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H. Eugene Stanley - One of the best experts on this subject based on the ideXlab platform.

  • Quantifying fluctuations in market liquidity: Analysis of the Bid-Ask Spread
    Physical Review E, 2005
    Co-Authors: Vasiliki Plerou, Parameswaran Gopikrishnan, H. Eugene Stanley
    Abstract:

    Quantifying the statistical features of the Bid-Ask Spread offers the possibility of understanding some aspects of market liquidity. Using quote data for the 116 most frequently traded stocks on the New York Stock Exchange over the two-year period 1994‐1995, we analyze the fluctuations of the average Bid-Ask Spread S over a time interval Dt. We find that S is characterized by a distribution that decays as a power law PhS . x j, x ˛zS, with an exponent zS< 3 for all 116 stocks analyzed. Our analysis of the autocorrelation function of S shows long-range power-law correlations, kSstdSst +tdl ,t ˛ms, similar to those previously found for the volatility. We next examine the relationship between the Bid-Ask Spread and the volume Q, and find that S , ln Q; we find that a similar logarithmic relationship holds between the transaction-level Bid-Ask Spread and the trade size. We then study the relationship between S and other indicators of market liquidity such as the frequency of trades N and the frequency of quote updates U, and findS, ln N and S, ln U. Lastly, we show that the Bid-Ask Spread and the volatility are also related logarithmically.

  • Quantifying fluctuations in market liquidity: analysis of the Bid-Ask Spread.
    Physical review. E Statistical nonlinear and soft matter physics, 2005
    Co-Authors: Vasiliki Plerou, Parameswaran Gopikrishnan, H. Eugene Stanley
    Abstract:

    Quantifying the statistical features of the Bid-Ask Spread offers the possibility of understanding some aspects of market liquidity. Using quote data for the 116 most frequently traded stocks on the New York Stock Exchange over the two-year period 1994-1995, we analyze the fluctuations of the average Bid-Ask Spread S over a time interval deltat. We find that S is characterized by a distribution that decays as a power law P[S>x] approximately x(-zeta(S) ), with an exponent zeta(S) approximately = 3 for all 116 stocks analyzed. Our analysis of the autocorrelation function of S shows long-range power-law correlations, (S(t)S(t + tau)) approximately tau(-mu(s)), similar to those previously found for the volatility. We next examine the relationship between the Bid-Ask Spread and the volume Q, and find that S approximately ln Q; we find that a similar logarithmic relationship holds between the transaction-level Bid-Ask Spread and the trade size. We then study the relationship between S and other indicators of market liquidity such as the frequency of trades N and the frequency of quote updates U, and find S approximately ln N and S approximately ln U. Lastly, we show that the Bid-Ask Spread and the volatility are also related logarithmically.

George H K Wang - One of the best experts on this subject based on the ideXlab platform.

  • trading volume bid ask Spread and price volatility in futures markets
    Journal of Futures Markets, 2000
    Co-Authors: George H K Wang
    Abstract:

    In this study, we examined the relations between trading volume, bid–ask Spread, and price volatility on four financial and metal futures. Hausman’s (1978) tests of specification confirmed that trading volume, bid–ask Spread, and price volatility are jointly determined. We estimated the parameters and elasticities of trading volume, bid–ask Spread, and price volatility in a three‐equation structural model, using the generalized method of moments (GMM) procedure. Results indicate that there was a positive relationship between trading volume and price volatility but an inverse relationship between trading volume and bid–ask Spread after we controlled for other factors. Furthermore, results show that price volatility had a positive relationship with bid–ask Spread and a negative relationship with lagged trading volume. In addition, we found that the ordinary least‐squares parameter estimates of each equation model were often severely underestimated in comparison with those consistent estimates obtained from the GMM estimation. Results from this study have important policy implications. Our results indicate that a transaction tax, which is analogous to a greater bid–ask Spread, will reduce trading volume, although the reduction is not as great as we previously estimated. © 2000 John Wiley & Sons, Inc. Jrl Fut Mark 20:943–970, 2000

  • Trading volume, bid–ask Spread, and price volatility in futures markets
    Journal of Futures Markets, 2000
    Co-Authors: George H K Wang, Jot Yau
    Abstract:

    In this study, we examined the relations between trading volume, bid–ask Spread, and price volatility on four financial and metal futures. Hausman’s (1978) tests of specification confirmed that trading volume, bid–ask Spread, and price volatility are jointly determined. We estimated the parameters and elasticities of trading volume, bid–ask Spread, and price volatility in a three‐equation structural model, using the generalized method of moments (GMM) procedure. Results indicate that there was a positive relationship between trading volume and price volatility but an inverse relationship between trading volume and bid–ask Spread after we controlled for other factors. Furthermore, results show that price volatility had a positive relationship with bid–ask Spread and a negative relationship with lagged trading volume. In addition, we found that the ordinary least‐squares parameter estimates of each equation model were often severely underestimated in comparison with those consistent estimates obtained from the GMM estimation. Results from this study have important policy implications. Our results indicate that a transaction tax, which is analogous to a greater bid–ask Spread, will reduce trading volume, although the reduction is not as great as we previously estimated. © 2000 John Wiley & Sons, Inc. Jrl Fut Mark 20:943–970, 2000

Vasiliki Plerou - One of the best experts on this subject based on the ideXlab platform.

