International equity flows and the predictability of US stock returns

We examined the link between international equity flows and US stock returns. Based on the results of tests of in‐sample and out‐of‐sample predictability of stock returns, we found evidence of a strong positive (negative) link between international equity flows and contemporaneous (one‐month‐ahead) stock returns. Our results also indicate that an investor, in real time, could have used information on the link between international equity flows and one‐month‐ahead stock returns to improve the performance of simple trading rules. Copyright © 2007 John Wiley & Sons, Ltd.     

Conditional predictability of daily exchange rates

At what forecast horizon is one time series more predictable than another? This paper applies the Diebold–Kilian conditional predictability measure to assess the out‐of‐sample performance of three alternative models of daily GBP/USD and DEM/USD exchange rate returns. Predictability is defined as a non‐linear statistic of a model's relative expected losses at short and long forecast horizons, allowing flexible choice of both the estimation procedure and loss function. The long horizon is set to 2 weeks and one month ahead and forecasts evaluated according to MSE loss. Bootstrap methodology is used to estimate the data's conditional predictability using GARCH models. This is then compared to predictability under a random walk and a model using the prediction bias in uncovered interest parity (UIP). We find that both exchange rates are less predictable using GARCH than using a random walk, but they are more predictable using UIP than a random walk. Predictability using GARCH is relatively higher for the 2‐weeks‐than for the 1‐month long forecast horizon. Comparing the results using a random walk to that using UIP reveals ‘pockets’ of predictability, that is, particular short horizons for which predictability using the random walk exceeds that using UIP, or vice versa. Overall, GBP/USD returns appear more predictable than DEM/USD returns at short horizons. Copyright © 2002 John Wiley & Sons, Ltd.     

Relationships between Australian real estate and stock market prices—a case of market inefficiency

This paper explores the relationship between the Australian real estate and equity market between 1980 and 1999. The results from this study show three specific outcomes that extend the current literature on real estate finance. First, it is shown that structural shifts in stock and property markets can lead to the emergence of an unstable linear relationship between these markets. That is, full‐sample results support bi‐directional Granger causality between equity and real estate returns, whereas when sub‐samples are chosen that account for structural shifts the results generally show that changes within stock market prices influence real estate market returns, but not vice versa. Second, the results also indicate that non‐linear causality tests show a strong unidirectional relationship running from the stock market to the real estate market. Finally, from this empirical evidence a trading strategy is developed which offers superior performance when compared to adopting a passive strategy for investing in Australian securitized property. These results appear to have important implications for managing property assets in the funds management industry and also for the pricing efficiency within the Australian property market. Copyright © 2002 John Wiley & Sons, Ltd.     

Equity return predictability,its determinants,and profitable trading strategies

This paper explains cross‐market variations in the degree of return predictability using the extreme bounds analysis (EBA). The EBA addresses model uncertainty in identifying robust determinant(s) of cross‐sectional return predictability. Additionally, the paper develops two profitable trading strategies based on return predictability evidence. The result reveals that among the 13 determinants of the cross‐sectional variation of return predictability, only value of stock traded (a measure of liquidity) is found to have robust explanatory power by Leamer's (1985) EBA. However, Sala‐i‐Martin's (1997) EBA reports that value of stock traded, gross domestic product (GDP) per capita, level of information and communication technology (ICT) development, governance quality, and corruption perception are robust determinants. We further find that a strategy of buying (selling) aggregate market portfolios of the countries with the highest positive (negative) return predictability statistic in the past 24 months generates statistically significant positive returns in the subsequent 3 to 12 months. In the individual country level, a trading rule of buying (selling) the respective country's aggregate market portfolio, when the return predictability statistic turns out positive (negative), outperforms the conventional buy‐and‐hold strategy for many countries.     

