Dealing with financial instability under a DSGE modeling approach with banking intermediation: A predictability analysis versus TVP-VARs

In the dynamic stochastic general equilibrium (DSGE) literature there has been an increasing awareness on the role that the banking sector can play in macroeconomic activity. We present a DSGE model with financial intermediation as in Gertler and Karadi (2011). The estimation of shocks and of the structural parameters shows that time-variation should be crucial in any attempted empirical analysis. Since DSGE modelling usually fails to take into account inherent nonlinearities of the economy, we propose a novel time-varying parameter (TVP) state-space estimation method for VAR processes both for homoskedastic and heteroskedastic error structures. We conduct an exhaustive empirical exercise to compare the out-of-sample predictive performance of the estimated DSGE model with that of standard ARs, VARs, Bayesian VARs and TVP-VARs. We find that the TVP-VAR provides the best forecasting performance for the series of GDP and net worth of financial intermediaries for all steps-ahead, while the DSGE model outperforms the other specifications in forecasting inflation and the federal funds rate at shorter horizons.     

Do Decomposed Financial Ratios Predict Stock Returns and Fundamentals Better?

We investigate the prediction of excess returns and fundamentals by financial ratios, which include dividend‐price ratios, earnings‐price ratios, and book‐to‐market ratios, by decomposing financial ratios into a cyclical component and a stochastic trend component. We find both components predict excess returns and fundamentals. Cyclical components predict increases in future stock returns, while stochastic trend components predict declines in future stock returns in long horizons. This helps explain previous findings that financial ratios in the absence of decomposition find weak predictive power in short horizons and some predictive power in long horizons. We also find both components predict fundamentals.     

Institutional Trade Persistence and Long‐Term Equity Returns

Recent studies show that single‐quarter institutional herding positively predicts short‐term returns. Motivated by the theoretical herding literature, which emphasizes endogenous persistence in decisions over time, we estimate the effect of multiquarter institutional buying and selling on stock returns. Using both regression and portfolio tests, we find that persistent institutional trading negatively predicts long‐term returns: persistently sold stocks outperform persistently bought stocks at long horizons. The negative association between returns and institutional trade persistence is not subsumed by past returns or other stock characteristics, is concentrated among smaller stocks, and is stronger for stocks with higher institutional ownership.     

Momentum in weekly returns: the role of intermediate‐horizon past performance

Gutierrez and Kelly (2008) recently documented momentum in weekly returns. Using the Australian market as a setting, we find that stocks with high 1‐week returns exhibit a continuation in returns up to 1 year after a brief initial return reversal. However, after controlling for the intermediate‐horizon past performance, the continuation in returns after 1‐week returns disappears. These findings suggest that different past investment horizons contain separate information about price momentum and that intermediate‐term trends dominate short‐term trends in driving future returns. Overall, we show that understanding momentum over different horizons facilitates the design of more profitable trading strategies.     

Return predictability: The dual signaling hypothesis of stock splits

This paper aims to differentiate between optimistic splits and overoptimistic/opportunistic splits. Although markets do not distinguish between these two groups at the split announcement time, optimistic (overoptimistic/opportunistic) splits precede positive (negative) long‐term buy‐and‐hold abnormal returns. Using the calendar month portfolio approach, we show that the zero‐investment, ex ante identifiable, and fully implementable trading strategy proposed in this paper can generate economically and statistically significant positive abnormal returns. Our findings indicate that pre‐split earnings management and how it relates to managers’ incentives, is an omitted variable in the studies of post‐split long‐term abnormal returns.     

Moments of multivariate regime switching with application to risk-return trade-off

We use a Fourier transform to derive multivariate conditional and unconditional moments of multi-horizon returns under a regime-switching model. These moments are applied to examine the relevance of risk horizon and regimes for buy-and-hold investors. We analyze the impact of time-varying expected returns and risk (variance and covariance) on portfolio allocations' “term structure”—portfolio allocations as a function of the investment horizon. Using monthly observations on S&P composite index and 10-year Government Bond, we find that the term structure of the optimal allocations depends on market conditions measured by the probability of being in bull state. At short horizons and when this probability is low, buy-and-hold investors decrease their holdings of risky assets. We also find that the conditional optimal portfolio performs quite well at short and intermediate horizons and less at long horizons.     

