Future Stock Market Research Articles

Cash Distributions and Returns

Discounted cash flow analysis suggests that high cash-flow-to-price ratios should predict high future stock return, low future cash flow growth, or both. Existing studies on the predictive power of the dividend-price ratio, however, produce evidence largely inconsistent with this prediction. In this paper, we address this issue by focusing on the total cash distributions that include both dividends and share repurchases net of seasoned equity offerings. Utilizing a long time series of the total-cash-distributions-to-price (tp) ratio constructed from CRSP data since 1927, we establish strong and persistent evidence of stock return predictability at the annual horizon. Based on a wide variety of evaluation methods, the tp ratio is both statistically and economically significant in predicting future stock market returns, and serves as a pervasive state proxy.

Liquidity and The Law of One Price: The Case of the Cash/Futures Basis

Deviations from no-arbitrage relations should be related to frictions associated with transacting; in particular to market illiquidity, because frictions impede arbitrage. Thus, financial market liquidity may play a key role in moving prices to fair values. At the same time, a wide futures/cash basis may trigger arbitrage trades and thereby affect liquidity. We test these ideas by studying the joint dynamic structure of aggregate NYSE market liquidity and the NYSE Composite index futures basis for a relatively long time-period, over 3000 trading days. We find that liquidity and the basis forecast each other in addition to being contemporaneously correlated. There is evidence of two-way Granger causality between the short-term absolute basis and effective spreads, and quoted and effective spreads Granger-cause longer-term absolute bases. These results are preserved after including a proxy for arbitrage financing costs, the Federal Funds rate, which bears an independent positive and significant relation with the short-term absolute basis. Impulse response functions indicate that shocks to the absolute basis predict future stock market liquidity. Overall, the evidence suggests that stock market liquidity enhances the efficiency of the futures/cash pricing system.

Die Verwendbarkeit der ZEW-Aktienindex-Prognosen für aktive Portfoliomanagement-Strategien / The Usefulness of ZEW Stock Market Forecasts for Active Portfolio Management Strategies

Zusammenfassung Einzeltitelempfehlungen von Banken oder Fachzeitschriften treffen in den vergangenen Jahren bei der Investorenschaft immer häufiger auf Misstrauen. Zunehmende Aufmerksamkeit wird hingegen indexbasierten aktiven Portfoliomanagement-Strategien gewidmet. Trotzdem werden nur selten Prognosen zur Entwicklung von Aktienindizes veröffentlicht. Im deutschsprachigen Raum publiziert lediglich das Zentrum für Europäische Wirtschaftsforschung (ZEW) regelmäßig Prognosen zu wichtigen internationalen Aktienindizes. Im vorliegenden Aufsatz wird der Frage nachgegangen, ob diese Prognosen geeignet sind, um darauf aktive Portfoliomanagement-Strategien aufzubauen. Neben der grafischen Gegenüberstellung von Prognosedaten und tatsächlichen Marktdaten werden die einschlägigen Wendepunktfehlerquoten, der Theilsche Ungleichheitskoeffizient „neuer Art“ (U2), der GOVA-Koeffizient und die Prognosequalitätsmatrix als Beurteilungsmaßstäbe herangezogen. Es zeigt sich, dass alle sechs untersuchten Prognosezeitreihen eine gegenwartsorientierte Verlaufsanpassung aufweisen. Fünf von sechs Prognosezeitreihen weisen eine schlechtere, die sechste eine nur minimal bessere Prognosequalität als die entsprechenden Zeitreihen naiver Prognosen auf. Insgesamt muss festgestellt werden, dass die Aktienindexprognosen des ZEW im Zeitraum von Oktober 1994 bis Dezember 2002 keine geeignete Grundlage für die Umsetzung indexbasierter aktiver Portfoliomanagement-Strategien darstellten.

The Role of Stock Markets in Current Account Dynamics: A Time-Series Approach

This paper develops a simple model to study the impact of stock markets on the current account. A closed-form solution for the current is derived from the optimal portfolio and consumption/saving choices of a representative agent. Formally, the model can be seen as a stock market-augmented version of the fundamental equation of the current account popularized by Jeffrey Sachs. It appears to shed light on recent developments in the U.S. current deficit. The model also shows how the current may help predict future stock market performance and/or endowment streams.

The Role of Stock Markets in Current Account Dynamics: A Time Series Approach

This paper develops a simple model to study the impact of stock markets on the current account. A closed-form solution for the current is derived from the optimal portfolio and consumption/saving choices of a representative agent. Formally, the model can be seen as a stock market-augmented version of the fundamental equation of the current account popularized by Jeffrey Sachs. It appears to shed light on recent developments in the U.S. current deficit. The model also shows how the current may help predict future stock market performance and/or endowment streams.

