Limits...
Impact of stock market structure on intertrade time and price dynamics.

Ivanov PCh, Yuen A, Perakakis P - PLoS ONE (2014)

Bottom Line: Specifically, we find that, compared to NYSE stocks, stocks registered on the NASDAQ exhibit significantly stronger correlations in their transaction timing on scales within a trading day.These findings do not support the hypothesis of universal scaling behavior in stock dynamics that is independent of company characteristics and stock market structure.Further, our results have implications for utilising transaction timing patterns in price prediction and risk management optimization on different stock markets.

View Article: PubMed Central - PubMed

Affiliation: Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts, United States of America; Harvard Medical School and Division of Sleep Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America; Institute of Solid State Physics, Bulgarian Academy of Sciences, Sofia, Bulgaria.

ABSTRACT
We analyse times between consecutive transactions for a diverse group of stocks registered on the NYSE and NASDAQ markets, and we relate the dynamical properties of the intertrade times with those of the corresponding price fluctuations. We report that market structure strongly impacts the scale-invariant temporal organisation in the transaction timing of stocks, which we have observed to have long-range power-law correlations. Specifically, we find that, compared to NYSE stocks, stocks registered on the NASDAQ exhibit significantly stronger correlations in their transaction timing on scales within a trading day. Further, we find that companies that transfer from the NASDAQ to the NYSE show a reduction in the correlation strength of transaction timing on scales within a trading day, indicating influences of market structure. We also report a persistent decrease in correlation strength of intertrade times with increasing average intertrade time and with corresponding decrease in companies' market capitalization-a trend which is less pronounced for NASDAQ stocks. Surprisingly, we observe that stronger power-law correlations in intertrade times are coupled with stronger power-law correlations in absolute price returns and higher price volatility, suggesting a strong link between the dynamical properties of intertrade times and the corresponding price fluctuations over a broad range of time scales. Comparing the NYSE and NASDAQ markets, we demonstrate that the stronger correlations we find in intertrade times for NASDAQ stocks are associated with stronger correlations in absolute price returns and with higher volatility, suggesting that market structure may affect price behavior through information contained in transaction timing. These findings do not support the hypothesis of universal scaling behavior in stock dynamics that is independent of company characteristics and stock market structure. Further, our results have implications for utilising transaction timing patterns in price prediction and risk management optimization on different stock markets.

Show MeSH

Related in: MedlinePlus

Relation between correlations in intertrade times and stock price dynamics.(a) Dependence of exponent  characterising power-law correlations in absolute logarithmic price return fluctuations, on correlation exponent  characterising intertrade times within a trading day. Data represent one hundred NYSE (Table 1) and one hundred NASDAQ (Table 2) stocks. We calculate price returns over 1-minute intervals and  over time scales from 8 to 180 minutes ( half a trading day, which is 390 minutes). The positive relationship between  and  indicates that stronger correlations in ITT are coupled with stronger correlations in price fluctuations. This finding suggests that price fluctuations are not merely a response to short-term bursts of trading activity [34], [16]: rather the fractal organisation of price fluctuations over a broad range of time scales is linked to the observed underlying scaling features in the series of intertrade times. (b) Strong relationship between correlations in ITT and correlations in price fluctuations over time scales larger than a trading day for NASDAQ stocks. In contrast, there is no corresponding positive relationship for NYSE stocks. This suggests a weaker coupling between trading dynamics and price formation under the NYSE market structure, over time horizons above a trading day. Dependence of stock price volatility  on (c) the correlation exponent  and (d) the average value of ITT for the same stocks as in (a). We calculate  as the standard deviation of daily logarithmic price returns over six-month periods, averaging over all six-month periods throughout the entire record of each stock. Our results show no strong dependence between stock price volatility  and average level of trading activity, rather the volatility appears sensitive to the strength of the temporal correlations in ITT. These findings suggest that scale-invariant features in transaction times may play an important role in price formation. Furthermore, both dynamic and static properties of stock prices appear to be influenced by market-specific features in transaction timing: stronger power-law correlations in ITT (higher values of ) for NASDAQ stocks are matched by stronger power-law correlations in price fluctuations (higher values of ) and higher volatility (), compared with NYSE stocks.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3974723&req=5

