Can Consumer Attitudes Forecast Household Spending in the United States? Further Evidence from the Michigan Survey of Consumers

This article examines the usefulness of various measures of consumer confidence in forecasting household spending in the United States. Using the reduced-form equation of Carroll, Fuhrer, and Wilcox (American Economic Review 84:1397-1408, 1994), we find that for the post-World War II period, the Index of Consumer Expectations is incrementally more informative about household spending than the Index of Consumer Sentiment for all categories of consumption examined. A similar conclusion emerges when Carroll, Fuhrer, and Wilcox's data set is used. Our overall results confirm the view that indices of consumer confidence reflect consumers' perception of future economic conditions. Also, the ability of these confidence indices to predict future consumption growth can be construed as a clear rejection of the random walk hypothesis of Hall (Journal of Political Economy 86:971-87, 1978).     

Forecasting the Yen/U.S. Dollar exchange rate: Empirical evidence from a capital enhanced relative PPP-based model

This study uses a relative purchasing power parity (PPP) model based on price indexes (consumer, CPI or traded-goods price indexes, TPI), interest rate differentials, and a linear forecasting technique to determine the horizon over which such a model outperforms a random walk in forecasting the Yen/U.S. Dollar exchange rates out-of-sample. The results improve if one adjusts a simple CPI-based PPP-model by interest rate differentials, while the best results are obtained using a TPI-based PPP-model. For example, the TPI-based model, adjusted by interest rate differentials, is able to statistically significantly outperform the pure random walk starting at forecast horizons of 1 month.     

The implications of monetary expansion in China for the US dollar

We examine the influence of rapid growth in China's money supply on the US dollar within a framework of monetary models of exchange rates. We develop out-of-sample forecasts of the US dollar exchange rate using US and global data on price level, output, and interest rates, and money supply data for the US, China, and the rest of the world for the period 1996–2013. Monetary model forecasts significantly outperform a random walk forecast in terms of mean squared forecast error in the long run. A monetary error correction model with sticky prices performs best. Rolling sample analysis indicates changes over time in the influence of Chinese money supply in forecasting the US dollar. The expectation is that rapid money growth in China would increase the demand for dollars thus raising the value of the dollar, yet our forecasts are to the contrary for the mid 2000s. This is consistent with anticipation of renminbi appreciation under China’s managed exchange rate, which made holding renminbi more attractive. With the break from a dollar peg in 2005 and subsequent currency appreciation, the distortion was alleviated and the forecast direction for the dollar became as expected.     

The Efficiency of the Chinese Commodity Futures Markets： Development and Empirical Evidence

I. Introduction In the early 1980s China set about transforming its moribund economy by adopting, in stages, various elements of the market-based systems. Among the reforms were thederegulation of prices and the introduction of financial markets for assets. The first stock market was set up in Shanghai in 1991 and in the same year the first commodities market opened in Zhengzhou, Henan Province. Since then, both the stock market and the commodities futures markets have expanded substantiall…     

FORECASTING PERFORMANCE OF AN ESTIMATED DSGE MODEL FOR THE SOUTH AFRICAN ECONOMY

We construct a small open‐economy New Keynesian dynamic stochastic general equilibrium (DSGE) model for South Africa with nominal rigidities, incomplete international risk sharing and partial exchange rate pass‐through. The parameters of the model are estimated using Bayesian methods, and its out‐of‐sample forecasting performance is compared with Bayesian vector autoregression (VAR), classical VAR and random‐walk models. Our results indicate that the DSGE model generates forecasts that are competitive with those from other models, and it contributes statistically significant information to combined forecast measures.     

Real Exchange Rate Forecasting and PPP: This Time the Random Walk Loses

This paper brings four new insights into the Purchasing Power Parity (PPP) debate. First, we show that a half-life PPP (HL) model is able to forecast real exchange rates better than the random walk (RW) model at both short and long-term horizons. Second, we find that this result holds if the speed of adjustment to the sample mean is calibrated at reasonable values rather than estimated. Third, we find that it is preferable to calibrate, rather than to elicit as a prior, the parameter determining the speed of adjustment to PPP. Fourth, for most currencies in our sample, the HL model outperforms the RW also in terms of nominal exchange rate forecasting.     

LIQUIDITY AND THE INFORMATIONAL EFFICIENCY OF AFRICAN STOCK MARKETS

The hypothesis that a stock market price index follows a random walk is tested for 11 African stock markets, Botswana, Côte d'Ivoire, Egypt, Ghana, Kenya, Mauritius, Morocco, Nigeria, South Africa, Tunisia and Zimbabwe using joint variance ratio tests with finite-sample critical values, over the period beginning in January 2000 and ending in September 2006. The iid random walk hypothesis is rejected in all 11 markets. In four stock markets, Egypt, Nigeria, Tunisia and South Africa, weekly returns are a martingale difference sequence. Liquidity is an important factor which contributes to whether a stock market follows a random walk.     

Is Violation of the Random Walk Assumption an Exception or a Rule in Capital Markets?

