The return on investment from proportional portfolio strategies

Dynamic asset allocation strategies that are continuously rebalanced so as to always keep a fixed constant proportion of wealth invested in the various assets at each point in time play a fundamental role in the theory of optimal portfolio strategies. In this paper we study the rate of return on investment, defined here as the net gain in wealth divided by the cumulative investment, for such investment strategies in continuous time. Among other results, we prove that the limiting distribution of this measure of return is a gamma distribution. This limit theorem allows for comparisons of different strategies. For example, the mean return on investment is maximized by the same strategy that maximizes logarithmic utility, which is also known to maximize the exponential rate at which wealth grows. The return from this policy turns out to have other stochastic dominance properties as well. We also study the return on the risky investment alone, defined here as the present value of the gain from investment divided by the present value of the cumulative investment in the risky asset needed to achieve the gain. We show that for the log-optimal, or optimal growth policy, this return tends to an exponential distribution. We compare the return from the optimal growth policy with the return from a policy that invests a constant amount in the risky stock. We show that for the case of a single risky investment, the constant investor's expected return is twice that of the optimal growth policy. This difference can be considered the cost for insuring that the proportional investor does not go bankrupt.

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Impact of Portfolio Strategies on Portfolio Performance and Risk

International Journal of Business Administration ◽

10.5430/ijba.v10n1p73 ◽

2018 ◽

Vol 10 (1) ◽

pp. 73

Author(s):

Zunera Shaukat ◽

Ahmad Shahzad

Keyword(s):

Asset Allocation ◽

Stock Prices ◽

Sharpe Ratio ◽

Portfolio Performance ◽

Dynamic Asset Allocation ◽

Portfolio Strategies ◽

Tactical Asset Allocation ◽

The Difference ◽

Post Hoc ◽

The Relationship

The Portfolio strategies are the effective investment tools pertaining to active and passive investment approaches. This signifies the investor’s inclination of buying and selling the risky and risk-free assets. The research includes four strategies namely buy and hold strategy, dynamic asset allocation, strategic asset allocation and tactical asset allocation along with their dimensions. Strategies based hypothetical portfolios are generated on the basis of 14 years’ stock prices (2005-2017). The annually and monthly risk-adjusted return ratios; Sharpe ratio, Treynor’s measure, CAPM and Jenson Alpha are calculated individually. Simulated annualized portfolios generate significant result with Sharpe and treynor measure. Alpha return is generated with buy and hold if based on growth in stock prices. For empirical result, One-way analysis of variance (ANOVA) is used for studying the relationship between the strategies. Post hoc Tukey’s test is applied to find the difference between the strategies. The ANOVA and Tukey’s post hoc test for monthly portfolios gives significant results with three measure Sharpe ratio, CAPM and Jenson Alpha. No empirical significant result is measured on the basis of treynor measure.

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FROM RAGS TO RICHES: ON CONSTANT PROPORTIONS INVESTMENT STRATEGIES

International Journal of Theoretical and Applied Finance ◽

10.1142/s0219024902001547 ◽

2002 ◽

Vol 05 (06) ◽

pp. 563-573 ◽

Cited By ~ 19

Author(s):

IGOR V. EVSTIGNEEV ◽

KLAUS REINER SCHENK-HOPPÉ

Keyword(s):

Financial Market ◽

Asset Allocation ◽

Trading Strategies ◽

Investment Strategies ◽

Exponential Rate ◽

Dynamic Asset Allocation ◽

Stationary Stochastic Processes ◽

Fixed Constant ◽

Allocation Strategies ◽

Constant Prices

This paper studies the performance of self-financing constant proportions trading strategies, i.e. dynamic asset allocation strategies that keep a fixed constant proportion of wealth invested in each asset in all periods in time. We prove that any self-financing constant proportions strategy yields a strictly positive exponential rate of growth of investor's wealth in a financial market in which prices are described by stationary stochastic processes and the price ratios are non-degenerate. This result might be regarded as being counterintuitive because any such strategy yields no increase of wealth under constant prices. We further show that the result also holds under small transaction costs, which is important for the viability of this approach, since constant proportions strategies require frequent rebalancing of the portfolio.

