Informational efficiency and behaviour within in-play prediction markets

Studies of financial market informational efficiency have proven burdensome in practice, because it is difficult to pinpoint when news breaks and is known by some or all the participants. We overcome this by designing a framework to detect mispricing, test informational efficiency and evaluate the behavioural biases within high-frequency prediction markets. We demonstrate this using betting exchange data for association football, exploiting the moment when the first goal is scored in a match as major news that breaks cleanly. There are pre-match and in-play mispricing and inefficiency in these markets, explained by reverse favourite-longshot bias (favourite bias). The mispricing tends to increase when the major news is a surprise, such as a goal scored by a longshot team late in a match, with the market underestimating their chances of going on to win These results suggest that, even in prediction markets with large crowds of participants trading state-contingent claims, significant informational inefficiency and behavioural biases can be reflected in prices.     

A model of information diffusion with asymmetry and confidence effects in financial markets

We present a model of multi-period continuous information diffusion in financial markets. We show that price and trading volume exhibit asymmetric term structures to information flow, where the diffusion rate accelerates more slowly at short horizons than it decelerates at long horizons. Bounded rationality is modelled by an endogenous trader confidence index which declines as stock price information becomes noisier, where lower confidence translates into lower trading volume and slower price accretion. Information diffusion slows and asymmetries are accentuated as traders lose confidence in information accuracy. Our empirical findings support the model's predictions of asymmetric momentum patterns and confidence effects.     

Forecasting stock prices with commodity prices: New evidence from Feasible Quasi Generalized Least Squares (FQGLS) with non-linearities

The complexities in modern stock markets make it imperative to unravel the possible predictors of their future values. This paper thus provides insights into the predictability of stock prices of the BRICS countries with large dependence on commodities either for foreign exchange earnings or industrial while accounting for the role of asymmetries. Essentially, empirical evidence abound for the high volatility in world commodity markets, thus making us to determine if positive and negative changes in commodity prices predict stock prices differently. In addition, unlike the traditional forecast models, our choice of forecast models additionally addresses certain statistical features, including conditional heteroskedasticity, serial dependence, persistence and endogeneity, inherent in the predictors, which have the potential of causing estimation bias. In all, we find evidence in favour of the ability of commodity prices to predict stock prices of Brazil, Russia and South Africa. Also, both the in-sample and out-of-sample forecast performances of the predicted models support asymmetries in a number of commodity prices in each of these three countries. Our results are robust to different data samples and forecast horizons.     

Fiscal Surprises at the FOMC

We examine a new set of U.S. fiscal forecasts from the FOMC briefing books. These forecasts are precisely those that were presented to monetary policymakers, and include frequently-updated estimates covering six complete business cycles and several fiscal-policy regimes. We detail the performances of forecast federal expenditures, revenues, surpluses, and structural surpluses in terms of their accuracy, bias, and efficiency. We find that forecast errors can be large economically, even at relatively short forecast horizons. While economic activity became less volatile after 1990, fiscal policy became harder to forecast. Finally, cyclically-adjusted deficit forecasts appear to be over-optimistic around both peaks and troughs of the business cycle, suggesting that fiscal policy is counter-cyclical in downturns and pro-cyclical in the early stages of recoveries.     

Forecasting global equity market volatilities

Motivated by a common belief that the international stock market volatilities are synonymous with information flow, this paper proposes a parsimonious way to combine multiple market information flows and assess whether cross-national volatility flows contain important information content that can improve the accuracy of international volatility forecasting. We concentrate on realized volatilities (RV) derived from the intra-day prices of 22 international stock markets, and employ the heterogeneous autoregressive (HAR) framework, along with two common diffusion indices that are constructed based on the simple mean and first principal component (PC) of the 22 stock market RVs, to forecast future volatilities of each market for 1-day, 1-week, and 1-month ahead. We provide strong evidence that the use of the cross-national information reflected by the simple and parsimonious common indices enhances the predictive accuracy of international volatilities at all forecasting horizons. Alternative volatility measures, estimation window sizes, and forecasting evaluation tests confirm the robustness of our results. Finally, our strategy of constructing common diffusion indices is also feasible for international market jumps.     

Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?

We study the potential merits of using trading and non-trading period market volatilities to model and forecast the stock volatility over the next one to 22 days. We demonstrate the role of overnight volatility information by estimating heterogeneous autoregressive (HAR) model specifications with and without a trading period market risk factor using ten years of high-frequency data for the 431 constituents of the S&P 500 index. The stocks’ own overnight squared returns perform poorly across stocks and forecast horizons, as well as in the asset allocation exercise. In contrast, we find overwhelming evidence that the market-level volatility, proxied by S&P Mini futures, matters significantly for improving the model fit and volatility forecasting accuracy. The greatest model fit and forecast improvements are found for short-term forecast horizons of up to five trading days, and for the non-trading period market-level volatility. The documented increase in forecast accuracy is found to be associated with the stocks’ sensitivity to the market risk factor. Finally, we show that both the trading and non-trading period market realized volatilities are relevant in an asset allocation context, as they increase the average returns, Sharpe ratios and certainty equivalent returns of a mean–variance investor.     

Accuracy, Bias, and Dispersion in Analysts' Earnings Forecasts: The Case of Cross-Listed Foreign Firms

This paper examines differences in analysts' earnings forecast characteristics for foreign incorporated non-U.S. firms cross-listed in the U.S. stock markets relative to a control sample of purely domestic firms. Examining summary earnings forecasts over the calendar years 1984 through 1989, this paper provides evidence that there are statistically significant differences in bias and accuracy between domestic and cross-listed foreign firms. Consistent with prior research, we find a horizon effect in accuracy; i.e., accuracy improves as we get closer to the actual earnings announcement for both types of firms. However, the differences in accuracy between the cross-listed and domestic firms persist only in the earlier forecast horizons where analysts' forecasts are less accurate for foreign cross-listed firms compared with domestic firms. The evidence is also consistent with analysts' exhibiting less optimism with respect to cross-listed foreign firms compared with the domestic firms. Finally, the paper also documents that there is a greater consensus among analysts for foreign cross-listed firms than for domestic firms.     

Examining the credibility of macroeconomic forecasts: Null of cointegration approach

In this study we examine the accuracy of forecasts of a select group of major macroeconomic variables, representing both the real and the financial sector of the economy. The theoretical foundations are similar to the one used to study exchange rate expectations, i.e. a verification of consistency and rationality in forecast formation. The empirical measure of accuracy is consistency in the expectation formation process, a precursor to rational forecasts. Here we examine the cointegration properties of the actual and forecast series (at multiple horizons) using the modern null of cointegration approach. A very reliable and continuos data set, the ASA-NBER survey is used. We find evidence of short (long) term expectational consistency (inconsistency) i.e. bandwagon effects and a mean reversion tendency in case of real variables, while the forecasts of financial variables are inconsistent across all forecast horizons.     

Herding behavior,market sentiment and volatility: Will the bubble resume?

This paper aims to investigate herding behavior and its impact on volatility under uncertainty. We apply a cross-sectional absolute deviation approach as well as Quantile Regression methods to capture the herding behavior in daily and monthly frequencies in US markets over several time-periods including the global financial crisis. In a novel attempt we modify the empirical CSAD herding modeling by introducing implied volatility as a measure of agent risk expectations. Our findings indicate that herding tends to be intense under extreme market conditions, as depicted in the upper high quantile range of the conditional distribution of returns. During crisis periods herding is observed at the beginning of the crisis and becomes insignificant towards the end. The US market herding behavior exhibits time-varying dynamic trading patterns that can be attributed e.g., to overconfidence or excessive “flight to quality” features, mostly observed in the aftermath of the global financial crisis. Moreover, implied volatility reveals asymmetric patterns and plays a key role in enforcing irrational behavior.     

Does judgment improve macroeconomic density forecasts?

