Equity Market Fair Value (Morningstar)

A relatively accurate fair value could give the trader a feel for whether an asset's current bid/offer is respectively high/low. Valuation of general equity markets, or particular sectors, would intuitively take a truckload of number crunching, luckily it is available at Morningstar.

About the Morningstar Fair Value Calculations
source: How Fair Value and Target Price Differ
"
To derive the fair value estimates, we use our proprietary discounted cash-flow (DCF) model. This model assumes that the stock's value is equal to the total of the free cash flows the company is expected to generate in the future, discounted back to the present. So, the first step is to project how much cash a firm is likely to produce over a number of years, and subtract the amount needed for capital improvements and increases in working capital to keep the business growing. Whatever profits are left over belong to the shareholders. The second step is to discount those profits to understand how much they are worth today.
 
As with any DCF model, the ending value is highly sensitive to the analyst's projections of future top- and bottom-line growth. In addition, the cost of capital, which is determined by the firm's capital structure and its riskiness, is another influential factor in the fair value estimate. (For more discussion of the nuts and bolts of Morningstar's DCF model, please see David Kathman's article "How Morningstar Values Stocks".)
" 

About Discounted Cash Flow Model
source: Wikipedia on DCF
"
The discounted cash flow formula is derived from the future value formula for calculating the time value of money and compounding returns.

where

• DPV is the discounted present value of the future cash flow (FV), or FV adjusted for the delay in receipt;
•  
• FV is the nominal value of a cash flow amount in a future period;
•  
• i is the interest rate, which reflects the cost of tying up capital and may also allow for the risk that the payment may not be received in full;
•  
• d is the discount rate, which is i/(1+i), i.e. the interest rate expressed as a deduction at the beginning of the year instead of an addition at the end of the year;
•  
• n is the time in years before the future cash flow occurs.

"

VIX trade effectiveness: quantitative or qualitative?

So on Thursday morning (near the Wed. close on NYSE), I made a post regarding my numbers (with + statistical expectancy) advising to short VIX futures, yet I did not follow through due to news of nuclear meltdown.


We can see that volatility did indeed drop significantly. This is not the first time, where I have gotten scared out of a good trade due to financial news bulletins. Looking back, performance would have been better if I had ignored the news and just stuck to the numbers entirely.

I am still uncertain if that is the wisest choice of action, but am leaning toward the "avoid all news" option at this point. Yes, it is completely against what most people believe, yet...

"
Whenever you find yourself on the side of the majority, it's time to pause and reflect.
"
-- Mark Twain

Free market sentiment charts

Market Harmonics provides updated sentiment figures for free. It saves some of us number crunching time, especially things like the Rydex NOVA/URSA and Nasdaq ratios (see below).


We can see that sentiment numbers have high absolute correlation with the underlying indices, and mostly represent dumb money. They are then pretty important for trading.

The Complete Arbitrage Deskbook (book review)

This book is pretty informative for anyone new to arbitrage. Stephane Reverre explains a fairly comprehensive field of existing means of arbitrage (up to the year of the book, 2001), covering a very wide range of instruments and explains both technical and economical grounds behind the mentioned market inefficiencies.


Prerequisite knowledge

A good understanding of statistics is necessary to fully understand some of the concepts from this book. The reader is also expected to have basic understanding of corporate finance and familiarity with intermediate financial mathematics.


Some interesting things from the book

Mathematical explanations for Index Arbitrage opportunities and expected mispricings (Index futures vs. Spot ETF, component portfolio, etc.)

Risk arbitrage exploiting scheduled corporate events (mergers and acquisitions)

Existing statistical arbitrage (e.g. pair trading) means

Examples of actual arbitrage opportunities and exploitations­­ off empirical data

Taleb's Code

I got this off Against Value at Risk excerpt (which deserves a later analysis), from Taleb's Fooled by Randomness. These concepts could apply to not just financial trading, but all little risk/reward scenarios we go through in everyday life.

