Active Portfolio Strategies Research Articles

Analysis of Stock Portfolio Performance Using Passive Strategy and Active Strategy with Single Index Model

This study aims to determine and analyze the differences in return and risk performance results of passive and active strategies using the Single Index Model. Then measure the return performance using the Sharpe Index, Treynor, and Jensen's Alpha. This research was conducted on Sri-Kehati index stocks for the period November 2019 to November 2021. Based on passive portfolio strategy research, there are three stocks that form the optimal portfolio, namely BBCA, BBRI, SIDO. While based on active portfolio strategy research, in the first year there were three stocks that formed the optimal portfolio, then in the second year there were seven stocks forming the optimal portfolio, namely BBCA, BBRI, DSNG, INCO, SIDO, SMGR, WIKA. Based on performance measurement using Sharpe, Treynor and Jensen's Alpha indices, the Active Portfolio Strategy is better than the Passive Portfolio Strategy. Meanwhile, based on the difference test using the Mann Whitney U test method, it can be concluded that there is no significant difference in return performance between passive and active portfolio strategies.

Effect of Portfolio Management Strategies on the Returns of Mutual Funds in Kenya

Abstract The study explored the effect of portfolio management strategies on portfolio returns of mutual funds in Kenya. The population of the study was all the mutual funds licensed by CMA as at 2018. The study concluded that portfolio management strategies have an impact on portfolio returns. In Kenya, the most preferred strategy was active portfolio strategy. Mutual funds that employed active and growth portfolio management strategy generated negative returns, although active strategy is the most preferred strategy, the costs that the strategy attracts leads to negative returns. Those that employed value and passive portfolio management strategies generated positive returns. The study recommends that mutual funds should use value and passive strategies as they produce positive returns, and this is because of the low cost incurred when using these strategies. Keywords: Portfolio Management Strategies, Returns, Mutual funds, Kenya.

It’s all in the timing again: simple active portfolio strategies that outperform naïve diversification in the cryptocurrency market

ABSTRACT This article analyzes whether cryptocurrencies’ inclusion improves stock portfolios’ performance and whether the application of portfolio selection methodologies would bring gains to investors in the digital currency market. Volatility Timing and Reward-to-Risk Timing methodologies were applied to a base containing only S&P 100 stocks, another containing only cryptocurrencies, and one mixing the previous two. The results suggest that the inclusion of cryptocurrencies has not brought performance gains to the stock portfolios and that the investor in the cryptocurrency market can benefit from the use of portfolio selection.

Cognitive Biases as an Integral Part of Behavioral Finance

Abstract The complex world of finance is characterised by numerous irrationalities that representatives of behavioral finance seek to explain by cognitive biases (flaws, inclinations or anomalies). Cognitive biases represent imperfect perception of reality and are caused by limited cognitive capacities of decision-makers. By analysing cognitive biases, the paper aims to answer the following questions that standard finance fails: Why active portfolio strategy is still the most influential strategy in portfolio management despite the mounting evidence of unsuitability of its application? Why investors prefer dividend payout over increase in capital value (dividend puzzle)? Why investors ignore benefits of investment diversification and choose to invest in a small number of shares of well-known local businesses (diversification puzzle)? Why investors avoid to sell “loser” shares and thus reduce the tax burden? Why shares of small companies usually bring higher returns compared to shares of large companies? The answers to these questions are obtained by using qualitative research methodology, which also represents the main result of the research.

Oil and Risk Premia in Equity Markets

We explore the oil-stock market nexus from a novel angle by examining the predictive role of oil prices over the excess returns associated with the market, size, book-to-market and momentum factors via bivariate cross-quantilograms. Our analysis of systematic risk premia across the four regions show that crude oil returns indeed capture predictive information regarding excess factor returns in stock markets, particularly those associated with market, size and momentum factors. However, the predictive power of oil return over excess factor returns is asymmetric and primarily concentrated on extreme quantiles, suggesting that large fluctuations in oil prices capture markedly different predictive information over stock market risk premia during up and down states of the oil market. The findings have significant implications for the profitability of factor or style based active portfolio strategies and suggest that the predictive information contained in oil market fluctuations could be utilized to enhance returns via conditional strategies based on these predictability patterns.

Investment strategy performance under tracking error constraints

Recent (2018) evidence identifies the increased need for active managers to facilitate the exploitation of investment opportunities found in inefficient markets. Typically, active portfolios are subject to tracking error (TE) constraints. The risk-return relationship of such constrained portfolios is described by an ellipse in mean-variance space, known as the constant TE frontier. Although previous work assessed the performance of active portfolio strategies on the efficient frontier, this article uses several performance indicators to evaluate the outperformance of six active portfolio strategies over the benchmark – subject to various TE constraints – on the constant TE frontier.