On the predictability of crude oil market: A hybrid multiscale wavelet approach

Past research indicates that forecasting is important in understanding price dynamics across assets. We explore the potentiality of multiscale forecasting in the crude oil market by employing a wavelet multiscale analysis on returns and volatilities of Brent and West Texas Intermediate crude oil indices between January 1, 2001, and May 1, 2015. The analysis is based on a shift-invariant discrete wavelet transform, augmented by an entropy-based methodology for determining the optimal timescale decomposition under different market regimes. The empirical results show that the five-step-ahead wavelet forecast that is based on volatilities outperforms the random walk forecast, relative to the wavelet forecast that is based on returns. Optimal wavelet causality forecasting for returns is suggested across all frequencies (i.e., daily–yearly), whereas for volatilities it is suggested only up to quarterly frequencies. These results may have important implications for market efficiency and predictability of prices on the crude oil markets.     

Forecasting Errors,Directional Accuracy and Profitability of Currency Trading: The Case of EUR/USD Exchange Rate

We provide a comprehensive study of out‐of‐sample forecasts for the EUR/USD exchange rate based on multivariate macroeconomic models and forecast combinations. We use profit maximization measures based on directional accuracy and trading strategies in addition to standard loss minimization measures. When comparing predictive accuracy and profit measures, data snooping bias free tests are used. The results indicate that forecast combinations, in particular those based on principal components of forecasts, help to improve over benchmark trading strategies, although the excess return per unit of deviation is limited. Copyright © 2016 John Wiley & Sons, Ltd.     

Do stock markets have predictive content for exchange rate movements?

This paper examines short‐horizon exchange rate predictability and investigates whether stock returns contain information for forecasting daily exchange rate movements. Inspired by the uncovered equity parity condition, we show that stock return differentials have in‐sample and out‐of‐sample predictive power for nominal exchange rates with short horizons (1‐day‐ahead predictions). That is, stock markets inform us about exchange rate movements, at least in the case of high‐frequency data.     

Forecasting Performance of Nonlinear Models for Intraday Stock Returns

We studied the predictability of intraday stock market returns using both linear and nonlinear time series models. For the S&P 500 index we compared simple autoregressive and random walk linear models with a range of nonlinear models, including smooth transition, Markov switching, artificial neural network, nonparametric kernel regression and support vector machine models for horizons of 5, 10, 20, 30 and 60 minutes. The empirical results indicate that nonlinear models outperformed linear models on the basis of both statistical and economic criteria. Specifically, although return serial correlation receded by around 10 minutes, return predictability still persisted for up to 60 minutes according to nonlinear models, even though profitability decreases as time elapses. More flexible nonlinear models such as support vector machines and artificial neural network did not clearly outperform other nonlinear models. Copyright © 2011 John Wiley & Sons, Ltd.     

Cross‐correlations and predictability of stock returns

Studies have shown that small stock returns can be partially predicted by the past returns of large stocks (cross‐correlations), while a larger body of literature has shown that macroeconomic variables can predict future stock returns. This paper assesses the marginal contribution of cross‐correlations after controlling for predictability inherent in lagged macroeconomic variables. Macroeconomic forecasting models generate trading rule profits of up to 0·431% per month, while the inclusion of cross‐correlations increases returns to 0·516% per month. Such results suggest that cross‐correlations may serve as a proxy for omitted macroeconomic variables in studies of stock market predictability. Macroeconomic variables are more important than cross‐correlations in forecasting small stock returns and encompassing tests suggest that the small marginal contribution of cross‐correlations is not statistically significant. Copyright © 2000 John Wiley & Sons, Ltd.     

Adaptive Evolutionary Neural Networks for Forecasting and Trading without a Data‐Snooping Bias

In this paper, we present two neural‐network‐based techniques: an adaptive evolutionary multilayer perceptron (aDEMLP) and an adaptive evolutionary wavelet neural network (aDEWNN). The two models are applied to the task of forecasting and trading the SPDR Dow Jones Industrial Average (DIA), the iShares NYSE Composite Index Fund (NYC) and the SPDR S&P 500 (SPY) exchange‐traded funds (ETFs). We benchmark their performance against two traditional MLP and WNN architectures, a smooth transition autoregressive model (STAR), a moving average convergence/divergence model (MACD) and a random walk model. We show that the proposed architectures present superior forecasting and trading performance compared to the benchmarks and are free from the limitations of the traditional neural networks such as the data‐snooping bias and the time‐consuming and biased processes involved in optimizing their parameters. Copyright © 2015 John Wiley & Sons, Ltd.     