This paper presents empirical evidence on how judgmental adjustments affect the accuracy of macroeconomic density forecasts. Judgment is defined as the difference between professional forecasters’ densities and the forecast densities from statistical models. Using entropic tilting, we evaluate whether judgments about the mean, variance and skew improve the accuracy of density forecasts for UK output growth and inflation. We find that not all judgmental adjustments help. Judgments about point forecasts tend to improve density forecast accuracy at short horizons and at times of heightened macroeconomic uncertainty. Judgments about the variance hinder at short horizons, but can improve tail risk forecasts at longer horizons. Judgments about skew in general take value away, with gains seen only for longer horizon output growth forecasts when statistical models took longer to learn that downside risks had reduced with the end of the Great Recession. Overall, density forecasts from statistical models prove hard to beat.     

WHAT DO WE KNOW ABOUT THE PROFITABILITY OF TECHNICAL ANALYSIS?

Abstract.  The purpose of this paper is to review the evidence on the profitability of technical analysis. The empirical literature is categorized into two groups, 'early' and 'modern' studies, according to the characteristics of testing procedures. Early studies indicate that technical trading strategies are profitable in foreign exchange markets and futures markets, but not in stock markets. Modern studies indicate that technical trading strategies consistently generate economic profits in a variety of speculative markets at least until the early 1990s. Among a total of 95 modern studies, 56 studies find positive results regarding technical trading strategies, 20 studies obtain negative results, and 19 studies indicate mixed results. Despite the positive evidence on the profitability of technical trading strategies, most empirical studies are subject to various problems in their testing procedures, e.g. data snooping, ex post selection of trading rules or search technologies, and difficulties in estimation of risk and transaction costs. Future research must address these deficiencies in testing in order to provide conclusive evidence on the profitability of technical trading strategies.     

Prediction market prices under risk aversion and heterogeneous beliefs

In this paper, we examine the properties of prediction market prices when risk averse traders have heterogeneous beliefs in state probabilities. We show that the equilibrium state prices equal the mean beliefs of traders about that state if and only if the traders’ common utility function is logarithmic. We also provide a necessary and sufficient condition ensuring that the state prices are systematically below or above the mean beliefs of traders, thus providing a rational explanation to the favorite-longshot bias in prediction markets.     

Fractional cointegration and price discovery in Canadian commodities

Using as a unifying theme commodities important to the Canadian economy, recently developed tools are applied to studying price discovery in the spot and futures markets. For each commodity the fractionally cointegrated vector autoregression (FCVAR) model of Johansen and Neilsen is estimated and tested against the special case of the conventional cointegrated vector autoregression (CVAR). These models characterize the fundamental value of a commodity as the common stochastic trend shared by its cointegrated spot and futures prices, and so price discovery can be analyzed using the permanent-transitory decomposition of Gonzalo and Granger. Model forecasts are evaluated and compared using a distributional result due to Clark and West. The generalization to fractional cointegration is found to be statistically significant. However the economic significance of this generalization—in terms of forecast accuracy and the profitability of mean–variance dynamic trading strategies—is more fragile than may have been appreciated.     

Financially constrained arbitrage in illiquid markets

This article analyzes trading strategies when arbitrageurs impact prices. Trades of financially constrained arbitrageurs are feedback functions of their capital, which depends on the amount traded. A component of arbitrage trading ensures financial flexibility. This hedging component explains why price deviations persist in spite of arbitrage. Financial constraints are responsible for volatile prices and for time variation in the correlations of prices across markets. Distortions arise when regulated firms can influence the dynamics of prices on which capital requirements are based. Under current value at risk (VaR) measures, large traders behave aggressively and have a cost advantage relative to other traders.     

Classification of intraday S&P500 returns with a Random Forest

Stock markets can be interpreted to a certain extent as prediction markets, since they can incorporate and represent the different opinions of investors who disagree on the implications of the available information on past and expected events and trade on their beliefs in order to achieve profits. Many forecast models have been developed for predicting the future state of stock markets, with the aim of using this knowledge in a trading strategy. This paper interprets the classification of the S&P500 open-to-close returns as a four-class problem. We compare four trading strategies based on a random forest classifier to a buy-and-hold strategy. The results show that predicting the classes with higher absolute returns, ‘strong positive’ and ‘strong negative’, contributed the most to the trading strategies on average. This finding can help shed light on the way in which using additional event outcomes for the classification beyond a simple upward or downward movement can potentially improve a trading strategy.     