MOMENTUM FAKTÖRÜ, PARA POLİTİKASI KARARLARI VE BÜYÜK FİYAT DEĞİŞİMLERİNE DAYALI YATIRIM STRATEJİLERİNİN PERFORMANSLARININ KARŞILAŞTIRILMASI: GEZİ VE REKREASYON SEKTÖRÜ UYGULAMASI

Gezi ve rekreasyon sektörü finans alanında karakteristik özellikleri nedeniyle özel olarak incelenmektedir. Gezi ve rekreasyon sektöründe pay senedi yatırımları belirli risk düzeyinde en yüksek getiri sağlandığında gerçekleşmektedir. Pay senetlerine olan yatırımlar ile birlikte sektörün ekonomik büyümesinde ve istihdam miktarında artış yaşanmaktadır. Araştırmanın amacı, Türkiye'de gezi ve rekreasyon sektörü üzerinde yatırımlarda uygulanabilecek farklı portföy değiştirme stratejilerinin performanslarını karşılaştırmaktır. Çalışmada momentum, para politikası değişimleri ve büyük fiyat değişimleri sinyallerini esas alan portföy değiştirme stratejileri incelenmiştir. Treynor, Sharpe ve Jensen ölçütlerine dayanan karşılaştırmalar gerçekleştirilmiştir. Elde edilen bulgular izlenecek portföy değiştirme stratejilerinin klasik satın al ve elde tut yatırımlarına göre daha başarılı olabileceğini göstermektedir. Riske dayalı performans ölçütleri, getiri ve portföy değeri açısından karşılaştırma yapıldığında büyük fiyat değişimlerine bağlı olarak oluşturulan strateji en başarılı performansı göstermektedir.

Performance persistence in Chinese securities investment funds

This study examines the performance persistence of Chinese equity securities investment funds during the period between May 2003 and May 2014. We apply the recursive portfolio formation methodology of Carhart (1997). The results from sorting funds either by past 4-factor alphas or by t-statistics of past alphas suggest that the top ranked decile portfolio yields statistically and economically significant forwarding looking alphas. In respect of past decile loser funds, there is no evidence that underperformance among Chinese loser funds persists. In addition, we apply the recursive portfolio formation methodology for alternative ‘smaller’ portfolios of a fixed size and find that almost all the smaller portfolios of past winning funds produce positive and statistically significant forward looking alphas. Hence an active portfolio strategy for the Chinese securities investment fund industry of selecting a small number of past outperforming funds may earn positive abnormal returns after the deduction of management fees.

Asset Allocation in the Chinese Stock Market: The Role of Return Predictability

In this article the authors investigate asset allocation in the Chinese stock market from the perspective of incorporating return predictability. Based on a host of return predictors, they find significant out-of-sample return predictability in the Chinese stock market. They then examine the performance of active portfolio strategies—such as aggregate market timing as well as industry, size, and value-rotation strategies—designed to profitably exploit return predictability. Strong evidence is found by the authors that these portfolio strategies incorporating return predictability can deliver superior performance—up to 600 basis points per annum and almost double the Sharpe ratios—compared with the passive buy-and-hold benchmarks that ignore return predictability.

Asset Allocation in the Chinese Stock Market:The Role of Return Predictability

While numerous studies have analyzed the asset allocation issue of US stock market from various angles, much less attention has been paid to the asset allocation issue of Chinese stock market. This article investigates the asset allocation in Chinese stock market from a perspective of incorporating return predictability. We find significant out-of-sample return predictability in Chinese stock market based on a host of return predictors. We then examine the performance of active portfolio strategies such as aggregate market timing strategy, and industry, size, and value rotation strategies to profitably exploit return predictability. We provide strong evidence that these portfolio strategies incorporating return predictability can deliver superior outperformance up to 600 basis points per annum and almost double the Sharpe ratios compared to the passive buy-and-hold benchmarks ignoring return predictability.

Portfolio strategies of fund managers in the Indian capital market

This paper examines whether Indian fund managers follow an active portfolio strategy. Interalia, the impact of asset size and market capitalisation on fund performance and the fund managers' ability to create value to the fund they manage is also addressed. The study finds that fund managers exhibit poor stock-selection skills and do not seem to exhibit any distinguishable ability in timing. It signals that they are unsuccessful in determining the right time to enter/exit the market.

FACTOR UNIQUENESS IN THE S&P 500 UNIVERSE: CAN PROPRIETARY FACTORS EXIST?

In this paper, we analyze factor uniqueness in the S&P 500 universe. The current theory of approximate factor models applies to infinite markets. In the limit of infinite markets, factors are unique and can be represented with principal components. If this theory would apply to realistic markets such as the S&P 500 universe, the quest for proprietary factors would be futile. We find that this is not the case: in finite markets of the size of the S&P 500 universe different factor models can indeed coexist. We compare three dynamic factor models: a factor model based on principal component analysis, a classical factor model based on industry, and a factor model based on cluster analysis. Dynamic behavior is represented by fitting vector autoregressive models to factors and using them to make forecasts. We analyze the uniqueness of factors using Procrustes analysis and correlation analysis. Forecasting performance of the factor models is analyzed by forming active portfolio strategies based on the forecasts for each model using sample data from the S&P 500 index in the 21-year period 1989–2010. We find that one or two factors which we can identify with global factors are common to all models, while the other factors for the factor models we analyzed are truly different. Models exhibit significant differences in performance with principal component analysis-based factor models appearing to behave better than the sector-based factor models.

