Computerized stock screening rules for portfolio selection

Recent studies have uncovered several systematic patterns that increase the probability that individual investors can select stock portfolios with excess returns. This study tests the feasibility of using a commercially available computerized stock screening program for investors to take advantage of these patterns. The screening program searches the three major exchanges and selects stocks on both fundamental and technical indicators: low price-to-sales ratio, small firm size, accelerating stock prices above their 50 day moving average, high trading volume, and high earnings growth. Of the 18 models tested between 1994 and 1998, those that allow for selection between exchanges yield portfolio returns that significantly exceed the average market indices.     

Optimum consumption and portfolio rules with money as an asset

This article generalizes Merton's optimum consumption and portfolio rules in continuous time by introducing money as a capital asset and allowing for uncertain inflation. Assuming that prices are log-normally distributed, a three-funds theorem is derived and the introduction of money is shown not to change the form of the standard inflation-adjusted CAPM but to change the market price of risk. The individual's consumption-portfolio problem is completely solved under uncertain inflation if his utility function is iso-elastic in its arguments. Comparative statics are used to assess the influence of changes in exogenous parameters on the individual's optimal rules.     

On the stability of portfolio selection models

One of the main issues in portfolio selection models consists in assessing the effect of the estimation errors of the parameters required by the models on the quality of the selected portfolios. Several studies have been devoted to this topic for the minimum variance and for several other minimum risk models. However, no sensitivity analysis seems to have been reported for the recent popular Risk Parity diversification approach, nor for other portfolio selection models requiring maximum gain–risk ratios.Based on artificial and real-world data, we provide here empirical evidence showing that the Risk Parity model is always the most stable one in all the cases analyzed with respect to the portfolio composition. Furthermore, the minimum risk models are typically more stable than the maximum gain–risk models, with the minimum variance model often being the preferable one. The Risk Parity model seems to be the most stable one also with respect to profitability when measured by the Sharpe ratio. However, the maximum gain–risk models, although quite sensitive to the input data, generally appear to attain better profitability results.     

The profitability of regression-based trading rules for the Shanghai stock market

This paper uses daily Shanghai A share data to evaluate the profitability of trading rules based on the predictability found in the return series. We find that the value of the trading-rule-based portfolio at the end of our sample is between 2 and 11 times that of an equity-buy-and-hold portfolio. We assess the robustness of the results in various ways: by carrying out various statistical tests, by varying the period over which the evaluation is carried out, by using a recursive estimation procedure for the forecasting equation, by incorporating transactions costs, and by considering weekly and monthly data.     

Strategy as a portfolio of real options

In financial terms, a business strategy is much more like a series of options than like a single projected cash flow. Executing a strategy almost always involves making a sequence of major decisions. Some actions are taken immediately while others are deliberately deferred so that managers can optimize their choices as circumstances evolve. While executives readily grasp the analogy between strategy and real options, until recently the mechanics of option pricing was so complex that few companies found it practical to use when formulating strategy. But advances in both computing power and our understanding of option pricing over the last 20 years now make it feasible to apply real-options thinking to strategic decision making. To analyze a strategy as a portfolio of related real options, this article exploits a framework presented by the author in "Investment Opportunities as Real Options: Getting Started on the Numbers" (HBR July-August 1998). That article explained how to get from discounted-cash-flow value to option value for a typical project; in other words, it was about reaching a number. This article extends that framework, exploring how, once you've worked out the numbers, you can use option pricing to improve decision making about the sequence and timing of a portfolio of strategic investments. Timothy Luehrman shows executives how to plot their strategies in two-dimensional "option space," giving them a way to "draw" a strategy in terms that are neither wholly strategic nor wholly financial, but some of both. Such pictures inject financial discipline and new insight into how a company's future opportunities can be actively cultivated and harvested.     

An analysis of portfolio selection with background risk

This paper investigates the impact of background risk on an investor’s portfolio choice in a mean–variance framework, and analyzes the properties of efficient portfolios as well as the investor’s hedging behaviour in the presence of background risk. Our model implies that the efficient portfolio with background risk can be separated into two independent components: the traditional mean–variance efficient portfolio, and a self-financing component constructed to hedge against background risk. Our analysis also shows that the presence of background risk shifts the efficient frontier of financial assets to the right with no changes in its shape. Moreover, both the composition of the hedge portfolio and the location of the efficient frontier are greatly affected by a number of background risk factors, including the proportion of background assets in total wealth and the correlation between background risk and financial risk.     

The profitability,costs and systematic risk of the post-earnings-announcement-drift trading strategy

This paper re-examines the profitability of the post-earnings-announcement-drift (PEAD) trading strategy using a practical simulation approach that aligns with a fund manager’s investment perspective. It allows us to calculate the break-even transaction costs of following a PEAD strategy, and permits the explicit incorporation of transaction costs. Using US data from 1974 to 2007, we show that the traditional event-study method understates the risk and overstates the abnormal return of the PEAD strategy. Accounting for transaction costs in a practical simulation framework, we show there is no abnormal return (alpha) from the PEAD strategy in multi-factor asset pricing regression analyses. These results are robust to sub-period analyses and alternative transaction cost measures. The effects of intraday timing and information risk on the PEAD strategy are also explored. Overall, our study shows that the practical aspects of implementing the PEAD strategy are vitally important to evaluating the risk and return of the strategy. We provide a practical, analytical tool that can be directly adopted by fund managers to study the PEAD strategy with their institutional parameters of transaction costs and market timing.     