pone-0092885-g006: Relation between correlations in intertrade times and stock price dynamics.(a) Dependence of exponent characterising power-law correlations in absolute logarithmic price return fluctuations, on correlation exponent characterising intertrade times within a trading day. Data represent one hundred NYSE (Table 1) and one hundred NASDAQ (Table 2) stocks. We calculate price returns over 1-minute intervals and over time scales from 8 to 180 minutes ( half a trading day, which is 390 minutes). The positive relationship between and indicates that stronger correlations in ITT are coupled with stronger correlations in price fluctuations. This finding suggests that price fluctuations are not merely a response to short-term bursts of trading activity [34], [16]: rather the fractal organisation of price fluctuations over a broad range of time scales is linked to the observed underlying scaling features in the series of intertrade times. (b) Strong relationship between correlations in ITT and correlations in price fluctuations over time scales larger than a trading day for NASDAQ stocks. In contrast, there is no corresponding positive relationship for NYSE stocks. This suggests a weaker coupling between trading dynamics and price formation under the NYSE market structure, over time horizons above a trading day. Dependence of stock price volatility on (c) the correlation exponent and (d) the average value of ITT for the same stocks as in (a). We calculate as the standard deviation of daily logarithmic price returns over six-month periods, averaging over all six-month periods throughout the entire record of each stock. Our results show no strong dependence between stock price volatility and average level of trading activity, rather the volatility appears sensitive to the strength of the temporal correlations in ITT. These findings suggest that scale-invariant features in transaction times may play an important role in price formation. Furthermore, both dynamic and static properties of stock prices appear to be influenced by market-specific features in transaction timing: stronger power-law correlations in ITT (higher values of ) for NASDAQ stocks are matched by stronger power-law correlations in price fluctuations (higher values of ) and higher volatility (), compared with NYSE stocks.

Mentions: Finally, we investigate if the market-mediated differences in long-range power-law correlations in ITT translate into differences in the scaling behaviour of price fluctuations of stocks registered on the NASDAQ and NYSE markets. To this end, in parallel with ITT we analyse the absolute price returns for each company in our database for both markets. For all stocks we observe a crossover at a trading day in the scaling function of price fluctuations [33], [24], from weaker to stronger correlations, corresponding to the crossover we observe for intertrade times. In addition we find that over time scales less than a day, stocks with stronger correlations in ITT exhibit stronger correlations in absolute price returns (Fig. 6a), as indicated by Pearson's test (, ). In particular, we find that the stronger correlations in ITT associated with the NASDAQ market structure (), are accompanied by stronger correlations in price fluctuations () over time scales within a trading day (Fig. 6a).


Impact of stock market structure on intertrade time and price dynamics.

Ivanov PCh, Yuen A, Perakakis P - PLoS ONE (2014)

Relation between correlations in intertrade times and stock price dynamics.(a) Dependence of exponent  characterising power-law correlations in absolute logarithmic price return fluctuations, on correlation exponent  characterising intertrade times within a trading day. Data represent one hundred NYSE (Table 1) and one hundred NASDAQ (Table 2) stocks. We calculate price returns over 1-minute intervals and  over time scales from 8 to 180 minutes ( half a trading day, which is 390 minutes). The positive relationship between  and  indicates that stronger correlations in ITT are coupled with stronger correlations in price fluctuations. This finding suggests that price fluctuations are not merely a response to short-term bursts of trading activity [34], [16]: rather the fractal organisation of price fluctuations over a broad range of time scales is linked to the observed underlying scaling features in the series of intertrade times. (b) Strong relationship between correlations in ITT and correlations in price fluctuations over time scales larger than a trading day for NASDAQ stocks. In contrast, there is no corresponding positive relationship for NYSE stocks. This suggests a weaker coupling between trading dynamics and price formation under the NYSE market structure, over time horizons above a trading day. Dependence of stock price volatility  on (c) the correlation exponent  and (d) the average value of ITT for the same stocks as in (a). We calculate  as the standard deviation of daily logarithmic price returns over six-month periods, averaging over all six-month periods throughout the entire record of each stock. Our results show no strong dependence between stock price volatility  and average level of trading activity, rather the volatility appears sensitive to the strength of the temporal correlations in ITT. These findings suggest that scale-invariant features in transaction times may play an important role in price formation. Furthermore, both dynamic and static properties of stock prices appear to be influenced by market-specific features in transaction timing: stronger power-law correlations in ITT (higher values of ) for NASDAQ stocks are matched by stronger power-law correlations in price fluctuations (higher values of ) and higher volatility (), compared with NYSE stocks.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3974723&req=5

