A momentum trading strategy based on the low frequency component of the exchange rate

In this paper, we develop a momentum trading strategy based on the low frequency trend component of the spot exchange rate. Using kernel regression and the high-pass filter of Hodrick and Prescott [Hodrick, R., Prescott, E., 1997. Post-war US business cycles: An empirical investigation. Journal of Money, Credit and Banking 29, 1–16], we recover the non-linear trend in the monthly exchange rate and use short-term momentum in this to generate buy and sell signals. The low frequency momentum trading strategy offers greater directional accuracy, higher returns and Sharpe ratios, lower maximum drawdown and less frequent trading than traditional moving average rules. Moreover, unlike traditional moving average rules, the performance of the low frequency momentum trading strategy is relatively robust across different time periods. The low frequency momentum trading strategy is also robust to the choice of smoothing parameter (in the case of the HP filter) and the distribution and bandwidth parameter (in the case of kernel regression) over a wide range of values.     

The forward E/P ratio and earnings growth

Valuation theories predict a negative relation between the earnings-to-price (E/P) ratio and future earnings growth, but prior studies have produced conflicting results. Using a growth measure that incorporates loss firms, this paper shows that the negative relation exists in the long term, but not in the short term. The results also show a U-shaped relation between the forward E/P ratio and earnings risk. Compared with high forward E/P firms which are inherently financially distressed, low forward E/P firms exhibit even higher incidence of loss and larger growth volatility in subsequent years. The wide distribution of earnings growth in the lowest forward E/P portfolio indicates that this portfolio includes not only star firms that generate the strongest earnings growth, but also firms that report the most negative earnings growth. This paper shows that the forward E/P ratio is a stronger predictor of future growth than the conventionally used trailing E/P ratio.     

Maxing out: Stocks as lotteries and the cross-section of expected returns

Motivated by existing evidence of a preference among investors for assets with lottery-like payoffs and that many investors are poorly diversified, we investigate the significance of extreme positive returns in the cross-sectional pricing of stocks. Portfolio-level analyses and firm-level cross-sectional regressions indicate a negative and significant relation between the maximum daily return over the past one month (MAX) and expected stock returns. Average raw and risk-adjusted return differences between stocks in the lowest and highest MAX deciles exceed 1% per month. These results are robust to controls for size, book-to-market, momentum, short-term reversals, liquidity, and skewness. Of particular interest, including MAX reverses the puzzling negative relation between returns and idiosyncratic volatility recently shown in 2 and 3.     

Economic valuation of liquidity timing

This paper conducts a horse-race of different liquidity proxies using dynamic asset allocation strategies to evaluate the short-horizon predictive ability of liquidity on monthly stock returns. We assess the economic value of the out-of-sample power of empirical models based on different liquidity measures and find three key results: liquidity timing leads to tangible economic gains; a risk-averse investor will pay a high performance fee to switch from a dynamic portfolio strategy based on various liquidity measures to one that conditions on the Zeros measure (Lesmond et al., 1999); the Zeros measure outperforms other liquidity measures because of its robustness in extreme market conditions. These findings are stable over time and robust to controlling for existing market return predictors or considering risk-adjusted returns.     

Do return prediction models add economic value?

We compare statistical and economic measures of forecasting performance across a large set of stock return prediction models with time-varying mean and volatility. We find that it is very common for models to produce higher out-of-sample mean squared forecast errors than a model assuming a constant equity premium, yet simultaneously add economic value when their forecasts are used to guide portfolio decisions. While there is generally a positive correlation between a return prediction model’s out-of-sample statistical performance and its ability to add economic value, the relation tends to be weak and only explains a small part of the cross-sectional variation in different models’ economic value.     