Dynamic Estimation of Volatility Risk Premia and Investor Risk Aversion from Option-Implied and Realized Volatilities

This paper proposes a method for constructing a volatility risk premium, or investor risk aversion, index. The method is intuitive and simple to implement, relying on the sample moments of the recently popularized model-free realized and option-implied volatility measures. A small-scale Monte Carlo experiment suggests that the procedure works well in practice. Implementing the procedure with actual S&P500 option-implied volatilities and high-frequency five-minute-based realized volatilities results in significant temporal dependencies in the estimated stochastic volatility risk premium, which we in turn relate to a set of underlying macro-finance state variables. We also find that the extracted volatility risk premium helps predict future stock market returns.

The Role of Stock Markets in Current Account Dynamics: Some Empirical Evidence

This paper tests a model of the role of stock markets in current dynamics, which is developed in a companion paper. With US data, the model performs better than the same model without stock markets. An insight given by the model is that the current account might help predict future stock market performance. This property receives some preliminary empirical confirmation. These results suggest that stock markets matter to the current account dynamics.

Can interest rate changes help predict future stock price movements? Evidence from the German market

By applying a linear regression model to monthly time series data from the German equity and money markets, this paper challenges the conventional viewpoint that historical data do not possess any explanatory power for future stock market returns.

The Role of Stock Markets in Current Account Dynamics: a Time Series Approach

This paper develops a simple model to study the impact of stock markets on the current account. The model allows for an arbitrary number of risky assets, which form an incomplete market, as well as a risk-free bond. A closed-form solution for the current is derived from the optimal portfolio and consumption/saving choices of a representative agent. Formally, the model can be seen as a stock-market-augmented version of the fundamental equation of the current account popularized by Sachs. In order to make the main points of the model clear, I first solve it taking prices as given. A general equilibrium is analyzed in a companion paper.I suggest that the model sheds light on the recent US current deficit. Some claim that this current deficit reflects over-optimistic -irrationally exuberant- expectations of future stock market performance. The model, however, suggests that it is optimal for a country to run a current deficit even if people do not expect the stock market boom to last. Another insight afforded by the model is that the current may help predict future stock market performance and/or future endowments streams. This forecasting property can be formally expressed by a set of Granger causality and Granger causal priority propositions.

An Empirical Investigation of the Performance Consequences of Nonfinancial Measures

Firms are increasingly implementing new performance measurement systems to track nonfinancial metrics such as customer and employee satisfaction, quality, market share, productivity, and innovation. This study examines the implications of nonfinancial performance measures included in compensation contracts on current and future performance. Contextual factors, environmental factors, and strategic plans vary across firms and, in turn, adopting appropriate nonfinancial measures determines the performance consequences of such measures. Our findings support the contention that firms that employ a combination of financial and nonfinancial performance measures have significantly higher mean levels of returns on assets and higher levels of market returns. Although we find evidence that the adoption of nonfinancial measures improves firms' current and future stock market performance, we find only partial support for accounting performance improvements. Overall, the results indicate that the association between the use of nonfinancial measures and firm performance is contingent on the firm's operational and competitive characteristics.

The Role of Stock Markets in Current Account Dynamics: Evidence from the United States

This paper tests a model of the role of stock markets in current account dynamics, developed in a companion paper. With U.S. data, the model performs better than the same model without stock markets. An insight given by the model is that the current account might help predict future stock market performance. This property receives some preliminary empirical confirmation. The results also suggest that stock markets matter to the current account dynamics.

The Role of Stock Markets in Current Account Dynamics: Evidence From the United States

This paper tests a model of the role of stock markets in current account dynamics, developed in a companion paper. With U.S. data, the model performs better than the same model without stock markets. An insight given by the model is that the current account might help predict future stock market performance. This property receives some preliminary empirical confirmation. The results also suggest that stock markets matter to the current account dynamics.

Captação de recursos por fundos de investimento e mercado de ações

O presente artigo analisa a relação contemporânea e defasada entre fluxos de entrada e saída de recursos nos fundos de ações no Brasil e o desempenho de alguns ativos fundamentais, tais como o Índice Bovespa, a taxa de câmbio comercial e a taxa de juros no mercado interbancário. Os principais objetivos do trabalho são (1) encontrar evidências de que o fluxo de recursos depende do desempenho desses ativos, com particular atenção para o Índice Bovespa, e (2) verificar se as estatísticas de captação de recursos podem ser utilizadas para prever preços futuros, principalmente o valor da carteira do Índice Bovespa. Com o auxílio de diversas ferramentas de análise de séries temporais, são obtidos resultados que indicam haver evidências da dependência entre captação de recursos e desempenho do Índice, mas que o conhecimento do fluxo de recursos não ajuda a prever seu o desempenho futuro, resultado compatível com a eficiência semiforte do mercado brasileiro de ações.

Predictabilty of Aggregate Stock Market Returns: Evidence Based on Glamour Stocks

It is now well documented that returns on the aggregate stock market are predictable and negatively autocorrelated over longer investment horizons. In this paper, we investigate the predictability of the aggregate stock market returns using past returns of glamour and value stocks. We find the relationships between returns of glamour and value stocks with future stock market returns are quite different. In particular, we find that annual excess returns on the stock market index are negatively related to the returns of glamour stocks in the previous 36-month period. In contrast, the past returns of value stocks do not have any explanatory power in predicting aggregate stock market excess returns. Furthermore, stock market returns, which are purged of the effects of the glamour stocks, do not have any reliable predictive power in explaining the future stock market returns. In contrast, the glamour stocks have a predictive power even after controlling for the information in the past market returns. Our evidence of the unique predictive ability of glamour stocks seems to be inconsistent with the time-varying market risk-premium explanation for the predictability of the aggregate stock market returns.

Is implied volatility an informationally efficient and effective predictor of future volatility?

We examine how well implied volatility forecasts future stock market volatility. If markets are efficient and the option pricing model is correct, the implied volatility calculated from option prices should be an unbiased and informationally efficient predictor of future volatility; that is, it should correctly impound all available information, including the asset’s price history. However, numerous studies have found that the implied volatility forecast is biased and/or is not informationally efficient. We re-examine this issue using S&P500 futures options data, a more active market which is less susceptible to measurement error than the OEX options market considered in most previous studies. Our findings are that, first, implied volatility has strong predictive power and generally subsumes the information in historical volatility; second, prediction and efficiency results are quite sensitive to the forecasting horizon and whether the data period covers the October 1987 stock market crash; and third, at least in the S&P500 options market, measurement error due to such factors as bid–ask spreads, non-synchronous price observations and discrete prices does not significantly affect the results.

Using Science and Technology Indicators to Manage R&D as a Business

The purpose of this article is to develop a method to predict future stock market performance of a company, using its technological quality as measured by its patent portfolio. This is accomplished by using a series of science and technology indicators developed from a unique patent database. Several studies have examined the relationship between various research and development (R&D) metrics and different measures of economic performance, yet few have explored this relationship using market-based measures of performance. Our findings show that an investment model based solely on this group of R&D metrics does substantially better than the Standard and Poor's (S&P) 500 Index.

The Death of the Risk Premium

The authors contend that most of the institutional investing community is expecting far higher returns than are realistic from current market levels. Extrapolating the past is the easiest, and worst, way to forecast the future. Unfortunately, most investors' return expectations are shaped by a simple extrapolation of either recent or long–term past returns. If, instead, the constituent parts of equity market returns are examined, we find that it is remarkably difficult to make a case for a positive equity risk premium (the premium of future stock market returns relative to bond yields) from current market levels. None of this analysis is contingent on any assumption that market P/E ratios or dividend yields should return to historical levels. If market levels are fair and are fully sustained in the years ahead, there is still little or no room for a positive equity risk premium. If there is not a positive risk premium, then actuarial return assumptions are likely to be too optimistic, with far–reaching implications for pension funding ratios, corporate earnings, future pension contributions, and appropriate asset allocation policy.

Future Asian stock market trends

Future Asian stock market trends

Volatility Forecasts, Trading Volume and the ARCH vs Option-Implied Volatility Tradeoff

Market expectations of future return volatility play a crucial role in finance; so too does our understanding of the process by which information is incorporated in security prices through the trading process. This paper seeks to learn something about both of these issues by investigating empirically the role of trading volume (a) in predicting the relative informativeness of volatility forecasts produced by ARCH models versus the volatility forecasts derived from option prices, and (b) in improving volatility forecasts produced by ARCH and option models and combinations of models. We find that if trading volume was low during period t-1 then ARCH is much more important than options for forecasting future stock market volatility. Conversely, if volume was high during period t-1, then option-implied volatility is much more important than ARCH for forecasting future volatility. Our findings reveal an important regime-switching role for trading volume and suggest that option markets may be more efficient in high volume states. Results from various tests also uncover possible sources of volume-related nonlinearity in the relationship between past and future return innovations as captured by asymmetric ARCH models.

The Relation between Stock Market Movements and NYSE Seat Prices

Exchange seat prices are widely reported and followed as measures of market sentiment. This paper analyzes the information content of NYSE seat prices using: (1) annual seat prices from 1869 to 1998, and (2) the complete record of trades, bids and offers for the seat market from 1973 to 1994. Seat market volumes have predictive power regarding future stock market returns, consistent with a model where seat market activity is a proxy for unobserved factors affecting expected returns. We find abnormally large price movements in seats prior to October 1987, consistent with the hypothesis that seat prices capture market sentiment.