Dynamic Volume-Return Relation of Individual Stocks

Llorente, Guillermo; Michaely, Roni; Saar, Gideon; Wang, Jiang

doi:10.1093/rfs/15.4.1005

Cited by 596 publications

(229 citation statements)

References 45 publications

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“…Lewellen (2002) found negative autocorrelation for stock portfolios after a year. Llorente et al (2002) found that the firstorder autocorrelation of daily returns is negative for stocks with large bid-ask spreads (-0.088) and small sizes (-0.076), but positive but very small for large stocks (0.003) and stocks with small bid-ask spreads (0.01). Bianco and Renò (2006) found negative serial correlation in the returns of Italian stock index futures for periods smaller than 20 minutes.…”

Section: Autocorrelationmentioning

confidence: 99%

The Efficient Market Hypothesis: Empirical Evidence

Sewell¹

2012

IJSP

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The efficient market hypothesis (EMH) has been the central proposition of finance since the early 1970s and is one of the most well-studied hypotheses in all the social sciences, yet, surprisingly, there is still no consensus, even among financial economists, as to whether the EMH holds. Five statistical analyses are conducted in an attempt to explicate such apparently contrary convictions. An analysis of daily, weekly, monthly and annual Dow Jones Industrial Average log returns found that first-order autocorrelation is small but positive for all time periods, with the autocorrelations for daily and weekly returns closest to zero, and thus an efficient market. A standard runs test showed that the hypothesis of independence is strongly rejected for daily returns, but accepted for weekly, monthly and annual returns, whilst the results of a more sophisticated runs test showed that daily, weekly and decreasing returns are the least consistent with an efficient market. Rescaled range analysis was conducted on the same data sets, and there was no significant evidence for the existence of long memory in the returns, a result consistent with market efficiency. Finally, from an analysis of investment newsletters it may be concluded that technical analysisas applied by practitioners-fails to outperform the market. I reconcile the fact that daily stock market log returns pass linear statistical tests of efficiency, yet non-linear forecasting methods can still generate above-average riskadjusted returns, whilst discretionary technical analysts fail to make abnormal returns.

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Section: Autocorrelationmentioning

confidence: 99%

The Efficient Market Hypothesis: Empirical Evidence

Sewell¹

2012

IJSP

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“…The empirical dependence of trading volume on the stock price return was studied by Lo and Wang (2006) and Llorente, Michaely, Saar (2002). Llorente, Michaely and Saar (2002) propose the model, for which the trading process is driven by investor's expectations of the future stock price returns and exposure to the risk in equilibrium conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Llorente, Michaely and Saar (2002) propose the model, for which the trading process is driven by investor's expectations of the future stock price returns and exposure to the risk in equilibrium conditions. Lo and Wang (2006) describe an intertemporal equilibrium model of stock trading and pricing with multiple assets and heterogeneous investors.…”

Section: Introductionmentioning

confidence: 99%

Stock Price and Trading Volume during Market Crashes

Remorov¹

2014

IJMS

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We present a model for stock price and volume behaviour during market crashes. The model incorporates a market mechanism for the share exchange between buyers and sellers while taking into account their cash balances. Using an analytical approach and the Monte-Carlo technique for the simulation of the trading volume, we analyzed the dynamics of the stock price and trading volume during market crashes. The trading volume was simulated through the trading exchange process using Monte-Carlo technique. We found that trading volume is inversely proportional to the square of the stock price in the case of the sharp price declines. This result is empirically supported in price and volume data for major recent US stock bankruptcies and market crashes, including Lehman Brothers Inc, Enron, Wachovia, Washington Mutual, Citigroup, Merrill Lynch, and MF Global. The results of the analytical approach may be used for marketing analysis of the sales in the case of the shocking market conditions.

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“…In summary, the return and volume are strongly related contemporaneously but there is a little evidence that either can be used to predict the other. Similarly, Llorente et al (2002) used the simple model to explore the dynamic relation between volume and returns. According to their model, returns generated by hedging-motivated trades reverse themselves, while returns generated by speculation-motivated trades tend to continue themselves.…”

Section: Introductionmentioning

confidence: 99%