Both the efficient market hypothesis and modern portfolio theory rest on the assumptions of the Gaussian probability distribution and independence of consecutive returns. This paper provides a brief excursion into the history of capital market research. A measure of long-range dependence (Hurst exponent) was applied to daily returns of selected stock indices and individual firms. The Hurst exponent was estimated using rescaled range analysis. The estimates are based on an unusually large sample of empirical-time series from capital markets. This method distinguishes whether the data-generating process follows random walk or exhibits antipersistent or persistent behavior. Both the efficient market hypothesis and modern portfolio theory assume that the data-generating process has no memory, i.e. follows Brownian motion. The random walk process is characterized by a Hurst exponent value of 0.5. Values greater than 0.5 and less than 1 indicate a persistence of local trends. Values between 0 and 0.5 indicate a process that reverts to the mean more often than a random process (mean-reverting process). The results indicated that the series of daily returns exhibit predominantly persistent or antipersistent behavior. Therefore, Brownian motion cannot be perceived as the norm for describing stock market behavior. These findings challenge the assumption of a random walk in stock prices, valuation models and assessment of risk.

     

Fear of Floating: Korea's Exchange Rate Policy after the Crisis

The purpose of this paper is to explore the question of whether a free floating exchange rate regime is a viable option for Korea. This paper divides the sample period into three subperiods: pre-crisis, crisis, and post-crisis. We then analyze the causal relationships among both levels and volatility of three financial variables: exchange rates, interest rates, and stock prices. By using Granger causality tests and variance decomposition, our empirical results show that causal relations among the three variables are weak during the post-crisis period, and furthermore, shocks in other financial markets do not have a significant contribution to explain the variations of each variable's forecast errors. Based on these empirical findings, we infer that the Korean government, having adopted the de jure freely floating exchange rate regime, is still fearful of floating for various reasons. J. Japan. Int. Econ., June 2001, 15(2), pp. 225–251. Department of Economics, Korea University, Seoul, Korea; Korea Institute for International Economic Policy, Seoul, Korea. Copyright 2001 Academic Press.Journal of Economic Literature Classification Numbers: F3, F4.     

The Role of Current Account Balance in Forecasting the US Equity Premium: Evidence From a Quantile Predictive Regression Approach

The purpose of this paper is to investigate whether the current account balance can help in forecasting the quarterly S&P500-based equity premium out-of-sample. We consider an out-of-sample period of 1970:Q3 to 2014:Q4, with a corresponding in-sample period of 1947:Q2 to 1970:Q2. We employ a quantile predictive regression model. The quantile-based approach is more informative relative to any linear model, as it investigates the ability of the current account to forecast the entire conditional distribution of the equity premium, rather than being restricted to just the conditional-mean. In addition, we employ a recursive estimation of both the conditional-mean and quantile predictive regression models over the out-of-sample period which allows for time-varying parameters in the forecast evaluation part of the sample for both of these models. Our results indicate that unlike as suggested by the linear (mean-based) predictive regression model, the quantile regression model shows that the (changes in the) real current account balance contains significant out-of-sample information when the stock market is performing poorly (below the quantile value of 0.3), but not when the market is in normal to bullish modes (quantile value above 0.3). This result seems to be intuitive in the sense that, when the markets are performing average to well, that is performing around the median and above of the conditional distribution of the equity premium, the excess return is inherently a random-walk and hence, no information, from a predictor (changes in the real current account balance) is able to predict the equity premium.     

Occupational Mobility Pattern in Chonju,Korea

This paper investigates the existence of the inter‐dependence between the Indian stock market and Asia's emerging markets since 1990. This study analyzes whether the MSCI Asian Index has significantly influenced the Bombay Stock Exchange Index before, during, and after the Asian financial crisis. To address this issue, the author first uses a rolling correlation, and conduct uni‐directional and bi‐directional causality tests using the Granger causality test. He then examines the impulse response functions and variance decompositions of forecast errors based on a VAR (vector auto‐regression) model. These tests provide evidence that the influence of the Asian market on the Indian market has increased during and after the Asian financial crisis. These results can be interpreted as evidence that the Indian market has been moving toward integration with other Asian markets.     

Are long-term return anomalies illusions?: Evidence from the spot Yen

This study investigates the sensitivity of the long-term return anomaly observed in the US$/Yen currency market to sample and method bias during the period from 11 October 1983 to 21 July 1999. Initially the CUSUM statistic is employed to identify subperiods of sign shifts in the mean returns. Then the dependence in these subperiods is investigated using the Hurst [Hurst, H.E., 1951. Trans. Am. Soc. Civil Eng. 116, 770–799] and the Lo [Lo, A.W., 1991. Econometrica 59 (5), 1279–1313] rescaled range statistics. The results suggest that rejection of the random null is conditional on both the procedure and the period being tested. The Hurst test may incorrectly lead to a rejection of the null hypothesis. However, the Lo test is a more appropriate approach to the data in this study. Thus previous research based on the Hurst test may have inadvertently introduced a method bias.     

