Optimal Investment Horizon Research Articles

Agency, Corporate Liquidity, and Optimal Investment Horizon

Agency, Corporate Liquidity, and Optimal Investment Horizon

Return on investment in REIT real estate funds

The article considers the shaping conditions of the diversified portfolio with abundance of REIT from various sectors of residential property, mortgage and mixed. Special focus will be on the choice of most attractive investment products with the “Return-Risk” Model offered by the authors. This model was tested on independent material, and the investment attractiveness results received were compared with other products such as common shares, exchange goods, money market, etc. The conclusion contains the data about the accumulated return which on average makes up 1,67%, and its dynamics allows to evaluate the optimal investment horizon.

Optimal Investment Horizon Tehran Price Index (Tepix) and Its Comparison with Indices of Automotive, Sugar, Pharmaceutical, Financial and Banking Industries

Optimal Investment Horizon Tehran Price Index (Tepix) and Its Comparison with Indices of Automotive, Sugar, Pharmaceutical, Financial and Banking Industries

Portfolio rebalancing with an investment horizon and transaction costs

In this paper we consider the problem of rebalancing an existing financial portfolio, where transaction costs have to be paid if we change the amount held of any asset. These transaction costs can be fixed (so paid irrespective of the amount traded provided a trade occurs) and/or variable (related to the amount traded). We indicate the importance of the investment horizon when rebalancing such a portfolio and illustrate the nature of the efficient frontier that results when we have transaction costs. We model the problem as a mixed-integer quadratic programme with an explicit constraint on the amount that can be paid in transaction cost. Our model incorporates the interplay between optimal portfolio allocation, transaction costs and investment horizon. We indicate how to extend our model to include cardinality constraints and present a number of enhancements to the model to improve computational performance. Results are presented for the solution of publicly available test problems involving up to 1317 assets.

Explaining Momentum Strategies Using Intrinsic Price Fluctuations

This paper focuses on cross-sectional equity momentum patterns by modeling a stock’s price path as the interaction between a long-term growth component and a number of fluctuating price components that oscillate around the long-term trend at various distinct frequencies. Using this specification, the results are consistent with a behavioural overreaction-to-private-information and underreaction-to- public-information explanation of cross-sectional explanation of momentum patterns. Cross-sectional momentum profitability is found to be robust to realistic transaction costs and a 6-month holding period appears to be the optimal tradeoff investment horizon between the short-term and the longer-term effect of the transaction costs. Simple stop-loss rules are shown to improve the performance of strategies with long-term holding horizon by discarding big and growth stocks, which achieve higher levels of price efficiency and therefore realise their momentum potential faster than small and value stocks.

INVERSE STATISTICS AND ASSET ALLOCATION EFFICIENCY

In this paper using inverse statistics analysis, the effect of investment horizon on the efficiency of portfolio selection is examined. Inverse statistics analysis is a general tool also known as probability distribution of exit time that is used for detecting the distribution of the time in which a stochastic process exits from a zone. This analysis was used in Refs. 1 and 2 for studying the financial returns time series. This distribution provides an optimal investment horizon which determines the most likely horizon for gaining a specific return. Using samples of stocks from Tehran Stock Exchange (TSE) as an emerging market and S&P 500 as a developed market, effect of optimal investment horizon in asset allocation is assessed. It is found that taking into account the optimal investment horizon in TSE leads to more efficiency for large size portfolios while for stocks selected from S&P 500, regardless of portfolio size, this strategy does not only not produce more efficient portfolios, but also longer investment horizons provides more efficiency.

European Equity Market Integration and Optimal Investment Horizons – Evidence from Wavelet Analysis

In the paper the process of equity market integration in Europe is examined from the wavelet perspective. The method applied is the Continuous Discrete Wavelet Transform that enables to perform global and local wavelet variance and correlation decompositions. In particular, questions about changes of the investment risk and the possibility of international portfolio diversification under different investment horizons are addressed. The study documents both convergence of the Central and Eastern European equity markets as well as their segmentation on the European market. The latter enables reduction of portfolio returns variability by an international portfolio diversification, especially for long investment horizons.

Optimal Investment Horizons for S&P CNX Nifty and Its Components

The distributions of the first passage time for the S&P CNX Nifty and its 50 constituent stocks are examined. Numerical analysis shows the ‘optimal’ investment horizon at 5% return level is about 15 days for the index and is most frequently distributed at seven days (range: 5 to 15 days) for the 50 constituent stocks. This suggests a complex dynamics between the index and its constituents in terms of feedback and feed-forward loops. We also examine the distribution of first passage times for six world indices, the Dow Jones Industrial, Hang Seng, FTSE, SSEC, Kospi and the Nikkei. These range between 13 days (for the Kospi) to 47 days for the FTSE. Two distinct regimes, for both positive and negative returns) are observed in the evolution of the optimal investment horizon over different return levels.