pone-0092885-g006: Relation between correlations in intertrade times and stock price dynamics.(a) Dependence of exponent characterising power-law correlations in absolute logarithmic price return fluctuations, on correlation exponent characterising intertrade times within a trading day. Data represent one hundred NYSE (Table 1) and one hundred NASDAQ (Table 2) stocks. We calculate price returns over 1-minute intervals and over time scales from 8 to 180 minutes ( half a trading day, which is 390 minutes). The positive relationship between and indicates that stronger correlations in ITT are coupled with stronger correlations in price fluctuations. This finding suggests that price fluctuations are not merely a response to short-term bursts of trading activity [34], [16]: rather the fractal organisation of price fluctuations over a broad range of time scales is linked to the observed underlying scaling features in the series of intertrade times. (b) Strong relationship between correlations in ITT and correlations in price fluctuations over time scales larger than a trading day for NASDAQ stocks. In contrast, there is no corresponding positive relationship for NYSE stocks. This suggests a weaker coupling between trading dynamics and price formation under the NYSE market structure, over time horizons above a trading day. Dependence of stock price volatility on (c) the correlation exponent and (d) the average value of ITT for the same stocks as in (a). We calculate as the standard deviation of daily logarithmic price returns over six-month periods, averaging over all six-month periods throughout the entire record of each stock. Our results show no strong dependence between stock price volatility and average level of trading activity, rather the volatility appears sensitive to the strength of the temporal correlations in ITT. These findings suggest that scale-invariant features in transaction times may play an important role in price formation. Furthermore, both dynamic and static properties of stock prices appear to be influenced by market-specific features in transaction timing: stronger power-law correlations in ITT (higher values of ) for NASDAQ stocks are matched by stronger power-law correlations in price fluctuations (higher values of ) and higher volatility (), compared with NYSE stocks.
Mentions: Finally, we investigate if the market-mediated differences in long-range power-law correlations in ITT translate into differences in the scaling behaviour of price fluctuations of stocks registered on the NASDAQ and NYSE markets. To this end, in parallel with ITT we analyse the absolute price returns for each company in our database for both markets. For all stocks we observe a crossover at a trading day in the scaling function of price fluctuations [33], [24], from weaker to stronger correlations, corresponding to the crossover we observe for intertrade times. In addition we find that over time scales less than a day, stocks with stronger correlations in ITT exhibit stronger correlations in absolute price returns (Fig. 6a), as indicated by Pearson's test (, ). In particular, we find that the stronger correlations in ITT associated with the NASDAQ market structure (), are accompanied by stronger correlations in price fluctuations () over time scales within a trading day (Fig. 6a).

Bottom Line: Specifically, we find that, compared to NYSE stocks, stocks registered on the NASDAQ exhibit significantly stronger correlations in their transaction timing on scales within a trading day.These findings do not support the hypothesis of universal scaling behavior in stock dynamics that is independent of company characteristics and stock market structure.Further, our results have implications for utilising transaction timing patterns in price prediction and risk management optimization on different stock markets.

View Article: PubMed Central - PubMed

Affiliation: Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts, United States of America; Harvard Medical School and Division of Sleep Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America; Institute of Solid State Physics, Bulgarian Academy of Sciences, Sofia, Bulgaria.

ABSTRACT
We analyse times between consecutive transactions for a diverse group of stocks registered on the NYSE and NASDAQ markets, and we relate the dynamical properties of the intertrade times with those of the corresponding price fluctuations. We report that market structure strongly impacts the scale-invariant temporal organisation in the transaction timing of stocks, which we have observed to have long-range power-law correlations. Specifically, we find that, compared to NYSE stocks, stocks registered on the NASDAQ exhibit significantly stronger correlations in their transaction timing on scales within a trading day. Further, we find that companies that transfer from the NASDAQ to the NYSE show a reduction in the correlation strength of transaction timing on scales within a trading day, indicating influences of market structure. We also report a persistent decrease in correlation strength of intertrade times with increasing average intertrade time and with corresponding decrease in companies' market capitalization-a trend which is less pronounced for NASDAQ stocks. Surprisingly, we observe that stronger power-law correlations in intertrade times are coupled with stronger power-law correlations in absolute price returns and higher price volatility, suggesting a strong link between the dynamical properties of intertrade times and the corresponding price fluctuations over a broad range of time scales. Comparing the NYSE and NASDAQ markets, we demonstrate that the stronger correlations we find in intertrade times for NASDAQ stocks are associated with stronger correlations in absolute price returns and with higher volatility, suggesting that market structure may affect price behavior through information contained in transaction timing. These findings do not support the hypothesis of universal scaling behavior in stock dynamics that is independent of company characteristics and stock market structure. Further, our results have implications for utilising transaction timing patterns in price prediction and risk management optimization on different stock markets.

Show MeSH
Related in: MedlinePlus