Mohammad Najand

A further investigation of the lead–lag relationship between the spot market and stock index futures: Early evidence from Korea

In this article, we investigate possible lead and lag relationship in returns and volatilities between cash and futures markets in Korea. Utilizing intraday data from the newly established futures market in Korea, we find that the futures market leads the cash market by as long as 30 minutes. This result is consistent with previous studies for the U.S. and other countries’ futures markets. With regard to volatility interaction between spot and futures markets, we find that, unlike the above results for returns, a bidirectional causality is more prevalent between cash and futures markets, and this relationship is entirely sample dependent. We also find that the trading volume has significant explanatory power for volatility changes in both spot and futures markets.

Causal relations among stock returns, inflation, real activity, and interest rates: Evidence from Japan

Previous studies reveal a negative correlation between stock returns and inflation but do not fully specify the relationship or resolve the direction of causality. The possible explanations are (1) that inflation shocks negatively affect real output and the negative relationship among stock returns and thus inflation serves as a proxy for the positive relationship between stock returns and real variables (i.e., inflation predicts real activity) and (2) that unexpected changes in stock prices cause changes in inflationary expectations. The authors apply the state space econometric method to investigate Japanese data concerning the existence and direction of Granger causation among stock returns, inflation, real activity, and interest rates.

Forecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models

The study examines the relative ability of various models to forecast daily stock index futures volatility. The forecasting models that are employed range from naive models to the relatively complex ARCH-class models. It is found that among linear models of stock index futures volatility, the autoregressive model ranks first using the RMSE and MAPE criteria. We also examine three nonlinear models. These models are GARCH-M, EGARCH, and ESTAR. We find that nonlinear GARCH models dominate linear models utilizing the RMSE and the MAPE error statistics and EGARCH appears to be the best model for forecasting stock index futures price volatility.

Structural models of exchange rate determination

Abstract This study compares the forecasting accuracy of state space techniques based on the monetary models of exchange rate with univariate and random walk models for four countries. It is found that these structural models outperform ARIMA and random walk models for all four countries. A state space vector that contains variables based on the monetary model easily outperforms random walk as well as ARIMA models for France, Germany, UK, and Japan during the sample period of this study.

Volatility changes in European currency exchange rates due to EMS announcements

Abstract This study examines the effects of announcements concerning European Monetary Union on the exchange rate volatilities of several European currencies. It is expected that when good news is portrayed in regard to a single currency this will be considered bad news, thus eliciting a negative reaction, for the German mark. Conversely, good news for a single currency should also be good news for weaker currencies, such as the Portuguese escudo, the Italian lira, the Greek drachma, and the Spanish peseta. In terms of volatility, a reaction to good news should be a reduction in volatility, as bad news should cause an increase in volatility. In total there are 22 announcements examined from January 1986 through September 1997. The German mark is observed to experience greater increases in volatility than decreases, as does the Italian lira. Portugal and Greece appear to react more strongly to positive news in that the decreases in volatility are on average greater than the increases.

Stock Return Predictability and Investor Sentiment: A High-Frequency Perspective

We explore the predictive relation between high-frequency investor sentiment and stock market returns. Our results are based on a proprietary dataset of high-frequency investor sentiment, which is computed based on a comprehensive textual analysis of sources from news wires, internet news sources, and social media. We find substantial evidence that intraday S&P 500 index returns are predictable using lagged half-hour investor sentiment. The predictive power is also found in other stock and bond index ETFs. We document that this sentiment effect is independent of the intraday momentum effect, which is based on lagged half-hour returns. While the intraday momentum effect only exists in the last half hour, the sentiment effect persists in at least the last two hours of a trading day. From an investment perspective, high-frequency investor sentiment also appears to have significant economic value when evaluated with market timing trading strategies. We find evidence that the return predictability is most likely driven by the trading activities of noise traders.

Reit Executive Compensation, Performance, and Management Power: Evidence from Panel Data

This study focuses on REIT CEO compensation. We utilize five different definitions for CEO compensation: salary, bonus, cash compensation, total compensation, and option awards. To capture the determinants of CEO compensation, we use the following performance measures: three-year stock returns, MVA (market value added), and Tobins q. We also examine the impact of managerial power on compensation. We utilize a panel data set to capture both the time-series and cross-sectional effects. Our panel data set captures both the time-series and cross-sectional effects. Previous work on REIT executive compensation has chiefly looked at compensation data on cross-sectional basis. We find performance and size do not influence the CEOs salary while, risk, term, title, ownership, and age have significant impacts. Contrary to previous findings and our expectations, bonuses are not influenced by risk, CEO power, or size.

The Role of U.S. Market on International Risk-Return Tradeoff Relations

We study the intertemporal risk-return tradeoff relations based on returns from 18 international markets. We find striking new empirical evidence that the inclusion of U.S. market variables significantly changes the estimated risk-return tradeoff relationship in international markets. The estimated risk aversion coefficient switches from mostly negative to mostly positive after the inclusion of these U.S. market variables even when the conditional variance model specification remains the same. Our results are consistent with the state variable interpretation of the U.S. market variables in the sense of Mertons Intertemporal CAPM.

News and Social Media Emotions in the Commodity Market

Purpose - Emotion plays a significant role in both institutional and individual investors’ decision making process. Emotions affect the perception of risk and the assessment of monetary value. However, there is a lack of empirical evidence available that addresses how investors’ emotions affect commodity market returns. This paper investigates whether media-based emotions can be used to predict future commodity returns. Design/methodology/approach - We examine the short-term predictive power of media-based emotion indices on the following five days’ commodity returns. The research adopts a proprietary dataset of commodity specific market emotions, which is computed based on a comprehensive textual analysis of sources from newswires, Internet news sources, and social media. Time series econometrics models (Threshold-GARCH and VAR) are employed to analyze fourteen years (01/1998-12/2011) of daily observations of the CRB commodity market index, crude oil and gold returns, and the market-level sentiment and emotions (optimism, fear, and joy). Findings - The empirical results suggest that the commodity specific emotions (optimism, fear, and joy) have significant influence on individual commodity returns, but not on commodity market index returns. Additionally, the research findings support the short-term predictability of the commodity specific emotions on the following five days’ individual commodity returns. Compared to the previous studies of news sentiment on commodity returns (Borovkova, 2011; Borovkova and Mahakena, 2015; Smales, 2014), this research provides further evidence of the effects of news and social media based emotions (optimism, fear and joy) in the commodity market. Additionally, this work proposes that market emotion incorporates both a sentimental effect and appraisal effect on commodity returns. Empirical results are shown to support both the sentimental effect and appraisal effect when market sentiment is controlled in crude oil and gold spot markets. Originality/value - This paper adopts the valence-arousal approach and cognitive appraisal approach to explain financial anomalies caused by investor emotions. Additionally, this is the first paper to explore the predictive power of investor emotions (optimism, fear and joy) on commodity returns.

Does the Net Flow of Funds Help to Predict the S&P 500 Index?

Although there is considerable evidence that stock returns are predictable, both individually and in aggregate, prior investigations as to whether share prices are significantly affected by demand shocks have mixed results. We extend prior research on this issue by testing the forecasting ability of five univariate and two multivariate models with respect to the S&P 500 Index. We hypothesize that the multivariate dynamic regression and state-space models, which incorporate both previous prices and current equity mutual fund flows, will result in more accurate predictions of future stock prices than univariate models. Our results support our hypothesis, both for one-month-ahead and one-quarter-ahead predictions.