CVaR sensitivity with respect to tail thickness

The sensitivity of a risk measure with respect to the parameters of the hypothesized distribution is a useful tool in investigating the impact of marginal rebalancing decisions on the portfolio return distribution and also in the analysis of the asymptotic variability of the risk estimator. We calculate the relative importance of the conditional value-at-risk (CVaR) sensitivity with respect to tail thickness and scale of the portfolio return distribution in the case of regularly varying tails and in the case of exponential and faster-than-exponential decay. We discuss the implications for asset return modeling and the asymptotic variability of the risk estimator.     

An analysis of commodity markets: What gain for investors?

In this paper we study whether the commodity futures market predicts the commodity spot market. Using historical daily data on four commodities—oil, gold, platinum, and silver—we find that they do. We then show how investors can use this information on the futures market to devise trading strategies and make profits. In particular, dynamic trading strategies based on a mean–variance investor framework produce somewhat different results compared with those based on technical trading rules. Dynamic trading strategies suggest that all commodities are profitable and profits are dependent on structural breaks. The most recent global financial crisis marked a period in which commodity profits were the weakest.     

Disagreement and asset prices

How do differences of opinion affect asset prices? Do investors earn a risk premium when disagreement arises in the market? Despite their fundamental importance, these questions are among the most controversial issues in finance. In this paper, we use a novel data set that allows us to directly measure the level of disagreement among Wall Street mortgage dealers about prepayment speeds. We examine how disagreement evolves over time and study its effects on expected returns, return volatility, and trading volume in the mortgage-backed security market. We find that increased disagreement is associated with higher expected returns, higher return volatility, and larger trading volume. These results imply that there is a positive risk premium for disagreement in asset prices. We also show that volatility in and of itself does not lead to higher trading volume. Instead, only when disagreement arises in the market is higher uncertainty associated with more trading. Finally, we are able to distinguish empirically between two competing hypotheses regarding how information in markets gets incorporated into asset prices. We find that sophisticated investors appear to update their beliefs through a rational expectations mechanism when disagreement arises.     

Front-running of mutual fund fire-sales

We show that a real-time trading strategy which front-runs the anticipated forced sales by mutual funds experiencing extreme capital outflows generates an alpha of 0.5% per month during the 1990–2010 period. The abnormal return stems from selling pressure among stocks that are below the NYSE mean size and cannot be attributed to the arrival of public information. While the largest stocks also exhibit downward price pressure, their prices revert before the front-running strategy can detect it. The duration of the anticipated selling pressure has decreased from about a month in the 1990s to about two weeks in the most recent decade. Our results suggest that publicly available information of fund flows and holdings exposes mutual funds in distress to predatory trading.     

Global,local, and contagious investor sentiment

We construct investor sentiment indices for six major stock markets and decompose them into one global and six local indices. In a validation test, we find that relative sentiment is correlated with the relative prices of dual-listed companies. Global sentiment is a contrarian predictor of country-level returns. Both global and local sentiment are contrarian predictors of the time-series of cross-sectional returns within markets: When sentiment is high, future returns are low on relatively difficult to arbitrage and difficult to value stocks. Private capital flows appear to be one mechanism by which sentiment spreads across markets and forms global sentiment.     

On the predictability of stock prices: A case for high and low prices

This paper contributes to technical analysis (TA) literature by showing that the high and low prices of equity shares are largely predictable only on the basis of their past realizations. Moreover, using their forecasts as entry/exit signals can improve common TA trading strategies applied on US equity prices. We propose modeling high and low prices using a simple implementation of a fractional vector autoregressive model with error correction (FVECM). This model captures two fundamental patterns of high and low prices: their cointegrating relationship and the long-memory of their difference (i.e., the range), which is a measure of volatility.     

Predicting bear and bull stock markets with dynamic binary time series models

Despite the voluminous empirical research on the potential predictability of stock returns, much less attention has been paid to the predictability of bear and bull stock markets. In this study, the aim is to predict U.S. bear and bull stock markets with dynamic binary time series models. Based on the analysis of the monthly U.S. data set, bear and bull markets are predictable in and out of sample. In particular, substantial additional predictive power can be obtained by allowing for a dynamic structure in the binary response model. Probability forecasts of the state of the stock market can also be utilized to obtain optimal asset allocation decisions between stocks and bonds. It turns out that the dynamic probit models yield much higher portfolio returns than the buy-and-hold trading strategy in a small-scale market timing experiment.     

