Performance Of Investment Strategies Research Articles

Hedging longevity risk in defined contribution pension schemes

Pension schemes all over the world are under increasing pressure to efficiently hedge longevity risk imposed by ageing populations. In this work, we study an optimal investment problem for a defined contribution pension scheme that decides to hedge longevity risk using a mortality-linked security, typically a longevity bond. The pension scheme promises a minimum guarantee which allows the members to purchase lifetime annuities upon retirement. The scheme manager invests in the risky and riskless assets available on the market, including the longevity bond. We transform the corresponding constrained optimal investment problem into a single investment portfolio optimization problem by replicating future contributions from members and the minimum guarantee provided by the scheme. We solve the resulting optimization problem using the dynamic programming principle. Through a series of numerical studies, we show that the longevity risk has an important impact on the investment strategy performance. Our results add to the growing evidence supporting the use of mortality-linked securities for efficient hedging of longevity risk.

Optimal Active Share

Active Share is a widely used measure of an active portfolio´s deviation from the benchmark holdings. Initial research by M. Cremers and A. Petajisto [1] indicated that managers with a high Active Share value tend to outperform others. That observation promoted Active Share value as a predictor of fund performance. However, in an unrelated study, S. Johnson, R. Kahn and D. Petrich [2] showed that alpha, risk and gearing can not be selected independently. They also showed that imposing enhanced gearing value through a penalty can result in a significant deterioration in investment strategy performance. These results can be immediately translated into an active space where the gearing target becomes an Active Share target. In this paper, we analyzed the impact of directly setting an Active Share target by a constraint. Our results show that constraining Active Share to a higher value than the one consistent with the selected risk target has only negative impact on investment strategy performance. While we agree that achieving a higher unconstrained value of an Active Share measure may be a good sign of “activeness”, our results illustrate that reaching this objective by adding a binding constraint is neither a good investment practice nor one that will translate into a better performance.

Hedging Longevity Risk in Defined Contribution Pension Schemes

Pension schemes all over the world are under increasing pressure to efficiently hedge the longevity risk posed by ageing populations. In this work, we study an optimal investment problem for a defined contribution pension scheme which decides to hedge the longevity risk using a mortality-linked security, typically a longevity bond. The pension scheme invests in the risky assets available in the market, including the longevity bond, by using the contributions from a representative scheme member to ensure a minimum guarantee such that the member is able to purchase a lifetime annuity upon retirement. We transform this constrained optimal investment problem into an unconstrained problem by replicating a self-financing portfolio of future contributions from the member and the minimum guarantee provided by the scheme. We solve the resulting optimisation problem using the dynamic programming principle and through a series of numerical studies reveal that the longevity risk has an important impact on the performance of investment strategies. Our results provide mathematical evidence supporting the use of mortality-linked securities for efficient hedging of the longevity risk.

The performance of UK stock recommendation revisions: Does brokerage house reputation matter?

AbstractOur study examines the impact of brokerage house (BH) reputation on the performance of investment strategies following stock recommendation revisions in the UK stock market. We develop two alternative proxies for BH reputation based either on the past positions on the annual Institutional Investor (II) All‐Europe Research Team or on the past recommendation performance of BHs. We find that BH reputation proxied by the past II rankings has no significant impact on the recommendation performance, suggesting that the II rankings are largely “popularity contests”. However, BH reputation proxied by the past year recommendation performance of BHs has a significantly positive impact on the recommendation performance in the next year, implying that the recommendation performance of BHs in the UK market is persistent. The bootstrap simulations further confirm that the observed performance persistence could be due to BH skill rather than BH luck (i.e., random chance).

The time‐varying performance of UK analyst recommendation revisions: Do market conditions matter?

AbstractThis study examines the time‐varying performance of investment strategies following analyst recommendation revisions in the UK stock market, with specific emphasis on the impact of changing market conditions. We find a negative relationship between the recommendation performance and market conditions as measured in terms of past market return and market volatility. In particular, the upgrade (downgrade) portfolio generates significantly positive (negative) net abnormal returns in bad market conditions (e.g., the dot‐com bubble burst in 2000 and the credit crisis in 2007), but not in other periods of time. Moreover, our non‐temporal threshold regression analysis shows that the reported negative relationship disappears when market conditions become better, i.e., when the past market return (market volatility) is higher (lower) than a certain level, indicating the importance of taking non‐linearity into account in the long sample period as examined in this study. Our time‐series bootstrap simulations further confirm that the superior recommendation performance in bad market conditions is not due to random chance; analysts have certain skills in making valuable up/downward revisions in bad markets.

