A theory of very short-time price change: security price drivers in times of high-frequency trading

Abstract

Academic research has identified several factors that affect price movements; however, the scenario changes abruptly in the case of very short time price changes (VSTPC). This topic is not specifically examined in the existing literature; nonetheless, the behavior of the market microstructure is quite different at the subsecond scale. Indeed, below a certain psychological time threshold, most factors typically influencing price changes cease to apply. This paper analyzes several parameters considered to affect price changes and identifies four of them as potentially influencing VSTPC. These factors are previous volatility, scarce liquidity, high quantity exchanged, and stop-loss (SL) orders (seldom mentioned in the literature). These four parameters are examined by means of a mathematical model, audit trail data analysis, Granger-causality testing, and agent-based model. The results of these four techniques converge to suggest a nonlinear combination of previous volatility, liquidity, and SL orders as the main causes of excess volatility. However, contrary to mainstream literature on trading time above a certain psychological threshold, the volumes exchanged are not integral agents for VSTPC. Currently, financial markets face many ultrafast orders, yet a coherent theory of price change at time scales incomprehensible by humans and only manageable by computers is still lacking. The theory presented in this paper attempts to fill this gap. The outcome of such a theory is important for purposes of market stability, crisis avoidance, investment planning, risk management, and high-frequency trading.

Comprehensive Delineation of Algorithmic High-Frequency Trading

“…By roughly discussing 4 types of relations between the underlying factors, Direct, Indirect, Behavioral, and Indirect behavioral, the paper concluded that the combination of previous volatility, scarce liquidity, and stop-loss orders is the prerequisite to high volatility in a very short period. Whereas, the high quantity of trading, though reasonable, is not to be proven [10].…”

Comparison of Aparch-Type Models: Does the Continuous and Jump Components of Realized Volatility Improve the Fitting?

“…The volatility of a financial asset plays an important role in the calculation and hedging of financial derivatives (such as options), as well as the measurement of risk in a financial or investment portfolio. The volatility phenomenon refers to how large an asset's prices change around the average price over a given period of time-this is a statistical measure of the commodity return margin [1]. The existence of volatility is closely related to risk in the market.…”

Is Technical Analysis Profitable on Renewable Energy Stocks? Evidence from Trend-Reinforcing, Mean-Reverting and Hybrid Fractal Trading Systems

“…Third, we utilize realistic constraints (budget, trading costs and long-only rule), as well as money (anti-Martingale) and risk (trailing stop) management policies to allow for valid empirical tests. Despite the significant importance of these practical constraints and policies, they are often ignored in the existing literature (see, for instance [22,23]). Hence, their inclusion in this paper would further contribute to the research in this area.…”