Market Timing Strategies Research Articles

Stock Market Mean Reversion and Portfolio Choice Over the Life Cycle

We solve for optimal consumption and portfolio choice in a life-cycle model with short-sales and borrowing constraints, undiversifiable labor income risk and a predictable, time-varying, equity premium and show that the investor pursues aggressive market timing strategies. Importantly, in the presence of stock market predictability, the model suggests that the conventional financial advice of reducing stock market exposure as retirement approaches is correct on average, but ignoring changing market information can lead to substantial welfare losses. Therefore, enhanced target-date funds (ETDFs) that condition on expected equity premia increase welfare relative to target-date funds (TDFs).

Practical Applications of On the Expected Performance of Market Timing Strategies

In order to match the Sharpe ratio of a buy-and-hold strategy in the US equity market, you will need a success ratio of almost 60% in a market-timing strategy, according to this research. What actually drives the risk-adjusted performance of market-timing strategies? This is an important question in the context of the ongoing debate on the performance of active strategies. In this report, Winfried Hallerbach—Senior Quantitative Researcher at Robeco Investment Management in Rotterdam, The Netherlands—explains how he extends existing concepts about active management to market-timing strategies. Hallerbach’s original research, published in The Journal of Portfolio Management’s Summer 2014 issue, offers new insights into what actually drives the risk-adjusted performance of market-timing strategies.

Why Market Timing Makes Sense In China:Re-Examining Local Investor Behavior

In October 2000, when, during a televised interview, the renowned economist and long-standing capital markets advocate Wu Jinglian called the Chinese A-share market a “casino,” the public outcry was immediate. The early responses focused on the need for improved market regulation and transparency, but a few prominent market participants and academics highlighted a more fundamental problem: A-share investors’ stubborn reliance on market timing strategies to generate return. The persistence of this latter issue is why today—despite nearly 15 years of regulatory development and investor education—the gambling metaphor still finds traction. For overseas investors, this type of speculative investor behavior only reinforces the already robust investment case for China’s capital markets. The success of China’s f irst wave of reforms has bolstered confidence that the positive trends in consumer spending, urbanization, and productivity will ultimately be ref lected in higher share prices. Financial liberalization will introduce new f lows into the markets and support higher valuations. Further, according to J.P. Morgan Asset Management [2014], current valuations are still attractive: the market’s average price/forward earnings ratio in the third quarter of 2004 was less than 10x, and the 10-year average of a composite valuation metric evaluating price versus forward earnings, book value, historical cashf low, and historical dividends, respectively, was close to one standard deviation behind the long-term global market average. China’s interbank f ixed income market is still in a relatively nascent stage, but recent long maturity corporate issuance, newly introduced regulation to improve credit risk transparency, and the development of new securitized bonds—including the planned launch of mortage-backed securities—offer foreign investors the opportunity to benef it from unprecedented structural reforms. Recent efforts to liberalize interest rates have already had a positive impact on f ixed income market liquidity, and China’s current policy real yield remains among the highest in the world. Despite this positive long-term outlook for China’s capital markets, overseas investors would benefit from a close examination of domestic speculative investment behavior, particularly in China’s A-share market. Market timing—in simple practice, predicting the outperformance of stocks relative to bonds—is still a vexing topic in financial market theory. In the absence of persistent, superior forecasting skill, market timing is considered irrational behavior. Further, devising a method to assess this forecasting skill is fraught with difficulty and contingent on a number of impractical assumptions. Yet neither these impediments nor the long-term poor performance of onshore market timing strategies have deterred China A-share investors. In China, at least, this activity, albeit “irrational,” clearly must have some rationale. The seminal work by Merton [1981] on evaluating asset manager total performance proves relevant here. Merton argues that a pure market timing strategy can be regarded as a long position in a zero-cost European call option on the market portfolio, with a strike price that aligns to the drift path of the risk-free rate. This analogy Why Market Timing Makes Sense In China: Re-Examining Local Investor Behavior

