Daily Trading Volume Research Articles

Trading strategies in the overnight money market: Correlations and clustering on the e-MID trading platform

We analyze the correlations in patterns of trading for members of the Italian interbank trading platform e-MID. The trading strategy of a particular member institution is defined as the sequence of (intra-) daily net trading volumes within a certain semester. Based on this definition, we show that there are significant and persistent bilateral correlations between institutions’ trading strategies. In most semesters we find two clusters, with positively (negatively) correlated trading strategies within (between) clusters. We show that the two clusters mostly contain continuous net buyers and net sellers of money, respectively, and that cluster memberships of individual banks are highly persistent. Additionally, we highlight some problems related to our definition of trading strategies. Our findings add further evidence on the fact that preferential lending relationships on the micro-level lead to community structure on the macro-level.

Forecasting volatility via stock return, range, trading volume and spillover effects: The case of Brazil

For the purpose of developing alternative approach for forecasting volatility, we consider heterogeneous VAR (HVAR) model which accommodates the market effects of different horizons, namely, daily, weekly and monthly effects, and examine the interdependence of stock markets in Brazil and the US, based on information of daily return, range and trading volume. To compare with the new approach, we also work with the univariate and multivariate GARCH models with asymmetric effects, trading volumes and fat-tails. The heteroskedasticity-corrected Granger causality tests based on the HVAR show the strong evidence of such spillover effects. We assess the value-at-risk thresholds for Brazil, based on the out-of-sample forecasts of the HVAR model, finding the new approach works satisfactory for the periods including the global financial crisis, without assuming heavy-tailed conditional distributions.

Northern Exposure: How Canadian Small Stock Investments Can Benefit Investors

We investigate the extent to which investors from around the world can benefit from investing in small market capitalization Canadian stocks. Using monthly data from 1950 to 2009, we show that the size effect has not lessened over the decades in Canada, despite earlier evidence to the contrary in the U.S. We show that with their low correlation to large stocks in Canada and other developed markets, Canadian small stocks represent a unique asset class, and as such an international investor who included a portion of Canadian small stocks would have had a much better return-to-risk reward than from including only large stocks from ten developed equity markets. Based on daily trading volume, we examine and discuss important investabilty issues overlooked in most similar studies. Finally, we corroborate U.S. findings that highlight the importance of returns in January in explaining the bulk of the size effect and examine the size effect in a variety of economic conditions including crises.

Financial factor influence on scaling and memory of trading volume in stock market

We study the daily trading volume volatility of 17,197 stocks in the US stock markets during the period 1989-2008 and analyze the time return intervals τ between volume volatilities above a given threshold q. For different thresholds q, the probability density function P(q)(τ) scales with mean interval 〈τ〉 as P(q)(τ)=〈τ〉(-1)f(τ/〈τ〉), and the tails of the scaling function can be well approximated by a power law f(x)∼x(-γ). We also study the relation between the form of the distribution function P(q)(τ) and several financial factors: stock lifetime, market capitalization, volume, and trading value. We find a systematic tendency of P(q)(τ) associated with these factors, suggesting a multiscaling feature in the volume return intervals. We analyze the conditional probability P(q)(τ|τ(0)) for τ following a certain interval τ(0), and find that P(q)(τ|τ(0)) depends on τ(0) such that immediately following a short (long) return interval a second short (long) return interval tends to occur. We also find indications that there is a long-term correlation in the daily volume volatility. We compare our results to those found earlier for price volatility.

Conditional Volatility, Volume Shocks, and GARCH Effects

Using daily closing level of S&P500 index and daily trading volume from 1/2/1990 to 12/31/2010, this study examines the relationship between conditional volatility and trading volume at the market level. It is shown that linking volatility to total volume does not extract all information. In fact it is found that at the market level total volume is not a good proxy for the information flow, the latent mixing variable in the Mixture of Distribution Hypothesis, and is unable to explain GARCH effects and has no impact on conditional volatility. However after partitioning volume into expected and unexpected components, private information proxied by unexpected volume shocks, although unable to reduce volatility persistence due to its random nature, is positively associated with conditional volatility. This relation is shown to be asymmetric in the sense that only positive unexpected volume shocks are the contributing factor and negative shocks have no impact. Moreover, evidence suggests that this often reported positive relationship between volume and volatility in equity market is mainly driven by two factors: Costly short selling constraint, and Maturity of the US equity market. Thus the positive relation between volatility and volume in a mature equity market where the number of traders can reasonably be assumed to be constant, is a manifestation of differing cost of taking long and short position. Results indicate that in futures market where the cost of taking long and short positions is symmetric, such a relationship cease to exist. Furthermore, it is documented that in an expanding market characterized by growing number of traders, consistent with the theory, this relationship is still significant but in reversed direction.