  • Quantifying fluctuations in market liquidity: Analysis of the Bid-Ask Spread
    Physical Review E, 2005
    Co-Authors: Vasiliki Plerou, Parameswaran Gopikrishnan, H. Eugene Stanley
    Abstract:

    Quantifying the statistical features of the Bid-Ask Spread offers the possibility of understanding some aspects of market liquidity. Using quote data for the 116 most frequently traded stocks on the New York Stock Exchange over the two-year period 1994‐1995, we analyze the fluctuations of the average Bid-Ask Spread S over a time interval Dt. We find that S is characterized by a distribution that decays as a power law PhS . x j, x ˛zS, with an exponent zS< 3 for all 116 stocks analyzed. Our analysis of the autocorrelation function of S shows long-range power-law correlations, kSstdSst +tdl ,t ˛ms, similar to those previously found for the volatility. We next examine the relationship between the Bid-Ask Spread and the volume Q, and find that S , ln Q; we find that a similar logarithmic relationship holds between the transaction-level Bid-Ask Spread and the trade size. We then study the relationship between S and other indicators of market liquidity such as the frequency of trades N and the frequency of quote updates U, and findS, ln N and S, ln U. Lastly, we show that the Bid-Ask Spread and the volatility are also related logarithmically.

  • Quantifying fluctuations in market liquidity: analysis of the Bid-Ask Spread.
    Physical review. E Statistical nonlinear and soft matter physics, 2005
    Co-Authors: Vasiliki Plerou, Parameswaran Gopikrishnan, H. Eugene Stanley
    Abstract:

    Quantifying the statistical features of the Bid-Ask Spread offers the possibility of understanding some aspects of market liquidity. Using quote data for the 116 most frequently traded stocks on the New York Stock Exchange over the two-year period 1994-1995, we analyze the fluctuations of the average Bid-Ask Spread S over a time interval deltat. We find that S is characterized by a distribution that decays as a power law P[S>x] approximately x(-zeta(S) ), with an exponent zeta(S) approximately = 3 for all 116 stocks analyzed. Our analysis of the autocorrelation function of S shows long-range power-law correlations, (S(t)S(t + tau)) approximately tau(-mu(s)), similar to those previously found for the volatility. We next examine the relationship between the Bid-Ask Spread and the volume Q, and find that S approximately ln Q; we find that a similar logarithmic relationship holds between the transaction-level Bid-Ask Spread and the trade size. We then study the relationship between S and other indicators of market liquidity such as the frequency of trades N and the frequency of quote updates U, and find S approximately ln N and S approximately ln U. Lastly, we show that the Bid-Ask Spread and the volatility are also related logarithmically.

Gerianta Wirawan Yasa - One of the best experts on this subject based on the ideXlab platform.

  • Analisis Trading Volume Activity dan Bid-Ask Spread Setelah Stock Split
    E-Jurnal Akuntansi, 2019
    Co-Authors: Luh Ade Wahyu Merthadiyanti, Gerianta Wirawan Yasa
    Abstract:

    Penelitian ini bertujuan untuk mengetahui apakah terdapat peningkatan trading volume activity dan penurunan Bid-Ask Spread setelah dilakukan stock split. Populasi pada penelitian ini adalah seluruh perusahaan di BEI yang melakukan stock split dimana berjumlah 234 perusahaan. Jumlah sampel yang diambil sebanyak 53 perusahaan, dengan metode non probability sampling, khususnya purposive sampling. Teknik pengambilan data yaitu teknik dokumentasi dari Bursa Efek Indonesia. Metode analisis yang digunakan adalah analisis paired sample t-test. Berdasarkan hasil analisis ditemukan bahwa terdapat peningkatan TVA setelah stock split yang menunjukan bahwa minat investor mengalami peningkatan karena harga saham yang rendah dan terdapat penurunan Bid-Ask Spread yang menunjukan bahwa likuiditas perdagangan saham mengalami peningkatan. Kata kunci: Trading volume, Bid-Ask, split

Yuliana Rachma Sulistiawati - One of the best experts on this subject based on the ideXlab platform.