Financial Econometics Series
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No. 2015_15   (Download full text)
Kumari Ranjeeni and Susan S Sharma
The impact of the Lehman Brothers’ Bankruptcy on the Performance of Chinese Sectors
This paper investigates the impact of the news announcement of the Lehman Brothers’ (LBs’) bankruptcy on the performance of Shanghai Stock Exchange (SSE) sectors. Unlike the assumption in this literature that firms are homogenous, we address the unknown issue: does LBs’ bankruptcy have a heterogenous effect on stock returns of sectors listed on SSE? We employ an event study approach and use daily data for a total of 845 firms grouped into nine sectors, we find fresh results, previously undocumented in this literature. First, our results show that unlike the United States (see Ranjeeni 2014), Chinese Energy and Financial sectors were insignificantly affected from LBs’ bankruptcy. This implies that these sectors can provide cross-country diversification opportunities for US investors during volatile periods. Second, we find statistically insignificant effect of LBs’ bankruptcy on the performance of the financial sector while most of the other sectors suffered significantly. This implies that the Chinese market level analysis conducted by Bianconi et al. (2013) is influenced by the performance of the financial sector. Finally, our results highlight on the heterogeneous effect of LBs’ bankruptcy on different Chinese sectors and at different time intervals surrounding the event.
JEL-Codes: G01, G11, G14, G33
Keywords: Lehman Brothers’ bankruptcy, Global Financial Crisis, abnormal returns, Chinese sectors, event study.
No. 2015_14   (Download full text)
Dinh H B Phan, Susan S Sharma and Paresh K Narayan
Intraday Volatility Interaction between the Crude Oil and Equity Markets
This paper investigates the price volatility interaction between the crude oil and equity markets in the US using five-minute data over the period 2009 to 2012. Our main findings can be summarised as follows. First, we find strong evidence to demonstrate that the integration of the bid-ask spread and trading volume factors leads to a better performance in predicting price volatility. Second, trading information, such as bid-ask spread, trading volume, and the price volatility from cross-markets, improves the price volatility predictability for both in-sample and out-of-sample analyses. Third, the trading strategy based on the predictive regression model that includes trading information from both markets provides significant utility gains to mean-variance investors.
Keywords: volatility; trading volume; bid-ask spread; cross-market; predictability; forecasting.
No. 2015_13   (Download full text)
Dinh H B Phan, Susan S Sharma and Paresh K Narayan
Stock Return Forecasting: Some New Evidence
This paper makes three contributions to the literature on forecasting stock returns. First, unlike the extant literature on oil price and stock returns, we focus on out-of-sample forecasting of returns. We show that the ability of the oil price to forecast stock returns depends not only on the data frequency used but also on the estimator. Second, out-of-sample forecasting of returns is sector-dependent, suggesting that oil price is relatively more important for some sectors than others. Third, we examine the determinants of out-of-sample predictability for each sector using industry characteristics and find strong evidence that return predictability has links to certain industry characteristics, such as book-to-market ratio, dividend yield, size, price earnings ratio, and trading volume.
Keywords: Stock returns; Oil price; Predictability; Forecasting; Out-of-sample.
No. 2015_12   (Download full text)
Dinh H B Phan, Susan S Sharma and Paresh K Narayan
Oil Price and Stock Returns of Consumers and Producers of Crude Oil
In this paper we investigate how differently stock returns of oil producers and oil consumers are affected from oil price changes. We find that stock returns of oil producers are affected positively by oil price changes regardless of whether oil price is increasing or decreasing. For oil consumers, oil price changes do not affect all consumer sub-sectors and where it does, this effect is heterogeneous. We find that oil price returns have an asymmetric effect on stock returns for most sub-sectors. We devise simple trading strategies and find that while both consumers and producers of oil can make statistically significant profits, investors in oil producer sectors make relatively more profits than investors in oil consumer sectors.
Keywords: Oil Price Returns; Stock Returns; Producer; Consumer; Profits.