Asset Allocation in the Chinese Stock Market:The Role of Return Predictability

While numerous studies have analyzed the asset allocation issue of US stock market from various angles, much less attention has been paid to the asset allocation issue of Chinese stock market. This article investigates the asset allocation in Chinese stock market from a perspective of incorporating return predictability. We find significant out-of-sample return predictability in Chinese stock market based on a host of return predictors. We then examine the performance of active portfolio strategies such as aggregate market timing strategy, and industry, size, and value rotation strategies to profitably exploit return predictability. We provide strong evidence that these portfolio strategies incorporating return predictability can deliver superior outperformance up to 600 basis points per annum and almost double the Sharpe ratios compared to the passive buy-and-hold benchmarks ignoring return predictability.

Can We Predict the Financial Markets Based on Internet Search Queries?

In this paper we look into the interaction of Google's search queries and several aspects of international equity markets. Using a novel methodology for selecting words and a VAR modeling approach, we study whether the search queries of finance related words can have an impact on market uncertainty, log returns and traded volume of four different english speaking countries. Our overall main results are positive, we find several words that have a robust impact over the explained variables. Particularly, the most robust result we find is that an increase of the search queries including the word stock predicts an increase of volatility and decrease of index prices in the next week. With the support of this finding, we investigate the performance of a market timing strategy based on the search frequency of this word and compare it against random words from the Word-Net database and a naive buy and hold strategy. The results of this empirical application are positive, indicating the potential of this new set of data in predicting financial markets.

Practical Applications of On the Expected Performance of Market Timing StrategiesOn the Expected Performance of Market Timing Strategies Winfried G. Hallerbach

Practical Applications of On the Expected Performance of Market Timing StrategiesOn the Expected Performance of Market Timing Strategies Winfried G. Hallerbach

The Use of Correlation Networks in Parametric Portfolio Policies

Correlation networks reveal a rich picture of market risk structure dynamics. A rather compact and well-organized sector correlation network is indicative of a healthy market, whereas a widely spread sector correlation network characterizes a more fragile market environment. Intuitively, some characteristics of the correlation network can serve as natural measures of systemic risk. Pursuing an equity market timing strategy we document the predictive content of these measures to translate into a meaningful portfolio utility. Moreover, this result continues to hold when controlling for common predictors of the equity risk premium. Not only can correlation networks be useful as an aggregate market timing signal but also in navigating the cross-section of equity sectors. We especially document a significant outperformance of peripheral versus central equity sectors that cannot be explained by momentum or low volatility effects. Finally, we implement a parametric portfolio policy that comprises the complete information content of the sector network topology conditional on a given level of risk aversion.

The real-life performance of market timing with moving average and time-series momentum rules

In this article, we revisit the myths regarding the superior performance of market timing strategies based on moving average and time-series momentum rules. These active timing strategies are very appealing to investors because of their extraordinary simplicity and because they promise substantial advantages over their passive counterparts. However, the ‘too good to be true’ reported performance of these market timing rules raises a legitimate concern as to whether this performance is realistic and whether investors can expect that future performance will be the same as the documented historical performance. We argue that the reported performance of market timing strategies usually contains a considerable data-mining bias and ignores important market frictions. To address these issues, we perform out-of-sample tests of these two timing models in which we account for realistic transaction costs. Our findings reveal that the performance of market timing strategies is highly overstated, to say the least.

On the Expected Performance of MarketTiming Strategies

The author derives expressions for the information ratio (IR) that can be expected from directional market-timing strategies. The results hold as accurate approximations and lift the Fundamental Law of Active Management proposed by Richard Grinold in 1989 to an operational level. In addition, the author separates time series-breadth (the timing frequency per strategy) from cross-section breadth (the number of separate markets), because they contribute differently to performance. The author shows that implementing volatility-weighted bet sizes, both in the time series context of a single underlying market and in the cross-section context of multiple markets, increases the expected timing IR. His theoretical results can be used as a benchmark for and reality check on the back-tested performance of timing strategies. He confirms the accuracy of his results by simulating timing strategies for equities and fixed income. <b>TOPICS:</b>Exchanges/markets/clearinghouses, volatility measures, statistical methods