"

Trader Risk Management Lore : Major Rules of Thumb

Rule 1 - Do not venture in markets and products you do not understand. You will be a sitting duck.

Rule 2 - The large hit you will take next will not resemble the one you took last. Do not listen to the consensus as to where the risks are (i.e. risks shown by VAR). What will hurt you is what you expect the least.

Rule 3 - Believe half of what you read, none of what you hear. Never study a theory before doing your own prior observation and thinking. Read every piece of theoretical research you can - but stay a trader. An unguarded study of lower quantitative methods will rob you of your insight.

Rule 4 - Beware of the trader who makes a steady income. Those tend to blow up. Traders with very frequent losses might hurt you, but they are not likely to blow you up. Long volatility traders lose money most days of the week.

Rule 5 - The markets will follow the path to hurt the highest number of hedgers. The best hedges are those you are the only one to put on.

Rule 6 - Never let a day go by without studying the changes in the prices of all available trading instruments. You will build an instinctive inference that is more powerful than conventional statistics.

Rule 7 - The greatest inferential mistake: this event never happens in my market. Most of what never happened before in one market has happened in another. The fact that someone never died before does not make him immortal. (Learned name: Hume's problem of induction).

Rule 8 - Never cross a river because it is on average 4 feet deep. Rule 9 - Read every book by traders to study where they lost money. You will learn nothing relevant from their profits (the markets adjust). You will learn from their losses.

"

The Jump & Dump!

I've been reading up a lot around liquidity and ran into the story of how two postgrads, Rahul Savani (Computer Science) and Ben Veal (Applied Math.), won the Penn-Lehman Automated Trading Competition in 2005. That's University of Pennsylvania and yeah, Lehman Bros (back in the good old days of selling mortgage backed assets).

Savani and Veal's algorithm, Jump and Dump, dominated the other algorithms completely in the competition, by realizing that all of the competing algorithms focused only on market data and not any "reality checks" on price sensibility. Commented by a Wall St. friend of theirs, "... Often a strategy is successful because it anticipates how the other market participants are likely behave/react and then exploits them. "

Here's how they did it, it was brilliant and deceptively simple!

"

The strategy of Savani and Veal is simple to describe and even elegant in its own twisted way. The basic idea is to "clear out" one side of the market --- for instance, to simply buy all shares in the sell book. This has the effect of leaving a buy book, and thus a bid, but no sell book, and thus no ask.

The next step is to immediately place a buy and sell order at a very large price --- larger than the highest price paid to clear out the sell book. Since there is no ask, and the bid is far below this large price, this pair of orders becomes the new bid and ask, effectively leaving the current buy book far below the bid/ask.

The third step is to then self-execute a small number of shares with the new bid or ask, thus causing the last execution price to also be near the new large bid/ask.

The effect of these three steps is to (a) leave the strategy with a large long position (from the initial purchase of the sell book), and (b) move the bid, ask, and last price to a price far above the prices paid to acquire the large long position.

You can see where this is heading. Any strategy that only places orders with limit prices relative to the current bid, ask, or last execution price will blindly follow the artificial inflation in the market created by these steps, and begin trading near the new price. As long as there is enough such liquidity at the new inflated prices --- and in the recent competition, there was plenty --- the Savani and Veal strategy can then quietly start dumping its long position for far more than it paid for it. Genius incarnate.

"

Return Distributions: Power Law > Gaussian

Power Law distributions explain fat-tail distributions much more accurately than Normal. I am aware that most academics are drilled about how Gaussian curves fit EVERYTHING in real life. That is nonsense. The existence of alternative distributions in math/stat textbooks that track empirical real life tell a very different story. Basically, extreme (potentially profitable) events occur MUCH MORE frequently than what Gaussian assumes.

According to someone who models stock returns with a normal distribution (this probably includes 99% of academic finance grads), the 1 day big market drops in 1929, 1987, 1998, 2008, Enron, GM, etc. are supposed to happen at most once every 10^30, or nonillion years. So it's numerically convenient, and obviously wrong, yet they embrace it as if it's the one and only truth.