No. 2015_11   (Download full text)
Joakim Westerlund, Paresh K Narayan and Xinwei Zheng
Testing For Stock Return Predictability In A Large Chinese Panel
This paper proposes a simple panel data test for stock return predictability that is flexible enough to accommodate three key salient features of the data, namely, predictor persistency and endogeneity, and cross-sectional dependence. Using a large panel of Chinese stock market data comprising more than one million observations, we show that most financial and macroeconomic predictors are in fact able to predict returns. We also show how the extent of the predictability varies across industries and firm sizes.
JEL-Codes: C22; C23; G1; G12.
Keywords: Panel data; Bias; Cross-section dependence; Predictive regression; Stock return predictability; China.
No. 2015_10   (Download full text)
Joakim Westerlund, Hande Karabiyik and Paresh K Narayan
Testing for Predictability in Panels with General Predictors
The difficulty of predicting returns has recently motivated researchers to start looking for tests that are either robust or more powerful. Unfortunately, the way that these tests work typically involves trading robustness for power or vice versa. The current paper takes this as its starting point to develop a new panel-based approach to predictability that is both robust and powerful. Specifically, while the panel route to increased power is not new, the way in which the cross-section variation is exploited to achieve also robustness with respect to the predictor is. The result is two new tests that enable asymptotically standard normal and chi-squared inference across a wide range of empirical relevant scenarios in which the predictor may be stationary, unit root non-stationary, or anything in between. The cross-section dependence of the predictor is also not restricted, and can be weak, strong, or indeed anything in between. What is more, this generality comes at no cost in terms of test construction. The new tests are therefore very user-friendly.
JEL-Codes: C22; C23; G1; G12.
Keywords: Panel data; Predictive regression; Predictor persistency; Cross-section dependence.
No. 2015_09   (Download full text)
Paresh K Narayan, Sagarika Mishra and Kannan Thuraisamy
Is Exchange Rate Trading Profitable?
We test whether exchange rate trading is profitable in the emerging markets of Brazil, China, India, and South Africa. Using momentum trading strategies applied to high frequency data, we discover that: (a) momentum-based trading strategies lead to statistically significant profits from the currencies of all four emerging markets; (b) the South African Rand is generally the most profitable, followed by the Brazilian Real and the Indian Rupee; (c) profits are persistent during the day and increase substantially from 1-minute trade to 120-minute trade; and (d) during the period of the global financial crisis currency profits were maximised.
Keywords: Exchange Rate; Emerging Markets; Momentum Trading Strategies; High Frequency Data; Profits.
No. 2015_08   (Download full text)
Paresh K Narayan and Rangan Gupta
Has Oil Price Predicted Stock returns for Over a Century?
This paper contributes to the debate on the role of oil prices in predicting stock returns. The novelty of the paper is that it considers monthly time-series historical data that span over 150 years (1859:10-2013:12) and applies a predictive regression model that accommodates three salient features of the data, namely, a persistent and endogenous oil price, and model heteroskedasticity. Three key findings are unraveled: First, oil price predicts US stock returns. Second, in-sample evidence is corroborated by out-sample evidence of predictability. Third, both positive and negative oil price changes are important predictors of US stock returns, with negative changes relatively more important. Our results are robust to the use of different estimators and choice of in-sample periods.
Keywords: Stock returns; Predictability; Oil price.
No. 2015_07   (Download full text)
Deepa and Paresh K Narayan
Are Indian Stock Returns Predictable?
In this paper we show that Indian stock returns, based on industry portfolios, portfolios sorted on book-to-market, and on size, are predictable. While we discover that this predictability holds both in in-sample and out-of-sample tests, predictability is not homogenous. Some predictors are important than others and some industries and portfolios of stocks are more predictable and, therefore, more profitable than others. We also discover that a mean combination forecast approach delivers significant out-of-sample performance. Our results survive a battery of robustness tests.
Keywords: Stock Returns; Predictability; Profits; Sectors; Rational asset pricing; India.
