Improved testing for the efficiency of asset pricing theories in linear factor models

This paper suggests a refinement of the standard T2 test statistic used in testing asset pricing theories in linear factor models. The test is designed to have improved power characteristics and to deal with the empirically important case where there are many more assets than time periods. This is necessary because the case of too few time periods invalidates the conventional T2. Furthermore, the test is shown to have reasonable power in cases where common factors are present in the residual covariance matrix

Using Bayesian variable selection methods to choose style factors in global stock return models

This paper applies Bayesian variable selection methods from the statistics literature to give guidance in the decision to include/omit factors in a global (linear factor) stock return model. Once one has accounted for country and sector, it is possible to see which style or styles best explains current asset returns. This study does not find compelling evidence for global styles as useful explanatory factors, once country and sector have been accounted for

On the Difficulty of Measuring Forecasting Skill in Financial Markets

The use of correlation between forecasts and actual returns is commonplace in the literature, often used as a measurement of investors’ skill. A prominent application of this is the concept of the Information Coefficient (IC). Not only can IC be used as a tool to rate analysts and fund managers but it also represents an important parameter in the asset allocation and portfolio construction process. Nevertheless, theoretical understanding of it has typically been limited to the partial equilibrium context where the investing activities of each agent have no effect on other market participants. In this paper we show that this can be an undesirable oversimplification and we demonstrate plausible circumstances in which conventional empirical measurements of IC can be highly misleading. We suggest that improved understanding of IC in a general equilibrium setting can lead to refined portfolio decision making ex ante and more informative analysis of performance ex post.Information Rati

Asset Management with Price Impact and Fair Treatment of Clients

In light of recent regulatory initiatives focusing on fair treatment of customers in financial markets, this paper examines the agency problem created by an asset manager with market impact, segregated accounts and preference-based contracts. It illustrates how aggregate client welfare and assets under management are affected by the order in which clients' accounts are sequentially traded and demonstrates that the manager is unlikely to have incentives for equal treatment of clients. Effectively, she may conduct limited invisible transfers of wealth among largely uninformed clients by granting preferential market access to some of them and this may be purely the result of her dollar-alpha maximization efforts rather than size/importance-based client discrimination. Increased transparency and/or effective regulation in this area seem socially desirable since the manager's incentives and client welfare generally appear to be misaligned

Social Welfare Issues of Financial Literacy

In the matter of financial literacy it is often supposed that more is automatically preferable to less. This paper considers to what extent this may be true generally, and specifically focuses on the case of investment forecasting skill (a significant component of an individual's financial literacy). We show that the while improved forecasting skill can increase an individual's own utility, the resulting increase in trading volume leads to higher asset price volatility. Under the plausible assumption that this volatility imposes disutility on non-investors, an interesting trade-off is exposed between the benefits of skill improvement which accrue to investors, and the costs suffered more broadly by society. The paper constructs a formal analytic framework in which to discuss these issues, examines under what conditions the marginal utility of skill is in fact monotonic for the individual and considers implications for policy-makers

What Proportion of Time is a particular Market inefficient?...Analysing market efficiency when equity prices follow Threshold Autoregressions.

We assume that log equity prices follow multi-state threshold autoregressions and generalize existing results for threshold autoregressive models, presented in Knight and Satchell (2012) for the existence of a stationary process and the conditions necessary for the existence of a mean and a variance; we also present formulae for these moments. Using a simulation study we explore what these results entail with respect to the impact they can have on tests for detecting bubbles or market efficiency. We find that bubbles are easier to detect in processes where a stationary distribution does not exist. Furthermore, we explore how threshold autoregressive models with i.i.d trigger variables may enable us to identify how often asset markets are inefficient. We find, unsurprisingly, that the fraction of time spent in an efficient state depends upon the full specification of the model; the notion of how efficient a market is, in this context at least, a model-dependent concept. However, our methodology allows us to compare efficiency across different asset markets

Time Series Momentum Trading Strategy and Autocorrelation Amplification

This article assumes general stationary processes for prices and derives the autocorrelation function for a general Moving Average (MA) trading rule to investigate why this rule is used. The result shows that the MA rule is popular because it can identify price momentum and is a simple way of tracing and exploiting price autocorrelation structure without necessarily knowing its precise structure. We focus on analyzing the impact of price momentum on the profitability of the MA rule because the price momentum effect tends to be stronger and more persistent than the return momentum effect

GARCH model with cross-sectional volatility; GARCHX models

This study introduces GARCH models with cross-sectional market volatility, which we call GARCHX model. The cross-sectional market volatility is equlvalent to common heteroskedasticity in asset specific returns, which was suggested by Connor and Linton (2001) as an important component in individual asset volatility. Using UK and US data, we find that daily return volatility can be better specified with GARCHX models, but GARCHX models do not necessarily perform better than conventional GARCH models in forecasting

The disappearance of style in the US equity market

This paper investigates the modelling of style returns in the US and the returns to style "tilts" based on forecasts of enhanced future style returns. We use hidden Markov model to build our forecasts. Our finding that style returns are less forecastible in more recent years is consistent with the hypothesis that style returns are the result of anomalies rather than risk premia. The erosion of anomalous returns as public awareness of their presence is translated into strategies that arbitrage away the excess returns seems to be a hypothesis consistent with our modelling results