A microeconomic quantitative financial analysis score for retail intra-day trading D. Askew Quant Oracle. *Revised July 15th 2020

Abstract

The paper presents the problem with the paucity of information in retail trading when determining how the market works, it's degree of speculation, and price prediction, relying arbitrarily on pseudo-applications of Technical Analysis. Moreover this paper sets out a new 'probability indicator' based on quantitative statistical analysis to help retail traders in their speculation of instrument's price before and after major economic events.

1. Introduction 2. Quantitative analysis 3. Our Methodology 4. Trading Signal 5. Indicator 6. Economic events 7. Conclusion 8. References

1. Introduction

Most of retail trading is a highly speculative exercise, particularly in FX spot, and other derivatives, due to a lack of tools that allow the ordinary investor to accurately determine volumes which may give a good indication of the price going up or down – e.g. volumes being an extension of the basic law of supply and demand.

In retail trading, technical analysis [TA] prevails as a method for determining price predication, pushed by most brokers and internet based 'educators'. This is despite TA providing no more support than a random number generator for price discovery, akin to astrology used by the ancient Babylonians to determine whether grain prices would be bearish or bullish (Lo; Hasanhodzic; 2011). TA can only describe what is happening at that exact precise point in time and what has happened in the past as per the Efficient Market Hypothesis [EMH].

EMH states that asset prices already contain all necessary information relevant to the price (Samuelson 1965); therefore it's impossible to 'beat the market' in the sense of prediction because everything is already always 'priced in' (Malkiel, 1973) with the exception of 'lagging' prices as seen in latency arbitrage and front running as used by HFT firms. The same holds true for Statistical Arbitrage (stat arb) which looks for inefficiencies and anomalies in price movement - such as disparities of price between exchanges e.g. cash to futures, futures to cash arbitrage. That said, EMH is still only a hypotheses and does not account for the full diverse range of human behaviour and was blamed for some of the oversight in allowing the crash of 2008, (Siegel; 2010).

2. Quantitative analysis

Quantitative analysis, involving mathematical and statistical analysis has long been the boon of sophisticated financial institutions who, left technical analysis at it's apex in the 1970s (Brock; Lakonishok, et al 1992 ) replacing TA which was the de-facto standard for price discovery before the age of electronic computation of financial data – see Munehisa 18th century; Dow 1890s; Schabacker 1920s. In financial markets, quantitative analysis offers modelling of volatility [1, 2, 3, 4], cash flows [5, 6, 7], speculative bubbles [8], and reveals discrete patterns such as fractals [9, 10] in price clustering.

Typically the most advanced microeconomic models of financial markets used in retail, rely on abstract and loose free parameters used in fundamental analysis and speculative support and resistance lines of 'chartists; leaving trading as an 'art', rather than a science of mathematical probabilities, used with a quantitative approach.

The 'QO score' indicator offered in this paper, provides retail traders with a mathematical underpinning for price discovery as a probability that the price will either be higher or lower come the end of the intra-day market (EOD – end of day).

3. Our Methodology

The QO probability score, is based on the methodology of Bak-Sneppen (BkSn) model, created by Perr Bak and Kim Sneppen. BkSn was orignally created to explain aspects of the fossil records which show distribution sizes reflecting the fitness of species, characterised by evolution and extinction events. More broadly BkSn's algorithm has been extended to determine the distribution of avalanches, landslides, earthquakes – providing a probability that the duration of such an event can be calculated. It is here that there can be found an analogous description in determining market events from speculative bubbles, to market crashes and rapid spikes.

A common occurring size of an bearish drop, is smaller than the median and the mean as seen in

Originally BkSn described N species associated with a fitness factor of F(i); the indexed integers i were in a lattice structure and the algo determined the least fittest species and replaced them alongside it's two adjoint neigbours (like a linked-list data structure) with a new species acquiring a new random fitness. This then sets a precedent for a new minimum required standard of fitness, below which species do not survive.

One interesting caveat is the most stable periods of growth are always characterised by an eventual 'collapse', with the longer and more stable the period of growth, the deeper and sharper the level extinction and increased fitness required to survive it [8].

In a financial application of the BkSn model a 'species' can be an instrument, fund, company or individual investor. Each species is represented by time t and it's fitness e.g.

Fluctuations in the fitness of one 'species' affects the evolution of others, i.e. co-evolution. The implications of this if it holds true for economic systems, means financial instruments linked by industry, can affect each other regardless of their own activity. Of particular note was, the longer the period of stability or growth of a price (e.g. a basket of tech stocks), the sharper and deeper the level of extinction and increased fitness to survive.

