an asset class in a fully optimizing asset allocation framework, subject to long-only constraints. In back-testing, recursive exercises based on an expanding window of data from February 2010 to February 2016, we find evidence that VIX should enter with non-negligible weight most portfolio strategies and that under many circumstances, long VIX positions may generate positive risk-adjusted performance benefits. However, the volatility positions that can be managed and traded through (one of) the most popular US exchange-traded notes (VXX) fails to deliver such realized, out-of-sample benefits under all utility functions and for a range of assumptions on investors’ risk aversion. Even though the turnover implied by VXX does not appear excessive, taking into account transaction costs worsens considerably its performance and even casts doubts as to whether volatility ought to be considered as an alternative asset class altogether. Direct strategies that trade appropriate futures on the VIX improve somewhat realized performance, but not enough to tilt over the balance of our conclusions.

International Real Estate Review

We examine the market for U.S. equity real estate investment trusts (REITs) for evidence of the volatility effect, in which low volatility stocks tend to outperform high volatility ones, as has been found in the general equity market by prior research. While there is some evidence of a volatility effect in the first ten years of the sample, this disappears in a more recent time period. Furthermore, we test the efficacy of low risk portfolio construction techniques and find that none perform any better than a market cap weighted portfolio ¡V although they are also no worse ¡V over any of the time periods examined. Thus, there is no evidence that using a risk-based portfolio design that emphasizes low volatility would improve portfolio performance for a REIT allocation.

Bezpośrednie i pośrednie inwestycje w metale szlachetne

The paper presents a direct and indirect investment in precious metals and relations between financial assets market and precious metals market. The research in precious metals market was carried in the period 2010–2016. This market offers a wide range of instruments. An indirect investment dominates a direct investment in precious metals. This article is limited only to analysis of precious metals funds. This article presents instruments available to individual clients. Investors could improve portfolio performance considerably by adding a significant exposure to the precious metals market.

Asset Pricing Anomalies: Two Hedge Factors with Negative Risk Premia Embedded in Portfolios!

The primary purpose of this research is to empirically test a new asset pricing model, the Relative Asset Pricing Model (RAPM), and to confirm whether hedge portfolios on two new risk factors highlighted in that model, and embedded in all portfolios, have negative and significant risk premia. In a number of specifications, this first test of RAPM appears to validate the importance of these two ubiquitous risk factors: (i) a liability proxy or Strategic Asset Allocation (SAA); and (ii) an index of traditional rebalancing back to the SAA (REBAL). A secondary goal of the research is to then examine what RAPM and the inclusion of these two risk factors mean for other commonly used factors (like Value Premium, Capitalization Premium, and Momentum Premium) that are increasingly being added to portfolios in a new trend called “factor-based investing”. In many regressions, adding these two new factors either individually or together causes traditional factors like Capitalization and – in fewer cases – Momentum to lose their significance. Valuation continues to be significant even in tests where the SAA is heavily biased to include Value indices. These findings have interesting implications for improving portfolio performance especially in a low-yielding environment. Investors would do well to consider an intelligent approach to managing portfolio drift. With respect to factor-based investing, two results stand out: (i) Valuation seems to be a very robust strategy; and (ii) early movers probably benefit before these strategies get incorporated into the industry-wide SAA and then lose significance – a seemingly obvious result but validated in the context of a robust asset pricing model, thereby also serving as a first test of (a special case of) the Adaptive Markets Hypothesis.

Volatility as an Alternative Asset Class: Does It Improve Portfolio Performance?

We investigate the potential role of Exchange Traded Products (Notes) as vehicles to trade volatility (here proxied by the VIX index) as an asset class in a fully optimizing asset allocation framework, subject to long-only constraints. In back-testing, recursive exercises based on an expanding window of data from February 2010 to February 2016, we find evidence that VIX should enter with non-negligible weight most portfolio strategies and that under many circumstances, long VIX positions may generate positive risk-adjusted performance benefits. However, the volatility positions that can be managed and traded through (one of) the most popular US exchange-traded notes (VXX) fails to deliver such realized, out-of-sample benefits under all utility functions and for a range of assumptions on investors’ risk aversion. Even though the turnover implied by VXX does not appear excessive, taking into account transaction costs worsens considerably its performance and even casts doubts as to whether volatility ought to be considered as an alternative asset class altogether. Direct strategies that trade appropriate futures on the VIX improve somewhat realized performance, but not enough to tilt over the balance of our conclusions.

The informational and non-informational compositions of UK fund managers’ dynamic herding in the stock market

This paper examines whether UK fund managers engage in herding behaviour in the stock market using the dynamic herding measure, whether their herding behaviour is different during bullish and bearish periods, whether most of their herding is informational, which types of informational reasons act as the main drivers of their herding and whether there are non-informational drivers of their herding. Our results reveal that UK fund managers engage in significant herding behaviour and that this behaviour does not differ significantly from bullish to bearish stock markets. Moreover, we confirm that there are weak positive correlations between fund managers? herding and stock returns within the subsequent year, which indicates that their herding is mainly informational. To improve portfolio performance, other investors could follow UK fund managers and purchase stocks overbought by them with at least 15 traders quarterly in the following one-year period, particularly for growth-type, sectorspecific and international-type funds. Moreover, because they are more likely to herd in large-capitalisation securities, the informational reasons driving managers? herding behaviour are mainly related to investigative herding. We also find that growth-type and international-type funds are more likely to herd with similar- type funds. This finding may result from reputational and characteristic herding, which illustrates that non-informational reasons for managers? herding still exist.