Is smarter better? A comparison of adaptive, and simple moving average trading strategies

This study examines the comparative performance of an Adaptive Moving Average (AMA) on the Australian All Ordinaries, Dow Jones Industrial Average, and Standard and Poor's 500 stock market indices. The theoretical advantage of the Adaptive Moving Average over fixed length Simple Moving Average (SMA) trading systems is its ability to automatically respond to changing market conditions dependant upon the level of volatility in the market. While the strategy is confirmed to have some market timing ability, the overall results show returns to the Adaptive Moving Average cannot compensate for the cost of trade therefore lending support for the use of a long run passive strategy.     

The disparity between long-term and short-term forecasted earnings growth

We find the disparity between long-term and short-term analyst forecasted earnings growth is a robust predictor of future returns and long-term analyst forecast errors. After adjusting for industry characteristics, stocks whose long-term earnings growth forecasts are far above or far below their implied short-term forecasts for earnings growth have negative and positive subsequent risk-adjusted returns along with downward and upward revisions in long-term forecasted earnings growth, respectively. Additional results indicate that investor inattention toward firm-level changes in long-term earnings growth is responsible for these risk-adjusted returns.     

Profiling high-frequency equity price movements in directional changes

Market prices are traditionally sampled in fixed time intervals to form time series. Directional change (DC) is an alternative approach to record price movements. Instead of sampling at fixed intervals, DC is data driven: price changes dictate when a price is recorded. DC provides us with a complementary way to extract information from data. It allows us to observe features that may not be recognized in time series. The argument is that time series and DC-based analysis complement each other. With data sampled at irregular time intervals in DC, however, some of the time series indicators cannot be used in DC-based analysis. For example, returns must be time adjusted and volatility must be amended accordingly. A major objective of this paper is to introduce indicators for profiling markets under DC. We analyse empirical high-frequency data on major equities traded on the UK stock market, and through DC profiling extract information complementary to features observed through time series profiling.     

Contrarian flows,consumption and expected stock returns

We investigate the relation between contrarian flows, consumption growth, and market risk premium. We construct a contrarian flows measure by summing up the capital flows to stocks that go against the total flow of the aggregate market. We show that the contrarian flows are negatively influenced by the same-quarter consumption growth. During bad times, the majority of investors who are affected by the negative shock reduce their equity exposure, and these extra supplies of risky assets are absorbed by contrarian investors who are least affected by the consumption shock. Using quarterly stock market data, we find that the contrarian flows forecast market returns at short-to-intermediate horizons. The predictability stems from the component that is explained by the consumption growth, and therefore the consumption growth contains valuable information about the market risk premium. Moreover, the predictability is stronger for growth stocks than for value stocks, and hence it negatively predicts the value premium. This is because the contrarian flows measure the market risk premium and growth stocks bear more discount rate risk than value stocks. Out-of-sample tests show that the main results are robust to data-snooping bias.     

On de-seasonalising adjusted-average formulae

The techniques of Borgan (1979) are extended to cover data with seasonal variations. Examples are given, and it is suggested that the formulae presented here give smoother results than those traditionally employed to deal with economic time series subject to seasonal variations.     

Trending time-varying coefficient market models

In this paper we study time-varying coefficient (beta coefficient) models with a time trend function to characterize the nonlinear, non-stationary and trending phenomenon in time series and to explain the behavior of asset returns. The general local polynomial method is developed to estimate the time trend and coefficient functions. More importantly, a graphical tool, the plot of the kth-order derivative of the parameter versus time, is proposed to select the proper order of the local polynomial so that the best estimate can be obtained. Finally, we conduct Monte Carlo experiments and a real data analysis to examine the finite sample performance of the proposed modeling procedure and compare it with the Nadaraya–Watson method as well as the local linear method.     

Non-parametric determination of real-time lag structure between two time series: the ‘optimal thermal causal path’ method

We introduce a novel non-parametric methodology to test for the dynamical time evolution of the lag–lead structure between two arbitrary time series. The method consists of constructing a distance matrix based on the matching of all sample data pairs between the two time series. Then, the lag–lead structure is searched for as the optimal path in the distance matrix landscape that minimizes the total mismatch between the two time series, and that obeys a one-to-one causal matching condition. To make the solution robust to the presence of a large amount of noise that may lead to spurious structures in the distance matrix landscape, we generalize this optimal search by introducing a fuzzy search by sampling over all possible paths, each path being weighted according to a multinomial logit or equivalently Boltzmann factor proportional to the exponential of the global mismatch of this path. We present the efficient transfer matrix method that solves the problem and test it on simple synthetic examples to demonstrate its properties and usefulness compared with the standard running-time cross-correlation method. We then apply our ‘optimal thermal causal path’ method to the question of the lag-dependence between the US stock market and the treasury bond yields and confirm our earlier results on an arrow of the stock markets preceding the Federal Reserve Funds’ adjustments, as well as the yield rates at short maturities in the period 2000–2003. Our application of this technique to inflation, inflation change, GDP growth rate and unemployment rate unearths non-trivial lag relationships: the GDP changes lead inflation especially since the 1980s, inflation changes leads GDP only in the 1980 decade, and inflation leads unemployment rates since the 1970s. In addition, our approach seems to detect multiple competing lag structures in which one can have inflation leading GDP with a certain lag time and GDP feeding back/leading inflation with another lag time.     

Profitability of time series momentum

We propose a continuous-time heterogeneous agent model consisting of fundamental, momentum, and contrarian traders to explain the significant time series momentum. We show that the performance of momentum strategy is determined by both time horizon and the market dominance of momentum traders. Specifically, when momentum traders are more active in the market, momentum strategies with short (long) time horizons stabilize (destabilize) the market, and meanwhile the market under-reacts (over-reacts) in short-run (long-run). This provides profit opportunity for time series momentum strategies with short horizons and reversal with long horizons. When momentum traders are less active in the market, they always lose. The results provide an insight into the profitability of time series momentum documented in recent empirical studies.     

Out‐of‐sample stock return predictability in emerging markets

This article builds on the widely debated issue of stock return predictability by applying a broad range of predictor variables and comprehensively considering the in‐sample and out‐of‐sample stock return predictability of ten advanced emerging markets. It compares forecasts from models with a single predictor variable, multiple predictor variables and a combination forecast approach. The results confirm the findings of Welch and Goyal (2008) for US data that only a limited number of individual predictor variables are able to deliver significant out‐of‐sample forecasts. However, a combination forecast approach provides statistically and economically significant out‐of‐sample forecast results.     

The option to stock volume ratio and future returns

We examine the information content of option and equity volumes when trade direction is unobserved. In a multimarket asymmetric information model, equity short-sale costs result in a negative relation between relative option volume and future firm value. In our empirical tests, firms in the lowest decile of the option to stock volume ratio (O/S) outperform the highest decile by 0.34% per week (19.3% annualized). Our model and empirics both indicate that O/S is a stronger signal when short-sale costs are high or option leverage is low. O/S also predicts future firm-specific earnings news, consistent with O/S reflecting private information.