Trading and non-trading period Internet information flow and intraday return volatility

doi:10.1016/j.physa.2016.01.086

Physica A: Statistical Mechanics and its Applications

Volume 451, 1 June 2016, Pages 519-524

https://doi.org/10.1016/j.physa.2016.01.086 Get rights and content

Highlights

•
We employ Baidu News as a proxy for information flow.
•
A positive impact of Internet information on volatility persistence is discovered.
•
Trading period Internet information is a better proxy for information flow.
•
The results provide evidences for the MDH.

Abstract

This paper employs the news appeared in Baidu News as the proxy for Internet information flow, separates them into trading period and non-trading period information and provides alternative evidence for the Mixture of Distribution Hypothesis (MDH). The empirical results show that the contemporary information can effectively reduce the volatility persistence; meanwhile, the lead information and the aggregate information also show some explanatory power. Some future directions are pointed out in the concluding remarks.

Introduction

The Mixture of Distribution Hypothesis (MDH) argues that the volatility of stock return is proportional to the rate of information flow at a given interval [1], [2], [3], [4]. Although this is a reasonable hypothesis linking the information flow and the “stylized facts” of stock return. Empirical validation is difficult in quantifying the intangible form of information as well as capturing the volatility of return. Recent studies show evidence that the Autoregressive Conditional Heteroskedasticity (ARCH) model can capture well the variation in the volatility process [5], [6]. In support of the argument of MDH, Lamoureux and Lastrapes [7] firstly introduce the daily trading volume as the proxy for information flow and show a significant explanatory power to the variance of daily returns. Wagner and Marsh [8] use the surprise volume (unexpected above-average trading activity) as the proxy for information flow and show that surprise volume can explain volatility persistence and excess kurtosis. Kalev et al. [9] employ the firm-specific announcements as the proxy for information flow and find that the inclusion of this variable in the conditional variance equation of the GARCH model reduces the volatility persistence. Although the above-mentioned empirical studies strongly support the MDH, they are subject to criticism. Firstly, the trading volume-based proxies may suffer from the nonstationary properties and endogeneity. Secondly, firm-specific announcement is not sufficient, which only represents one aspect of the information flow.

In a seminal work by Zhang et al. [10], they employ the number of information appeared in Baidu News as the proxy for information flow, make a comprehensive comparison with the existing proxy and find that this Internet information-based proxy can reduce the volatility persistence of the SZSE SME PRICE INDEX. Our paper is a naturally extension of their work and contribute to the literature in two aspects. Firstly, we extract the information from Baidu News for individual stock, rather than focus on the stock index. Observing the explanatory for individual stock provides additional evidence for MDH. Secondly, given to the availability of the Internet information, we divide the information into trading period information and non-trading period information, include each of them into the conditional variance equation of the GARCH model and observe the decreases of the volatility persistence.

This paper is also in line to a broad literature on using Internet information for financial economics research [11], [12], [13], [14], [15]. Specifically, Da et al. [16] use the search frequency in Google Trends as the proxy for investor attention and find that this direct proxy can predict stock prices in the next 2 weeks and long-run underperformance of IPO. In a similar way, Zhang et al. [17] use the search frequency of stock name in Baidu Index as the proxy for investor attention and show that the quantified variable can predict the abnormal return and trading volume. Preis et al. [18] analyze changes in Google query volumes for search item related to finance and find patterns can be viewed as “early warning signs” of stock market movement. Moat et al. [19] also document changes in Wikipedia contain early signs of stock market movement.

The rest of the article is organized as follows. Section 2 describes the data. Section 3 presents the mode setup as well as the empirical results. Section 4 is the concluding remarks.

Section snippets

Data description

The dataset comprises daily returns for 10 actively traded stocks in CSI 300 Index,¹

Models

In line with previous empirical studies [7], [8], [9] and by making a direct comparison, we also adopt the GARCH (1, 1) model. Because the GARCH (1, 1) specification is a parsimonious characterization of conditional variance, which fits many financial time series quite well [20]. Therefore, the model can be expressed in the following equations: $r_{t} = μ + ε_{t}, ε_{t} | Ω_{t - 1} \sim (0, h_{t}^{2})$ $h_{t}^{2} = ω + α ε_{t - 1}^{2} + β h_{t - 1}^{2}$ where $r_{t}$ represents the stock return in D2 and D4, respectively, which is calculated as the closing price

Concluding remarks

This paper connects to employing Baidu News as the proxy for information flow, incorporates this proxy into the conditional variance equation of the GARCH (1, 1) model and provides some evidence the MDH with trading and non-trading period Internet information for individual stock return volatility persistence. Although the empirical results show that the volatility persistence is most decreased when including the contemporary information, there is still some unexplained volatility persistence.

Acknowledgment

This work is supported by the National Natural Science Foundation of China (71532009, 71201112 and 71320107003).

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View full text

Trading and non-trading period Internet information flow and intraday return volatility

Highlights

Abstract

Introduction

Section snippets

Data description

Models

Concluding remarks

Acknowledgment

J. Econometrics

J. Bank. Finance

Physica A

Ecol. Modell.

J. Econometrics

A subordinated stochastic process model with finite variance for speculative prices

Econometrica

The stochastic dependence of security price changes and transaction volumes: implications for the mixture-of-distributions hypothesis

Econometrica

The price variability-volume relationship on speculative markets

Econometrica

Transaction data tests of the mixture of distributions hypothesis

J. Finan. Quant. Anal.

Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation

Econometrica

Heteroskedasticity in stock return data: volume versus GARCH effects

J. Finance