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Do Asset Price Drops Foreshadow Recessions?
John C. Bluedorn (IMF), et al. | Oct 2013
This paper examines the usefulness of asset prices in predicting recessions in the G-7 countries. It finds that asset price drops are significantly associated with the beginning of a recession in these countries. In particular, the marginal effect of an equity/house price drop on the likelihood of a new recession can be substantial. Equity price drops are, however, larger and are more frequent than house price drops, making them on average more helpful as recession predictors. These findings are robust to the inclusion of the term-spread, uncertainty, and oil prices. Lastly, there is no evidence of significant bias resulting from the rarity of recession starts.

Predicting Financial Markets with Google Trends and Not so Random Keywords
D. Challet and A.B.H. Ayed (Ecole Centrale Paris) | (Aug 2013)
We check the claims that data from Google Trends contain enough data to predict future financial index returns. We first discuss the many subtle (and less subtle) biases that may affect the back-test of a trading strategy, particularly when based on such data. Expectedly, the choice of keywords is crucial: by using an industry-grade back-testing system, we verify that random finance-related keywords do not to contain more exploitable predictive information than random keywords related to illnesses, classic cars and arcade games. We however show that other keywords applied on suitable assets yield robustly profitable strategies, thereby confirming the intuition of Preis et al. (2013).

News versus Sentiment: Comparing Textual Processing Approaches for Predicting Stock Returns
S. L. Heston (University of Maryland) and N.R. Sinha | Sep 2013
This paper uses a dataset of over 900,000 news stories to test whether news can predict stock returns. It finds that firms with no news have distinctly different average future returns than firms with news. We measure sentiment with the Harvard psychosocial dictionary used by Tetlock, Saar-Tsechansky, and Macskassy (2008), the financial dictionary of Loughran and McDonald (2011), and a proprietary Thomson-Reuters neural network. Simpler processing techniques predict short-term returns that are quickly reversed, while more sophisticated techniques predict larger and more persistent returns. Conforming previous research, daily news predicts stock returns for only 1-2 days. But weekly news predicts stock returns for a quarter year. Positive news stories increase stock returns quickly, but negative stories have a long-delayed reaction.

Some Thoughts on Making Long-Term Forecasts for the World Economy
S. Fardoust (World Bank) and A. Dhareshwar | Nov 2013
Countries and international organizations working on longer-range development issues depend on long-term quantitative projections and scenario analysis. Such forecasting has become increasingly challenging, thanks to the rapid pace of globalization, technological progress, the interplay among them, and enhanced connectivity among people. As a result, seemingly isolated events can quickly lead to wide-ranging and lasting regional or even global consequences. This paper examines the problem of long-term economic forecasting in the face of increased complexity and uncertainty. With the benefit of hindsight, it scrutinizes past long-term qualitative and quantitative projections for the 1990s in order to draw lessons on how an institution can and should conduct long-term forecasting and policy analysis. The main conclusions are that policy makers and researchers across the world urgently need to see the big picture if they are to deal with the specific challenges and opportunities they will face over the long term as economies and global linkages undergo major structural changes under conditions of considerable uncertainty and volatility. Global institutions need to have strong research programs that work in close collaboration with other international organizations, academic centers, and independent experts on important long-term development issues ("blue sky" issues) and megatrends. These institutions need to build on their comparative strengths and form teams of in-house researchers and global experts who work on state-of-the-art models related to globalization, technological progress and innovations, climate change, demographic shifts, population, and labor force quality and their policy implications at both the global and country levels. Researchers should be encouraged to consider how global challenges such as financial crises, climate change, and infectious diseases can lead to breaks in economic trends and regime change and how such breaks affect economic activity. Alternative scenarios need to be created that incorporate the views of contrarian forecasters, including forecasts of possible shocks.

Uncertainty and Heterogeneity in Factor Models Forecasting
M. Luciani (Universite Libre de Bruxelles) and L. Monteforte | Nov 2013
In this paper, we exploit the heterogeneity in the forecasts obtained by estimating different factor models to measure forecast uncertainty. Our approach is simple and intuitive. It consists first in selecting all the models that outperform some benchmark model, and then in constructing an empirical distribution of the forecasts produced by them. We interpret this distribution as a measure of uncertainty. We illustrate our methodology by means of a forecasting exercise using a large database of Italian data from 1982 to 2009.

