Hubert Dichtl

Are stock markets really so inefficient? The case of the “Halloween Indicator” ☆

The old and simple investment strategy “Sell in May and Go Away” (also referred to as the “Halloween effect”) enjoys an unbroken popularity. Recent studies suggest that the Halloween effect even strengthened rather than weakened since its first publication by Bouman and Jacobsen (2002). We implement regression models as well as Hansen’s (2005) “Superior Predictive Ability” test to analyze whether stock markets are really so inefficient. In line with the predictions of market efficiency, our results reject the hypothesis that a trading strategy based on the Halloween effect significantly outperforms.

Dollar-Cost Averaging and Prospect Theory Investors: An Explanation for a Popular Investment Strategy

Dollar-cost averaging requires investing equal amounts of an investment sum step-by-step in regular time intervals. Previous studies that assume expected utility investors were unable to explain the popularity of dollar-cost averaging. Statman [1995] argues that dollar-cost averaging is consistent with the positive framework of behavioral finance. We assume a prospect theory investor who implements a strategic asset allocation plan and has the choice to shift the portfolio immediately (comparable to a lump sum) or on a step-by-step basis (dollar-cost averaging). Our simulation results support Statmans [1995] notion that dollar-cost averaging may not be rational but a perfectly normal behavior.

On the Popularity of the CPPI Strategy: A Behavioral-Finance-Based Explanation and Design Recommendations

The constant proportion portfolio insurance (CPPI) strategy is frequently used on both the institutional and the retail sides of the asset management industry. While standard finance theory struggles to justify its popularity, this article attempts to explain the widespread use of the CPPI strategy by referring to elements of behavioral finance. We run bootstrap as well as Monte Carlo simulations for the CPPI strategy and for simple benchmark strategies in order to evaluate the outcomes using cumulative prospect theory. Our simulation results indicate that the CPPI strategy is the preferred strategy for a prospect theory investor. The analysis provides hints at how a CPPI-based investment product should be designed in order to meet the preferences of a prospect theory investor as well as possible.

Where is the Value Added of Rebalancing? A Systematic Comparison of Alternative Rebalancing Strategies

We compare the performance of different rebalancing strategies under realistic market conditions by reporting statistical significance levels. Our analysis is based on historical data from the United States, the United Kingdom, as well as Germany and comprises three different classes of rebalancing (namely periodic, threshold, and range rebalancing). Despite cross-country differences, we provide evidence that both excessive as well as too infrequent rebalancing lead to an inferior risk-adjusted portfolio performance. Specifically, the optimal rebalancing strategy seems to be quarterly periodic rebalancing for all three countries under investigation.This study compares the performance of different rebalancing strategies under realistic market conditions by reporting statistical significance levels. Our analysis is based on historical data from the United States, the United Kingdom, and Germany and comprises three different classes of rebalancing (periodic, threshold, and range rebalancing). Despite cross-country differences, our history-based simulation results show that all rebalancing strategies outperform a buy-and-hold strategy in terms of Sharpe ratios, Sortino ratios, and Omega measures. The differences in risk-adjusted performance are not only statistically significant, but also economically relevant. However, the choice of a particular rebalancing strategy is of only minor economic importance.

Testing Rebalancing Strategies for Stock-Bond Portfolios: Where Is the Value Added of Rebalancing?

We apply a stationary bootstrap approach that enables us to test the value added of rebalancing for stock-bond portfolios using historical data from the United States, the United Kingdom, and Germany. Analyzing the Sharpe ratio, the Omega measure, and the Sortino ratio as simple measures of value added, our historical simulation results provide strong evidence that all rebalancing strategies significantly outperform a buy-and-hold strategy. This outperformance is attributable to reduced risk, while there are no statistical differences in returns between rebalancing and buy-and-hold. Therefore, we conclude that it is a risk management argument, which justifies the widespread use of rebalancing in investment practice: The primary objective of any rebalancing strategy is the reduction of risk relative to a given asset allocation.

Testing Rebalancing Strategies for Stock-Bond Portfolios Across Different Asset Allocations

ABSTRACT We compare the risk-adjusted performance of stock–bond portfolios between rebalancing and buy-and-hold across different asset allocations by reporting statistical significance levels. Our investigation is based on a 30-year dataset and incorporates the financial markets of the United States, the United Kingdom and Germany. To draw useful recommendations to investment management, we implement a history-based simulation approach which enables us to mimic realistic market conditions. Even if the portfolio weight of stocks is very low, our empirical results show that a frequent rebalancing significantly enhances risk-adjusted portfolio performance for all analysed countries and all risk-adjusted performance measures.

Investing in Gold - Market Timing or Buy-and-Hold?

The literature on gold is dominated by empirical studies on its diversification, hedging, and safe haven properties. In contrast, the question “When to invest in gold?�? is generally not analyzed in much detail. We test more than 4,000 seasonal, technical, and fundamental timing strategies for gold and find evidence for some market timing ability and economic gains relative to a passive buy-and-hold benchmark. However, since the results are not robust to data-snooping biases and limited to specific evaluation periods, we conclude that our findings support the efficiency of the gold market.

A Bootstrap-Based Comparison of Portfolio Insurance Strategies

This study presents a systematic comparison of portfolio insurance strategies. In order to test for statistical significance of the differences in downside performance risk measures between pairs of portfolio insurance strategies, we use a bootstrap-based hypothesis test. Our comparison of different strategies considers the following distinguishing characteristics: static versus dynamic; initial wealth versus cumulated wealth protection; model-based versus model-free; and strong floor compliance versus probabilistic floor compliance. Our results show that the classical portfolio insurance strategies synthetic put and CPPI provide superior downside protection compared to a simple stop-loss trading rule, also resulting in significantly higher Omega ratios. Analyzing more recently developed strategies, neither the TIPP strategy (as an ‘improved’ CPPI strategy) nor the dynamic VaR-strategy provide significant improvements over the more traditional portfolio insurance strategies. The attractiveness of the dynamic VaR-strategy strongly depends on the quality of the estimates for the required input parameters, in particular, the equity risk premium. However, if an investor possesses superior forecasting skills, other active (market timing) strategies may exist which generate higher (risk-adjusted) returns compared to a protected passive stock market investment.