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April 2012

Infographic: Is the Black Scholes Model Responsible for the Credit Crunch

April 27, 2012 by tigergb   Comments (0)

There is a large debate on whether we should blame the Black Scholes model for the credit crisis, for example, Guardian publishes an article "The mathematical equation that caused the banks to crash" discussing the issue that the Black-Scholes equation was the mathematical justification for the trading that plunged the world's banks into catastrophe.

Should we? I don't think so, the black scholes is just a weapon, it is the person who use it improperly should be blamed instead. This infographic is a simple defense of the Black Scholes model.
a defense of black scholes model

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Top Hedge Fund in London Week in Review

April 27, 2012 by tigergb   Comments (0)

A Generalized Measure of Riskiness: a generalized options’ implied measure of riskiness based on the risk neutral return distribution of financial securities is able to provide asset allocation implications and successfully predict the cross section of 1-, 3-, 6-, and 12-month ahead risk-adjusted returns of individual stocks.

Identifying financial crises in real time: we develop a new measure to study the behavior of stochastic time series, which permits to distinguish events which are different from the ordinary, like financial crises.

Free Historical Intra-Daily Data: how to download free intraday data from Google Finance.

New ranking of London's hedge funds: ranking by size.
 London's hedge funds
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Trading Stocks with Stochastic Oscillator

April 27, 2012 by tigergb   Comments (0)

Developed by George Lane in the 1950s, the Stochastic Oscillator is one of the most popular stock trading indicators, that provide good signals in many Forex pairs, stocks and commodities. In this article we will describe how to profit with it and catching bottoms and tops.

First of all it is important to understand the formula of the Stochastic Oscillator:

Main Stochastic (%K) = 100 * (Closing Price - Lowest Close of Last 5 Bars) / (Highest High of Last 5 Bars - Lowest Close of Last 5 Bars)
Signal Stochastic (%D) = 3-Period Exponential Moving Average of the Main Stochastic

From the formula we can derive that the main stochastic is showing us the relative location of current price in relation to the range of last 5 bars. Low readings indicate that price is near a support level (the lowest point of the range) and high readings indicate that price is near a resistance level (the highest point of the range).

Most traders enter trades when the main stochastic crosses the signal stochastic line - when a cross is from below it is a long signal, and when the cross is from below it is a short signal.



Another trading method is to enter trades when the Stochastic Oscillator crosses the 60 level (long trade), and when it crosses the 40 level (for shotr trade). It is a trend-following approach that works well in stock charts with strong trends.



It is remarkable that an indicator that was developed 60 years ago is still useful and still generates powerful signals to this day, on many stocks and commodities.

One can also improve the formula of the Stochastic to take into account ranges that are shifting: Channels instead of parallel trends. The improved formula would show the location of price in relation to the boundaries of regression channel, giving much more accurate signals that take into account the trend as well, and not just flat high and low.

We highly recommend getting to know this indicator and mastering the trading systems presented here. It can provide very accurate signals, both trend-following and reversal signals, and can provide you with trading edge.

This is a guest post by Steve Sollheiser, a writer and a stock trader. Visit his site, www.StockChartPatterns.org for more articles about chart patterns.
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Free Historical Intra-Daily Data

April 27, 2012 by tigergb   Comments (0)

The last decade we have seen a significant increase in the demand for high frequency data. This is explained for a large part by an increased attention of the academic world in algoritmic trading. Moreover, as lot of papers suggest, the profitability has been shifted to an intra-daily format. In this segment, speed is what counts. For instance, Scholtus and Van Dijk (2012) state that strategies that yield a positive return when they experience no delay, a delay of 200 milliseconds is enough to lower their performance significanlty. Given the competition on the market from large institutions, such as JP Morgan and Morgan Stanley, a private investor has always a competative disadvantage due to its lack of the required technology. Nevertheless, there is always room for improvement in the modelling of stochastic intra-daily processes such as the VWAP and daily volatility.

A key ingredient in these research areas is proper and clean (historical and up-to-date) intra-daily data. On the web there are various resources available, but most of them require a relatively high fee. Other solutions require the use of a specific software. However, there are ways to retrieve intra-daily data for free using Google Finance and also without any software.


Using Matlab


If you are familiar with MatLab you can use parts of the package 'Volume Weighted Average Price from Intra-Daily Data' by Semin Ibisevic referenced at Qoppa Investment Society. This package allows you to
(1) retrieve intra-daily stock price data from Google Finance; (2) calculate the VWAP at the end of each trading day; and (3) transform intra-daily data to a daily format. It is a relatively flexible function as it only requires the user to input the ticker symbol and the exchange where the security is listed on. Additionally, the user can define the frequency of the data (1 second or higher) and the period (for instance past 10 days).


Without software


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Good Strategy Bad Strategy Week in Review 130412

April 27, 2012 by tigergb   Comments (0)

RExcel: call R in Excel.

Speed up your R code using a just-in-time (JIT) compiler: A simple trick to speed up your R code.

Assessing Models of Individual Equity Option Prices: This article investigates option models in the encompassing class of stochastic volatility, return-jumps, and volatility-jumps.

