Betting on War? Oil Prices, Stock Returns and Extreme Geopolitical Events
Knut Nygaard (Oslo Metropolitan U.) and L.Q. Sørensen (Storebrand Asset Mgt.)
July 2023
We show that the ability of oil price changes to predict stock returns is largely limited to five extreme geopolitical events: the 2022 invasion of Ukraine, the 2003 invasion of Iraq, the 1990/91 Persian gulf war, the 1986 OPEC collapse, and the 1973 Arab-Israel war. In the counterfactual scenario where these events did not occur, the t-statistics are reduced on average 75% as compared to that reported by Driesprong, Jacobsen, and Maat (2008). We also find that a market-timing trading strategy based on oil price changes typically generates insignificant abnormal returns, contradicting previously published results. Our findings serve as an example of how a significant predictor in a time series forecasting regression does not necessarily constitute a useful or profitable market-timing signal.
Economic Fundamentals and Stock Market Valuation: A CAPE-based Approach
Maria Ludovica Drudi and Federico Nucera (Bank of Italy)
November 2022
This paper estimates a fair-value model, based on macroeconomic fundamentals, of the Shiller Cyclically Adjusted Price-to-Earnings (CAPE) ratio. By performing a multi-country analysis, we find that CAPE – a widely used metric for stock market valuations – is, in general, positively related to economic growth and negatively related to the real long-term interest rate and to measures of economic volatility computed using industrial production and inflation data. Empirical evidence arising from predictive regressions of real stock market returns indicates that deviations of CAPE from its estimated fair value are negatively related to future stock returns. A prediction model based on these deviations outperforms, in many cases, a model based on the CAPE levels both in sample and out of sample.
Stock Market Bubbles and the Forecastability of Gold Returns (and Volatility)
David Gabauer (independent researcher), et al.
June 2023
Firstly, we use the Multi-Scale LPPLS Confidence Indicator approach to detectboth positive and negative bubbles at short-, medium- and long-term horizons forthe stock markets of the G7 and the BRICS countries. We were able to detect majorcrashes and rallies in the 12 stock markets over the period of the 1st week of January,1973 to the 2nd week of September, 2020. We also observed similar timing of strong(positive and negative) LPPLS indicator values across both G7 and BRICS countries,suggesting interconnectedness of the extreme movements in these stock markets.Secondly, we utilize these indicators to forecast gold returns and its volatility, usinga method involving block means of residuals obtained from the popular LASSOroutine, given that the number of covariates ranged between 42 to 72, and goldreturns demonstrated a heavy upper tail.We found that, our bubbles indicators,particularly when both positive and negative bubbles are considered simultaneously,can accurately forecast gold returns at short- to medium-term, and also time-varyingestimates of gold returns volatility to a lesser extent.Our results have importantimplications for the portfolio decisions of investors who seek a safe haven duringboom-bust cycles of major global stock markets.
Forecasting Cryptocurrency Returns
Nilanjana Chakraborty (independent researcher)
May 2023
This paper studies two cryptocurrencies and finds that their prices can be estimated or forecasted better than their returns because returns being ratios of prices, do not always exhibit the economic relationship that may exist between two price series. However, average returns use multiple prices in their ratios that capture the economic behavior of the price series. Further, the forecasting performance of traditional preceding return models are compared with those of preceding average return models and the latter are found to generally give better results in terms of Root Mean Square Error (RMSE) and average return on investments (ARoIs).
Forecasting Stock Returns
David Rapach (Atlanta Fed) and Guofu Zhou (Washington U. in St. Louis)
March 2023
We survey the literature on stock return forecasting, highlighting the challenges faced by forecasters as well as strategies for improving return forecasts. We focus on U.S. equity premium forecastability and illustrate key issues via an empirical application based on updated data. Some studies argue that, despite extensive in-sample evidence of equity premium predictability, popular predictors from the literature fail to outperform the simple historical average benchmark forecast in out-of-sample tests. Recent studies, however, provide improved forecasting strategies that deliver statistically and economically significant out-of-sample gains relative to the historical average benchmark. These strategies—including economically motivated model restrictions, forecast combination, diffusion indices, and regime shifts—improve forecasting performance by addressing the substantial model uncertainty and parameter instability surrounding the data-generating process for stock returns. In addition to the U.S. equity premium, we succinctly survey out-of-sample evidence supporting U.S. cross-sectional and international stock return forecastability. The significant evidence of stock return forecastability worldwide has important implications for the development of both asset pricing models and investment management strategies.
Business Cycles and Portfolio Tail Risk Forecasting
Clara Zhou (Macquarie University), et al.
December 2022
We propose a new 3-step resampling approach to forecast portfolio tail risk conditional on the economic state. The approach first predicts economic states using a set of macroeconomic and financial variables. We then forecast the joint distribution of multiple assets in the portfolio according to the forecasted economic states. Finally, we calculate portfolio tail risk measures using the forecasted joint return distribution. This approach favorably accounts for time variation in the higher co-moments of the joint distribution of the returns of assets in the portfolio, and is applicable to large-scale portfolios. In an out-of-sample forecasting analysis, the new approach outperforms a set of widely-used existing models for portfolio tail risk forecasting.
Time-Varying Risk Aversion and International Stock Returns
Massimo Guidolin (Bocconi University), et al.
July 2023
We estimate an aggregate time-varying risk aversion function using option, stock return and macroeconomic data for a sample of 8 countries. We document that, in most of the countries, the degree of risk aversion is countercyclical. Moreover, we show that the estimated risk aversion function forecasts monthly stock index returns up to 12 months ahead. This effect is statistically significant in panel regressions, and it survives the inclusion of additional control variables. Finally, we show that the estimated time-varying risk aversion function provides useful information to an investor who aims at timing the market. An investment strategy that uses the estimated time-varying risk aversion measure to solve a mean-variance asset allocation problem, delivers significant returns.
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
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