When Do Stop-Loss Rules Stop Losses?

Stop-loss rules-predetermined policies that reduce a portfolio's exposure after reaching a certain threshold of cumulative losses-are commonly used by retail and institutional in- vestors to manage the risks of their investments, but have also been viewed with some skep- ticism by critics who question their e±cacy. In this paper, we develop a simple framework for measuring the impact of stop-loss rules on the expected return and volatility of an arbitrary portfolio strategy, and derive conditions under which stop-loss rules add or subtract value to that portfolio strategy. We show that under the Random Walk Hypothesis, simple 0/1 stop-loss rules always decrease a strategy's expected return, but in the presence of momen- tum, stop-loss rules can add value. To illustrate the practical relevance of our framework, we provide an empirical analysis of a stop-loss policy applied to a buy-and-hold strategy in U.S. equities, where the stop-loss asset is U.S. long-term government bonds. Using monthly returns data from January 1950 to December 2004, we find that certain stop-loss rules add 50 to 100 basis points per month to the buy-and-hold portfolio during stop-out periods. By computing performance measures for several price processes, including a new regime- switching model that implies periodic "flights-to-quality", we provide a possible explanation for our empirical results and connections to the behavioral finance literature.Investments; Portfolio Management; Risk Management; Performance Attribution; Behavioral Finance

Investment risk taking by institutional investors

This paper is the first that formally compares investment risk taking by pension funds and insurance firms. Using a unique and extended dataset that covers the volatile investment period 1995-2009, we find that, in the Netherlands, insurers take substantially less investment risk than pension funds, even though a market risk capital charge for insurers is yet absent. This result can be explained from financial distress costs, which only insurers face. We also find that institutional investors' risk taking is determined by their risk bearing capacity, where this risk bearing capacity depends on capital, size, reinsurance, underwriting risk and human and financial wealth per pension plan participant. Finally, and in line with the ownership structure hypothesis, stock insurers are found to take significantly more investment risk than mutual insurers.Portfolio Choice, Insurance Companies, Pension Funds, Ownership Structure

When do stop-loss rules stop losses?

We propose a simple analytical framework to measure the value added or subtracted by stop-loss rules-predetermined policies that reduce a portfolio's exposure after reaching a certain threshold of cumulative losses-on the expected return and volatility of an arbitrary portfolio strategy. Using daily futures price data, we provide an empirical analysis of stop-loss policies applied to a buy-and-hold strategy using index futures contracts. At longer sampling frequencies, certain stop-loss policies can increase expected return while substantially reducing volatility, consistent with their objectives in practical applications. Keywords: Investments; Portfolio management; Risk management; Asset allocation; Performance attribution; Behavioral financ

Self-referential behaviour, overreaction and conventions in financial markets

We study a generic model for self-referential behaviour in financial markets, where agents attempt to use some (possibly fictitious) causal correlations between a certain quantitative information and the price itself. This correlation is estimated using the past history itself, and is used by a fraction of agents to devise active trading strategies. The impact of these strategies on the price modify the observed correlations. A potentially unstable feedback loop appears and destabilizes the market from an efficient behaviour. For large enough feedbacks, we find a `phase transition' beyond which non trivial correlations spontaneously set in and where the market switches between two long lived states, that we call conventions. This mechanism leads to overreaction and excess volatility, which may be considerable in the convention phase. A particularly relevant case is when the source of information is the price itself. The two conventions then correspond then to either a trend following regime or to a contrarian (mean reverting) regime. We provide some empirical evidence for the existence of these conventions in real markets, that can last for several decades.Comment: 15 pages, 12 .eps figure

