The Bookshop Guru
Making money trading in stocks, shares, commodities, currencies for the investment banks and institutions in today’s cyber space currently relies heavily on ‘algo-trading’. Algo-trading is a term used to describe the algorithmic software which is used to gain that alpha edge against an increasing frenetic trading market where milliseconds can mean millions in buy and sell decisions in veiled trading markets.
There are a host of different strategies deployed in ‘algo-trading’ to achieve that elusive alpha edge. Some algorithms are designed to slice and dice trades into smaller multiply trades in the hope that another algo system doesn't spot the pattern and alert the opposition and lose the potential profitable edge. Pattern spotting software of all shades and colors are deployed to gain the alpha edge and crunching huge amounts of data waiting for the triggers to buy or sell.
The inherent problem with algorithms whether their based on neural networks, genetic algorithms, knowledge discovery systems or any other type of pattern recognition software is the paradox in automated pattern discovery systems in extracting random data sets and the association of interestingness as a measure of the usefulness of the data patterns revealed, because there is no prior knowledge of the likely interesting data associations to be found before automation. “If you do not expect it, you will not find the unexpected, for it is hard to find and difficult.” (Padmanabhan & Tuzhilin, 1999)
The essence of this is that it is easy, to spot a pattern, because it is already there – so designing algorithms to look for certain criteria and predicting a pattern would be more profitable. The use of artificial intelligence, is still in its infancy relative to how much we really understand about the capacity of the human brain. We only use about 10% of our brain capacity, and the rest is mostly conjecture and theories. The human brain has billions of neuron connections, yet even the most advanced neural networks only use a handful of connections and they are used in a wide range of applications such as stock market analysis and recognising trends, cancer screening classification etc.
It’s not about computer power like the SETI project where spare computer processing power cycles are combined from around the world, to number crunch and analyse segments of the night sky. The problem is that artificial intelligence, is based essentially on learning, and adapting to the data sets, while at the same time, using classification to modify its process – but, it’s not forward thinking, it doesn't need more computer processing power or memory, it lacks perception – the ability we humans call intuition.
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Some of the most successful sage investors (like - Warren Buffet) have built fortunes based largely on simple investment strategies, over the long term. Short term fluctuations, and gambling on the outcome, is the global daily routine of investment institutions and banks, and how they make their money, but, the paradox is that when one is making money, the other is not. It remains to be seen if real transparency exists in these trades or is hidden within veiled accountancy practices. We have seen the outcome of greed, in cases like Enron and Barings bank – when the light is switched on all is revealed. Don't get mistaken, computers and software systems are vital for a multitude of social and economic purposes, but they don’t have the alpha edge, in my opinion.
The human mind will always be more powerful than any computer because it has that hidden element of perception. An example of this would be George Soros who made a mint in the 1990’s. Anyone for polo?
Are you listening, what are you dreaming?
In my latest book “It’s Never Too Late” read how dreams do come true, but be careful what you wish for. Understand the secret of greed and you will attain one of the secrets of prosperity. The book will also take you on a journey and explores love, money, luck, and much more.
Hey, Chuck. Did you bring any spending money? Viva la vida loca.
Conducting Survey into Precognitive Choices