How will AI resolve your wealth problem!
AMI（www.aminpo.org）Innovation and Investment Forum was held in Toronto, Ontario Canada on Oct. 29th. Representatives from global leading investment institutions, top innovation companies and leading enterprises attended the event which covered opportunities in AT/FinTech and Healthcare industry.
My interview with ZF where Bogdan asks me fascinating questions on psychology, markets, information, AI etc.
Finance is a key frontier for AI. Imagine coming back from vacation and talking to your virtual assistant about your investment portfolio and wondering how he does it, quarter after quarter, year after year. Managing money is the real human AI. It has to talk, it has to think, it has to have intuition and it has to make money.
Why should an investment analyst need a special skill set different from that of a sports analyst, a beverage company executive, a scientist, a social change agent, or the key decision makers in the government. Data, and its interpretation, is what connects us all despite our domains. We may not always be able to reconcile sub-atomic data with stock market data as both of them vibrate at a different frequency, but that does not change some universal laws which are present in every data set, irrespective of its natural source. Statistical laws are not only at the heart of physics, but also drive how we look at data. This means that there is the overlooked influence of ‘Data Universality’. Universality can be defined as “The aspects of a system’s behavior which are independent of the behavior of its components. And even systems whose elements differ widely may nevertheless have common emergent features”, Therefore, Data Universality can be defined as the “common universal behavior of any data set irrespective of its organic source of generation or derivation.” The talk on Data Universality will explain the disconnect between Pareto’s 80-20 and Galton’s Mean Reversion; how investing styles can be simplified; how Capital Asset Pricing Model can be transformed into a framework for modeling growth and decay of any natural variable driven by data.
This was London 2014. I am standing tall with the veterans Greg Morris, Trevor Neil MSTA MCSI ACI-UK and my friend Alex Spiroglou, DipTA(ATAA), CFTe who beat me to the Charles Dow Awards. :-)
Proud of you Alex Spiroglou, DipTA(ATAA), CFTe. You broke the door. Congratulations.
Market technicians bring a lot of value to capital markets. And in the world of markets, where it's easy to believe in your trading skillset, systematic training as a technician goes a long way in avoiding ruin. Over the decade, I have steered away from the subject to become a pure quant but my formative years as a technician have made me a better quant.
And just in case we forget, Charles Dow created the Dow Industrials Index, the Wall Street Journal, and the Dow Theory. Charles Dow was a true market technician and a successful indexer.
And if you think you are smart trader, you should try reading Alex's paper and set up your benchmark.
https://lnkd.in/grdbCskJ
CMT Association, Inc.
Alex we should have a bash when you are here next or when I come down to London.
When I first met him, I had the feeling that a movie star was in front of me. He looks like a movie star, but he thinks like a visionary. He comes from India, the largest democracy in the world, a country with an incredible past, and a bright future; a land of choices, a country with as many Gods as you can count, and a wonderful cuisine. Not to mention the incredible philosophical, religious, and cultural traditions and also the amazing scientific discoveries. That’s why it was not very clear to me, at the beginning, what he was doing here, in Romania, in Cluj. After a while, I understood.
There are many universal laws, but few or none are used to understand markets. Stock markets are natural systems and express universal laws. Building a case for an application built around such laws is not easy, as failure or noise effects universality too. Mean reversion is a universal law, which has also suffered from failure and noise. The patterns and noise in mean reversion has been witnessed across subjects like Behavioral Finance, Intermarket Analysis, and Statistics etc. However, little has been done to understand the failure of mean reversion. The talk will focus on comprehending the failures of mean reversion and how it could be redefined into a better statistical and universal risk management framework, have cross sector application like investment management and predicting trends etc.