Improving Investment Decisions with Quant Analysis

Below are some excerpts on a SeekingAlpha article -- that highlights "the application of quant analyses to produce meaningful and objective investment information."  

With so much at stake in the finance and investment world, there are constant debates about investment performance, asset allocation, active vs. passive approaches, and more. In this article, we review recent articles on the application of quantitative models to the investment world - and look at the stock market's technical outlook.

History can tell us where we have been -- and a scientific and mathematical approach can help guide our investment decisions going forward. Quantitative analytics can be applied to many areas within finance, ranging from asset allocation and risk management to trading / investment strategies and the growing interest in alternative assets.

Quantitative approaches can be used to improve investment decisions by producing objective analysis. The goal is to improve investment results and the probability of repeatability. Math and scientific methods can help put history and patterns on our side. Here are some important takeaways:

• Varied interests and motivations can reduce optimal investment decisions.
• Performance data is not necessarily useful in predicting future results, especially when studying short-term manager performance.
• Behavioral finance has shown that many investors overreact to recent events, and tend to ignore long-term historical patterns.
• Mathematical models can minimize the negative impacts of greed and fear, improving discipline -- and taking emotion out of the equation.
• Robust quantitative analysis can improve "forward information" and repeatability.

Systematic and scientific approaches can yield meaningful information and help improve long-term performance by providing objective analysis to investment committees.

Read more here:

http://seekingalpha.com/article/879811-improving-investment-decisions-with-quantitative-analysis

Anatomy of a Trade

I was talking to some friends and associates recently about market action and the "setup of a trade."  If you are interested in trading systems, below are some notes from our conversations.

By way of background, our quantitative trading systems are technical in nature and are tuned and researched for market action, time-frame, and execution.  We focus on the intermediate to long-term time horizon -- with shorter-term approaches "in the mix" for diversification and risk management reasons.

In particular, the notes focus on trade entry and execution.  We would all like to enter a trade at the best possible price/trade entry.  On the other hand, we have heard some great traders and "market wizards" such as Paul Tudor Jones, Bill Eckhardt, or Richard Dennis -- say they prefer trades and markets where execution seems bad; or markets that are "running away" from them.

In the long run (based on experience; also, data backs this up) -- our trading strategies are designed so that they can enter trades "at the market."  Sometimes, we'll be able to get a better price; other times, the trade will move away from us (along with profits) -- so going "at the market" is okay.  If we want to "be long" in a market, we should initiate the trade.    

Below is an example of why some market wizards PREFER buying markets that are running away from them. 

_____ 

Say, we want to buy Market X and the price is 100.00.

• (A) Trade Initiation -- at trade initiation, our trade expectations might be something like this.  
• 47% chance of Profitable Trade (yes, less than 1/2 the trades are profitable...)
• Avg Gain = 1700 (but avg gain is greater than avg loss); 
• Avg Loss = 1000
• In the long run, this kind of trade action will lead to profits, even with less than 50% of the trades profitable.
• (B) "Bargain" (?!) Trade -- if we wait a bit on pricing -- and the price is now 99.25, we might think, "Oh great, this is a good deal."  However, note that this is not the exact match of all trades in the bucket in category (A).  This "Bargain" trade is a subset of category (A).  
• Note that "noise" will sometimes work for trade entries and sometimes against trade entry.
• However, within the complete dataset of trades, there IS an area where "noise" becomes "information."  
• And roughly, the numbers for this trade might NOW be something like this: 
• 44% chance of Profitable Trade (market is moving against the desired long position)
• Avg Gain == 1600
• Avg Loss == 1100
• Note that this subset has slightly lesser trade parameters -- because there is "more information" based on the lower price. 

Although this example is simplified, our technical and quantitative trading strategies take these factors --  as well as other quant research and volatility / risk management -- into consideration.