Financial market time series (FMTS) remain the subject of intense study in modern econometrics. Whilst there are statistical approaches to analysing the complex and chaotic way FMTS autocorrelate over time, there has been no adequate way to identifying such series from graphs. This paper reports on a technique for directly converting them to auditory data and some experiments to newly demonstrate that a FMTS can be easily distinguished aurally from a number of artificially generated uncorrelated time series of variously similar structures.
[para]There is an ongoing debate about the real structure of the way market prices fluctuate over time. Louis Bachelier conjectured that price fluctuation in day–to–day exchange–traded securities is essentially a random walk (Bachelier 1900). If Bachelier’s conjecture is correct, as is assumed by the Efficient Market Hypothesis, prices would exhibit no autocorrelation and thus a Gaussian Noise Time Series (GNTS) would be a good approximation to the way prices move in real markets.
[para]Subsequent investigations by Mandelbrot and others have shown that real markets exhibit much larger variability compared to GNTS (Mandelbrot and Hudson 2004; Peters 1991): returns show greater kurtosis and/or skew than a normal distribution (Peinke et al. 2004) as well as exhibiting momentary autocorrelations. Modern econometrics and financial engineering place considerable import on understanding such phenomena because the increased likelihood of extreme events indicates greater market volatility than if the market was a GNTS and this impacts on market modelling, risk assessment, options pricing and portfolio theory in general.
[para]Frysinger played back market price data directly as a sound waveform. He reported that results proved difficult to interpret probably because the stock market does not follow physical-acoustic resonance laws (Frysinger 1990). This paper demonstrates, using Australian All-Ordinaries Index data, how a translation can be made to follow such resonance laws that can be shown experimentally to maintain temporal autocorrelation characteristics.
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References
Bachelier, L. 1900. Théorie de la Spéculation Annales de l’Ecole Normale Superieure pp. 21-86 translated into English by A.J. Boness in The Random Character of Stock Market Prices, edited by P.H. Cootner (MIT Press 1967), pp. 17-78.
Frysinger, S. P. 1990. "Applied research in auditory data representation" in Extracting Meaning from Complex Data: Processing, Display, Interaction. Santa Clara, California, SPIE-The International Society for Optical Engineering.
Mandelbrot, B.B. and R.L Hudson. 2004. The (Mis)behaviour of markets. Basic Books, NY, USA.
Peters, E E. 1991. Chaos and order in the capital markets. John Wiley & Sons, Inc. NY.
Peinke, J., M Siefert, S. Barth, C. Renner, F. Reiss, M. Wâcher, M and R. Friedrich. 2004. “Fat tail statistics and beyond”. In Advances in Solid State Physics Vol. 44, 363-374.
Authors: David Worrall
Event: SF08: Search and Information Extraction from Audio Data Workshop