Tuesday, July 04, 2017

What Is Making Money Now In Financial Markets

I recently wrote on the topic of how trading has sharply moved in an evidence-based direction.  That has enormous implications for trading process.  Specifically, it means that traders are spending the bulk of their time researching opportunities in markets, not staring at screens and putting on trades.  The successful trader is looking less and less like an intuitive market wizard and more and more like an insightful, disciplined researcher.

Read carefully the recent blog post from The Mathematical Investor describing which hedge funds are outperforming the others--and outperforming the markets.  It's a relative handful of funds that are performing very well and gaining assets.  These funds have several significant advantages:

*  They utilize high frequency algorithms to place trades and manage positions, making trade management a profit center, but also freeing up traders' time from the tasks of execution.  In many situations, it is far more time and cost-effective to systematize execution than to hire a small army of discretionary execution traders.

*  They have the capability of ferreting signals from large, complex data sets, allowing them to generate edges in trading not available to casual inspection and intuitive processing. 

*  They have the bandwidth to develop many models in many markets, creating highly diversified sources of returns and more reliable revenue streams.

These advantages are conferred by superior processing power (supercomputers); superior programming capabilities (capacity to store and access data and automate processes); superior data sets (more information and more unique information); and superior mathematical expertise (better ways to transform and analyze large data sets without overfitting).

The point that The Mathematical Investor is making is not that quant trading is superior to discretionary trading.  It's that mathematically sophisticated trading/investing has been superior to everything else, including lower-level quant that attempts to systematize discretionary insights by applying basic statistics and modeling methods.

In that sense, trading is looking a lot like weather forecasting.  Forecasting was once a wholly discretionary activity based upon the reading of cloud patterns and the feel of the air.  Later, it became possible to quantify such variables as wind speed, air pressure, and humidity and use this information to understand weather patterns and make forecasts.  At present, computer models capture the complexities of interacting weather systems to make forecasts that would be impossible to an individual forecaster looking at a thermometer and barometer.  

Where I am seeing the greatest success of individual traders is in niche strategies and markets, where inefficiencies are most likely to be present.  Many of these exist in strategies and markets where there is limited liquidity and hence limited participation of sophisticated funds.  An example would be certain commodities and companies, where detailed knowledge of the market and industry can still confer an edge in trading.  This is akin to a small business person finding a niche in a community he or she knows well, thus performing well in spite of the presence of large retail firms.  Specialization, uniqueness, and detailed product knowledge are likely to outperform generalist trading strategies as sophisticated market participants continue to claim the most liquid sources of alpha.

Further Reading:  Becoming an Evidence-Based Trader