Hedging Longevity Risk in Defined Contribution Pension Schemes

Pension schemes all over the world are under increasing pressure to efficiently hedge the longevity risk posed by ageing populations. In this work, we study an optimal investment problem for a defined contribution pension scheme which decides to hedge the longevity risk using a mortality-linked security, typically a longevity bond. The pension scheme invests in the risky assets available in the market, including the longevity bond, by using the contributions from a representative scheme member to ensure a minimum guarantee such that the member is able to purchase a lifetime annuity upon retirement. We transform this constrained optimal investment problem into an unconstrained problem by replicating a self-financing portfolio of future contributions from the member and the minimum guarantee provided by the scheme. We solve the resulting optimisation problem using the dynamic programming principle and through a series of numerical studies reveal that the longevity risk has an important impact on the performance of investment strategies. Our results provide mathematical evidence supporting the use of mortality-linked securities for efficient hedging of the longevity risk.

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.

Leveraged ETF Pairs: An Empirical Evaluation of Portfolio Performance

This paper examines the performance of investment strategies involving leveraged and inverse leveraged ETF pairs. As in Jiang and Peterburgsky’s (2017) simulation analysis, the empirical analysis in this paper indicates that simple portfolios of bull/bear short positions constructed to approximately mimic an underlying index (such as the S&P 500) outperform the index on a risk-adjusted basis. The outperformance asymptotically diminishes as the investor’s rebalance bounds tighten. Additionally, the results support the theoretical positive relationship between outperformance and index standard deviation and negative relationship between outperformance and index absolute return.

ANALYSIS OF THE SELF-ORGANIZING MAP-BASED INVESTMENT STRATEGY

The following paper presents the application of Self-Organizing Maps (SOM) to construct and apply investment strategy on the stock market. Characteristics of this type of neural network and their influence on the investment strategy performance are verified. Considered parameters include the SOM size, here connected to the size of the training set (number of examples). The average number of patterns per neuron was selected as the appropriate measure. Other aspects of the SOM analysis included conscience mechanism, which allows more neurons to be stimulated during the learning process, method of weights updating, determining the number of stimulated neurons. Additionally, the impact of the correlation between features was verified to eliminate redundant ones. Performance of each designed network was verified against the simple investment strategy, generating “buy”, ”sell” and “hold” signals based on the average Rate of Return (RoR). Results show that SOMs with the conscience mechanism outperform their simpler configurations. Elimination of correlated data also improves performance of the SOM-based investment strategy.

Performance of Asset Allocation Strategies in Europe: An Out-of-Sample Assessment

Optimal diversification gain is frequently more than offset by estimation error, leading to a well documented gap between the in-sample and out-of-sample performance of investment strategies. Our findings suggest that popular attempts to address the estimation error can be counterproductive in improving the realized performance. In particular, potential diversification deteriorates if the original strategy outperforms the naive benchmark; whereas the performance improves – up to the suboptimal 1/N return levels – in the case of bad performing strategies. We argue that constraining the weight variability reduces the negative effects of the estimation error but they still fail to improve the diversification potential.

Risk Measurement Choice in Selecting REITs: Evidence from the U.S. Market

Executive Summary. The returns of real estate investments trusts (REITs) in the United States are not normally distributed and should therefore not be analyzed using the standard mean-variance approach. In this paper, we compare REIT performance rankings based on the Sharpe ratio with those using risk-adjusted performance (RAP) measures that do not assume the normality of the return distribution. The results demonstrate that the rankings obtained by different risk measures are significantly correlated with the Sharpe measure but some of them can show a high degree of time persistence with respect to the Sharpe index. We also study the usefulness of RAP rankings in selecting REITs by comparing the results of a naïve diversified portfolio with those of portfolios concentrating on only the top REITs identified using different RAP measures. The results demonstrate that past information is useful in improving REIT portfolio performance and the choice to consider a more complex RAP measure can increase the yearly performance of investment strategy by around 1%–2%, especially for value-weighted portfolios and/or multiple-year horizons.

Performance of investment strategies in the absence of correct beliefs

We study an evolutionary market model with long-lived assets. We show that in the absence of correct beliefs, the strategy which is “closer” to the Kelly rule cannot be driven out of the market. This means that this strategy will either dominate or at least survive. Our techniques are borrowed from the theory of random dynamical systems.