Optimal holding period for a real estate portfolio

PurposeThe purpose of this paper is to offer a framework for computing optimal investment holding periods for real estate portfolios.Design/methodology/approachThe analysis is set within a standard DCF modelling framework and it is shown that it is not adapted to offer sufficient insight into the mechanics leading to optimal holding periods. A richer framework is offered that enables the portfolios terminal value to behave according to a simple diffusion process.FindingsThe findings show that optimal holding periods for real estate investment portfolios exist within very precise conditions. The key parameters are the investor's weighted average cost of capital (WACC), the cash flow growth rate during the investment period, and the investment's net initial yield. The key finding is (loosely speaking) that, if the investor's cost of capital is outpaced by (the sum of) the portfolio's net initial yield and the cash flow growth rate, then an optimal holding period exists and can be precisely computed. Numerical examples are provided to illustrate these findings.Originality/valueStandard financial theory does not specify a consistent methodology for choosing the optimal investment horizon in investment analysis and in particular in discounted cash flow (DCF) modelling. This problem may be particularly acute in real estate investment analysis and valuation, as investment horizons are often arbitrarily chosen. The paper proves that investment horizon may strongly influence net present value.

Gain–loss asymmetry for emerging stock markets

Stock indexes for some European emerging markets are analyzed using an investment-horizon approach. Austrian ATX index and Dow Jones have been studied and compared with several emerging European markets. The optimal investment horizons are plotted as a function of an absolute return value. Gain–loss asymmetry, originally found for American DJIA index, is observed for all analyzed data. It is shown, that this asymmetry has different character for emerging and for established markets. For established markets, gain curve lies typically above loss curve, whereas in the case of emerging markets the situation is just the opposite. We propose a measure quantifying the gain–loss asymmetry that clearly exhibits a difference between emerging and established markets.

Optimal investment horizons for stocks and markets

The inverse statistics is the distribution of waiting times needed to achieve a predefined level of return obtained from (detrended) historic asset prices [I. Simonsen, M.H. Jensen, A. Johansen, Eur. Phys. J. 27 (2002) 583; M.H. Jensen, A. Johansen, I. Simonsen, Physica A 234 (2003) 338]. Such a distribution typically goes through a maximum at a time coined the optimal investment horizon, τ ρ * , which defines the most likely waiting time for obtaining a given return ρ . By considering equal positive and negative levels of return, we reported in [M.H. Jensen, A. Johansen, I. Simonsen, Physica A 234 (2003) 338] on a quantitative gain/loss asymmetry most pronounced for short horizons. In the present paper, the inverse statistics for 2 3 of the individual stocks presently in the DJIA is investigated. We show that this gain/loss asymmetry established for the DJIA surprisingly is not present in the time series of the individual stocks nor their average. This observation points towards some kind of collective movement of the stocks of the index (synchronization).

Inverse Statistics for Stocks and Markets

In recent publications, the authors have considered inverse statistics of the Dow Jones Industrial Averaged (DJIA). Specifically, we argued that the natural candidate for such statistics is the investment horizons distribution. This is the distribution of waiting times needed to achieve a predefined level of return obtained from detrended historic asset prices. Such a distribution typically goes through a maximum at a time coined the optimal investment horizon Tp, which defines the most likely waiting time for obtaining a given return p. By considering equal positive and negative levels of return, we reported in on a quantitative gain/loss asymmetry most pronounced for short horizons. In the present paper, this gain/loss asymmetry is re-visited for 2/3 of the individual stocks presently in the DJIA. We show that this gain/loss asymmetry established for the DJIA surprisingly is not present in the time series of the individual stocks. The most reasonable explanation for this fact is that the gain/loss asymmetry observed in the DJIA as well as in the SP500 and Nasdaq are due to movements in the market as a whole, i.e., cooperative cascade processes (or 'synchronization') which disappear in the inverse statistics of the individual stocks.

Inverse statistics in stock markets: Universality and idiosyncracy

Investigations of inverse statistics (a concept borrowed from turbulence) in stock markets, exemplified with filtered Dow Jones Industrial Average, S&P 500, and NASDAQ, have uncovered a novel stylized fact that the distribution of exit times τ ρ , defined as the waiting time needed to obtain a certain increase ρ in the price, follows a power law p ( τ ρ ) ∼ τ ρ - α with α ≈ 1.5 for large τ ρ and the optimal investment horizon τ ρ * scales as ρ γ when ρ is not too small (Eur. Phys. J. B 27 (2002) 583–586; Physica A 324 (2003) 338–343; Int. J. Mod. Phys. B 17 (2003) 4003–4012). We have performed extensive analyses based on unfiltered daily indices and stock prices as well as high-frequency (5-min) records in numerous stock markets all over the world. Our analysis confirms that the power-law distribution of exit times with an exponent of about α = 1.5 is universal for all the data sets analyzed. In addition, all data sets show that the power-law scaling in the optimal investment horizon holds, but with idiosyncratic exponents. Specifically, γ ≈ 1.5 for the daily data in most of the developed stock markets and the 5-min high-frequency data, while the γ values for the daily indexes and stock prices in emerging markets are significantly less than 1.5. We show that there is little chance that the discrepancy in γ is due to the difference in sample sizes of the two kinds of stock markets.