End-of-Day Stock Trading Volume Prediction with aTwo-Component Hierarchical Model

We present a hierarchical model approach for predicting the end-of-day stock trading volume (total daily volume). It effectively combines two sources of information: the trading volume already accumulated from the beginning of the trading day to the time of the prediction, and the historical daily trading volume dynamics. The hierarchical model assumes an ARMA–GARCH model for the daily volume series and a Gaussian multinomial distribution for the volume distribution within the trading day. This model is an extension of the stable-seasonal model in Chen and Fomby [1999] used for tourism prediction. We also present out-of-sample prediction performance, with a comparison to several simpler approaches, using real volume series, where the improvement is quite significant (30% to 40%). <b>TOPICS:</b>Statistical methods, information providers/credit ratings, exchanges/markets/clearinghouses

A multifractal detrended fluctuation analysis of trading behavior of individual and institutional traders in Tehran stock market

Employing the multifractal detrended fluctuation analysis (MF-DFA), the multifractal properties of trading behavior of individual and institutional traders in the Tehran Stock Exchange (TSE) are numerically investigated. Using daily trading volume time series of these two categories of traders, the scaling exponents, generalized Hurst exponents, generalized fractal dimensions and singularity spectrum are derived. Furthermore, two main sources of multifractality, i.e. temporal correlations and fat-tailed probability distributions are also examined. We also compare our results with data of S&P 500. Results of this paper suggest that for both classes of investors in TSE, multifractality is mainly due to long-range correlation while for S&P 500, the fat-tailed probability distribution is the main source of multifractality.

Relationship Between Trading Volume And Stock Exchange Performance: A Case From Karachi Stock Exchange

Over the past fifty years, much work has been done trying to understand the relationship between volume and price changes of individual stocks. In this article, we attempt to introduce and discuss some of these articles and take inference for relationship of traded volume and aggregate stock exchange index performance. This article presents an empirical analysis of the relationship between trading volume and performance of stock exchange index on a given day in the Pakistani market. Just like individual stocks, the relationship between changes in KSE 100 index and trading volume, irrespective of the direction of the index change, is significantly positive across all three alternative measures of daily trading volume (the daily number of shares traded; the daily total monetary value of shares traded; the daily number of equity trades).

Volume, Intraday and Overnight Returns for Volatility Prediction: Profitability or Accuracy?

This article presents a comprehensive analysis of the relative ability of three information sets --- daily trading volume, intraday returns and overnight returns --- to predict equity volatility. We investigate the extent to which statistical accuracy of one-day-ahead forecasts translates into economic gains for technical traders. Various profitability criteria and utility-based switching fees indicate that the largest gains stem from combining historical daily returns with volume information. Using common statistical loss functions, the largest degree of predictive power is found instead in intraday returns. Our analysis thus reinforces the view that statistical significance does not have a direct mapping onto economic value. As a byproduct, we show that buying the stock when the forecasted volatility is extremely high appears largely profitable, suggesting a strong return-risk relationship in turbulent conditions.

Modeling and forecasting trading volume index: GARCH versus TGARCH approach

Volatility has been described as an indicator of uncertainty which has implications for investment decisions, risk management as well as monetary policy. This paper investigates the pattern of volatility in the daily trading volume index of Hong Kong stock exchange. The empirical evidence provided in this paper suggests that TGARCH specification is superior to GARCH specification. This is particularly important when one is dealing with the case of asymmetric information that captures the leverage effect of the volatile stock market.

Liquidity and Asset Pricing: Evidence from Daily Data over 1926 to 2005

Using the new CRSP compilation of daily trading volume data from 1926 to 1962, this paper conducts a detailed analysis of liquidity from 1926 to 2005. It distinguishes liquidity risk from liquidity as a characteristic and presents new evidence on the importance of liquidity risk in asset pricing. The liquidity risk premium is as strong in the pre-1963 period as in the post 1963-period, and is the most significant and persistent premium in a comparison with size, value, and momentum premiums. Liquidity as a firm characteristic lacks significant predictive power beyond liquidity risk and the liquidity-augmented CAPM provides a good description of expected returns.

Migration of Trading and the Introduction of Single Stock Futures on the Underlying U.S. Stocks

This study investigates where liquidity and informed trading takes place following the introduction of single stock futures (SSF) contracts on the OneChicago futures exchange. Specifically, we analyze the size and composition of proportional spreads for two sets of stocks, those that have single stock futures contracts and a matched control sample that does not have such contracts. We find that, after controlling for changes in spread determinants, the average proportional spreads, on average, decrease significantly after SSF are introduced. For NYSE stocks, while the average daily trading volume in the cash market is reduced by 389,000 shares, we find a corresponding increase in the average percentage of the adverse selection component in the spread of the cash asset. This pattern indicates a migration of liquidity trading to the SSF market as fund managers appear to adjust their portfolio positions in the secondary SSF market rather than in the primary stock market.