The Impact of Corporate Governance on Market Performance

In this study, we examine how corporate governance affects portfolio management. Investors are expected to opt for a judicious trade-off between risk and return. Such an objective can be achieved through the contribution of corporate governance which is assumed to align manager and shareholders interests and minimize agency conflict.Our results show that governance structure affects risk and return. However, this impact is not the same whether we use market or accounting measures (whether before or after 2000). Moreover, we find that governance quality has an impact though various mechanisms on excess return and idiosyncratic risk for active portfolio strategies.

It’s All in the Timing: Simple Active Portfolio Strategies that Outperform Naïve Diversification

AbstractDeMiguel, Garlappi, and Uppal (2009) report that naïve diversification dominates mean-variance optimization in out-of-sample asset allocation tests. Our analysis suggests that this is largely due to their research design, which focuses on portfolios that are subject to high estimation risk and extreme turnover. We find that mean-variance optimization often outperforms naïve diversification, but turnover can erode its advantage in the presence of transaction costs. To address this issue, we develop 2 new methods of mean-variance portfolio selection (volatility timing and reward-to-risk timing) that deliver portfolios characterized by low turnover. These timing strategies outperform naïve diversification even in the presence of high transaction costs.

Two Staged Portfolio Optimizations

The primary goal of this work is to determine if an active portfolio optimization strategy utilizing a two staged optimization approach outperforms an ordinary optimization technique. Both portfolio optimization models are based on Markowitz’s Modern Portfolio Theory (MPT), which relies on assets’ mean, variance, and correlation to maximize returns at any given level of risk. For the two staged optimization approach the process of optimization is applied twice. In the first stage, it is used to select an optimal portfolio of industries, and in the second stage optimization is applied to determine an optimal portfolio consisting of stocks within each industry. Our research indicates that portfolios formed based on ordinary optimization outperforms two staged portfolios and Market indexes by 37% during a bear market (2002) and outperforms Dow Jones Industrial Average and S&P 500 by more than 13% during a bull market (2003). The performance of each model was determined by the capital gains and the dividend returns during the 2002 to 2003 time period.

Long-Term Tactical Asset Allocation

This paper evaluates a long-term tactical asset allocation strategy that takes advantages of perceived short-term opportunities in the capital market while benchmarked against the long-term strategic portfolio. The portfolio strategy makes the portfolio weights depend directly on changes in state variables, and uses an expanded asset menu in the Markowitz framework. The optimal portfolio consists of a benchmark portfolio that controls the active risk, and a pure overlay portfolio that adds active return to the whole portfolio. The empirical analysis shows that the information ratio of the optimal portfolio under the single-period strategy is 0.85. However, an investor may want to constrain the active portfolio strategy such that the tactical asset allocation's risk profile is matched with the strategic asset allocation in the long term. The certainty equivalent to a mean-variance investor with risk aversion of 10 is less than 10 basis points under the unconstrained portfolio strategy and 1.3% under the constrained portfolio strategy, both relative to passive benchmark indexing strategy. Hence, the investor is better off with the constrained active portfolio management.

It’s All in the Timing: Simple Active Portfolio Strategies that Outperform Naive Diversification

DeMiguel et al. (2009) report that naive diversification dominates mean-variance optimization in out-of-sample asset allocation tests. Our analysis suggests that this is largely due to their research design, which focuses on mean-variance efficient portfolios that are subject to high estimation risk and extreme turnover. We find that mean-variance optimization outperforms naive diversification under many circumstances, but its advantage can easily be eroded by transactions costs. This motivates us to propose two types of mean-variance timing strategies, both characterized by low turnover. These strategies outperform naive diversification even in the presence of relatively high transactions costs. In contrast to DeMiguel et al. (2009), therefore, we conclude that using sample information to guide portfolio selection yields substantial benefits.

It's All in the Timing: Simple Active Portfolio Strategies that Outperform Naive Diversification

It's All in the Timing: Simple Active Portfolio Strategies that Outperform Naive Diversification

Simple Active Portfolio Strategies that Outperform Naive Diversification

Simple Active Portfolio Strategies that Outperform Naive Diversification

Capital Regulation and Banks' Financial Decisions

This paper develops a stochastic dynamic model to examine the impact of capital regulation on banks' financial decisions. In equilibrium, lending decisions, capital buffer and the probability of bank failure are endogenously determined. Compared to a flat-rate capital rule, a risk-sensitive capital standard causes the capital requirement to be much higher for small (and riskier) banks and much lower for large (and less risky) banks. Nevertheless, changes in actual capital holdings are less pronounced due to the offsetting effect of capital buffers. Moreover, the non-binding capital constraint in equilibrium implies that banks adopt an active portfolio strategy and hence the counter-cyclical movement of risk-based capital requirements does not necessarily lead to a reinforcement of the credit cycle. In fact, the results from the calibrated model show that the impact on cyclical lending behavior differs substantially across banks. Lastly, the analysis suggests that the adoption of a more risk-sensitive capital regime can be welfare-improving from a regulator's perspective, in that it causes less distortion in loan decisions and achieves a better balance between safety and efficiency.