Probabilistic Market Timing Using Bull Bear Cycle Statistics

Under the historical market view of binary bull and bear cycles, what is an ex ante optimal trading strategy? Similar to an original optimal stopping time model (Dai, et al 2010, 2011) to maximize long term investment returns, we introduce a market timing strategy based on the probability that the market is currently in a bull or bear regime. In doing so, we adjust the Wonham filter (1965) for the regime conditional probability calculation to allow time-varying volatilities and utilize expected bull/bear regime risk-adjusted average returns as statistically estimated parameter inputs. Further, we derive explicit formula to approximate the optimal probabilistic buying or selling thresholds and extend the analytical formulation to allow both long only and long/flat/short positions for a hysteresis trading implementation. Considering the fractal characteristics of stock market indexes and other asset class index data at different frequencies, we devise a systematic procedure to separate historical bull bear regimes and obtain detailed market cycle statistics. This allows using different index gain, loss and duration thresholds to define historical bull/bear cycles in the same manner as an earlier study (Wagner, 1992). We back test the methodology on two broad stock indexes: S&P 500 Index (SPX 1928-2014) and Shanghai Stock Exchange Composite Index (SSEC 1995-2014). The results, including that of an out-of-sample test case on the SPX since 1940 using 10-cycle look-back rolling statistics, indicate out-performance over the buy-and-hold approach and a popular trend following technical trading strategy, the Golden Cross.

Are Flows Costly to Etf Investors?

Authorized participants create and redeem exchange-traded fund (ETF) shares in response to supply and demand imbalances in the secondary market. For the majority of ETFs, share creations and redemptions take place as in-kind transactions, but a growing number of ETFs feature cash creations and redemptions. The authors investigate whether ETF flows are costly to investors. Using a reasonable proxy for the potential cost of fund flows, they find evidence of underperformance associated with flows to ETFs featuring cash creations and redemptions and tracking international benchmarks. Because ETF flows do not predict subsequent returns and the authors do not find evidence the funds are susceptible to market-timing strategies, the costs likely stem from the transaction costs associated with the portfolio trades that are necessary to accommodate cash liquidity. <b>TOPICS:</b>Exchange-traded funds and applications, exchanges/markets/clearinghouses, in markets

Measuring Market Timing: An InternationalExamination of Market Timing Conditions

The objective of this article is to introduce an expost measure of market timing independent of luck or skill in order to identify when market conditions are more favorable to successful market timing and to evaluate the conclusions of several previous academic studies. We propose that basic market conditions rotate from favoring buy-and-hold investing to favoring market timing strategies, and that these conditions can be measured in any equity market by tracking the results of a constant skilled market timer. We seek to identify what market return conditions are more conducive to successful timing for this constant-skilled investor and when these conditions have been present over our sample period. The MTBH metric was calculated for 44 countries for 14 years and compares findings of eight previous studies of the timing ability in mutual fund managers, stock traders, option traders, and individual investors across several U.S. and international indexes. The MTBH metric is particularly accurate in explaining the positive timing results for options traders and hedge fund managers, but not as accurate in explaining mutual fund timing. The MTBH metric provides an alternative explanation for non-persistent market-timing results outside of the investors’ behavior or skill and can be applied to any foreign or domestic market to evaluate managers’ timing results. <b>TOPICS:</b>Security analysis and valuation, quantitative methods

Dynamic Asset Allocation Strategies Basedon Unexpected Volatility

The author documents that at the aggregate stock market level, unexpected volatility is negatively related to expected future returns, and positively related to future volatility. The author demonstrates how the predictive ability of unexpected volatility can be utilized in dynamic asset allocation strategies that deliver a substantial improvement in terms of risk-adjusted performance as compared to traditional buy-and-hold strategies. In addition, the author shows that active strategies based on unexpected volatility outperform the popular active strategy with a volatility target mechanism, and have some edge over the popular market timing strategy with a 10-month simple moving average rule. <b>TOPICS:</b>Portfolio construction, analysis of individual factors/risk premia, performance measurement

Dynamic Asset Allocation Strategies Based on Unexpected Volatility

In this paper we document that at the aggregate stock market level the unexpected volatility is negatively related to expected future returns and positively related to future volatility. We demonstrate how the predictive ability of unexpected volatility can be utilized in dynamic asset allocation strategies that deliver a substantial improvement in risk-adjusted performance as compared to traditional buy-and-hold strategies. In addition, we demonstrate that active strategies based on unexpected volatility outperform the popular active strategy with volatility target mechanism and have the edge over the widely reputed market timing strategy with 10-month simple moving average rule.