The optimal asset allocation for South African real return investors

This research aims to establish the optimal asset allocations for targeting specific real returns over short, medium and long-term investment horizons. The joint returns are modelled with data-centric methods that are empirical and non-parametric in nature, and are able to capture the dependencies of returns over time. The asset classes that are considered are South African (SA) equities, SA bonds, SA cash, SA property, global equities, global bonds, global cash, and global property. The returns of each asset class are modelled, each class with its own empirical distribution based on monthly returns from 1972 to 2017. The monthly returns are grouped in a block of rolling periods of varying block lengths in order to attempt to capture dependencies across time. These blocks of data are resampled in order to simulate the distributions of returns of portfolios with their own unique empirical distribution. The optimal portfolios are derived using a genetic algorithm, showcasing how these extremely versatile optimisation tools can be used in combination with resampling methods to find the optimal portfolio for virtually any criterion. A comparison is also made to the traditional mean-variance optimal portfolios, yielding an estimate of the bias in mean-variance optimisation’s (MVO) optimal weights. It is investigated how these optimal portfolios are influenced by the choice of risk criterion and investment horizon. The effect of the most important and consequential nuisance parameter in this research’s model, the block length, is discussed. The relationships established between the characteristics of optimal portfolios and investment horizon and risk criterion and the comparisons with classic MVO should be of interest to investors and investment professionals alike. Economic and market regimes are “identified” on the basis of economic and market data, consequently the resampling probabilities will be unequal. The optimal weights conditional on regimes are derived. Both static and changing regimes are considered. Lastly, an out-of-sample backtest of the performance of the optimal portfolios conditional on the regime across time at six month intervals is conducted from 1983 to 2017. It shows that out of the three block lengths tested for a single investment horizon of 36 months, a block length of 24 months yielded the best overall risk-adjusted performance, on average. Conditioning for regimes is shown to generally outperform the unconditional approach. The improvements are marginal and further research is recommended to investigate the performance for longer investment horizons and other values of the two tuning parameters, block length and tactical pressure. The higher level aim of this work is to present a broad sense of how data-driven nonparametric methods can be used in conjunction with metaheuristic procedures. The objective of combining these techniques is to find optimal portfolios under very general conditions and with very few assumptions regarding the underlying distributions

Essays on asset pricing

The dissertation consists of three chapters that represent separate papers in the area of asset pricing. The first chapter studies investors optimal asset allocation problem in which mean reversion in stock prices is captured by explicitly modeling transitory and permanent shocks. The second chapter focuses on option pricing with stochastic dividend yield. In this work, we present an option formula which does not depend on the dividend yield risk premium. In the final chapter, we work on commodity derivative pricing under the existence of stochastic convenience yield. In this paper, we discuss a Gaussian complete market model of commodity prices in which the stochastic convenience yield is assumed to be an affine function of a weighted average of past commodity price changes

Global Tactical Cross-Asset Allocation: Applying Value and Momentum Across Asset Classes

In this paper we examine global tactical asset allocation (GTAA) strategies across a broad range of asset classes. Contrary to market timing for single asset classes and tactical allocation across similar assets, this topic has received little attention in the existing literature. Our main finding is that momentum and value strategies applied to GTAA across twelve asset classes deliver statistically and economically significant abnormal returns. For a long top-quartile and short bottom-quartile portfolio based on a combination of momentum and value signals we find a return of 12% per annum over the 1986-2007 period. Performance is stable over time, also present in an out-of-sample period and sufficiently high to overcome transaction costs in practice. The return cannot be explained by potential structural biases towards asset classes with high risk premiums, nor the Fama French and Carhart hedge factors. We argue that financial markets may be macro inefficient due to insufficient ‘smart money’ being available to arbitrage mispricing effects away.momentum;GTAA;global asset allocation;value effect

Optimal time series momentum

We develop a continuous-time asset price model to capture the time series momentum documented recently. The underlying stochastic delay differential system facilitates the analysis of effects of different time horizons used by momentum trading. By studying an optimal asset allocation problem, we find that the performance of time series momentum strategy can be significantly improved by combining with market fundamentals and timing opportunity with respect to market trend and volatility. Furthermore, the results also hold for different time horizons, the out-of-sample tests and with short-sale constraints. The outperformance of the optimal strategy is immune to market states, investor sentiment and market volatility