Combined Value Investment Strategy Performance

Combined Value Investment Strategy Performance

On the Performance of Investment Strategies with DEA Measures

On the Performance of Investment Strategies with DEA Measures

Long-Term Strategies for the Investment Information System

The demand for personal asset allocation planning is rising in this M-shaped era. The diversification of financial products is also on the rise in order to comply with the trends of internationalization and globalization. It is essential that investors perceive their own investment preference and risk tolerance, and understand the operation and risk of each investment vehicle. In addition, investors should choose investment vehicles to accommodate their needs, to develop their investment strategies and methods, and to conduct an effective asset allocation and financial planning on their assets. The trick to fund investment for the investor is to have a healthy investment mentality. However, it is always easier to plan than to actually act put into practice. The sentiment reflected from investment varies as the expectation and risk preference differs for every individual investor. Besides, some investors are weak in self-control and easily influenced by the market and environment, the unsettled sentiment can thus impact the realization of the ultimate fund yields. The importance of adopting appropriate investment strategy is attracting attentions as the implication beneath buy low and sell high can be seen through long term observation to the full economic cycle. This research assessed the performance of investment strategy by using investment information system to select long-term investment strategy. The data used in this study are Taiwan stock exchange weighted price index and the United States Stand & Poor's 500 index (S&P 500). The result has showed the highest return could be obtained from the lump-sum investment strategy.

The effects of mean reversion on alternative investment strategies

PurposeThe purpose of this study is to investigate the relationship between predictability in return and investment strategy performance. Two measures that characterize investment strategies within a mean‐variance framework, an activity measure and a style measure, are developed and the performance of alternative strategies (e.g. contrarian, momentum, etc.) is examined when risky asset returns are mean reverting.Design/methodology/approachReturns are assumed to follow a multivariate Ornstein‐Uhlenbeck process, where reversion to a time‐varying mean is governed by an additional variable set, similar to that proposed by Lo and Wang (1995). Depending on its parameterization, this process is capable of producing an autocorrelation pattern consistent with empirical evidence, that is, positive autocorrelation in short‐horizon returns and negative autocorrelation in long‐horizon returns.FindingsThe results, for four uninformed investment strategies and assuming that returns are generated by a simple univariate Ornstein‐Uhlenbeck process, show that the unadjusted returns from the contrarian (momentum) strategy are greater than those from the other strategies when the mean reversion parameter, α, is greater than (less than) one. The results are expected, given the relationship between α and the first‐order autocorrelation in returns. The risk level (measured by either the standard deviation of returns or beta) of the contrarian strategy is the lowest at essentially all levels of mean reversion and the risk‐adjusted returns from the contrarian strategy, measured by the both the Sharpe and Treynor ratios, dominate those from the other strategies.Research limitations/implicationsIn future research, a number of issues not considered in this study may be investigated. The style measure developed here can be used to determine whether the results obtained hold when an informed, mean‐variance efficient active strategy is employed. In addition, the performance of both the informed and uninformed strategies may be examined under the assumption that the risky return process follows a multivariate Ornstein‐Uhlenbeck process. This work should provide findings that facilitate the separation of fund risk due to dynamic strategies from that due to time‐varying expected returns.Practical implicationsThe methodology used here may be easily extended to consider a number of important issues, such as the frequency of portfolio rebalancing, transactions costs, and multiple asset portfolios, that are encountered in practice.Originality/valueThe approach used here provides insight into how predictability affects the relative performance of tactical investment strategies and, thus, may serve as a basis for determining the magnitude and persistence in autocorrelation required for active investment strategies to yield profits significantly different from those of passive strategies. In this sense, this study may have appeal for both academics and investment professionals.

Cross-Industry, Cross-Country Allocation

Recent empirical evidence has demonstrated that both global industry factors and country factors are important determinants of equity prices. In light of this evidence, we describe a cross-industry, cross-country allocation framework for making active global equity investment decisions. We present a forecasting approach to predicting the relative performance of industries in each of 22 developed country equity markets and demonstrate that a blend of style signals provides an effective way to predict the return performance of these assets. The out-of-sample portfolio performance of investment strategies based on these forecasts for the 1991–2001 period would have provided annual gross returns in excess of the world benchmark return of 400 bps a year with one-way turnover of 50 percent. Conventional global risk models cannot explain this outperformance. Thus, explaining this “anomaly” is a challenge for the investment and academic communities.