Things I learned the past few years trading the stock markets
Rocko Chen
October 07, 2008
The content of my blog has drifted away from the mathematical side of things for a good while now; I wanted to express some personal thoughts toward the financial industry at this point. Nothing is as it seems.
Pure Quantitative Studies are Over-Rated
The search for an elegant mathematical system to both explain the past and exploit future moves of the financial markets continues, yet so far I have not found anything of significant value to study historical prices. Too many assumptions regarding distributions, probabilities, rational trading behavior, and etc. have done more harm than good (e.g. consider the credit crisis and the half quadrillion derivative shenanigans).
So for anyone starting out and trying to figure out patterns, wedges, flags, trend-lines, let me save you some time and suggest that it is all a waste of time. Studying prices and price derived "dependent" variables will not result in consistently profitable trading.
Some Things that have "worked"
The news. Trading against mainstream financial media has worked exceptionally well. All you have to do is put yourself in their shoes, and think critically of motivations behind each public announcement, and more often than not obvious answers lie behind the veils of deception. Remember my article on Goldman Sachs and oil?
Quantified sentiment. Only by observing what most traders are actually doing, can one truly assess realistic likelihood of liquidity in the markets and therefore directional bias. One example of such would be the ISE sentiment index.
Credit risk assessment. An easy way to find good candidates for shorts is to look for a company that issues bonds of very high Yield to Maturity. The higher the YTM, the more willing investors "imply" high credit risk of the issuing business. At the same time, if the company is willing to pay such high interest for borrowing, it MUST be desperate for cash. This can not be faked by playful accounting.
Market depth of ECNs. Observing bid/ask volumes off BatsTrading or ARCA do provide a significant edge in day trading, unfortunately my current schedule does not allow for late nights every week day.
Existing research
My research this moment lies applying Support Vector Machines in neural networks for financial forecasting. Sounds impressive, though the mathematics gets so convoluted I get easily lost within the numbers, let alone trying to predict the markets for the next day or two. Nevertheless, I still search for something that "works" on the very short horizon.
I believe that methods MUST exist to determine high probability direction bias of stock indexes at any point in time, and finding answers from pure mathematics is improbable. From this point my interest has turned toward that of economics, politics, and behavior of the financial institutions.
Pure Quantitative Studies are Over-Rated
The search for an elegant mathematical system to both explain the past and exploit future moves of the financial markets continues, yet so far I have not found anything of significant value to study historical prices. Too many assumptions regarding distributions, probabilities, rational trading behavior, and etc. have done more harm than good (e.g. consider the credit crisis and the half quadrillion derivative shenanigans).
So for anyone starting out and trying to figure out patterns, wedges, flags, trend-lines, let me save you some time and suggest that it is all a waste of time. Studying prices and price derived "dependent" variables will not result in consistently profitable trading.
Some Things that have "worked"
The news. Trading against mainstream financial media has worked exceptionally well. All you have to do is put yourself in their shoes, and think critically of motivations behind each public announcement, and more often than not obvious answers lie behind the veils of deception. Remember my article on Goldman Sachs and oil?
Quantified sentiment. Only by observing what most traders are actually doing, can one truly assess realistic likelihood of liquidity in the markets and therefore directional bias. One example of such would be the ISE sentiment index.
Credit risk assessment. An easy way to find good candidates for shorts is to look for a company that issues bonds of very high Yield to Maturity. The higher the YTM, the more willing investors "imply" high credit risk of the issuing business. At the same time, if the company is willing to pay such high interest for borrowing, it MUST be desperate for cash. This can not be faked by playful accounting.
Market depth of ECNs. Observing bid/ask volumes off BatsTrading or ARCA do provide a significant edge in day trading, unfortunately my current schedule does not allow for late nights every week day.
Existing research
My research this moment lies applying Support Vector Machines in neural networks for financial forecasting. Sounds impressive, though the mathematics gets so convoluted I get easily lost within the numbers, let alone trying to predict the markets for the next day or two. Nevertheless, I still search for something that "works" on the very short horizon.
I believe that methods MUST exist to determine high probability direction bias of stock indexes at any point in time, and finding answers from pure mathematics is improbable. From this point my interest has turned toward that of economics, politics, and behavior of the financial institutions.