دانلود مقاله ISI انگلیسی شماره 13446
عنوان فارسی مقاله

پیش بینی VNET : مدل پویایی های عمق بازار

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
13446 2001 30 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
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عنوان انگلیسی
Predicting VNET: A model of the dynamics of market depth
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Financial Markets, Volume 4, Issue 2, April 2001, Pages 113–142

کلمات کلیدی
ساختار بازار - اطلاعات نامتقارن - نقدینگی بازار سهام - عمق بازار - منحنی واکنش بازار - مدل - اطلاعات - داده
پیش نمایش مقاله
پیش نمایش مقاله پیش بینی VNET : مدل پویایی های عمق بازار

چکیده انگلیسی

The paper proposes a new intraday measure of market liquidity, VNET, which directly measures the depth of the market corresponding to a particular price deterioration. VNET is constructed from the excess volume of buys or sells associated with a price movement. As this measure varies over time, it can be forecast and explained. Using NYSE TORQ data, it is found that market depth varies with volume, transactions, and volatility. These movements are interpreted in terms of the varying proportion of informed traders in an asymmetric information model. When an unbalanced order flow is transacted in a surprisingly short time relative to that expected using the Engle and Russell (Econometrica 66 (1998) 1127) ACD model, the depth is further reduced providing an estimate of the value of patience. The analysis is repeated for 1997 TAQ data revealing that the parameters of the relationships changed only modestly, despite shifts in market volume, volatility, and minimum tick size. A dynamic market reaction curve is estimated with the new data.

مقدمه انگلیسی

Over the past decade, equity market activity has increased dramatically in terms of both trading volume and price volatility. From one perspective, the ability of the stock market to handle an increasing number of daily transactions points to greater liquidity. However, the large price fluctuations that accompanied many of the high-volume days indicate that the market did not absorb the additional transactions without some degree of price impact. The net effect on the cost of trading is by no means obvious. Clearly neither volume nor volatility is a direct measure of liquidity, although they are closely connected. Beyond the bid–ask spread, few established measures of market liquidity are available and several are measurable only cross-sectionally. To the extent that stock market liquidity is a time-varying process, it may be possible to forecast when the market will be most accommodative to incoming trade activity. A tool capable of distinguishing and predicting shifts in market depth would be particularly valuable to institutional traders conducting high-volume trades in a particular stock. In addition, risk managers seeking ways to measure liquidity risk should find the prediction of market reaction curves useful. Not only would this present the possibility of computing price deterioration from a known quantity of portfolio holdings, but it also would offer a menu of liquidation costs depending upon the unwind strategy chosen. This paper introduces a new, intraday statistic for market depth. Quoted depth reflects the number of shares that can be bought or sold at a particular bid or offer price. The new statistic, VNET, measures the number of shares purchased minus the number of shares sold over a period when prices moved a certain increment, and it is therefore a measure of realized depth for a specific price deterioration. VNET is constructed in event-time, similar to Cho and Frees (1988), and can be measured repeatedly throughout the trading day to capture the short-run dynamics of market liquidity. Motivated by the asymmetric information models in the market microstructure literature, a predictive model of intraday market depth is developed and estimated for 17 stocks from the NYSE's TORQ data set. As anticipated, VNET is observed to vary both over time and across stocks. The results show VNET to be a function of the magnitude and timing of current and lagged transaction flows. The transactions data used to derive our measure of market depth presumably were themselves optimized according to investor criteria. Thus, time variation in expected VNET must be a result of agents who chose not to completely smooth liquidity over time, such as information-based traders. The prediction of VNET based on a valid conditioning set can only be precisely associated with market depth under the assumption that the contemplated trades are treated by the market in the same way that trades were treated historically. That is, a well-known troubled hedge fund might find that the depth available to it would be less than that forecast because the trades would be identifiable. Conversely, an index fund might find greater depth than predicted. In the next section, the liquidity concept is specified, then in Section 3 the market microstructure theory is discussed. Section 4 describes the TORQ data, and Section 5 presents the estimation results. Section 6 tests the robustness of these findings using a more current data sample, and Section 7 concludes.

نتیجه گیری انگلیسی

NYSE transaction and quote data are used to identify, measure, and model intraday variations in the market depth of individual stocks. Our models for the length of a price-duration (PTIME) and the net directional volume traded within a price-duration (VNET) are estimated over two distinct time periods, producing roughly similar parameter estimates. The relative stability of the relationship between quote revisions and trading behavior suggests that our price-duration based microstructure approach may indeed touch upon some of the fundamental determinants of equity market liquidity and volatility. The empirical analysis explores the depth of " nancial markets, which to this point has been di $ cult to quantify. By de " ning price-durations as the time between substantial adjustments in the midpoint of the quotes, a measure of the one-sided trading behind price movements can be obtained. With this new statistic, VNET, we are able to estimate the shape of the market reaction curve, both ex ante and ex post. Our models of VNET reveal that the realized depth of the market varies according to internal trading conditions. In general, the market traits associated with a higher likelihood of price adjustment following a given amount of one-sided volume (small VNET) are similar to those corre- sponding to low liquidity as represented by tightness (wide bid } ask spread) in earlier studies. This result is important in that it uni " es our de " nition of depth with more traditional views of liquidity. The models propose some strategies of how to trade large volume at the least cost. First, it may seem obvious that the greater the overall trading volume, the more of a nominal imbalance will be accepted by the market. However, the percentage imbalance between buys and sells su $ cient to move prices declines with the total number of shares traded. The number of transactions per duration also appears to reduce the depth of the market. This supports the notion that market thickness is generally a consequence of informed traders # ooding the market after a semi-private news event. The empirical models " nd that movements in VNET are negatively correlated with movements in the bid } ask spread. Along with providing evidence that the new statistic is a valid measure of liquidity, this relationship adds another trading strategy component, albeit an obvious one: when the market is tight it will also lack depth. In addition, the positive impact of the expected duration length on expected VNET suggests that when the market is volatile it will o ! er less depth. Finally, unanticipated shocks to the length of a price-durations, represented by PTIME } ERR, also increase realized depth. With respect to large volume trading strategies, this result carries the implication that patience may greatly reduce transaction costs. These results carry the implication that trading behavior may play a signi " cant role in shaping and predicting the intraday liquidity of the stock market

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