The brilliant options trader, writer of The Black Swan, and once a professor at MIT, Taleb mentioned in Haug's Derivative Models on Models that the state of academic finance is intellectually insulting. Having met some folks from local business schools, I must agree.

It would seem more out of political reasons for this persistent blind faith of normal distributions in modern finance. As an overwhelming majority of current financial practices rely on Gaussian moments such as standard deviations, which we now know is completely meaningless in financial time series, an official recognition of this error would mean job loss for university staff, so-called analysts, and embarrassment for a number of people who came up with useless concepts such as "modern portfolio theory", "CAPM", "Black-Scholes Option Pricing", "VaR", and etc.

Is it intentional deception or incompetence? I'd say a bit of both.

Portfolio Theory, and practice

Even though "Modern" Portfolio Theory is pretty old, and holds a lot of false assumptions around market completeness and diversification, it could be applied practically for a portfolio of positively expected strategies instead of simple assets vulnerable to systemic risk.

Original thoughts
So the original assumption around increasing the number of asset holdings is to lower the standard deviation of return, i.e. "unsystemic risk" (see below).

This is pretty easy to do by simply taking positions in index ETFs.

So what's the deal with the "Undiversifiable or Market Risk"? That includes things like credit risk, counter-party risk, basically everything that shows why buy & hold does not turn out well.

Actual application

Replacing asset holdings with trading strategies that exploit fundamental inefficiencies where systemic risks become opportunities for profit, and all of sudden portfolio theory becomes practical for the real world. It's like running a casino, to minimize swings in revenue, hosting a whole bunch of games helps the Law of Large Numbers kick in just a bit sooner; and everybody's happy.

The "Hot Chicks" Indicator

This is according to google trends (see above). It appears that periods where Dow Jones exceeds Hot Chicks in relative search volume by a few sigmas (standard deviations), a bullish market likely follows, and vice versa.

Easy $$$!

Implied correlation index KCJ

KCJ is the S&P500 implied correlation index, you can see it at yahoo finance, KCJ link.

It's pretty interesting as like the VIX, it has a negative correlation to the S&P500 index. What makes it cool is that as a correlation, it's implicitly bounded. It could definitely offer an edge for directional forecasts of both underlying markets and volatilities.

So much for words, look at the chart!

A Non-Random Walk Down Wall Street (Free PDF)

Awesome stuff, this is where Andrew W. Lo and A. Craig MacKinlay show analytically (i.e. statistical evidence) that stock prices are not random, but actually deterministic. The Princeton University Press has taken the trouble to make it freely available in PDF format.

(Click on the book cover for link)

Is complicated math necessary for profitable trading?

An old blog post from Paul Wilmott addresses the too often accepted belief, "the more complicated the mathematics the better". An example he gave involves the Heston Stochastic Volatility Process, where you need to solve a PDE (Partial Differential Equation) involving numerical integration in complex space, i.e.

...

...


So the next logical question remains, does all that work improve forecast accuracy significantly? More importantly, would it offer significantly more efficient volatility arbitrage strategies? We need empirical findings!


The fact that the above applies "standard arbitrage arguments", an assumption of no arbitrage, makes it not as desirable. Wilmott makes a really good point here, "So, many know all the ins and outs of the most advanced volatility models based in the classical no-arbitrage world. Well, what if your job is to find volatility arbitrage opportunities?"

Using The TICK For Profitable Trading

The NYSE TICK (ticker symbol ^tick at prophet.net or $tick at stockcharts.com) gives a net difference between stocks moving up against those on the decline, and it could help provide an edge for short term traders. As this indicator moves in a mean reverting fashion, interim emotional buying and selling become easier to identify.

Applying TICK for profitable trading

Naturally, you want to buy if TICK closed at extremely low levels the previous day, and vice versa. However there is more, and I will provide an example for a buying opportunity.