Using Artificial Neural Networks to forecast Exchange Rate,including VAR‐VECM residual analysis and prediction linear combination

The Euro US Dollar rate is one of the most important exchange rates in the world, making the analysis of its behavior fundamental for the global economy and for different decision‐makers at both the public and private level. Furthermore, given the market efficiency of the EUR/USD exchange rate, being able to predict the rate's future short‐term variation represents a great challenge. This study proposes a new framework to improve the forecasting accuracy of EUR/USD exchange rate returns through the use of an Artificial Neural Network (ANN) together with a Vector Auto Regressive (VAR) model, Vector Error Corrective model (VECM), and post‐processing. The motivation lies in the integration of different approaches, which should improve the ability to forecast regarding each separate model. This is especially true given that Artificial Neural Networks are capable of capturing the short and long‐term non‐linear components of a time series, which VECM and VAR models are unable to do. Post‐processing seeks to combine the best forecasts to make one that is better than its components. Model predictive capacity is compared according to the Root Mean Square Error (RMSE) as a loss function and its significance is analyzed using the Model Confidence Set. The results obtained show that the proposed framework outperforms the benchmark models, decreasing the RMSE of the best econometric model by 32.5% and by 19.3% the best hybrid. Thus, it is determined that forecast post‐processing increases forecasting accuracy.     

Consumption risk and the cross-section of government bond returns

In this paper we provide a consumption-based explanation of risk in nominal US Treasury bond portfolios. We use a consumption-CAPM with Epstein–Zin–Weil recursive preferences. Our model introduces two sources of risk: uncertainty about current consumption (reflected in contemporaneous consumption growth) and uncertainty about prospects of consumption in a long run (reflected in innovations to expectations about future consumption growth). We use a novel approach to estimate pricing factors in our model: we employ a factor-augmented VAR model with common factors, extracted from a large panel of macroeconomic and financial data, as state variables. We find that the important source of risk in US bonds is related to uncertainty in prospects in future consumption and it induces a positive and significant risk premium. We find as well that covariance risk related to innovations in expectations about future consumption growth is greater for long term bond portfolios than for short term bond portfolios, which is consistent with a duration measure of risk and justifies why long term bonds require greater premium than short term bonds. Our model explains well the cross-sectional variation in average excess returns of bonds with different maturities over the period 1975–2011 and compares favorably with competing models.     

Expectations‐Based Management

The authors begin by summarizing the results of their recently published study of the relation between stock returns and changes in several annual performance measures, including not only growth in earnings and EVA, but changes during the year in analysts' expectations about future earnings over three different periods: (1) the current year; (2) the following year; and (3) the three‐year period thereafter. The last of these measures—changes in analysts' expectations about three‐ to five‐year earnings—had by far the greatest explanatory “power” of any of the measures tested. Besides being consistent with the stock market's taking a long‐term, DCF approach to the valuation of companies, the authors' finding that investors seem to care most about earnings three to five years down the road has a number of important implications for financial management: First, a business unit doesn't necessarily create shareholder value if its return on capital exceeds the weighted average cost of capital—nor does an operation that fails to earn its WACC necessarily reduce value. To create value, the business's return must exceed what investors are expecting. Second, without forecasting returns on capital, management should attempt to give investors a clear sense of the firm's internal benchmarks, both for existing businesses and new investment. Third, management incentive plans should be based on stock ownership rather than stock options. Precisely because stock prices reflect expectations, the potential for prices to get ahead of realities gives options‐laden managers a strong temptation to manipulate earnings and manage for the short term.     

Why do large shareholders adopt a short‐term versus a long‐term investment horizon in different firms?