No. 2015_06   (Download full text)
Paresh K Narayan, Sagarika Mishra and Seema Narayan
New Empirical Evidence on the Bid-Ask Spread
In this paper, we model the determinants of spread for 734 firms listed on the NYSE over the period 1 January 1998 to 31 December 2008. We propose a panel data model of the determinants of spread. There are four main messages emerging from our work. We find a statistically significant effect of volume on spread inconsistent with the work of Johnson (2000). On price, we find mixed results, consistent with the literature. On the effect of price volatility on spread, our results are completely the opposite of the cross-sectional literature but sides with the relatively recent work of Chordia et al. (2001). We allow for persistence of spread as a determinant of spread and find significant evidence of spread persistence across all 16 sectors. Finally, we examine size effects and find statistically strong evidence of size effects based on the relationship between price and spread, persistence and spread, and volatility and spread.
Keywords: Bid-Ask; Spread; NYSE; Panel Data.
No. 2015_05   (Download full text)
Paresh K Narayan and Ruipeng Liu
A Unit Root Model for Trending Time-series Energy Variables
In this paper, we propose a GARCH-based unit root test that is flexible enough to account for; (a) trending variables, (b) two endogenous structural breaks, and (c) heteroskedastic data series. Our proposed model is applied to a range of time-series, trending, and heteroskedastic energy variables. Our two main findings are: first, the proposed trend-based GARCH unit root model outperforms a GARCH model without trend; and, second, allowing for a time trend and two endogenous structural breaks are important in practice, for doing so allows us to reject the unit root null hypothesis.
Keywords: Time-series; Energy; Unit Root; Trending Variables.
No. 2015_04   (Download full text)
Paresh K Narayan, Susan S Sharma and Kannan Thuraisamy
Can Governance Quality Predict Stock Market Returns? New Global Evidence
We develop country-level governance indices using governance risk factors and examine whether country-level governance can predict stock market returns. We find that country-level governance predicts stock market returns only in countries where governance quality is poor. For countries with well-developed governance, there is no evidence that governance predicts returns. Our findings also confirm that investors in countries with weak governance can utilise information contained in country-level governance indicators to devise profitable portfolio strategies.
Keywords: Predictability; Returns; Governance; Country characteristics.
No. 2015_03   (Download full text)
Paresh K Narayan and Joakim Westerlund
Does Cash Flow Predict Returns?
In this paper, we propose the hypothesis that cash flow and cash flow volatility predict returns. We categorize firms listed on the New York Stock Exchange into sectors, and apply tests for both in-sample and out-of-sample predictability. While we find strong evidence that cash flow volatility predicts returns for all sectors, the evidence obtained when using cash flow as a predictor is relatively weak. Estimated profits and utility gains also suggest that it is cash flow volatility that is more relevant as a source of information than cash flow.
JEL-Codes: C12; C22.
Keywords: Cash Flow Volatility; Returns; Predictability; Panel Data; Sectors
No. 2015_02   (Download full text)
Paresh K Narayan
An Analysis of Sectoral Equity and CDS Spreads
In this paper, we find that CDS return shocks are important in explaining the forecast error variance of sectoral equity returns for the USA. The CDS return shocks have different effects on equity returns and return volatility in the pre-crisis and crisis periods. It is the post-Lehman crisis period in which the effects of CDS return shocks are the most dominant. Finally, we construct a spillover index and find that it is time-varying and explains a larger share of total forecast error variance of sectoral equity and CDS returns for some sectors than for others.
Keywords: Equity Returns; CDS Spread; Forecast Error Variance; Spillover.
No. 2015_01   (Download full text)
Paresh K Narayan and Ruipeng Liu
A GARCH Model for Testing Market Efficiency
In this paper we propose a generalised autoregressive conditional heteroskedasticity (GARCH) model-based test for a unit root. The model allows for two endogenous structural breaks. We test for unit roots in 156 US stocks listed on the NYSE over the period 1980 to 2007. We find that the unit root null hypothesis is rejected in 40% of the stocks, and only in four out of the nine sectors the null is rejected for over 50% of stocks. We conclude with an economic significance analysis, showing that mostly stocks with mean reverting prices tend to outperform stocks with non-stationary prices.