Combining equilibrium,resampling, and analyst’s views in portfolio optimization

This paper proposes the use of a portfolio optimization methodology which combines features of equilibrium models and investor’s views as in Black and Litterman (1992), and also deals with estimation risk as in Michaud (1998). In this way, our combined methodology is able to meet the needs of practitioners for stable and diversified portfolio allocations, while it is theoretically grounded on an equilibrium framework. We empirically test the methodology using a comprehensive sample of developed countries fixed income and equity indices, as well as sub-samples stratified by geographical region, time period, asset class and risk level. In general, our proposed combined methodology generates very competitive portfolios when compared to other methodologies, considering three evaluation dimensions: financial efficiency, diversification, and allocation stability. By generating financially efficient, stable, and diversified portfolio allocations, our methodology is suitable for long-term investors such as Central Banks and Sovereign Wealth Funds.     

A new approach to predicting analyst forecast errors: Do investors overweight analyst forecasts?

I provide evidence that investors overweight analyst forecasts by demonstrating that prices do not fully reflect predictable components of analyst errors, which conflicts with conclusions in prior research. I highlight estimation bias in traditional approaches and develop a new approach that reduces this bias. I estimate characteristic forecasts that map current firm characteristics into forecasts of future earnings. Contrasting characteristic and analyst forecasts predicts analyst forecast errors and revisions. I find abnormal returns to strategies that sort firms by predicted forecast errors, consistent with investors overweighting analyst forecasts and predictable biases in analyst forecasts influencing the information content of prices.     

Momentum or contrarian investment strategies: Evidence from Dutch institutional investors

This paper analyses investment strategies of three types of Dutch institutional investors - pension funds, life insurers and non-life insurers - over the period 1999-2005. We use balance sheet and cash flow data, including purchases and sales of equity, fixed income and real estate. We trace asset reallocations back to both active trading and revaluations and link investment decisions to firm-specific characteristics and macroeconomic variables. Overall, our results indicate that all three investor types tend to be contrarian traders, i.e. they buy past losers and sell past winners. Especially pension funds showed this behaviour in the most turbulent part of the sample - the crash of 2002 and early 2003 - implying that these institutions have a stabilising impact on financial markets when this is needed most. Life insurers tend to be contrarian traders when they have a high proportion of unit-linked policies, while non-life insurers are contrarian when they have a more risky business model.     

Detecting structural breaks and identifying risk factors in hedge fund returns: A Bayesian approach

Extending previous work on asset-based style factor models, this paper proposes a model that allows for the presence of structural breaks in hedge fund return series. We consider a Bayesian approach to detecting structural breaks occurring at unknown times and identifying relevant risk factors to explain the monthly return variation. Exact and efficient Bayesian inference for the unknown number and positions of the breaks is performed by using filtering recursions similar to those of the forward–backward algorithm. Existing methods of testing for structural breaks are also used for comparison. We investigate the presence of structural breaks in several hedge fund indices; our results are consistent with market events and episodes that caused substantial volatility in hedge fund returns during the last decade.     

Competition among mutual funds

We examine the impact of the entry of new mutual funds on incumbents using the overlap in their portfolio holdings as a measure of competitive intensity. This simple metric delivers powerful economic results. Incumbents that have a high overlap with entrants subsequently engage in price competition by reducing management fees. Distribution fees, however, rise so that investors do not benefit as much from price competition. Funds with high overlap also experience quantity competition through lower investor flows, have lower alphas, and higher attrition rates. These effects only appear after the late 1990s, at which point there appears to be an endogenous structural shift in the competitive environment. We conclude that the mutual fund market has evolved into one that displays the hallmark features of a competitive market.