I ask why the same large shareholders have different investment horizons. Using data for 1998–2013, I examine four fundamental firm policies for their potential influence on blockholders’ investments with different time horizons. The panel ordinary least squares, difference‐in‐difference (using the Sarbanes‐Oxley Act), logistic, and dynamic generalized method of moments regression analyses reveal that blockholders adopt a short‐term horizon in smaller firms with a less independent board, high leverage, and high dividends while the same blockholders keep their investments longer in firms with a more independent board and low dividends. Under various economic conditions, different firm characteristics gain importance in blockholders’ decision on short‐term versus long‐term investments.     

Banks’ equity performance and the term structure of interest rates

Using an extensive global sample, this paper investigates the impact of the term structure of interest rates on bank equity returns. Decomposing the yield curve to its three constituents (level, slope and curvature), the paper evaluates the time‐varying sensitivity of the bank's equity returns to these constituents by using a diagonal dynamic conditional correlation multivariate GARCH framework. Evidence reveals that the empirical proxies for the three factors explain the variations in equity returns above and beyond the market‐wide effect. More specifically, shocks to the long‐term (level) and short‐term (slope) factors have a statistically significant impact on equity returns, while those on the medium‐term (curvature) factor are less clear‐cut. Bank size plays an important role in the sense that exposures are higher for SIFIs and large banks compared to medium and small banks. Moreover, banks exhibit greater sensitivities to all risk factors during the crisis and post‐crisis periods compared to the pre‐crisis period; though these sensitivities do not differ for market‐oriented and bank‐oriented financial systems.     

Attracting Long‐Term Shareholders

Corporate CEOs often say they don't hear enough from shareholders about strategic issues related to long‐term value creation. At the same time, they claim to hear with predictable regularity from short‐term investors about their success (or failure) in hitting consensus earnings targets. But as the authors of this article begin by noting, there is mounting evidence that companies get the shareholders they deserve—that companies that provide quarterly earnings guidance and otherwise focus investor attention on near‐term earnings targets tend to attract more transient investors. The authors go on to argue that companies with a compelling long‐term vision can expect to benefit not only from more farsighted managerial decision‐making, but also from building a base of longer‐term investors who share management's view of success, and how it can and ought to be achieved. Such a shift in strategic focus and disclosure toward longer‐run performance creates a virtuous cycle—one in which companies that gain the interest and backing of investors with longer horizons end up reinforcing management's confidence to undertake value‐adding investments in their company's future. Even if most companies can't pick their shareholders, they can develop an investor engagement strategy designed to attract long‐term investors. In this article, the chairman and president of FCLTGlobal outline the underlying strategy behind long‐term investor relations and the four key components of such an approach.     

Dividend yields and expected stock returns: alternative procedures for inference and measurement

Alternative ways of conducting inference and measurement forlong-horizon forecasting are explored with an application todividend yields as predictors of stock returns. Monte Carloanalysis indicates that the Hansen and Hodrick (1980) procedureis biased at long horizons, but the alternatives perform better.These include an estimator derived under the null hypothesisas in Richardson and Smith (1991), a reformulation of the regressionas Jegadeesh (1990), and a vector autoregression (VAR) as inCampbell and Shiller (1988), Kandel and Stambaugh (1988), andCampbell (1991). The statistical properties of long-horizonstatistics generated from the VAR indicate interesting patternsin expected stock returns.     

Putting the New Keynesian DSGE Model to the Real‐Time Forecasting Test

The paper compares the quality of real‐time forecasts from a standard medium‐scale New Keynesian dynamic stochastic general equilibrium (DSGE) model to those from the Survey of Professional Forecasters (SPF) and DSGE‐VARs. It is shown that the DSGE model is relatively successful in forecasting the U.S. economy. This is especially true for forecasts conditional on SPF nowcasts, in which case the forecasting power of the DSGE turns out to be similar or better than that of the SPF for all the variables and horizons. An important weakness of the benchmark DSGE model is the poor absolute performance of its point forecasts and rather badly calibrated forecast densities.     