Keywords: Efficient Market Hypothesis; GARCH; Unit Root; Structural Break; Stock Price.
No. 2014_16   (Download full text)
Sagarika Mishra and Sandip Dhole
Stock Price Comovement: Evidence from India
This study examines the extent to which stock prices comove in an emerging economy, India. We first document that stocks listed on the National Stock Exchange (NSE) comove. Further, we find that synchronicity is positively associated with growth and earnings volatility and negatively associated with business group affiliation and leverage.
JEL-Codes: G14, M41
Keywords: Stock Price Synchronicity, Business Groups, Firm Growth, Leverage, Earnings Volatility
No. 2014_15   (Download full text)
Joakim Westerlund and Sagarika Mishra
A Practical Note on the Determination of the Number of Factors Using Information Criteria with Data-Driven Penalty
As is well known, when using an information criterion to select the number of common factors in factor models the appropriate penalty is generally indetermine in the sense that it can be scaled by an arbitrary constant, c say, without affecting consistency. In an influential paper, Hallin and Liˇska (Determining the Number of Factors in the General Dynamic Factor Model, Journal of the American Statistical Association 102, 603�617, 2007)proposes a data-driven procedure for selecting the appropriate value of c. However, by removing one source of indeterminacy, the new procedure simultaneously creates several new ones,which make for rather complicated implementation, a problem that has been largely overlooked in the literature. By providing an extensive analysis using both simulated and real data, the current paper fills this gap.
JEL-Codes: C12; C13; C33.
Keywords: Panel data; Common factor model; Information criterion; Data-driven penalty.
No. 2014_14   (Download full text)
Paresh Kumar Narayan, Huson Ali Ahmed, Susan S Sharma and KP Prabheesh
How Profitable is the Indian Stock Market?
In this paper, using a range of technical trading and momentum trading strategies, we show that the Indian stock market is profitable. We find robust evidence that investing in some sectors is relatively more profitable than investing in others. We show that sectoral heterogeneity with respect to profitability is a result of the gradual diffusion of information from the market to the sectors. Specifically, we show that while the market predicts returns of sectors, the magnitude of predictability varies with sector. Our results are robust to a range of trading strategies.
Keywords: Momentum; Technical Trading; Profits; Sectors; Stock Market; India; Predictability.
No. 2014_13   (Download full text)
Joakim Westerlund and Paresh Kumar Narayan
Testing for Predictability in Panels of Small Time Series Dimensions with an Application to Chinese Stock returns
The few panel data tests for predictability of returns that exist are based on the prerequisite that both the number of time series observations, T, and the number of crosssection units, N, are large. As a result, these tests are impossible for stock markets where lengthy time series data are scarce. In response to this, the current paper develops a new test for predictability in panels where T ≥ 2 but N is large, which seems like a much more realistic assumption when using firm-level data. As an illustration, we consider the Chinese stock market, for which data is only available for 17 years but where the number firms is relatively large, 160.
JEL-Codes: C22; C23; G1; G12.
Keywords: Panel data; Predictive regression; Stock return predictability; China.
No. 2014_12   (Download full text)
Joakim Westerlund, Milda Norkute and Paresh K Narayan
A Factor Analytical Approach to the Efficient Futures Market Hypothesis
Most empirical evidence suggests that the efficient futures market hypothesis, henceforth referred to as EFMH, stating that spot and futures prices should cointegrate with a unit slope on futures prices, does not hold, a finding at odds with many theoretical models. This paper argues that these results can be attributed in part to the low power of univariate tests, and that the use of panel data can generate more powerful tests. The current paper can be seen as a step in this direction. In particular, a newly developed factor analytical approach is employed, which is very general and, in addition, free of the otherwise so common incidental parameters bias in the presence of fixed effects. The approach is applied to a large panel covering 17 commodities between March 1991 and August 2012. The evidence suggests that the EFMH cannot be rejected once the panel evidence has been taken into account.
JEL-Codes: C12; C13; C33; C36.
Keywords: Dynamic panel data models; Unit root; Factor analytical method; Efficient market hypothesis; Futures markets.
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