Noise sensitivity of portfolio selection under various risk measures

We study the sensitivity to estimation error of portfolios optimized under various risk measures, including variance, absolute deviation, expected shortfall and maximal loss. We introduce a measure of portfolio sensitivity and test the various risk measures by considering simulated portfolios of varying sizes N and for different lengths T of the time series. We find that the effect of noise is very strong in all the investigated cases, asymptotically it only depends on the ratio N/T, and diverges (goes to infinity) at a critical value of N/T, that depends on the risk measure in question. This divergence is the manifestation of a phase transition, analogous to the algorithmic phase transitions recently discovered in a number of hard computational problems. The transition is accompanied by a number of critical phenomena, including the divergent sample to sample fluctuations of portfolio weights. While the optimization under variance and mean absolute deviation is always feasible below the critical value of N/T, expected shortfall and maximal loss display a probabilistic feasibility problem, in that they can become unbounded from below already for small values of the ratio N/T, and then no solution exists to the optimization problem under these risk measures. Although powerful filtering techniques exist for the mitigation of the above instability in the case of variance, our findings point to the necessity of developing similar filtering procedures adapted to the other risk measures where they are much less developed or non-existent. Another important message of this study is that the requirement of robustness (noise-tolerance) should be given special attention when considering the theoretical and practical criteria to be imposed on a risk measure.     

Testing the growth option theory: the profitability of enhanced momentum strategies in Australia

Recent literature relates growth option theory to various return regularities. Sagi and Seasholes (2007) (S&S) develop a model that explains momentum profitability using growth option theory. We test the model’s predictions in the Australian market by examining three momentum strategies. Two of these strategies examine the profitability of momentum strategies conditioned on stocks characteristics, whereas the third conditions on previous market returns. Our results are largely supportive of the S&S model. As predicted by S&S, the two strategies that use firm‐specific characteristics yield a higher profit than a simple momentum strategy. The third strategy that conditions on the previous market return also leads to differences in momentum profitability between bull and bear markets, but these differences are small and largely insignificant.     

The implications of the new capital adequacy rules for portfolio management of credit assets

Over the past several years, there has been an extensive discussion among practitioners and academics about whether and how a portfolio management approach could help banks to better manage risk capital and create shareholder value. In this article, the authors argue that there are four key drivers which require banks to move from a transactional to a more portfolio management like approach when managing credit assets. These are: structural changes in the credit markets, inefficiencies of risk transfer in lending markets, ballooning debt levels in the US, and the proposed changes for capital adequacy. The authors see the latter not as a one-time change in capital adequacy rules, but more as a first step towards full convergence between risk capital and regulatory capital for credit risk. These changes require banks to accelerate their efforts to build first class portfolio management skills and capabilities. Achieving best practice credit portfolio management is rewarded with attractive opportunities for shareholder value creation and enables bank to successfully compete going forward.     

Parameter uncertainty in portfolio selection: Shrinking the inverse covariance matrix

The estimation of the inverse covariance matrix plays a crucial role in optimal portfolio choice. We propose a new estimation framework that focuses on enhancing portfolio performance. The framework applies the statistical methodology of shrinkage directly to the inverse covariance matrix using two non-parametric methods. The first minimises the out-of-sample portfolio variance while the second aims to increase out-of-sample risk-adjusted returns. We apply the resulting estimators to compute the minimum variance portfolio weights and obtain a set of new portfolio strategies. These strategies have an intuitive form which allows us to extend our framework to account for short-sale constraints, transaction costs and singular covariance matrices. A comparative empirical analysis against several strategies from the literature shows that the new strategies often offer higher risk-adjusted returns and lower levels of risk.     

Disentangling the role of variance and covariance information in portfolio selection problems

The covariation among financial asset returns is often a key ingredient used in the construction of optimal portfolios. Estimating covariances from data, however, is challenging due to the potential influence of estimation error, specially in high-dimensional problems, which can impact negatively the performance of the resulting portfolios. We address this question by putting forward a simple approach to disentangle the role of variance and covariance information in the case of mean-variance efficient portfolios. Specifically, mean-variance portfolios can be represented as a two-fund rule: one fund is a fully invested portfolio that depends on diagonal covariance elements, whereas the other is a long-short, self financed portfolio associated with the presence of non-zero off-diagonal covariance elements. We characterize the contribution of each of these two components to the overall performance in terms of out-of-sample returns, risk, risk-adjusted returns and turnover. Finally, we provide an empirical illustration of the proposed portfolio decomposition using both simulated and real market data.     

