I recently mentioned lessons that I was learning in developing a trading system. Since 3 AM this morning, I've been taking apart every signal from the past several years and investigating the factors differentiating the successful signals from the unsuccessful ones.
My sense, for what it's worth, is that far too little attention is paid to the value of qualitative research in markets. Qualitative research refers to systematic observation that is designed to *generate* hypotheses, not test or validate them. Qualitative research does not replace quantitative work; its purpose is different.
We can think of qualitative research as theory building research. It is the observation that we perform to develop an understanding of phenomena.
When Darwin collected his notebooks of observations from nature, he organized the information in a way that enabled him to generate an explanatory framework: evolution. That framework not only explained existing observations; it suggested new ones. It is the testing of those fresh observations that forms the backbone of quantitative research.
"There is nothing more practical than a good theory," psychologist Kurt Lewin once observed. His point was that science aims at more than prediction: it seeks explanation. Indeed, it is through understanding that we are able to generate predictions.
A technical presentation of the structure of scientific theories can be found here. One theme that has emerged over the course of philosophy of science is the role of models and analogies in generating theories and explanations. We explain something we don't know well by casting it in terms of what is better known. This process of analogy helps us think about complex, partially understood phenomena in novel ways: the good theory is practical to the extent that it leads us to observations we otherwise would have never made.
As I pour through data, I increasingly realize that equating a "scientific" approach to markets with a predictive one is a limited and limiting perspective. There are many predictive statements generated regarding markets daily; few of them are backed by formal efforts at explanation and understanding. Before we enter the laboratory--like Darwin--we need to fill in our notebooks and view our investigations as ways of generating hypotheses, not conclusions.
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My sense, for what it's worth, is that far too little attention is paid to the value of qualitative research in markets. Qualitative research refers to systematic observation that is designed to *generate* hypotheses, not test or validate them. Qualitative research does not replace quantitative work; its purpose is different.
We can think of qualitative research as theory building research. It is the observation that we perform to develop an understanding of phenomena.
When Darwin collected his notebooks of observations from nature, he organized the information in a way that enabled him to generate an explanatory framework: evolution. That framework not only explained existing observations; it suggested new ones. It is the testing of those fresh observations that forms the backbone of quantitative research.
"There is nothing more practical than a good theory," psychologist Kurt Lewin once observed. His point was that science aims at more than prediction: it seeks explanation. Indeed, it is through understanding that we are able to generate predictions.
A technical presentation of the structure of scientific theories can be found here. One theme that has emerged over the course of philosophy of science is the role of models and analogies in generating theories and explanations. We explain something we don't know well by casting it in terms of what is better known. This process of analogy helps us think about complex, partially understood phenomena in novel ways: the good theory is practical to the extent that it leads us to observations we otherwise would have never made.
As I pour through data, I increasingly realize that equating a "scientific" approach to markets with a predictive one is a limited and limiting perspective. There are many predictive statements generated regarding markets daily; few of them are backed by formal efforts at explanation and understanding. Before we enter the laboratory--like Darwin--we need to fill in our notebooks and view our investigations as ways of generating hypotheses, not conclusions.
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