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Dynamic surplus optimization with performance- and index-linked liabilities

European Actuarial Journal ◽

10.1007/s13385-021-00292-z ◽

2021 ◽

Author(s):

Sascha Desmettre ◽

Markus Wahl ◽

Rudi Zagst

Keyword(s):

Optimal Investment ◽

Investment Strategy ◽

Insurance Companies ◽

Investment Strategies ◽

Insurance Company ◽

Expected Return ◽

Close Link ◽

Closed Form Solutions ◽

Risky Investment ◽

The Impact

AbstractThe increasing importance of liability-driven investment strategies and the shift towards retirement products with lower guarantees and more performance participation provide challenges for the development of portfolio optimization frameworks which cover these aspects. To this end, we establish a general and flexible terminal surplus optimization framework in continuous time, allowing for dynamic investment strategies and stochastic liabilities, which can be linked to the performance of an index or the asset portfolio of the insurance company. Besides optimality results in a fairly general surplus optimization setting, we obtain closed-form solutions for the optimal investment strategy for various specific liability models, which include the cases of index-linked and performance-linked liabilities and liabilities which are completely or only partially hedgeable. We compare the results in numerical examples and study the impact of the performance participation, unhedgeable risk components, different ways of modeling the liabilities and the relative risk aversion parameter. We find that performance- or index-linked liabilities, which provide a close link between the wealth of the insurance company and its liabilities, allow for a higher allocation in the risky investment. On the other hand, unhedgeable risks reduce the allocation in the risky investment. We conclude that, aiming at a high expected return for the policy holder, insurance companies should try to connect the performance of insurance products closely to the wealth and minimize unhedgeable risks.

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Carry-Based Expected Returns for Strategic Asset Allocation

The Journal of Portfolio Management ◽

10.3905/jpm.2018.45.2.068 ◽

2019 ◽

Keyword(s):

Risk Factors ◽

Asset Allocation ◽

Expected Returns ◽

Present Value ◽

Expected Return ◽

Strategic Asset Allocation ◽

Present Value Model ◽

Geographic Regions ◽

Asset Classes ◽

Value Model

In this article, the author investigates expected return forecasting methodologies and their application in an asset allocation context. Although present value model–related methods are popular in practice, little is known about their performance when used for asset allocation. An intuitive and traceable carry-based method is developed by the author and tested and benchmarked against competing alternatives. The results are evaluated from different perspectives, and the obtained returns are regressed on well-known risk factors. The proposed methodology outperformed other return forecasting variants on various metrics and generated significant alphas regardless of the weight determination approach used. The methodology can be extended to further asset classes and geographic regions and provides a framework for allocating assets strategically.

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Dynamic Asset Allocation Techniques

British Actuarial Journal ◽

10.1017/s1357321700005742 ◽

2009 ◽

Vol 15 (3) ◽

pp. 573-655 ◽

Cited By ~ 2

Author(s):

S. Jarvis ◽

A. Lawrence ◽

S. Miao

Keyword(s):

Asset Allocation ◽

Investment Strategy ◽

Trading Strategies ◽

Investment Strategies ◽

Dynamic Asset Allocation ◽

Dynamic Investment ◽

Tactical Asset Allocation ◽

Relationship Of ◽

The Relationship

ABSTRACTInvestment strategy is often static, punctuated by infrequent reviews. For most long-term investors, this practice results in large risks being taken that could otherwise be managed with a more dynamic investment policy. The bulk of this paper is aimed at analysing and describing two multi-period investment strategy problems — in order to derive potential dynamic strategies. Along the way, we show how static investment strategies can fail to deliver an investor's long-term objectives and describe the relationship of our work to other areas of the finance literature. This paper does not cover trading strategies such as Tactical Asset Allocation.This paper sets out two main approaches to the multi-period problem. The first approach optimises a utility function. The second approach uses partial differential equation (PDE) technology to optimise a target statistic (in this case, TailVaR) subject to return and long-only constraints.

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Timing Idiosyncratic Volatility and Dynamic Asset Allocation

10.31235/osf.io/9kber ◽

2020 ◽

Author(s):

Yun Shi

Keyword(s):

Transaction Cost ◽

Portfolio Selection ◽

Asset Allocation ◽

Weighted Average ◽

Idiosyncratic Volatility ◽

Time Varying ◽

Dynamic Asset Allocation ◽

Expected Return ◽

Alternative Strategies ◽

The Common

We solve a portfolio selection problem when both expected return, idiosyncratic volatility, and transaction cost are time-varying. Our optimal strategy suggests trading partially toward a dynamic aim portfolio, which is a weighted average of expected future tangency portfolio and is highly influenced by the common fluctuation of idiosyncratic volatility (CIV). When CIV is high, the investor would invest less and trade less frequently to avoid risk and transaction cost. Moreover, the investor trades more closely to the aim portfolio with a more persistent CIV signal. Our strategy outperforms alternative strategies empirically and the benefits mainly come from timing idiosyncratic volatility.

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