Forecasting Realized Volatility of Daily Returns
B. Gmuer (Quantica Capital) and S. Malamud | Nov 2013
Despite the vast academic literature on modelling stochastic volatility, many finance practitioners still use the simple "RiskMetrics" approach of J. P. Morgan (1997). In this paper, we evaluate this approach using a universe of 47 liquid futures contracts, including equity index, currency, commodity, and bond futures. We find that the RiskMetrics approach is indeed efficient for forecasting shorter term (5-10 day) realized volatility of daily returns; however, it is quite inefficient for forecasting longer term realized volatility. We show that a simple regression-based modification of the RiskMetrics approach generates efficient realized volatility forecasts over an interval of horizons, from 5 days to 2 months. Our forecasting procedure is implementable in real time, and its performance is comparable (and sometimes even superior) to that of ARCH-type models.

Anchoring the yield curve using survey expectations
Carlo Altavilla (University of Naples Parthenope) | Oct 2013
The dynamic behavior of the term structure of interest rates is difficult to replicate with models, and even models with a proven track record of empirical performance have underperformed since the early 2000s. On the other hand, survey expectations are accurate predictors of yields, but only for very short maturities. We argue that this is partly due to the ability of survey participants to incorporate information about the current state of the economy as well as forward-looking information such as that contained in monetary policy announcements. We show how the informational advantage of survey expectations about short yields can be exploited to improve the accuracy of yield curve forecasts given by a base model. We do so by employing a flexible projection method that anchors the model forecasts to the survey expectations in segments of the yield curve where the informational advantage exists and transmits the superior forecasting ability to all remaining yields. The method implicitly incorporates into yield curve forecasts any information that survey participants have access to, without the need to explicitly model it. We document that anchoring delivers large and significant gains in forecast accuracy for the whole yield curve, with improvements of up to 52% over the years 2000-2012 relative to the class of models that are widely adopted by financial and policy institutions for forecasting the term structure of interest rates.

Animal Spirits and Credit Cycles
Paul De Grauwe and Corrado Macchiarelli (London School of Economics) | Nov 2013
In this paper we extend the behavioral macroeconomic model as proposed by De Grauwe (2012) to include a banking sector. The behavioral model takes the view that agents have limited cognitive limitations. As a result, it is rational to use simple forecasting rules and to subject the use of these rules to a fitness test. Agents then are driven to select the rule that performs best. The behavioral model produces endogenous and self-fulfilling movements of optimism and pessimism (animal spirits). Our main result is that the existence of banks intensifies these movements, creating a greater scope for booms and busts. Thus banks do not create but amplify animal spirits. The policy conclusion we derive from this result is that the central bank has an important responsibility for stabilizing output. Output stabilization is an instrument to “tame the animal spirits”. This has the effect of improving the tradeoff between inflation and output volatility.

What Central Bankers Need to Know about Forecasting Oil Prices
Christiane Baumeister and Lutz Kilian (Bank of Canada) | May 2013
Forecasts of the quarterly real price of oil are routinely used by international organizations and central banks worldwide in assessing the global and domestic economic outlook, yet little is known about how best to generate such forecasts. Our analysis breaks new ground in several dimensions. First, we address a number of econometric and data issues specific to real-time forecasts of quarterly oil prices. Second, we develop real-time forecasting models not only for U.S. benchmarks such as West Texas Intermediate crude oil, but we also develop forecasting models for the price of Brent crude oil, which has become increasingly accepted as the best measure of the global price of oil in recent years. Third, we design for the first time methods for forecasting the real price of oil in foreign consumption units rather than U.S. consumption units, taking the point of view of forecasters outside the United States. In addition, we investigate the costs and benefits of allowing for time variation in vector autoregressive (NYSE:VAR) model parameters and of constructing forecast combinations. We conclude that quarterly forecasts of the real price of oil from suitably designed VAR models estimated on monthly data generate the most accurate forecasts among a wide range of methods including forecasts based on oil futures prices, no-change forecasts and forecasts based on regression models estimated on quarterly data.

Source: Research Review: Forecasting Markets And Macro