The effectiveness of the VaR-based portfolio insurance strategy: An empirical analysis

This paper proposes an approach to constructing the insured portfolios under the VaR-based portfolio insurance strategy (VBPI) and provides a comprehensive analysis of its hedging effectiveness in comparison with the buy-and-hold (B&H) as well as the constant proportion portfolio insurance (CPPI) strategies in the context of the Chinese market. The results show that both of the insurance strategies are able to limit the downward returns while retaining certain upside returns, and their capabilities of reshaping the return distributions increase as the guarantee or the confidence level rises. In general, the VBPI strategy tends to outperform the CPPI strategy in terms of both the degree of downside protection and the return performance.     

Robust optimization of conditional value at risk and portfolio selection

This paper deals with a portfolio selection model in which the methodologies of robust optimization are used for the minimization of the conditional value at risk of a portfolio of shares.     

Trend definition or holding strategy: What determines the profitability of candlestick charting?

We ask what determines the profitability of candlestick trading strategies. Is it the definition of trend and/or the holding strategy that one uses in candlestick charting analysis? To answer this, we systematically consider three definitions of trend and four holding strategies. Applying candlestick trading strategies to the DJIA component data, we find that regardless of which definition of trend is used, eight three-day reversal patterns with a Caginalp–Laurent holding strategy are profitable when we set the transaction cost at 0.5% and after we account for data-snooping bias, while the patterns with a Marshall–Young–Rose holding strategy are not profitable. For sensitivity analysis, we also find that our results are not qualitatively changed on a lower transaction cost of 0.1%, or when we conduct the subsample analyses based on three equal periods and three distinct market conditions. When considering a more volatile market, evidence in favor of candlestick trading strategies is strengthened.     

Competing on capabilities: the new rules of corporate strategy

In the 1980s, companies discovered time as a new source of competitive advantage. In the 1990s, they will discover that time is only one piece of a more far-reaching transformation in the logic of competition. Using examples from Wal-Mart and other highly successful companies, Stalk, Evans, and Shulman of the Boston Consulting Group provide managers with a guide to the new world of "capabilities-based competition." In today's dynamic business environment, strategy too must become dynamic. Competition is a "war of movement" in which success depends on anticipation of market trends and quick response to changing customer needs. In such an environment, the essence of strategy is not the structure of a company's products and markets but the dynamics of its behavior. To succeed, a company must weave its key business processes into hard-to-imitate strategic capabilities that distinguish it from its competitors in the eyes of customers. A capability is a set of business processes strategically understood--for example, Wal-Mart's expertise in inventory replenishment, Honda's skill at dealer management, or Banc One's ability to "out-local the national banks and out-national the local banks." Such capabilities are collective and cross-functional--a small part of many people's jobs, not a large part of a few. Finally, competing on capabilities requires strategic investments in support systems that span traditional SBUs and functions and go far beyond what traditional cost-benefit metrics can justify. A CEO's success in building and managing a company's capabilities will be the chief test of management skill in the 1990s. The prize: companies that combine scale and flexibility to outperform the competition.     

Size matters: Optimal calibration of shrinkage estimators for portfolio selection

We carry out a comprehensive investigation of shrinkage estimators for asset allocation, and we find that size matters—the shrinkage intensity plays a significant role in the performance of the resulting estimated optimal portfolios. We study both portfolios computed from shrinkage estimators of the moments of asset returns (shrinkage moments), as well as shrinkage portfolios obtained by shrinking the portfolio weights directly. We make several contributions in this field. First, we propose two novel calibration criteria for the vector of means and the inverse covariance matrix. Second, for the covariance matrix we propose a novel calibration criterion that takes the condition number optimally into account. Third, for shrinkage portfolios we study two novel calibration criteria. Fourth, we propose a simple multivariate smoothed bootstrap approach to construct the optimal shrinkage intensity. Finally, we carry out an extensive out-of-sample analysis with simulated and empirical datasets, and we characterize the performance of the different shrinkage estimators for portfolio selection.     

Testing the effect of portfolio holdings disclosure in an environment absent of mandatory disclosure

This study examines a number of portfolio disclosure regimes with respect to accuracy and susceptibility to copycat behaviour in an environment absent of mandatory disclosure. We find that periodic portfolio disclosure tends to underestimate true excess performance as well as idiosyncratic risk in top‐quartile fund managers, with longer inter‐reporting intervals tending to result in greater differences. ‘Copycat funds’ following the disclosed holdings of top‐tier managers significantly underperform the underlying fund, while copycats following bottom‐tier managers significantly outperform the underlying fund. Our findings suggest that periodic reporting at monthly intervals or longer would not affect fund alpha generation.     

Correlation structure forecasting & ex ante portfolio selection strategies in the Japan market

Using monthly data for 160 stocks covering January 1977 to December 1991, we find that both the Historical Mean and the Industry Mean Models dominate the Global Mean and the Single Index Models. In theex-ante portfolio selection, the Historical Model dominates all other models when evaluated against the benchmark of the Global Minimum Variance Portfolio but a combination of historical correlation structure and Bayes-Stein Shrinkage expected returns dominates other models when the Optimal Tangency Portfolio is used as a benchmark for evaluation.