Towards modelling and measuring the non-random walk down wall street


LSBU Business School are pleased to present a third instalment of its Professional Lecture Series. When lockdown began last year, we maintained our commitment to address the learning needs of our staff, students, local business communities & beyond by delivering a series of virtual lectures with the aim of providing professional training & insights. Given that we are still operating remotely and following on from the success of last year's lectures, colleagues from LSBU's Business School have come together to develop an insightful & engaging new programme to carry us through to summer 2021.

An opportunity open to all, and with the broad business community with whom we are engaged in mind, our aim is to support the community and facilitate networks, especially in light of how the pandemic has affected so many business owners. This is reflected in many of the topics we will be addressing.

'Towards modelling and measuring the non-random walk down wall street' with Dr. Gurjeet Dhesi, Interim Director of Research for LSBU's Business School

Description: We have heard of the classic book by Burton G. Malkiel and now well-known phrase “A random walk down wall street “or putting it another way “The best we can predict about tomorrows price of an asset is today’s price”.!!! ………If this is the case then we would say that investors behave in a so-called rational manner. This then allows the application of the Geometric Brownian Motion model to model the histogram of the returns of assets, and it follows a “normal distribution”. However empirical observation of histograms of asset prices shows a peakier distribution with “fat tails”.

An innovative extension of Geometric Brownian Motion model is developed. Simulations based on this so called “Irrational Fractional Brownian Motion” with optimal weighting factors, substantially outperform the basic Geometric Brownian Motion model in terms of fitting the returns distribution of historic data price indices hence modelling the peakiness and fat tails.

With the Irrational Fractional Brownian Motion model, we re-examine agent behaviour reacting to time dependent news on the returns thereby modifying a financial market evolution. We specifically discuss the role of financial news or economic information positive or negative feedback of such irrational (or contrarian) agents upon the price evolution. We observe a kink-like effect reminiscent of soliton behaviour, suggesting how analysts' forecasts errors induce stock prices to adjust accordingly, thereby proposing a measure of the irrational force in a market.

Subsequently we forecast the numerical value of the fat tail(s) in asset returns distributions using the irrational fractional Brownian motion model. Optimal model parameter values are obtained from fits to consecutive daily 2-year period returns of S&P500 index over [1950–2016], generating 33-time series estimations. Through an econometric model, the kurtosis of returns distributions is modelled as a function of these parameters. Subsequently an auto-regressive analysis on these parameters advances the modelling and forecasting of kurtosis and returns distributions, providing the accurate shape of returns distributions and measurement of Value at Risk.


1pm - Welcome & Zoom functionality

1.05pm - Lecture: Towards modelling and measuring the non-random walk down wall Street

1.35pm - Q & A plus Networking

2pm - Close

This event will be delivered online using Zoom. The joining instructions will be emailed to you the day before the event takes place.

To check out the other event in the LSBU Business School Lecture Series, click here.



Gurjeet Dhesi gained his BSc in Physics from University College London with first class honours in 1984. He then moved to the University of Birmingham where his postgraduate studies were in Theoretical Physics. He completed his PhD in Mathematics (Theoretical Physics) in 1988. Further to this he also holds a Masters in Business Analysis with distinction from the University of Leicester.

Gurjeet is an active and experienced PhD supervisor in the area of Quantitative Studies applied in the domain of Finance, Business and Economics and has published in a wide range of subject areas.