The UK equity market around the ex-split date

Using UK stock market data this study unveils positive abnormal returns on and around the ex-split date. These excess returns are partially predictable using the publicly available information prior to the ex-split date. There is also a persistent increase in the post-split volatility of these stocks with the results being robust to the choice of the volatility proxy. Post-split volatility is found to be positively related to trading activity. Contrary to the US findings, volatility dynamics following the stock split are better captured by changes in the daily trading volume rather than by the number of trades.

Modelling Regulatory Change V's Volume, of Trading Effects in HSIF and HSI Volatility

In an earlier paper, we adopted a bi-variate BEKK–GARCH framework and employed a systematic approach to examine structural breaks in the Hang Seng Index and Index Futures market volatility. Switching dummy variables were included and tested in the variance equations to check for any structural changes in the autoregressive volatility structure due to the events that have taken place in the Hong Kong market surrounding the Asian markets crisis. In this paper, we include measures of daily trading volume from both markets in the estimation. Likelihood ratio tests indicate the switching dummy variables become insignificant and the GARCH effects diminish but remain significant. There is some evidence that the Sequential Arrival of Information Model (SIM) provides a platform to explain these market induced effects when volume of trade is accounted for.

The evolution of the January effect

This paper extends recent studies of the January effect by investigating the evolution of the daily pattern of the effect across size deciles. Our evidence documents a sizable mean reverting component beginning in the latter part of January and a shorter duration of the seasonal effect. Despite lower abnormal returns in the second part of January, higher abnormal returns in the first part of January keep the magnitude of the January effect unchanged. Further analysis of daily trading volumes suggests a stable trading volume intensity in the first part of January and a substantial decline in trading volume intensity in the second part of January.

Herding, momentum and investor over-reaction

In this paper we study the impact of noise or quality of prices on returns. The noise arises from herding by market participants beyond what is justified by information. We construct a firm-quarter-specific measure of speculative intensity (SPEC) based on autocorrelation in daily trading volume adjusted for the amount of information available, and find that speculative intensity has a significant positive impact on returns. Both cross-sectional and time series variation in SPEC are consistent with conventional wisdom, and with implications of theories of herding as in DeLong et al. (1990, J Political Econ 98(4):703–738). We find that high-SPEC firms drive the returns to momentum trading strategies and that investor over-reaction is significant only in the case of high-SPEC firms.

Power law and evolutionary trends in stock markets

We model the distribution of daily stock trading volume using the power law and document a new phenomenon. The power law exponent systematically increases with time suggesting that trading is becoming increasingly concentrated in a subset of stocks.

The Liquidity-Augmented CAPM over 1926 to 1963

This paper examines the two-factor model of Liu (2006) using the recent CRSP compilation of daily trading volume data between 1926 and 1962. I find that the liquidity premium is as strong for the early period as for the post 1963-period, and it is the most significant and persistent premium compared with those associated with size, book-to-market, turnover, return-to-volume ratio, and past return. The two-factor model performs well in explaining the cross-section of stock returns. The evidence suggests that the original results of Liu are robust to the earlier period. In addition, liquidity as a firm characteristic lacks significant predictive power beyond liquidity risk.

Informational Efficiency in Futures' Markets in India's National Stock Exchange

This paper provides estimates of overall informational efficiency in futures markets on India's National Stock Exchange. We do not examine the price reaction to any public announcement. Instead, we invoke the Hellwig (1980) model, and exploit the property that for futures contracts the terminal value can be treated as observable, to obtain estimates of the overall signal to signal plus noise ratio in markets for single-stock and index futures on India's National Stock Exchange. The variance-covariance parameters governing futures prices and terminal values can be inverted to obtain estimates of the primitive parameters of the Hellwig (1980) model. This lets us identify the MLEs of the precision of private information and the variance of liquidity motivated trades. The signal to signal plus noise ratio - our measure of overall informational efficiency - is a function of these primitive parameters. Our primary findings show that there is considerable variation across firms in these parameters despite only large active firms being available for futures trading. Overall informational efficiency is decreasing in univariate analyses with open interest and average daily trading volume in futures and the underlying equity. In a multivariate analysis it declines in open interest in the futures market and in the trading volume of the underlying equity but is increasing in the trading volume of in the futures market. The NIFTY index shows a higher signal to signal plus noise ratio than for any of the firms. This is consistent with the idea that less manipulability is associated with greater informational efficiency.

Does Hong Kong government intervention stabilise the stock market? Analysis of the trading volume and volatility

The purchase of Hang Seng Index component stocks by the Hong Kong government has an immediate effect of reducing the daily trading volume for the 33 stocks involved, which, in turn, leads to a reduction in volatility in the stock market. However, once we account for the effect of a decline in trading volume, our results show that government intervention does no have additional effects on the volatility of stock returns for the majority of the Hang Seng Index component stocks. On the other hand, over a longer horizon, an increase in volatility is observed despite a decline in trading volume. Our results suggest that Hong Kong government intervention stabilises the market only in the short run through a reduction in free float in the market. However, in the long run, the effect of government intervention is less clear.