Forecast-error-based estimation of forecast uncertainty when the horizon is increased

Past forecast errors are employed frequently in the estimation of the unconditional forecast uncertainty, and several institutions have increased their forecast horizons in recent times. This work addresses the question of how forecast-error-based estimation can be performed if there are very few errors available for the new forecast horizons. It extends the results of Knüppel (2014) in order to relax the condition on the data structure that is required for the SUR estimator to be independent of unknown quantities. It turns out that the SUR estimator of the forecast uncertainty, which estimates the forecast uncertainty for all horizons jointly, tends to deliver large efficiency gains relative to the OLS estimator (i.e., the sample mean of the squared forecast errors for each individual horizon) in the case of increased forecast horizons. The SUR estimator is applied to the forecast errors of the Bank of England, the US Survey of Professional Forecasters, and the FOMC.     

The usefulness of oil price forecasts—Evidence from survey predictions

This paper evaluates survey forecasts for crude oil prices and discusses the implications for decision makers. A novel disaggregated data set incorporating individual forecasts for Brent and Western Texas Intermediate is used. We carry out tests for unbiasedness, sign accuracy, and forecast encompassing, followed by the computation of coefficients for topically oriented trend adjustments and the Theil's U measure. We also control for the forecast horizon finding heterogeneous results. Forecasts are more precise for shorter horizons, but less accurate than the naïve prediction. For longer horizons, topically oriented trend adjustments become more pronounced, but forecasters tend to outperform the naïve predictions.     

Country and industry convergence of equity markets: International evidence from club convergence and clustering

This study employs the panel convergence methodology developed by Phillips and Sul (2007) to explore the convergence dynamics of international equity markets. The analysis considers both country and industry effects. While traditional portfolio management strategies usually follow a top-down procedure, assuming that country-level effects drive financial aggregates (e.g., stock returns) our empirical results suggest that the equity markets of 37 of the 42 counties in our sample do form a unified convergence club. The empirical findings, however, also show more numerous stock-price convergence clubs in certain industries. That is, country factors play a more important role in explaining the actual convergence in real stock prices than industry factors. Conversely, the volatility of stock prices exhibits much more evidence of convergence than stock prices. These findings should assist portfolio managers in the design and implementation of appropriate portfolio management strategies. Regulatory authorities also can benefit in the design of financial regulation.     

Analysts' Forecasts of German Firms' Earnings: a Comparative Analysis

This paper examines analysts' forecasts of the annual earnings per share of German firms over the period of February 1987 to December 1995. The German case is particularly interesting as the accounting and institutional structures vary from those in more thoroughly researched markets such as the U.S. or U.K. The paper therefore considers the features of the German forecasting environment which distinguish it from the Anglo-American model, and whether these might be reflected in forecasting performance. The results for Germany show that the accuracy of analysts' forecasts improves as the forecast horizon shortens, are less accurate than a naive prediction model over longer horizons, and contain a positive bias. When the results for Germany are contrasted with the results for the U.K., as reported in a recent paper, they are found to be a little less accurate but the positive bias is greater in U.K. forecasts. Taken overall the forecasting process in Germany appears to be less efficient than in the U.K., but this may be due to the distinct features of the German forecasting environment.     

Forecasting commodity price indexes using macroeconomic and financial predictors

Using a long sample of commodity spot price indexes over the period 1947–2010, we examine the out-of-sample predictability of commodity prices by means of macroeconomic and financial variables. Commodity currencies are found to have some predictive power at short (monthly and quarterly) forecast horizons, while growth in industrial production and the investment–capital ratio have some predictive power at longer (yearly) horizons. Commodity price predictability is strongest when based on multivariate approaches that account for parameter estimation error. Commodity price predictability varies substantially across economic states, being strongest during economic recessions.