Empirical estimations of price–response functions

2020 ◽  
pp. 82-104
Author(s):  
Erik Haugom
Keyword(s):  
Response Functions ◽  
Price Response

scholarly journals Price response functions and spread impact in correlated financial markets

2021 ◽  
Vol 94 (4) ◽  
Author(s):  
Juan C. Henao-Londono ◽  
Sebastian M. Krause ◽  
Thomas Guhr
Keyword(s):  
Financial Markets ◽  
Stock Prices ◽  
Signal Strength ◽  
Price Formation ◽  
Response Functions ◽  
Trading Costs ◽  
Research Focus ◽  
Data Set ◽  
Price Response ◽  
The Impact

AbstractRecent research on the response of stock prices to trading activity revealed long-lasting effects, even across stocks of different companies. These results imply non-Markovian effects in price formation and when trading many stocks at the same time, in particular trading costs and price correlations. How the price response is measured depends on data set and research focus. However, it is important to clarify how the details of the price response definition modify the results. Here, we evaluate different price response implementations for the Trades and Quotes (TAQ) data set from the NASDAQ stock market and find that the results are qualitatively the same for two different definitions of time scale, but the response can vary by up to a factor of two. Furthermore, we show the key importance of the order between trade signs and returns, displaying the changes in the signal strength. Moreover, we confirm the dominating contribution of immediate price response directly after a trade, as we find that delayed responses are suppressed. Finally, we test the impact of the spread in the price response, detecting that large spreads have stronger impact.

Flexible estimation of price response functions using retail scanner data

2007 ◽  
Vol 14 (6) ◽  
pp. 383-393 ◽  
Author(s):  
Winfried J. Steiner ◽  
Andreas Brezger ◽  
Christiane Belitz
Keyword(s):  
Response Functions ◽  
Scanner Data ◽  
Price Response

Estimating Irregular Pricing Effects: A Stochastic Spline Regression Approach

1998 ◽  
Vol 35 (1) ◽  
pp. 16-29 ◽  
Author(s):  
Kirthi Kalyanam ◽  
Thomas S. Shively
Keyword(s):  
Hierarchical Bayes ◽  
Data Sets ◽  
Response Functions ◽  
Spline Regression ◽  
Price Response ◽  
Product Categories ◽  
Hierarchical Bayes Model ◽  
Pricing Decisions ◽  
Bayes Model ◽  
Regression Approach

Markets respond to prices in complex ways. Multiple factors such as price points, odd pricing, and just-noticeable differences often cause steps and spikes in response. The result is market response functions that are frequently nonmonotonic. However, existing regression-based approaches employ functions that are inherently monotonic, which thereby limits representation of important irregularities. In this article, the authors use a stochastic spline regression approach in the framework of a hierarchical Bayes model that permits the estimation of irregular pricing effects and apply the approach to data sets from several product categories. A simulation study indicates that the stochastic spline approach is flexible enough to accommodate irregular response functions. The empirical results show that there are irregularities in own-price response for most of the brands examined and that there are important profit implications of these irregular response functions in pricing decisions. The authors find that the irregularities in the response functions include sales increases associated with odd prices in the range of 12% to 76%, flatness at the extremes of the range of observed prices, and kinks in the response function that are consistent with segmentation effects.

Stock Price Response to Transacting Technology Rights: Quick Reversal of Good Fortunes?!

2012 ◽  
Author(s):  
Abdelaziz Chazi ◽  
Ashraf Khallaf ◽  
Yi (Ian) Liu ◽  
Zaher Zantout
Keyword(s):  
Stock Price ◽  
Price Response
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