Does geographic location matter to stock return predictability?

Building on recent and growing evidence that geographic location influences information diffusion, this paper examines the relation between firm's location and the predictability of stock returns. We hypothesize that returns on a portfolio composed of firms located in central areas are more likely to follow a random walk than returns on a portfolio composed of firms located in remote areas. Using a battery of variance ratio tests, we find strong and robust support for our prediction. In particular, we show that the returns on a portfolio composed of the 500 largest urban firms follow a random walk; however, all variance ratio tests reject the random walk hypothesis for a portfolio that includes the 500 largest rural firms. Our results are robust to alternative definitions of firm's location and portfolio formation.     

Improving moving average trading rules with boosting and statistical learning methods

We present a system for combining the different types of predictions given by a wide category of mechanical trading rules through statistical learning methods (boosting, and several model averaging methods like Bayesian or simple averaging methods). Statistical learning methods supply better out‐of‐sample results than most of the single moving average rules in the NYSE Composite Index from January 1993 to December 2002. Moreover, using a filter to reduce trading frequency, the filtered boosting model produces a technical strategy which, although it is not able to overcome the returns of the buy‐and‐hold (B&H) strategy during rising periods, it does overcome the B&H during falling periods and is able to absorb a considerable part of falls in the market. Copyright © 2008 John Wiley & Sons, Ltd.     

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

A variety of recent studies provide a skeptical view on the predictability of stock returns. Empirical evidence shows that most prediction models suffer from a loss of information, model uncertainty, and structural instability by relying on low‐dimensional information sets. In this study, we evaluate the predictive ability of various lately refined forecasting strategies, which handle these issues by incorporating information from many potential predictor variables simultaneously. We investigate whether forecasting strategies that (i) combine information and (ii) combine individual forecasts are useful to predict US stock returns, that is, the market excess return, size, value, and the momentum premium. Our results show that methods combining information have remarkable in‐sample predictive ability. However, the out‐of‐sample performance suffers from highly volatile forecast errors. Forecast combinations face a better bias–efficiency trade‐off, yielding a consistently superior forecast performance for the market excess return and the size premium even after the 1970s.     

Non-linear prediction of security returns with moving average rules

Over the years, investors and the technical analysts have devised hundreds of technical market indicators in an effort to forecast the trend of a security market. Recent literature provides evidence that these rules may provide positive profits after accounting for transaction costs. This clearly contradicts the theory of the efficient market hypothesis which states that security prices cannot be forecasted from their past values or other past variables. This paper uses the daily Dow Jones Industrial Average Index from January 1963 to June 1988 to examine the linear and non-linear predictability of stock market returns with buy—sell signals generated from the moving average rules with a band between the short and the long averages. Strong evidence of non-linear predictability is found in the stock market returns by using the past buy and sell signals of these rules.     

Foreign exchange market prediction with multiple classifiers

Foreign exchange market prediction is attractive and challenging. According to the efficient market and random walk hypotheses, market prices should follow a random walk pattern and thus should not be predictable with more than about 50% accuracy. In this article, we investigate the predictability of foreign exchange spot rates of the US dollar against the British pound to show that not all periods are equally random. We used the Hurst exponent to select a period with great predictability. Parameters for generating training patterns were determined heuristically by auto‐mutual information and false nearest‐neighbor methods. Some inductive machine‐learning classifiers—artificial neural network, decision tree, k‐nearest neighbor, and naïve Bayesian classifier—were then trained with these generated patterns. Through appropriate collaboration of these models, we achieved a prediction accuracy of up to 67%. Copyright © 2009 John Wiley & Sons, Ltd.     