Alongside a low TICK value, this usually means below -800 for me, price action also matters. If accompanied by a dip in the indexes, then an opportunity is present for a buying entry. On the open of the next day, get in ONLY if price opens below the close of the previous day.

The opposite works for short positions. This scheme provides a slight edge for the learning trader. If liquidated at the end of the days, these theoretical opened positions would have all ended profitably for the past two months on the SPY (see above graphs). Yes it takes a heck of a lot of patience to trade it, well at least it would help your trading performance in the positive while the search for high returns goes on.

About Victor Niederhoffer's Experiences

In 1979, Victor Niederhoffer turned $50K into $20Million within 18 months, and became one of the first traders known for quantitative perception. Like any other business, his performance has had ups and downs throughout the decades, and perhaps we could all learn something from the experiences.

I came across this article a few days ago, and it really begs the question of practicality concerning quantitative finance developments today. Mr. Niederhoffer had assumed that stock markets generally moved upwards, a fundamental concept behind current theories in mathematical finance, and this belief had hurt his performance.

Stock prices drift downwards, too

The assumption that stock fundamental values generally stay static while risk-free interest rate serves as an upward drift parameter, i.e. value added via inflation, simply does not apply for actual, realistic market behavior. Companies fail, and more often than not speculative bubbles become mistaken for genuine added value.

Ignoring “unlikely” risks, weaknesses of applied mathematical models, generally makes blowing up a statistical eventuality (Mr. Niederhoffer had blown several hedge funds, but had always managed to get back up and fight another day). No matter how infrequently storms occur, boats are built to withstand the heaviest of them. So should your trading strategy.

Change of paradigm

Fundamentally and statistically, it does not take much for businesses to fail, and yet it requires everything for a moment or two of success. Business and liquidity cycles all point to very dissimilar perspectives than that of Mr. Niederhoffer, or the typical public investor.

It does not matter if your convictions are right or wrong, your objective remains to come out profitable. Pride plays no part in the markets. With that, maybe it is time to take a deeper look into your risk management, so that you WILL survive the worst of times.

Regression outlier trading via Excel

Now that we know how to import data into Excel, let me explain a simple technique for an edge the public crowd largely remains clueless of. I used to apply it myself but not today as I now utilize strategies that require less patience, and hence release of this information does not conflict with my own trading interests.

Basic Concept

This analysis basically exploits statistical outliers. Exponential growth does not sustain (but decay does), both in nature and financial markets. Fundamentally, exponential growth of stock prices occur when the public crowd drifts off to fantasy land and keeps buying while institutional traders await to take profit.

To exploit this phenomenon, you search for growing stock(s) and make downside bets as prices surpass exponential levels. A second degree polynomial regression analysis makes this possible. Profit taking, or short-covering, occurs when price returns to the regression line. See below for steps.

Steps

1) I have decided to use data for SPY from 2001 to today, basically from the beginning of the last bull market.

2) With daily “High” prices studied and highlighted, click on the “Chart Wizard” button. (Because the strategy calls for downside bets, the optimal entry levels would lie in the historical-high statistics.)

3) Choose XY (Scatter Graph)

4) Keep clicking “next” until you see “As New Sheet” or “As object in”, and choose As New Sheet, and name it whatever, in this case “SPY Regression”, and press “Finish”.

5) Once the chart becomes opened, click on “Tools”, then “Add Trendline”, and pick Polynomial, order “2”, and then hit “OK”.

6) Now the chart holds the regression line nicely for the historical data, woohoo!

Applying This Edge

• The regression line must curve upwards
• Take short position(s) as price jumps up way above the regression line
• Cover position(s) as price reverts back to regression line

As I’ve stressed in the past, do NOT apply a hard stop loss. That would seriously hurt performance. Remember that this strategy only works on the downside, not upside. If you do not understand why betting on failure has a higher expectancy, read this post.

Good trading.