A need for Screening Tests

A number of industries could become so much more "genuine", i.e. with some "screening tests".

Let's look at the academic level, if Business Schools advocate high probability (60%) of finding executive level employment for graduates, then a screening test could be something along the lines of "OK, then let's do a contract where if the grad doesn't get employed, the school would refund 60% of the tuition...".

Or in the financial industry. The currently popular management + incentive fees are not feasible for the average investor. With the fixed management fee, no management team has any incentive to actually perform for the investor(s), they could simply take opposite directions for half of the investors for 1 time step, then vice versa, and still make a guaranteed management fee income while investors bleed to death.

Screening Test for an investment firm (Mutual funds, hedge funds... etc.)

How about this, having an incentive fee ONLY, and if the fund suffers from a loss, then the management shares that loss with the investors. This little step could immediately filter out the snake oil salesmen, who we all know are rampant in the industry.

Game Theory utilized by CIA

Bruce Bueno de Mesquita (a professor at New York University and senior fellow at the Hoover Institution at Stanford), has helped the American CIA, via Game Theory, predict foreign geopolitical moves years ahead of time with 90% accuracy. This is pretty cool.

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I can predict events and decisions that involve negotiation or coercion, cooperation or bullying

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Here's a TED video where he speaks about politics, predictions, mathematics!

Quant skills

Let's look at some skills in demand today at Quant Finance Jobs. The employers are mostly hedge funds and the average salary: $200K USD + %PnL (Percentage of Profit/Loss).

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1) Quant Trading Analyst-Algorithmic Trading Team-London

London, United Kingdom

Interested candidates should have extensive experience in the following;
• Time Series Econometrics
• Alpha Construction
• Bayesian Statistics
• Transaction Cost Modelling
• Black Litterman modelling
• Portfolio optimisation

2) Ultra high frequency Statistical Arbitrage Trader

New York, United States of America

Requirements:-

Candidates will have a background in the high frequency trading space, with experience creating and managing strategies with a high Sharpe Ratio, high ROC and holding little to no overnight positions.
3+ years experience of researching, back testing and deploying systematic trading strategies straddling multiple asset classes including equity index, currency, fixed income and commodities. Futures experience would be ideal.
Ivy League calibre PhD in a hard science

Strong to expert programming skills in C++.
Candidates should be innovative and analytical thinkers with strong communication skills.

3) High Frequency Quant Trader

New York or London, United States of America

Required Skills and experience
PhD from a top tier University in Computer Science, Mathematics, Statistics, Engineering or related subject
Strong hands on programming experience in C++
Experience with analytical packages such as Mathematica, Matlab, PyLab or R
Between 1-3 years experience of developing, implementing and trading high frequency trading strategies across any asset class.
Strong quantitative skills and experience
Passion for solving complex problems and drive to success

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We can see that strong mathematics is a must, and computer science a close 2nd, or an ivy league PhD in a "Hard Science" (I'm thinking physics, statistics). So knowing what the hedge funds possess and utilize, how does the average private trader compete and survive, profitably?

Just some ideas

• Invest time and energy to gain necessary skills to level the playing field
  
• Find ways to quantify institutional buying/selling pressure (but risk being always a bit behind)
  
• Raise enough money to influence and exploit the markets like Soros or Buffet
  
• Hire a quant...

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)

Subprime mortgages in NZ

Overheard from a very popular Auckland talk radio station this morning during an ad break, "... little or no down payment mortgages!... no down payment for those under $300,000!" Yeah, in America those're called "Subprime Mortgages", it actually precipitated a Subprime Mortgage Crisis :D

So it begins, party time in New Zealand!!!

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?"

Implied/Realized volatility arbitrage

Pretty good article of exploiting index volatility discrepancies for a statistically expected profit.

Key Concepts

Volatility arbitrage is morphing from a niche institutional

strategy to mass market, index-linked products.

Volatility arbitrage strategies attempt to take advantage of the

difference between the implied volatility of an asset and its

realized volatility.

Variance swaps are ideally suited to capturing the difference

between implied and realized volatility.

Volatility arbitrage indices, such as the S&P 500 Volatility

Arbitrage Index, measure the performance of a tradable short

variance swap strategy that is long implied volatility and short

realized volatility.

Since 1990, the S&P 500 Volatility Arbitrage Index has

outperformed the S&P 500 at an annualized rate of more than

three percentage points while having one-third of benchmark

volatility. It has never had a twelve-month negative return period.

Article Link

It's pretty clear that profit potential off this strategy lies in a deep understanding of the math involved (e.g. GARCH, cointegration, ARIMA, and etc.)