An assessment of the economic value of non-linear foreign exchange rate forecasts

Recent research suggests that non-linear methods cannot improve the point forecasts of high-frequency exchange rates. These studies have been using standard forecasting criteria such as smallest mean squared error (MSE) and smallest mean absolute error (MAE). It is, however, premature to conclude from this evidence that non-linear forecasts of high-frequency financial returns are economically or statistically insignificant. We prove a proposition which implies that the standard forecasting criteria are not necessarily particularly suited for assessment of the economic value of predictions of non-linear processes where the predicted value and the prediction error may not be independently distributed. Adopting a simple non-linear forecasting procedure to 15 daily exchange rate series we find that although, when compared to simple random walk forecasts, all the non-linear forecasts give a higher MSE and MAE, when applied in a simple trading strategy these forecasts result in a higher mean return. It is also shown that the ranking of portfolio payoffs based on forecasts from a random walk, and linear and non-linear models, is closely related to a non-parametric test of market timing.     

Direction‐of‐change forecasting using a volatility‐based recurrent neural network

This paper investigates the profitability of a trading strategy, based on recurrent neural networks, that attempts to predict the direction‐of‐change of the market in the case of the NASDAQ composite index. The sample extends over the period 8 February 1971 to 7 April 1998, while the sub‐period 8 April 1998 to 5 February 2002 has been reserved for out‐of‐sample testing purposes. We demonstrate that the incorporation in the trading rule of estimates of the conditional volatility changes strongly enhances its profitability, after the inclusion of transaction costs, during bear market periods. This improvement is being measured with respect to a nested model that does not include the volatility variable as well as to a buy‐and‐hold strategy. We suggest that our findings can be justified by invoking either the ‘volatility feedback’ theory or the existence of portfolio insurance schemes in the equity markets. Our results are also consistent with the view that volatility dependence produces sign dependence. Copyright © 2008 John Wiley & Sons, Ltd.     

Price–Dividend Ratios and Stock Price Predictability

A long‐standing puzzle to financial economists is the difficulty of outperforming the benchmark random walk model in out‐of‐sample contests. Using data from the USA over the period of 1872–2007, this paper re‐examines the out‐of‐sample predictability of real stock prices based on price–dividend (PD) ratios. The current research focuses on the significance of the time‐varying mean and nonlinear dynamics of PD ratios in the empirical analysis. Empirical results support the proposed nonlinear model of the PD ratio and the stationarity of the trend‐adjusted PD ratio. Furthermore, this paper rejects the non‐predictability hypothesis of stock prices statistically based on in‐ and out‐of‐sample tests and economically based on the criteria of expected real return per unit of risk. Copyright © 2011 John Wiley & Sons, Ltd.     

Forecasting stock returns an examination of stock market trading in the presence of transaction costs

The paper presents new evidence on the predictability of excess returns on common stocks for the Standard and Poor's 500 and the Dow Jones Industrial portfolios at the monthly, quarterly, and annual frequencies. It shows that recursive predictions obtained on the basis of the excess returns regressions are capable of correctly predicting a statistically significant proportion of the signs of the actual returns. The paper also shows that the switching portfolios constructed on the basis of the signs of the recursive predictions mean-variance dominate the respective market portfolios when trading takes place on a quarterly or annual basis. This result holds even under a high transaction cost scenario. However, due to the larger number of transactions at the monthly frequency the monthly switching portfolios only mean-variance dominate the respective market portfolios when transaction costs are zero or low.     

Impact of Macroeconomic Announcements on US Equity Prices: 2009–2013

Returns of several US equity exchange‐traded funds on the days of major macroeconomic announcements are examined for the period of January 2009 to July 2013. The ARMA+GARCH model with external linear regression terms that describe announcement events and their surprises is used. It is found that mean daily returns may be notably higher on the announcement days than those for the buy‐and‐hold strategy, though their difference may be not statistically significant. The ISM Manufacturing Reports, Non‐Farm Payrolls, International Trade Balance, Index of Leading Indicators, Housing Starts, and Jobless Claims turn out to be the most statistically significant factors in the model. Three trading strategies that realize daily returns on the various macroeconomic announcement days are compared with the buy‐and‐hold strategy. The choice of announcements with statistically significant regression coefficients yields higher mean daily returns and better Sharpe ratios but possibly lower compound returns. Transaction costs may significantly affect profitability of these trading strategies. Copyright © 2015 John Wiley & Sons, Ltd.