The 52-Week High and Momentum Investing

When coupled with a stock's current price, a readily available piece of information—the 52‐week high price–explains a large portion of the profits from momentum investing. Nearness to the 52‐week high dominates and improves upon the forecasting power of past returns (both individual and industry returns) for future returns. Future returns forecast using the 52‐week high do not reverse in the long run. These results indicate that short‐term momentum and long‐term reversals are largely separate phenomena, which presents a challenge to current theory that models these aspects of security returns as integrated components of the market's response to news.     

Competition and risk‐taking in investment banking

How does competition affect the investment banking business and the risks individual institutions are exposed to? Using a large sample of investment banks operating in seven developed economies over 1997–2014, we apply a panel VAR model to examine the relationships between competition and risk without assuming any a priori restrictions. Our main finding is that investment banks’ higher risk exposure, measured as a long‐term capital‐at‐risk and return volatility, was facilitated by greater competitive pressures for both boutique investment banks and full‐service investment banks. Overall, we find some evidence that more competition leads to more fragility before and during the recent financial crisis.     

Loyalty‐Shares: Rewarding Long‐term Investors

In the face of growing concern about investors' excessive focus on quarterly earnings, recent research has found new evidence of the benefits of a committed long‐term shareholder base, whether in terms of higher profitability, R&D investment, greater integration of ESG factors, or a reduced cost of capital. In light of this evidence, this article takes up the challenge of proposing a market solution to this problem. Although much has already been done in the financial industry to lengthen the outlook of executives by imposing longer vesting periods for stock options, a significant fraction of institutional shareholders continues to have a short‐term orientation. The authors propose that companies try to attract a more long‐term‐oriented shareholder base by modifying the form of the common share contract to include a special reward for buy‐and‐hold investors. The type of contract proposed—called a “loyalty share”—is a call‐warrant attached to each share that is exercisable at a specified time‐horizon—say, three years—and exercise price, but is nontransferable and hence has value only if the share is held for the entire length of the specified “loyalty period.” Such a reward is expected to encourage a longer‐term valuation outlook, since those shareholders seeking the loyalty reward are likely to be those who are most confident about the company's ability to increase its value through the expiration of the loyalty period.     

Risk‐Adjusted Long‐Term Contrarian Profits: Evidence from Non‐S&P 500 High‐Volume Stocks

Can trading volume help unravel the long‐term overreaction puzzle? With portfolios of non‐S&P 500 NYSE stocks, we show that (1) both the high‐ and low‐volume (abnormal volume) contrarian portfolios earn a much higher market‐adjusted excess return than the normal‐volume contrarian portfolio, (2) however, when leverage‐induced risk is factored in, excess returns from contrarian portfolios with normal‐ and low‐volume stocks are insignificant, (3) only excess returns from high‐volume contrarian stocks are significant and cannot be explained by the time‐varying risk and return framework, and (4) such high‐volume, risk‐adjusted excess returns arise mainly from winner (glamour) stocks.     

Vector autoregressions and reduced form representations of DSGE models

The performance of dynamic stochastic general equilibrium models is often tested against estimated VARs. This requires that the data-generating process consistent with the DSGE theoretical model has a finite order VAR representation. This paper discusses the assumptions needed for a finite order VAR(p) representation of a DSGE model to exist. When a VAR(p) is only an approximation to the exact infinite order VAR, the truncated VAR(p) may return largely incorrect estimates of the impulse response function. The results do not hinge on small-sample bias or on incorrect identification assumptions. But the bias introduced by truncation can lead to bias in the identification of the structural shocks. Identification strategies that work in the exact VAR representation perform poorly in the truncated VAR.     

Do long‐term institutional investors foster corporate innovation?

This article examines the effect of institutional investors’ investment horizons on firms’ innovation activities. We conjecture that the presence of long‐term institutional investors mitigates managerial myopia, prompting firms to generate greater corporate innovation outputs. Using data on patents and patent citations for US firms, we find that institutions’ investment horizons are positively related to the number of patents and patent citations. We also document that long‐term (short‐term) institutional ownership is positively (negatively) related to the innovation outputs. This article makes an additional contribution to the corporate innovation literature by addressing the positive role of long‐term institutional investors.