The Voluntary Inclusion of Forecasts in the MD&A Section of Annual Reports*

Abstract. In this study, we appeal to theories advanced by Darrough and Stoughton (1990) to enhance our understanding of why some firms may voluntarily include directional forecasts in their annual reports while others do not. The data are consistent with their predictions that a firm's disclosure policy reflects its concern for both financial market valuation and product market competition. We find that for “good news firms, the probability of forecasting is increasing in the financing requirements but decreasing in the threat of competitor entry. The converse holds for “bad news” firms. These results lend further empirical support to the observation that the familiar good news hypothesis tested in the management earnings forecast literature offers only a partial explanation for the decision to forecast. Interestingly, however, even after controlling for financial and product market considerations, an overall voluntary disclosure bias still exists in the data. The data also provide support for the OSC's concern about a voluntary disclosure bias. Only 17.5 percent of our sample forecasts represent revisions downward relative to the previous year's results. However, in contrast to the OSC's concern about a general lack of forward-looking disclosures in annual reports, 35.9 percent of our sample firms include directional forecasts in their MD&A or elsewhere in the annual report.     

Forecasting China's economic growth and inflation

Although macroeconomic forecasting forms an integral part of the policymaking process, there has been a serious lack of rigorous and systematic research in the evaluation of out-of-sample model-based forecasts of China's real GDP growth and CPI inflation. This paper fills this research gap by providing a replicable forecasting model that beats a host of other competing models when measured by root mean square errors, especially over long-run forecast horizons. The model is shown to be capable of predicting turning points and to be usable for policy analysis under different scenarios. We find that M2 supply, rather than interest rates, is a key variable for forecasting macroeconomic variables. Annual GDP growth for the next five years is predicted to be close to the 6.5% official target and a future GDP growth path is predicted to be of L-shape rather than U-shape.     

Investors' Misweighting of Firm-Level Information and the Market's Expectations of Earnings

Prior studies use fundamental earnings forecasts to proxy for the market's expectations of earnings because analyst forecasts are biased and are available for only a subset of firms. We find that as a proxy for market expectations, fundamental forecasts contain systematic measurement errors analogous to those in analysts' biased forecasts. Therefore, these forecasts are not representative of investors' beliefs. The systematic measurement errors from using fundamental forecasts to proxy for market expectations occur because investors misweight the information in many firm-level variables when estimating future earnings, but fundamental forecasts are formed using the historically efficient weights on firm-level variables. Thus, we develop an alternative ex ante proxy for the market's expectations of future earnings (“the implied market forecast”) using the historical (and inefficient) weights, as reflected in stock returns, that the market places on firm-level variables. A trading strategy based on the implied market forecast error, which is measured as the difference between the implied market forecast and the fundamental forecast, generates excess returns of approximately 9 percent per year. These returns cannot be explained by investors' reliance on analysts' biased forecasts. Overall, our results reveal that market expectations differ from both fundamental forecasts and analysts' forecasts.     

Short-term forecasting of prices for the Russian wholesale electricity market based on neural networks

The article considers the possibility of using neural networks for the short-term forecasting of electricity prices in the day-ahead market (DAM) based on factors strictly determined for the forecast period. A set of six factors has been determined, which allows an hourly forecast of the DAM price to be constructed for a month in each of the four seasons with a high accuracy. The proposed model shows low average errors in forecasting the price for each hour of the month and in turn allows possible significant price deviations to be anticipated.     

Exchange Rate Forecasting: Results from a Threshold Autoregressive Model

Structural models of exchange rate determination rarely forecast the exchange rate more accurately than a naive random walk model. Recent innovations in exchange rate modeling indicate that changes in the exchange rate may follow a self-exciting threshold autoregressive model (SETAR). We estimate a SETAR model for various monthly US dollar exchange rates and generate forecasts for the estimated models. We find: (1) nonlinearities in the data not uncovered by the standard nonlinearity tests and (2) that the SETAR model produces better forecasts than the naive random walk model.     

Forecasting macroeconomic variables in emerging economies

Forecasting macroeconomic variables in rapidly changing emerging economies presents a number of challenges. In addition to structural changes, the time-series data are usually available only for a short number of periods, and predictors are available in different lengths and frequencies. Dynamic model averaging (DMA), by allowing the forecasting model to change dynamically over time, permits the use of predictors with different lengths and frequencies for the purpose of forecasting in a rapidly changing economy. This study uses DMA to forecast inflation and growth in Vietnam, Thailand, Philippines, Sri Lanka and Ghana. We compare its forecasting performance with a wide range of other time-series methods. We find that the size and composition of the optimal predictor set changed, indicating changes in the economic relationships over time. We also find that DMA frequently produces more accurate forecasts than other forecasting methods for both inflation and economic growth in the countries studied.     

上海股票市场空气质量指数的实证研究

空气质量能够通过情绪渠道和信息渠道去影响投资者的决策和投资行为,从而影响股票市场。采用2014年5月14日至2017年2月17日的上证指数、全国PM2.5指数以及百度指数数据,对空气质量如何影响股票市场及其影响力度进行实证分析。结果显示空气质量能够显著地影响股票市场,且季节性紊乱会加剧这一影响力度。此外空气质量对上证工业指数的影响大于上证综指,表明空气质量对特定行业的影响大于整体市场,而信息获取方式又会影响到影响的方向和力度。