Predictable Financial Crises
Robin Greenwood (Harvard University), et al.
Using historical data on post-war financial crises around the world, we show that crises are substantially predictable. The combination of rapid credit and asset price growth over the prior three years, whether in the nonfinancial business or the household sector, is associated with about a 40% probability of entering a financial crisis within the next three years. This compares with a roughly 7% probability in normal times, when neither credit nor asset price growth has been elevated. Our evidence cuts against the view that financial crises are unpredictable “bolts from the sky” and points toward the Kindleberger-Minsky view that crises are the byproduct of predictable, boom-bust credit cycles. The predictability we document favors macro-financial policies that “lean against the wind” of credit market booms.
Text-Based Recession Probabilities
Helena Le Mezo and Massimo Minesso Ferrari (European Central Bank)
This paper proposes a new methodology based on textual analysis to forecast U.S. recessions. Specifically, the paper develops an index in the spirit of Baker et al. (2016) and Caldara and Iacoviello (2018) which tracks developments in U.S. real activity. When used in a standard recession probability model, the index outperforms the yield curve based forecast, a standard method to forecast recessions, at medium horizons, up to 8 months. Moreover, the index contains information not included in yield data that are useful to understand recession episodes. When included as an additional control to the slope of the yield curve, it improves the forecast accuracy by 5% to 30% depending on the horizon. These results are stable to a number of different robustness checks, including changes to the estimation method, the definition of recessions and controlling for asset purchases by major central banks. Yield and textual analysis data also outperform other popular leading indicators for the U.S. business cycle such as PMIs, consumers’ surveys or employment data.
Seismonomics: Listening to the Heartbeat of the Economy
Luca Tiozzo Pezzoli (European Comm.) and Elisa Tosetti (Ca Foscari U. of Venice)
February 7, 2021
Seismic sensors continuously record a wide range of ground vibrations that are not necessarily related to earthquake activity, but are rather caused by human activity such as industrial processes, urban and air traffic. In this paper we show that human-generated seismic noise provides valuable information about the economic developments of a particular area, thus offering policymakers a useful tool for monitoring the heartbeat of that economy. We adopt a set of techniques developed within the seismic literature to disentangle sources of ground motion and propose a novel, daily indicator measuring vibrations caused by human activity. To demonstrate the usefulness of our procedure, we collect a huge data set made of nearly 20 years of continuously recorded seismic data in Beijing, China, and use our vibration indicator to forecast daily variations in regional industrial production.
Our findings suggest that seismic data closely tracks business cycle fluctuations, with significant enhancements in the forecasting performance during economic crises. Our results support the usefulness of seismology as a nowcasting and forecasting tool in the area of economics and business, particularly for monitoring regional economies, for which timely and up-to-date indicators of economic activity are often not available, or for tracking national economies in periods of disruption of many key statistics.
Does Twitter Strengthen Volatility Forecasts? Evidence from the S&P 500, DJIA and Twitter Sentiment Analysis
Eli Kranefuss and Daniel K. N. Johnson (Colorado College)
February 15, 2021
The role of public sentiment in stock market volatility has recently become increasingly relevant. Twitter, in theory, offers an inexpensive way to measure real-time public sentiment. We take advantage of a natural experiment to assess the potential improvement that social media adds to forecast performance of ARIMA and ARFIMA models of realized volatility using E-mini S&P 500 (ES) and E-mini DJIA (YM) futures contracts. Comparing models over time, we find that accounting for Twitter sentiment strengthens out-of-sample volatility forecasts across all time periods. While statistical significance exists, economic significance is harder to quantify, and it is unclear if a first-mover advantage exists from continuously monitoring real time Twitter sentiment.
Aggregate Financial Misreporting and the Predictability of U.S. Recessions
Messod D. Beneish (Indiana University)
February 23, 2021
We rely on the theoretical prediction that financial misreporting peaks before economic busts to examine whether aggregate ex ante measures of the likelihood of financial misreporting improve the predictability of U.S. recessions. We consider six measures of misreporting and show that the Beneish M-Score significantly improves out-of-sample recession prediction at longer forecasting horizons. Specifically, relative to leading models based on yield spreads and market returns, M-Score increases the average probability of a recession across forecast horizons of six-, seven-, and eight-quarters-ahead by 56 percent, 79 percent, and 92 percent, respectively. These findings are robust to alternative definitions of interest rate spreads, and to controlling for the federal funds rate, investor sentiment, and aggregate earnings growth. We show that the performance of M-Score likely arises because firms with high M-Scores tend to experience negative future performance. Overall, this study provides novel evidence that accounting information can be useful to forecasters and regulators interested in assessing the likelihood of U.S. recessions a few quarters ahead.
Financial Analysts’ Forecasts Have Improved Significantly in the Post-Reg FD Period
Pengguo Wang (Xfi, University of Exeter)
January 17, 2021
Reducing the amount of private information in corporate disclosures does not necessarily reduce the accuracy of analysts’ forecasts. This paper applies model-based earnings forecasts as a benchmark that is immune from disclosure of private information and evaluates the relative performance of analysts’ forecasts of earnings against the benchmark. It finds that the I/B/E/S consensus forecasts in general outperform the benchmark forecasts in the post-Reg FD period, while they underperform the benchmark in the pre-Reg FD period. It seems that Reg FD is a watershed. The difference-in-difference analysis confirms that the accuracy of analysts’ consensus forecasts of earnings has improved significantly following the passage of Reg FD.
Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno