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Erdogan, Bennett, Ozyildirim

Review of Finance, forthcoming

Recession Prediction Using Yield Curve and Stock Market Liquidity Deviation Measures

Oral Erdogan, Paul Bennett , Cenktan Ozyildirim Abstract This paper extends the benchmark Estrella and Hardouvelis (1991) term spread approach to recession forecasting by including the stock market macro liquidity deviation factor. We use a probit framework to predict US business cycles, as defined by the NBER between 1959Q1 and 2011Q4. We find that combining the yield curve parameter with the stock market liquidity deviation significantly improves our ability to predict the onset of a US recession, based both on in-sample and out-of-sample tests. In addition, changes in stock market depth further increase the accuracy of the model. Our findings suggest that economic forecasters and those charged with conducting economic stabilization policy more generally would benefit from monitoring not only the yield curve but also stock market depth and liquidity, and their deviation from one another. Keywords: Yield curve, macro liquidity deviation, stock market depth, recession, probit model JEL Codes: G01, G15, E43, E44

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Electronic copy available at: http://ssrn.com/abstract=2253180



1. Introduction The ability to predict the likelihood of financial crisis has become more valued than ever in the wake of the difficulties affecting world economies since 2008. Central bankers, private forecasters, investors, and business professional generally want to know which indicators provide reliable and potential forecasts of recession or crisis. One of the most widely relied on indicators has been the maturity yield curve, which when downward sloping has been found to be consistent with a heightened probability of a period of negative or sharply lower real economic growth. The idea that an inverted yield curve can signal a recession was formalized empirically by a number of researchers in the past decades, including Laurent (1988), Harvey (1988, 1989), Stock and Watson (1989), Chen (1991), and Estrella and Hardouvelis (1991). These studies mainly focused on using the term spread to project the probability that the US economy will be in recession several quarters in the future. Of these studies, Estrella and Hardouvelis (1991) and later Estrella and Mishkin (1995) provided the most comprehensive documentation of the strong predictive content of the spread for output, including its ability to predict a binary recession indicator in probit regressions. Confirming the earlier results on the usefulness of the spread between long- term and short-term interest rates for forecasting GDP growth, Hamilton and Kim (2002) showed how to decompose this effect into an expectations effect and a term premium effect. Estrella et al. (2003) demonstrated that binary models were more stable than continuous models used to predict recessions in both Germany and the United States. Nyholm (2007) employed a three-state regime Nelson Siegel parametric model to define inverted yield curves instead of yield spreads and showed that the probability of a flat or downward sloping yield curve state could be used to predict a recession several months in advance.

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Electronic copy available at: http://ssrn.com/abstract=2253180



The use of a U.S. government yield curve arguably makes sense for predicting crises or recessions because it is forward looking and implicitly projects future risk free interest rates, in the context of a highly liquid and informationally efficient market. By calculating a maturity yield curve using most recently issued and actively traded “on-the-run” government bills, notes, and bonds, forecasters can lower issue-specific liquidity effects on market yields and increase the relative share of market expectation of future interest rates in computing the yield curve.1 Lower forward interest rates indicate current market expectations of falling future short-term interest rates. This in turn could reflect lower expected inflation premia or, more relevant to the topic at hand, lower expected real interest rates (or lower required riskfree real returns) associated with a slack economic environment, or both. A closely related interpretation is that government securities provide a haven from prospective credit problems in the event of a recession, causing the prices of treasuries to rise as investors flee risk, accepting low or even negative interest rates as the price of safety. In turn, because the rates on very short term treasuries are anchored by central bank monetary intervention policy and because some investors have portfolio preferences or hedging needs for longer maturities and durations, yields on treasury notes and bonds will tend to fall more dramatically than the rates on bills, causing the yield curve to slope more downward. Even when expectations are imprecise, speculators or particularly well informed traders may seek to capitalize on an increased probability of crisis by stocking up on the most liquid assets, in anticipation of trading them at favorable terms for illiquid assets that initially less informed investors maybe forced to sell at the onset of a crisis (Acharya, Shin, and Yorulmazer, 2011). As the relative prices of illiquid assets begin to weaken, speculators may bid for liquid low yielding assets as temporary stores of value until the market turns. Either for speculative leverage or for maintaining duration matches, some of these purchases of liquid assets may

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See Bennett, Garbade, and Kambhu (2000).

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focus on longer maturity treasury notes and bonds, flattening or inverting the maturity yield curve. While a resulting inversion of the treasury yield curve is a sign that well informed market participants see an increased probability of a recession, many such participants are also active in equities trading. It is therefore also likely that a fixed income “flight to quality” will correlate contemporaneously with conditions in equity markets. Indeed, it is difficult to imagine economic distress signals affecting the yield curve but not affecting equities markets at the same time. Information shocks to the economy and the bond market can disrupt equity market liquidity in many ways. As participants become more uncertain about stock valuations, they may become more conservative in the depth of bids or offers they provide, raising the price movements associated with large sales or purchases. This in turn may tend to reduce the volumes of securities bought and sold, as knowledgeable market makers become more reluctant to take positions and others take note. More fundamentally, equity market valuations depend on projected earnings, and new inklings of downturns in a company’s sales can be expected to show up very quickly in its stock price. Using panel data analysis, Erdogan and Ozyildirim (2005) demonstrate that macro liquidity, measured as equity market volume, has a positive and significant relationship over time with macro depth, measured as valuation. As Erdogan (1996) explains, parallel or “balanced” growth in macro depth and macro liquidity characterizes the healthy development of financial markets. But the independent movement or growing deviation of these two common financial depth measures signals market imbalance. It is argued that when financial markets advance, both macro depth and macro liquidity increase in parallel. But when macro depth increases faster than liquidity, an adjustment is likely to follow, affecting the broader economy.

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This structure can be explained in several ways. One is based on the degree of optimism in the financial markets. When there is a lot of optimism about business and profit prospects, investors will bid up the stock prices (raise Q). This in turn gives incentives to existing companies to raise capital and invest, for private companies to go public, and for governments to sell government-owned companies to the public. However, when the most sophisticated investors sense that the economy may be peaking and that the market is may be overvalued, they become reluctant to participate further, which could dampen the amount of trading even as prices stay high or rising on thin volume. A physical analogy is the way a ball thrown straight up slows and stalls before it falls. There is a moment when the "less informed money" and momentum investors are keeping prices high or rising, and companies are trying to issue as much new stock as possible, while the smart money is quietly trickling away ("rebalancing"). This boom-bust tendency might be more exaggerated in markets without well-developed futures or other derivative markets, which would make it easier for investors to short the most egregiously overpriced assets to get out sooner, as the fundamentals begin to shift. Thus the exuberance episodically experienced by participants in the financial markets, appears not only as “escalated asset values”, but also as deviations of macro liquidity and depth. Of course, labeling a period of over-exuberance is only possible in hindsight. But to be able to determine whether a rise in asset values is an anomaly, it may be helpful to examine the relative behavior of macro liquidity and macro depth for a signal of coming recession. While US equities market prices are generally regarded as efficient and liquid, less use has been made of variations in stock market liquidity and deepness data as a macroeconomic forecasting tool. A possible reason is that, compared with bond yields, stock prices tend to be even more volatile and “bubbly”, and hence prone to sending false signals of

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economic recession. An empirical link just between equity market prices and economic activity was investigated early by Mitchell and Bums (1938), Fischer and Merton (1984), Barro (1990), Fama (1981) and Harvey (1989). A surprising but common finding of those studies was that stock returns themselves were not reliable predictors of economic activity. Indeed, another line of research (see Fama 1981 1990, Harvey1989, Stock and Watson 1989) found only a mixed ability of equity returns to predict macroeconomic conditions. Following Fama (1990) and Schwert (1990), Choi et al. (1999) examine the relationship between the industrial production (IP) growth rates and lagged real stock returns for the G-7 countries. Estrella and Mishkin (1998) find strong marginal predictive power for stock returns to predict U.S. recessions at up to 3 quarters ahead in models that include the yield curve spread. Alike, Stock and Watson (2003) use equity returns in their output forecasting models. More recently, Colombage (2009) demonstrates that the financial markets in Japan, Switzerland, the UK, and the US Granger-cause real output. Nyberg (2010) finds that stock returns along with the term spread is also found to be a useful predictor for both the US and German recession periods. However Mili et al. (2012) found that the contemporaneous U.S. yield curve spread has no marginal predictive power for Euro area GDP growth in a model with a large number of variables, including a Euro-zone yield curve spread that is significant.

Campbell et al. (2001) proposed that the variance of stock returns, rather than the returns themselves, could have predictive content for output growth. Using in-sample statistics, they found evidence that high equity price volatility in one quarter signals low macroeconomic growth in the next, consistent with the notion that higher uncertainty in financial markets presages or contributes to near-term economic weakness. Increased stock price volatility will also be a characteristic of a market in which participants are less willing to provide liquidity, for example due to heightened uncertainty about the economic outlook.

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In addition to the previous literature using the stock returns to predict recessions or outputs, Levine and Zervos (1998) and Erdogan et al. (2012) investigated the relationship between the stock market liquidity and growth. In both studies market liquidity is measured as the value of the trades of domestic shares on domestic exchanges divided by GDP. While the Levine and Zeros study states that stock market liquidity and the degree of banking development are both positively and robustly correlated with contemporaneous and future rates of economic growth, capital accumulation, and productivity growth; Erdogan et al. (2012) demonstrate that the stock market liquidity measure is a significant factor to predict an upcoming recession. As noted, Erdogan et al. (2012) use a recession indicator called Macro Liquidity Deviation (MLD). In this approach, large deviations from the normal statistical relationship between the volume of equity trading relative to GDP (“macro liquidity”) and the outstanding value of listed securities relative to GDP (“macro depth”) signal an elevated likelihood of an economic recession. Like the Estrella yield spread approach, the MLD uses current data on market conditions, but the MLD focuses on surprise deviations from normal of equity market liquidity relationships. Indeed, it seems likely that an unusual configuration of treasury yields and a disruption of equity market liquidity relationships are both signals that knowledgeable traders have reasons – broadly recognized or not – to reduce the amount of liquidity they offer and instead to buy up liquid assets in anticipation of recession. Like the Estrella and Mishkin (1998), Nyberg (2010) and Mili et al. (2012), this paper combines yield curve and equity market data, but with an emphasis on equity market liquidity as an indicator instead of equity prices or return per se. In the following sections we use both the Estrella and the MLD measures to test whether combining them significantly enhances the ability to forecast recession. We find that adding the MLD measure, as well as monthly changes of macro depth, to the Estrella model significantly improves the forecasts of

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recession both in-sample and out-of-sample tests. This confirms that the highly informationsensitive equity and treasury bond markets are useful indicators of enhanced risk of economic recession or crisis. Because the fixed-income and equity measures are not highly collinear, the two in combination present a significantly improved assessment of the risk of economic crisis. Section 2 below describes the data and methodology used in the study. Section 3 presents the results of the probit analysis. Section 4 discusses the findings and concludes.

2. Data and Methodology We begin by revisiting crisis prediction with the yield curve spread, and then we see whether adding an MLD measure further raises the model’s recession prediction power. Since previous literature (Estrella and Hardouvelis (1991), Estrella and Mishkin (1995, 1998), Wheelock and Wohar (2009)) has shown that an interest rate spread measure is more successful in forecasting recessions than in predicting the output growth rate, we aim to forecast recessions, using a probit methodology. First, following Estrella and Hardouvelis (1991) and Estrella and Mishkin (1995), we re-estimate the following probit model:

P ( X t = 1) = Φ(α + β * SPREADt −4 )

(1)

where Xt=1 when the economy is in recession in period t and Φ(.) is the cumulative standard density function. SPREAD is the quarterly average of monthly interest rate spread data calculated as the difference between the interest rate on 10-year Treasury bonds and 3-month

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Treasury bills2. The time lag used in the model is 4, which means that the study analyzes the ability of SPREAD to predict recessions 1 year ahead, similar to previous studies. The quarterly data is used to estimate the model, since GDP data required to calculate the macro liquidity and depth is available quarterly. The dummy variable for recessions (Xt) is defined according to NBER classification. Next, following Erdogan et al. (2012) to estimate the macro liquidity deviation, the following linear model is estimated in this research:

MLt = β1 + β 2 MDt + ε t

(2)

where ML stands for macro liquidity and MD for macro depth. To calculate macro depth and macro liquidity, we used the ratio of the end of quarter NYSE market capitalization to nominal GDP and the ratio of quarterly trading volume in NYSE to nominal GDP. The aim is not only to define the relationship between MD and ML, but also to measure deviations from this relationship using the residuals obtained from this regression. MD is expected to have a significant positive coefficient. Subsequently, the lags of the residuals, used to measure deviations from a balanced macro liquidity (MLD) defined by the regression equation above, are inserted to the Equation (1) as an independent variable to observe whether MLD provides incremental power beyond SPREAD to predict recessions. Finally, we also add the lagged values of first difference of MD (DMD) to the probit equation, to check whether equity market data contains extra information that is not contained in SPREAD or MLD:

P ( X t = 1) = Φ(α + β1 * SPREADt −4 + β1 *DMLt −l + β3 *DMDt −k )

(3)

where k, and l are the lag numbers for DMD and DML respectively.

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Moneta (2005) shows that the spread between ten-year government bond rate and the three-month interbank rate provides the best results among 10 different interest rate spreads used to predict recessions in the euro area.

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The data analyzed covers the period between first quarter of 1959 and fourth quarter of 2011. According to classification rule based on NBER data, there are 30 recession quarters and 178 non-recession quarters in the sample.

3. Findings 3.1 In Sample Prediction First, we re-estimate the yield-curve based forecasting model using the 4th lag of the SPREAD as the only regressor. Table 1 gives the categorical descriptive statistics of the explanatory variable. The 4th quarter ahead interest rate spread is -0.025 on average during recession quarters compared to 1.576,suggesting that SPREAD (-4) can be used as a discriminating factor. Table 1: Categorical Descriptive Statistics of SPREAD(-4) Non-recession Months

Recession Months

All

1.5506 1.1560 178

-0.1414 1.0486 30

1.3065 1.2852 208

Mean Standard Deviation # of observations

Table 2 provides the results of re-estimation of the yield-curve-only model. The coefficient of the 4th lag of SPREAD is significant. The pseudo-R2, a measure of fit proposed by Estrella (1998), is 0.24, indicating a sufficient goodness of fit3. The in-sample prediction results are also reported in Table 2. Using a 50% cut-off probability, the percentage of correct predictions is 88%. On the other hand, the percentage of correct predictions for recession periods is only 27%.

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The pseudo-R2 is defined by;

⎛ log( Lu ) ⎞ pseudo − R = 1 − ⎜ ⎟ ⎝ log( Lc ) ⎠

− (2/ n )log( Lc )

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where Lu is the likelihood of the full model and Lc is the likelihood of the intercept only model.

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Review of Finance, forthcoming Table 2: Probit estimation output

C

-0.518 (0.162)*

SPREAD(-4)

-0.721 (0.198)*

Pseudo R-squared

0.24 Prediction Evaluation (Success rate with 50% cut -off probability) Predicted / Total Recession months ( P=1) 8/30 Predicted / Total Non-recession months ( P=0) 175/178 The values in the parentheses are corrected standard deviations of corresponding coefficient estimates, calculated using the Newey-West (1987) technique. The number of Newey-West lags is four. *, **, *** indicates the significance at 1%, 5%, and 10% respectively.

Next, we estimate Equation (2) to obtain the macro liquidity deviation. The regression output reported in Table 3 exhibits the relationship between macro depth and macro liquidity4. In this context, it is seen that when the depth increases, macro liquidity would also increase by nearly 4.733 times the increase in the macro depth in NYSE. The residuals obtained from the regression equation are used to define the deviations of macro liquidity with respect to the macro depth values, namely Macro Liquidity Deviation (MLD). Following Erdogan et al. (2012), this study claims that when there are such deviations between macro depth and liquidity, it signals a recession. Table 3: Regression output of Equation (2) C

-7.049758 (0.841)*

Macro Depth

4.733 (0.293)*

R2 0.55 The values in the parentheses are standard deviations of corresponding coefficient estimates. *, **, *** indicates the significance at 1%, 5%, and 10% respectively.

To enhance the recession model defined by Equation (1), we insert the lags of MLD obtained from the residuals of regression on Equation (2). Table 4 provides the results of the

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The p-values of ADF UnitRoot Test for MD and ML are 0.47 and 0.95 respectively.

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probit model using the 4th lag of SPREAD and the second lag of MLD5. Both of the regressors have significant coefficients at a 1% significance level. Compared to the probit model with solely SPREAD (-4), the Pseudo-R2 and in-sample prediction evaluation results have improved by the inclusion of MLD (-2)6. Table 4: Probit estimation output C

-0.482 (0.242)**

SPREAD(-4)

-0.812 (0.165)*

MLD(-2)

0.110 (0.034)*

Pseudo-R2

0.33 Prediction Evaluation (Success rate with 50% cut -off probability) Predicted / Total Recession months ( P=1) 13/30 Predicted / Total Non-recession months ( P=0) 172/178 The values in the parentheses are corrected standard deviations of corresponding coefficient estimates, calculated using the Newey-West (1987) technique. The number of Newey-West lags is four. *, **, *** indicates the significance at 1%, 5%, and 10% respectively.

Table 5 gives the categorical descriptive statistics of the second lag of MLD. The table shows that macro liquidity deviation increases enormously two quarters before a recession. The average of MLD two quarters before a recession is 1.94, whereas it is only 0.44 for non-recession periods, supporting the argument that MLD (-2) can be used as a discriminating factor. Table 5: Categorical Descriptive Statistics of MLD(-2) Non-recession Months Recession Months Mean Standard Deviation # of observations

All

-0.443

1.941

-0.102821

3.904

6.261

4.381427

180

30

210

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The probit model is estimated by inserting the first 8 lags of MLD one by one. Based on the Pseudo-R2 goodness of fit statistics addition of MLD (-2) provides the best results. Only the MLD (-2) and SPREAD (-4) results are reported here. 6 It should also be noted that the outputs of Table 4 and Table 2 are not exactly comparable, since the forecasting horizon is 4 for the SPREAD(-4) model, and 2 for the SPREAD(-4) & DML(-2) model.

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Finally, we also add the lags of change in macro depth (DMD) to the model to check whether they contain extra information to improve the model. As a result of this experimentation, the first 2 lags of DMD are included in the probit equation. This model, which uses the fourth lag of the interest rate spread (SPREAD (-4)), the second lag of the market liquidity deviation (DML(-2)) and the first and second lags of the first difference of market depth (DMD(-1) & DMD(-2)), is referred as the extended model throughout the paper. The estimated coefficients of the extended model are given in Table 6. All parameters have significant coefficients and both goodness of fit tests and prediction evaluation analysis indicate that inclusion of lags of DMD provide better results. This shows that the change in macro depth of the stock market contains extra information beyond liquidity deviation and interest rate spread.

Table 6: Probit estimation output C

-0.776

DMD(-1)

-3.870

(0.219)* (0.889)*

DMD(-2)

-3.140 (0.603)*

SPREAD(-4)

-0.884 (0.124)*

MLD(-2)

0.078 (0.034)**

Pseudo-R2

0.49

Prediction Evaluation (Success rate with 50% cut -off probability) Predicted / Total Recession months ( P=1) 19/30 Predicted / Total Non-recession months ( P=0) 171/178 The values in the parentheses are corrected standard deviations of corresponding coefficient estimates, calculated using the Newey-West (1987) technique. The number of Newey-West lags is four. *, **, *** indicates the significance at 1%, 5%, and 10% respectively.

Figure 1 provides the estimated probability of a recession for all three probit models. All three models provide significant signals for recession periods. The responses of models involving equity market factors (MLD (-2)) and the first two lags of DMD) give stronger and

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sharper results compared to the probit using solely SPREAD (-4). Especially for the last two crises (i.e. 2001 and 2008), the performance of the extended model is also superior. Supporting the arguments in the previous literature that the interest rate spread’s power as a leading indicator has weakened since 1980s, the performance of the SPREAD (-4) model is far from sufficient. For instance the probability of a recession calculated by SPREAD (-4) model reached its peak since the last recession in 2002 at 46.2% for the first quarter 2008. However, the average of the estimated probabilities of the SPREAD (-4) model for the 6 recession quarters in 2008 crisis is only 19.6%. On the other hand, the average for the same period of the extended model is 90.5%. Of course, while comparing these models, we need to take into account the fact that forecasting horizon is shorter for the extended model.

Figure 1: Comparison of the estimated recession probabilities by the extended model and spread (-4) model

1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0

NBER Rec. Quarters

Extended Model 14

SPREAD(-‐4)



3.2 Out-of-sample prediction To examine the out-of-sample forecasting power of the models, we calculated onestep ahead forecasts for each quarter starting from last quarter of 1999, as if we can reach only the information available to market participants in that particular quarter. To obtain the out-of-sample forecasts for each quarter, we estimate all of the regression equations discussed above using a subsample, which starts with the first observation (1959Q1) and ends with the given period. Next, we add the next quarter to the subsample and run the regressions again. Following Estrella (1998) and Moneta (2005), we use the pseudo-R2 to measure the forecasting error. Table 7 compares the R-squared statistics of the 49 recursive estimations for both models. The median of the R-squared statistics of the SPREAD (-4) model is only 29.4%, well below the 44.4% of the extended model that employs MLD (-2) and the first 2 lags of DMD.

Table 7: Out-of-sample Pseudo- R2of the models estimated (Ex-post forecast period: 1999Q1 - 2011Q4) Pseudo-R2

Spread (-4)

Spread(-4) & DML(-2)

Extended Model

7.4%

47.7%

73.4%

Figure 2 compares the one-step ahead forecasted probabilities of SPREAD (-4) model and the extended model. There are 9 recession quarters according to NBER definition between Q41999 and Q4 2011. With 50% cut-off point, the extended model is able to predict eight of them, whereas the number of correctly predicted recessions by the SPREAD (-4) model is only one. We should note that the extended model is optimized for a 1‐quarter horizon, whereas the model with the spread alone is known to be much better at a 4‐quarter horizon

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pointed out earlier. A useful result in the figure is that the extended model has performed well at predictions 1 quarter ahead since about the year 2000.

Figure 2: One-step ahead forecasts for SPREAD(-4) and the extended model 1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0

NBER Rec. Quarters

The extended model

SPREAD(-‐4)

These findings highlight that the yield curve and macro liquidity deviation measures complement one another in forecasting crises. A possible explanation is that, as inflation falls, a declining proportion of the fluctuation in the bond market yield spread is information and a rising proportion is uninformative noise. This is consistent with the weaker indicator performance of the yield curve in the latter part of our sample period. In the post sample period, as noted, government asset purchase programs likely further muddied the information signal associated with the yield curve, but likely had less affect on the equity-based indicators. The addition of the macro deviation measure is a potentially powerful way to boost the ability of market observers or policy makers to monitor the systemic risk in the system, even as inflation and the efficacy of the yield curve indicator fluctuate. While the information aggregating properties of equity market prices has long been appreciated, the 16



formulation of the data into the macro liquidity deviation measure forms a key metric for capturing and aggregating the crisis forecasting information in stock markets, based not only on prices but also on market liquidity. Thus the combined set of measures provides a more reliable indication of the likelihood of crisis.

4. Discussion

and Conclusion

This paper finds that combining the yield curve and the market liquidity deviation measures improves our in-sample and out-of-sample ability to predict the onset of a US recession. According to the prediction model, the 4th lag of quarterly average term spread between 10 year and 3 months rates, the first two lags quarterly changes of the market capitalization to GDP (macro depth), and the second lag of the macro liquidity deviation (MLD) are found significant. The 4th lag of the term spread appears to give the initial signal for the crisis; then stock market conditions clearly signal a heightened crisis likelihood, leading up to the initial crisis quarter. Especially the quarter before the crisis, the MLD variable strongly confirms the upcoming recession. Questions remain for future research. Has the equity market liquidity begun to increase in importance relative to the yield spread, given the historically low level of interest rates and the Fed’s interventions directly into longer maturity securities? The Federal Reserve is aware of the Estrella yield curve analysis. As it becomes more familiar with the macro liquidity deviation indicator, will the market’s anticipation of policy response itself affect the ability of the model of forecast crisis? Being able to gauge the probability of the need for aggressive central bank action is likely to further enable timely policy intervention to blunt a recessionary impulse. Moreover, the question remains whether there are analogous indicator variable that could be helpful to authorities in Europe, Japan, and elsewhere, as they attempt to anticipate and mute macroeconomic recessions or crises.

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References Acharya, V. V., Shin, H. and Yorulmazer, T. (2011) Crisis Resolution and Bank Liquidity, Review of Financial Studies 24, 2166-2205. Barro, R. J. (1990) The Stock Market and Investment, Review of Financial Studies 3, 115-31. Bennett, P., Garbage, K. and Kamahi, J. (2000) Enhancing the Liquidity of U.S. Treasury Securities in an Era of Surplus, Federal Reserve Bank of New York Economic Policy Review, April 2000; reprinted in summary in the CFA Digest, Association for Investment Management and Research, Fall 2000, 60-61. Campbell, J. Y., Lettau, M., Malkiel, B. G. and Xu Y. (2001) Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk, Journal of Finance 56, 1-43. Chen, N. (1991) Financial Investment Opportunities and the Macroeconomy, Journal of Finance 46, 529-554. Choi, J. J., Hauser, S. and Kopecky, K. J. (1999) Does the Stock Market Predict Real Activity? Time Series Evidence from the G-7 Countries, Journal of Banking & Finance 23, 1771-1792. Colombage, S. R. N. (2009) Financial Markets and Economic Performances: Empirical Evidence from Five Industrialized Economies, Research in International Business and Finance 23, 339–348. Erdogan, O. (1996) Comparable Approach to the Theory of Efficient Markets: A Modified Capital Asset Pricing Model for Maritime Firms, Capital Markets Board, Ankara. USA LOC: 720187.

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Erdogan, O., Bennett, P. and Ozyildirim, C. (2012) An Early Warning Signal of Financial Crisis by using the Deepness and Liquidity in Stock Markets, The International Review of Applied Financial Issues and Economics 4-1, 58-63. Erdogan, O. and Ozyildirim, C. (2005) Predicting Financial Crisis Using the Relationship between the Stock Market Depth and Liquidity, Marmara University International Finance Conference, Istanbul, 9-10 June. Estrella, A. (1998) A New Measure of Fit for Equations with Dichotomous Dependent Variables, Journal of Business and Economic Statistics 16, 198-205. Estrella, A. (2005) Why Does the Yield Curve Predict Output and Inflation? The Economic Journal 115, 722-744. Estrella, A. and Hardouvelis, G. A. (1991) The Term Structure as a Predictor of Real Economic Activity, Journal of Finance 46, 555-76. Estrella, A. and Mishkin F. S. (1995) The Term Structure of Interest Rates and Its Role in Monetary Policy for the European Central Bank, National Bureau of Economic Research Working Paper, 5279. Estrella, A. and Mishkin F.S. (1997) The predictive power of the term structure of interest rates in Europe and the United States: Implications for the European Central Bank, European Economic Review 41, 1375-1401. Estrella, A. and Mishkin, F. S. (1998) Predicting U.S. Recessions: Financial Variables as leading Indicators, Review of Economics and Statistics 80, 45-61. Estrella, A., Rodrigues, A. R., and Schich, S. (2003) How Stable Is the Predictive Power of the Yield Curve? Evidence from Germany and the United States, The Review of Economics and Statistics 85, 629-644. Fama, E. (1981) Stock Returns, Real Activity, Inflation and Money, American Economic Review 71, 545-65.

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Fama, E. (1990) Stock Returns, Expected Returns, and Real Activity, Journal of Finance 45, 1089-1108. Fischer, S., and Merton, R. C. (1985) Macroeconomics and Finance: The Role of the Stock Market, NBER Working Papers, 1291. Hamilton, J. and Kim, D.H. (2002) A Reexamination of the Predictability of Economic Activity using the Yield Spread, Journal of Money, Credit, and Banking 34-2, 340360. Harvey, C. (1988) The Real Term Structure and Consumption Growth, Journal of Financial Economics 22, 305-333. Harvey, C. R. (1989) Forecasts of Economic Growth from the Bond and Stock Markets, Financial Analysts Journal 45, 38-45. Laurent, R. D. (1988) An Interest Rate-Based Indicator of Monetary Policy, Federal Reserve Bank of Chicago Economic Perspectives 12, 3-14. Levine, R. and Zervos, S. (1998) Stock Markets, Banks, and Economic Growth, The American Economic Review 88, 537-558. Mili, M., Sahut, J. and Teulon, F. (2012) Nonlinear and Asymmetric Linkages between Real Growth in the Euro Area and Global Financial Market Conditions: New Evidence, Economic Modelling 29, 734-741. Mitchell, W. C. and Bums, F. (1938) Statistical Indicators of Cyclical Revivals, NBER Bulletin, 69. Reprinted in: Moore, G.H., (Eds.) Business Cycle Indicators, 1961, Princeton: Princeton U. Press. Moneta, F. (2005) Does the Yield Spread Predict Recessions in the Euro Area? International Finance 8, 263–301 Newey, W.K. and West, K.D. (1987) A simple, Positive Semi-definite, Heteroscedasticity and Autocorrelation Consistent Covariance Matrix, Econometrica 55, 703-708.

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Nyberg, H. (2010) Dynamic Probit Models and Financial Variables in Recession Forecasting, Journal of Forecasting 29, 215–230. Nyholm, K. (2007) A New Approach to Predicting Recessions, Economic Notes by Banca Monte dei Paschi di Siena SpA 36, 27–42 Schwert, G.W. (1990) Stock Returns and Real Economic Activity: A Century of Evidence, Journal of Finance 45, 1237-1257. Stock, J. H. and Watson, M. W. (1989) New Indexes of Coincident and Leading Economic Indicators, in: Blanchard O.J., Fischer S., (Eds.), NBER Macroeconomics Annual 1989, 352-94. Stock, J. H., and Watson, M. W. (2003) Forecasting Output and Inflation: The Role of Asset Price, Journal of Economic Literature 41, 788-829. Wheelock, D. C. and Wohar, M. E. (2009) Can the Term Spread Predict Output Growth and Recessions? A Survey of the Literature, Federal Reserve Bank of St. Louis Review 91, 419-40.

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