An illustration of this can be seen clearly in the 'Dot-com bubble' (2000) in which companies failed to survive due to the financial fitness required when the 'bubble popped' e.g. Actua Corporation; Boo.com; ; Broadcast.com; CyberRebate; ; ; eToys.com; ; Pets.com; Startups.com;

Theoretically this provides some commentary to the disparities created by 'trickle-down economics' favoured by neo-classical economists which have an unequal distribution of money creation, vis-a-vis the financialised economy and the real economy. Rising assets prices create a new level of 'fitness' or wealth required to achieve financial security for the retail trader too.

The average fitness of a 'species' in the BkSn is a mean average of individual fitness, where fitness is synonymous with 'financial fitness'.

The financial application of is an estimate of the market price, with the fitness an estimate of the market index as a result of all it's actors.

We use to determine groupings of market agents as opposed to individuals; their impact upon market price at the time , t is determined by

The 'raw price' is multiplied by the coalescence of the group due to it's analogous impact upon others calculated as

At each point the group with the lowest price has to 'adapt' ; or 'die', which thereby also affects it's neighbours i.e. financially linked industries or other correlated instruments. 4. Trading Signal

BkSn is used to predict bear conditions above a threshold of ; a trading signal can be given from an analysis of the statistics over intervals. The first part of a bearish drop is expressed by

The size of the drop is defined by the ephemeral duration, thus the time this lasts can be described as:

in accordance with the power law of .

From there a trading signal, above the threshold allows for the ability to calculate 'recovery' in time intervals i.e.

5. Indicator

This indicator is designed for retail intra-day traders (day traders), because of the problems and pseudo- application of technical analysis. Using the methodology and trading signal from the modified BkSn algorithm, offers the potential for price discovery as a probability score.

This indicator is based on specific economic driven events and their impact on the market price using the modified BkSn algorithm. It's ouput is a probability of an instrument increasing or decreasing during an interval e.g. the end of day [EOD] of the intra-day market.

Illustration:

Economic Event: UK Manufacturing PMI. Date: 0930hrs GMT; November 16th 2019. Result: Figures, higher than forecast. Instrument: GBPUSD QO score: 71.3%

Conclusion: 71.3% GBP will increase against USD by End of Day (EOD) 16th November 2019.

6. Economic events

The QO score is based on economic events e.g. quarterly earnings, PMIs, CPIs, job data, because reactionary market forces generate volatility. Often retail traders see the most profit or loss trading during times of peak volatility occurring after economic events (Paulos, J.A. 2003; p.58; 187); so addition support during these periods is crucial.

That's not to say volatility is to be avoided, there has to be movement in the retail market for ordinary traders to make a return of any significance during the intra-day session (day trading), otherwise it's fruitless. It's true, day-traders could use an FX carry trading strategy to glean interest, or hold overnight positions, but this takes a far more illiquid approach, which may defeat the objective of using derivative trading instruments as a 'boost' to an overall portfolio.

7. Conclusion

The QO score can provide a probability score that a financial instrument will move higher or lower by the end of day (EOD), after a the result of an economic event e.g. CPI data, job data, quarterly earnings etc.

8. References

[1] T. Bollerslev, H.O. Mikkelsen, J. Econometrics 73 (1996) 151-184. [2] F.J. Breidt, N. Crato, P. De Lima, J. Econometrics 83 (1998) 325-348. [3] R. Cont, J. da Fonseca, in Takayasu, H (Ed.), Empirical science of financial fluctuation, pp. 230-239, Springer , Tokyo, 2002. [4] A.W. Lo, Econometrica 59 (1991) 1279-1313. [5] Z. Ding, R.F. Engle, C.W.J. Granger, J. Empir. Fin. 1 (1993) 83- 106. [6] Z. Ding, C.W.J. Granger, J. Econometrics, 73 (1996) 61-77. [7] Z. Ding, C.W.J.Granger, J. Econometrics, 73 (1996) 185-215. [8] D. Sornette, A. Helmstetter, Phys. Rev. Lett. 89 (2002) 158501 -158504. [9] https://www.sciencedirect.com/science/article/abs/pii/S037843710401221X?via%3Dihub [10] https://www.researchgate.net/publication/300913688_Benoit_Mandelbrot_in_finance [11] Andrew W. Lo; Jasmina Hasanhodzic (2011); The Evolution of Technical Analysis;John Wiley & Sons. [12] Malkiel (1973); A Random Walk Down Wall Street. [13] Brock; Lakonishok; LeBaron (1992) Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. The Journal of Finance (1992): 1731–1764. [14] Paulos, J.A. (2003); A Mathematician Plays the Stock Market. [15 ] Siegel, Laurence B. (2010). "Black Swan or Black Turkey? The State of Economic Knowledge and the Crash of 2007–2009"