pith. sign in

arxiv: 1903.11530 · v2 · pith:YVPWRU37new · submitted 2019-03-27 · 💱 q-fin.TR · q-fin.CP

Market Dynamics: On Directional Information Derived From (Time, Execution Price, Shares Traded) Transaction Sequences

classification 💱 q-fin.TR q-fin.CP
keywords marketpriceexecutiontradedsharestimeapproachchange
0
0 comments X
read the original abstract

A new approach to obtaining market--directional information, based on a non-stationary solution to the dynamic equation "future price tends to the value that maximizes the number of shares traded per unit time" [1] is presented. In our previous work[2], we established that it is the share execution flow ($I=dV/dt$) and not the share trading volume ($V$) that is the driving force of the market, and that asset prices are much more sensitive to the execution flow $I$ (the dynamic impact) than to the traded volume $V$ (the regular impact). In this paper, an important advancement is achieved: we define the "scalp-price" ${\cal P}$ as the sum of only those price moves that are relevant to market dynamics; the criterion of relevance is a high $I$. Thus, only "follow the market" (and not "little bounce") events are included in ${\cal P}$. Changes in the scalp-price defined this way indicate a market trend change - not a bear market rally or a bull market sell-off; the approach can be further extended to non-local price change. The software calculating the scalp--price given market observations triples (time, execution price, shares traded) is available from the authors.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Trade Execution Flow as the Underlying Source of Market Dynamics

    q-fin.CP 2025-11 unverdicted novelty 5.0

    Execution flow I = dV/dt is shown experimentally to be the fundamental source of market dynamics via a Radon-Nikodym derivative framework that auto-detects thresholds and time scales, validated on real data, plus a Ch...