Inverse Fractal Statistics in Turbulence and Finance

We consider inverse statistics in turbulence and financial data. By inverse statistics, also sometimes called exit time statistics, we "turn" the variables around such that the fluctuating variable becomes the fixed variable, while the fixed variable becomes fluctuating. In that sense we can probe distinct regimes of the data sets. In the case of turbulence, we obtain a new set of (multi)-scaling exponents which monitor the dissipation regime. In the case of economics, we obtain a distribution of waiting times needed to achieve a predefined level of return. Such a distribution typically goes through a maximum at a time called the optimal investment horizon[Formula: see text], since this defines the most likely waiting time for obtaining a given return ρ. By considering equal positive and negative levels of return, we report on a quantitative gain-loss asymmetry most pronounced for short horizons.

Inverse statistics in economics: the gain–loss asymmetry

Inverse statistics in economics is considered. We argue that the natural candidate for such statistics is the investment horizons distribution. This distribution of waiting times needed to achieve a predefined level of return is obtained from (often detrended) historic asset prices. Such a distribution typically goes through a maximum at a time called the optimal investment horizon, τ ρ ∗ , since this defines the most likely waiting time for obtaining a given return ρ. By considering equal positive and negative levels of return, we report on a quantitative gain–loss asymmetry most pronounced for short horizons. It is argued that this asymmetry reflects the market dynamics and we speculate over the origin of this asymmetry.

Optimal investment horizons

In stochastic finance, one traditionally considers the return as a competitive measure of an asset, i.e., the profit generated by that asset after some fixed time span Δt, say one week or one year. This measures how well (or how bad) the asset performs over that given period of time. It has been established that the distribution of returns exhibits “fat tails” indicating that large returns occur more frequently than what is expected from standard Gaussian stochastic processes [1-3]. Instead of estimating this “fat tail” distribution of returns, we propose here an alternative approach, which is outlined by addressing the following question: What is the smallest time interval needed for an asset to cross a fixed return level of say 10%? For a particular asset, we refer to this time as the investment horizon and the corresponding distribution as the investment horizon distribution. This latter distribution complements that of returns and provides new and possibly crucial information for portfolio design and risk-management, as well as for pricing of more exotic options. By considering historical financial data, exemplified by the Dow Jones Industrial Average, we obtain a novel set of probability distributions for the investment horizons which can be used to estimate the optimal investment horizon for a stock or a future contract.

Optimal Investment Horizons

In stochastic finance, one traditionally considers the return as a competitive measure of an asset, i.e., the profit generated by that asset after some fixed time span, say one week or one year. This measures how well (or how bad) the asset performs over that given period of time. It has been established that the distribution of returns exhibits fat tails indicating that large returns occur more frequently than what is expected from standard Gaussian stochastic processes [1, 2, 3]. Instead of estimating this fat tail distribution of returns, we propose here an alternative approach, which is outlined by addressing the following question: What is the smallest time interval needed for an asset to cross a fixed return level of say 10%? For a particular asset, we refer to this time as the investment horizon and the corresponding distribution as the investment horizon distribution. This latter distribution complements that of returns and provides new and possibly crucial information for portfolio design and risk-management, as well as for pricing of more exotic options. By considering historical financial data, exemplified by the Dow Jones Industrial Average, we obtain a novel set of probability distributions for the investment horizons which can be used to estimate the optimal investment horizon for a stock or a future contract.

Inverse Statistics in Economics: The Gain-Loss Asymmetry

Inverse statistics in economics is considered. We argue that the natural candidate for such statistics is the investment horizons distribution. This distribution of waiting times needed to achieve a predefined level of return is obtained from (often detrended) historic asset prices. Such a distribution typically goes through a maximum at a time called the {\em optimal investment horizon}, $\tau^*_\rho$, since this defines the most likely waiting time for obtaining a given return $\rho$. By considering equal positive and negative levels of return, we report on a quantitative gain-loss asymmetry most pronounced for short horizons. It is argued that this asymmetry reflects the market dynamics and we speculate over the origin of this asymmetry.

Misconceptions about Optimal Equity Allocation and Investment Horizon

Our analysis and results offer some resolution to a debate that has continued for 30 years. We demonstrate that, in contrast to prior claims, no conflict exists between the implications of economic models of choice under uncertainty and a rational investor preference for larger equity allocations at longer investment horizons. Using examples of expected utility of wealth and mean–variance utility, we show that the effect of a longer horizon on risky-asset allocation is consistent with increasing or decreasing optimal equity allocations, even under the assumption of constant relative risk aversion.