Market Predictability

Active investors, such as hedge funds, mutual funds, proprietary traders, individuals and other asset managers, often have multiple strategies to predict returns. These predictors may have different mean reversion (alpha decay) rates. Short-term strategies will have a high mean reversion rate and long-term strategies have a low one. The investors may seek to exploit all the predictors to form a strategy that predicts returns more accurately, minimizes risks and also minimizes transactions costs. Garleanu and Pedersen analyze this problem in “Dynamic Trading with Predictable Returns and Transaction Costs”. The paper has not been published yet, but a pre-print is available on their web site.

There is widespread evidence of excess return predictability in …nancial markets. For the foreign exchange market a number of studies have documented that the predictability of excess returns is closely related to the predictability of expectational errors of excess returns. In this paper we investigate the link between the predictability of excess returns and expectational errors in a much broader set of …nancial markets, using data on survey expectations of market participants in the stock market, the foreign exchange market, and the bond and money markets in various countries. Results are striking. First, in markets where there is signi…cant excess return predictability, expectational errors of excess returns are predictable as well, with the same sign and often even with similar magnitude. This is the case for forex, stock and bond markets. Second, in the only market where excess returns are generally not predictable, the money market, expectational errors are not predictable either. These …ndings suggest that an explanation for the predictability of excess returns must be closely linked to an explanation for the predictability of expectational errors.

This paper is concerned with empirical and theoretical basis of the Efficient Market Hypothesis (EMH). The paper begins with an overview of the statistical properties of asset returns at different frequencies (daily, weekly and monthly), and considers the evidence on return predictability, risk aversion and market efficiency. The paper then focuses on the theoretical foundation of the EMH, and show that market efficiency could co-exit with heterogeneous beliefs and individual irrationality so long as individual errors are cross sectionally weakly dependent in the sense defined by Chudik, Pesaran, and Tosetti (2010). But at times of market euphoria or gloom these individual errors are likely to become cross sectionally strongly dependent and the collective outcome could display significant departures from market efficiency. Market efficiency could be the norm, but it is likely to be punctuated with episodes of bubbles and crashes. The paper also considers if market inefficiencies (assuming that they exist) can be exploited for profit.

This paper analyzes whether web search queries predict stock market activity in a sample of the largest European stocks. We provide evidence that i) an increase in web searches for stocks on Google engine is followed by a temporary increase in volatility and volume and a drop in cumulative returns. ii) An increase for web search queries for the market index leads to a decrease in the returns of the index as well as of the stock index futures and an increase in implied volatility. iii) Attention interacts with behavioral biases. The predictability of web searches for return and liquidity is enhanced when firm prices and market prices hit a 52-week high and diminished when the market hits a 52-week low. iv) Investors tend to process more market information than firm specific information in investment decisions, confirming limited attention theory.

•Efficiency concepts

•EMH implies Martingale Property

•Evidence I:Return Predictability

•Mispricingversus Risk-factor

•Informational (market) efficiency concepts

•Asymmetric Information and Price Signal

•Evidence II:Event Study Methodology

•Grossman-Stiglitz Paradox

•Evidence III:Fund Managers’ Out/underperformance

We attempt to explain stock market dynamics in terms of the interaction among three variables: market price, investor opinion and information flow. We propose a framework for such interaction and apply it to build a model of stock market dynamics which we study both empirically and theoretically. We demonstrate that this model replicates observed market behavior on all relevant timescales (from days to years) reasonably well. Using the model, we obtain and discuss a number of results that pose implications for current market theory and offer potential practical applications

There is an ongoing debate about whether the equity premium is predictable or not. Early authors—e.g., Keim and Stambaugh (1986), Campbell (1987), Campbell and Shiller (1988), Fama and French (1989), Kothari and Shanken (1997), Pontiff and Schall (1998), Baker and Wurgler (2000), and Lettau and Ludvigson (2001)—find that some financial variables have significant forecasting power for excess stock market returns. Campbell and Cochrane (1999) and others have also developed rational expectations models to explain the observed stock return predictability. However, Goyal and Welch (2006) have conducted a comprehensive examination of the existing evidence using the data updated to 2004, and show quite convincingly that there is little support for stock return predictability, especially in the out-of-sample context. Cochrane (2006) points out that because dividend growth has negligible predictable variation and the dividend yield is quite volatile, the dividend yield must forecast stock market returns. Cochrane also provides simulation results to show that for the size of samples commonly used in the empirical studies, the out-of-sample test based on the linear specification has low power to detect stock return predictability. Moreover, some recent studies, e.g., Lewellen (2004), Campbell and Thompson (2005), and Lettau and Nieuwerburgh (2006), have documented some out-of-sample predictability by using alternative forecasting specifications.

The Hurst exponent, proposed by H. E. Hurst [1] for use in fractal analysis [2],[3], has been applied to many research fields. It has recently become popular in the finance community [4],[5],[6] largely due to Peters’ work [7],[8]. The Hurst exponent provides a measure for longterm memory and fractality of a time series. Since it is robust with few assumptions about underlying system, it

has broad applicability for time series analysis. The values of the Hurst exponent range between 0 and 1. Based on the Hurst exponent value H, a time series can be classified into three categories. (1) H=0.5 indicates a random series. (2) 0<H<0.5 indicates an anti-persistent series. (3) 0.5<H<1 indicates a persistent series. An antipersistent series has a characteristic of “mean-reverting”, which means an up value is more likely followed by a down value, and vice versa. The strength of “meanreverting” increases as H approaches 0.0. A persistent series is trend reinforcing, which means the direction (up

or down compared to the last value) of the next value is more likely the same as current value. The strength of trend increases as H approaches 1.0. Most economic and financial time series are persistent with H>0.5.