Market timing and trading strategies using asset rotation: non-neutral market positioning for exploiting arbitrage opportunities

We present empirical results on the statistical and economic viability of a market timing and trading strategy that is based on a pairwise rotation between two risky assets. Using data on equity exchange traded funds, and models for both the returns and the volatility of the underlying assets, we compare the performance of the suggested models with the standard benchmarks of a buy-and-hold strategy and an equally weighted portfolio. The underlying intuition for the use of such a strategy rests with literature on sign and volatility predictability. The rotation strategy, as we apply it in this paper, is not risk-neutral and assumes the presence of arbitrage opportunities in the markets and short-term trends. Furthermore, the model specification uses the interplay between relative returns and relative volatilities in picking-up the asset with the highest return. Our results show that even a naive model that is based on a moving average of relative returns can outperform both benchmarks and that more elaborate specifications for the rotation model may yield additional performance gains. We also find that, in many cases, the rotation strategy yields statistically significant sign predictions of the relative returns and volatility. While our results are conditional on the data that we have used in our analysis they, nevertheless, support the market-timing literature and show that an active trading strategy can be based on the concept of rotation.

The Profitability of Market Timing Strategies in Europe

This paper tests the moving-average strategy, dual moving average strategy, filter rule strategy and support-and-resist strategy and finds that all of these strategies can outperform the buy-and-hold strategy. The results are not driven by the momentum effect and even with the inclusion of transaction costs, the dual moving average strategy and filter rule strategy are more profitable than the buy-and-hold strategy. Furthermore, results show that the volatility and beta of the buy-and-hold strategy is larger than any of the market timing strategies tested. Therefore, using technical analysis reduces risk.

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.

Stock Market Timing: A Utility-Based Analysis

Many private and institutional investors attempt to time the market and generate abnormal returns by periodically switching their portfolio allocations between the stock market and the cash market based on their return predictions. However, most academic studies emphasize that a successful market timing strategy requires a prediction accuracy that is usually not observable in reality. While existing studies evaluate the outcomes based on traditional return and risk measures, we develop a “simulated market timer” that does not require a specific forecast model and adopt a decision theoretic framework to compare market timing with a buy-and-hold strategy. Our bootstrap-based simulations show that market timing can even add value for some investors with only moderate prediction skills.

The intra-day performance of market timing strategies and trading systems based on Japanese candlesticks

We develop market timing strategies and trading systems to test the intra-day predictive power of Japanese candlesticks at the 5-minute interval on the 30 constituents of the DJIA index. Around a third of the candlestick rules outperform the buy-and-hold strategy at the conservative Bonferroni level. After adjusting for trading costs, however, just a few rules remain profitable. When we correct for data snooping by applying the SSPA test on double-or-out market timing strategies, no single candlestick rule beats the buy-and-hold strategy after transaction costs. We also design fully automated trading systems by combining the best-performing candlestick rules. No evidence of out-performance is found after transaction costs. Although Japanese candlesticks can somewhat predict intra-day returns on large US caps, we show that such predictive power is too limited for active portfolio management to outperform the buy-and-hold strategy when luck, risk, and trading costs are correctly measured.

“Sell in May and Go Away” Just Won’t Go Away

The authors performed an out-of-sample test of the sell-in-May effect documented in previous research. Reducing equity exposure starting in May and levering it up starting in November persists as a profitable market-timing strategy. On average, stock returns are about 10 percentage points higher for November–April half-year periods than for May–October half-year periods. The authors also found that the sell-in-May effect is pervasive in financial markets.