مقاله انگلیسی رایگان در مورد توانایی پیش بینی چرخه کسب و کار با نقدینگی بازار بورس – امرالد 2018

Purpose – The purpose of this paper is to predict real gross domestic product (GDP) growth and business cycles by using information from both liquidity and volatility measures. Design/methodology/approach – The paper estimates liquidity and volatility measures from over 5,000 NYSE rms and extracts a common factor, which the paper calls uncertainty. In-sample and out-ofsample forecasting tests are used to determine the ability of the uncertainty factor to predict growth in real GDP, industrial production, consumer price index, real consumption and changes in real investment. Findings – The paper finds that on average, positive shocks to the uncertainty factor occur in the quarters preceding and at the beginning of a recession. During the quarters toward the end of recessions, there are negative shocks to uncertainty on average. Originality/value – Previous research has explored using either liquidity or volatility to forecast economic activity. The paper bridges the two branches of research and finds a link to real GDP growth and business cycles.

Introduction

Recently, there has been a large branch of literature that examines market liquidity. Pástor and Stambaugh (2003), Acharya and Pedersen (2005), Chen (2005) and Sadka (2006), all look into systematic liquidity risk. Additionally, as there are several different measures of liquidity, many studies have focused on identifying a common systematic liquidity factor (Chordia et al., 2000; Hasbrouck and Seppi, 2001; Eckbo and Norli, 2002; Korajczyk and Sadka, 2008). Amihud, Hameed, Kang and Zhang (2015) examine 45 countries and find evidence that the illiquidity premium exists in a number of international markets. With the most recent financial crisis, there has been an interest in the apparent causal link between a reduction in liquidity and an economic slowdown. Czauderna et al. (2015) find a bidirectional link between liquidity (as measured using exchange traded funds) and market returns in the German stock market between July 2006 and June 2010. Wagner and Winter (2013) include liquidity risk in models to evaluate daily mutual fund performance for funds focused on the European market. When using a six-factor model including liquidity and idiosyncratic risk, they find that a large number of funds have significant loadings on both the liquidity and idiosyncratic risk factors. In measuring exposure to the liquidity risk factor, they find that on average, fund managers prefer liquid stocks. In a paper by Næs et al. (2011), they show that this link between liquidity and recessions is not a recent phenomenon but has existed in past recessions as well. They find that, on average, there is an increase in illiquidity before a recession followed by an increase in liquidity during the tail end of the recession. Furthermore, measures of liquidity help in forecasting future real gross domestic product (GDP) growth. Switzer and Picard (2016) also examine measures of aggregate liquidity and their relation to economic cycles. Rather than using a linear model, as was done by Næs et al. (2011), Switzer and Picard (2016) use nonlinear models and find the relationship between aggregate liquidity measures and the real economy to be significantly less pronounced. In addition to linking liquidity to business cycles, several papers have explored stock market volatility and its relation to real macroeconomic variables and business cycles (Schwert, 1989; 1990; Hamilton and Gang, 1996). Hamilton and Gang (1996) find that stock volatility may be useful in forecasting economic activity. We merge these two trains of thought and check if there is any added benefit from considering liquidity and volatility jointly. In Carlston (2012), multiple daily liquidity and volatility measures are estimated from daily stock prices and returns. Common factors, what we will term as “uncertainty”, are extracted across all of the liquidity and volatility measures. He finds that this common risk factor carries a significant premium and helps explain the cross section of expected returns. In this paper, we follow the methodology of Næs et al. (2011) to explore a possible link between this common liquidity and volatility measure and the real economy and business cycles.