Good Strategy Bad Strategy: The Difference and Why It Matters: The author describes what strategy IS, and describes how to distinguish good from bad. It's the kind of stuff that's obvious - but only AFTER you've had it pointed it out to you.

Fallacies of Valuing Bonds With Near 0% Interest Rates: Investors hate inflation, but they love TIPS.
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Prof Hal White Passed Away Week in Review 020412

April 27, 2012 by tigergb   Comments (0)

Smile in Motion: An Intraday Analysis of Asymmetric Implied Volatility: on average, about 99% of the intraday variation of implied volatility can be explained by moneyness and changes in the index level. Compared to the typical smile regression with moneyness alone, about 50% of the remaining errors can be attributed to movements in the underlying index.

A Simple Way to Estimate Bid-Ask Spreads from Daily High and Low Prices: We develop a bid-ask spread estimator from daily high and low prices. Daily high (low) prices are almost always buy (sell) trades. Hence, the high-low ratio reflects both the stock’s variance and its bid-ask spread.

How to Prevent Other Financial Crises: Policy recommendations to avoid additional crises, focused on "less is more" simple rule of "captain goes down with ship." These aim at eliminating the combination of statistical moral hazard and scientism-misuse of probabilistic methods that got us into trouble.

A No BS Guide to the Basics of Parallelization in R:a simple example to use multi-core in R to speed-up.

Markets, Ethics and Mathematics - A Defence of Mathematics: should we blame mathematics for the crisis? a defence of mathematics.

The Impact of Quantitative Easing on the U.S. Term Structure of Interest Rates: a paper by Jarrow and Li (2012) estimates the impact of the Federal Reserveís 2008 - 2011 quantitative easing (QE) program on the U.S. term structure of interest rates.

Halbert L. White, Jr., 1951-2012: It is with great sadness that UCSD Economics Professor Hal White passed away Saturday morning after an extended struggle with cancer.
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Assistant Professor Salary: China vs. UK

April 27, 2012 by tigergb   Comments (0)

I will hopefully submit my PhD thesis by September, never thought I would one day work in academia, but started to seriously plan what to do after graduation: go back to industry or begin a new career at university?
china vs uk
Spent this weekend searching for opportunities both in UK and China, with emphasis on assistant professor at finance department. The results are really surprising, I heard the salary in UK is low, but never thought it is soooooooooo low, are you kidding me, UK? Except London Business School, other top universities pay extremely low, with a range of 30k GBP to 50k GBP for junior lecturer (equivalent to assistant professor) based on advertisement.

As a comparison, below is a salary list for those top mainland universities in China in GBP (econ for economics department, fin for finance department, all for assistant professor):
Shandong U (econ) 25k
Wuhan U (econ) 26k
Renmin Business School (fin) 40k
Shandong (econ) 20k
THU Shenzhen (fin) 50k
CQTBU (econ) 15k
PKU SOE (econ) 24k
SWUFE (econ) 25k
Renmin Labor and HR (econ) 32k
Fudan SOE (econ) 35k
ZJU (econ/fin) 35k
SHUFE (fin) 35k
THU SEM (econ/fin) 36k
Renmin Hanqing (fin) 39k
PKU Guanghua (econ/fin) 40k
PKU HSBC (econ) 40k
SWUFE (fin) 50k
PKU HSBC (fin) 50k
CEIBS (fin) 65k
SAIF (fin) 68k
CKGSB (econ) 125k
CKGSB (fin) 190k

Many universities in China pay similarly as in UK, I wish this post is an April fools day joke, but it is TRUE. I know we can't just compare salary when choosing a university, but still, are you kidding me, UK?

Please leave a comment if my number isn't correct.
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Markets, Ethics and Mathematics - A Defence of Mathematics

April 27, 2012 by tigergb   Comments (0)

This article is a guest post by Dr Timothy Johnson.

In the aftermath of the Credit Crisis it became popular to blame quants and mathematics for the Credit Crisis. In November, 2008, a former French prime minister, Michel Rocard, wrote in Le Monde that “mathematicians are guilty (unwittingly) of crimes against humanity”. More seriously, the following March, the UK’s financial regulator, the Financial Services Authority published the Turner Review on the causes and cures of the crisis where it identified one of the causes as a “misplaced reliance in sophisticated mathematics”. Wired wrote about The Formula That Killed Wall Street and the FT followed up on the Wired report.

As the dust settled, http://c0182732.cdn1.cloudfiles.rackspacecloud.com/fcic_final_report_full.pdf" target="_blank" rel="nofollow">The Financial Crisis Inquiry Comission Report gave a more thoughtful analysis. They mentioned maths and quants, but only in passing. Their conclusion was that there had been a “systemic breakdown in accountability and ethics”, which had resulted in lax regulation and excessive borrowing.

In one respect the FCIC conclusions are positive for mathematicians, the Crisis wasn’t their fault. On the other hand, if the problems were rooted in ethics, then surely maths has no role in preventing future Crisis. Maths is just another tool, like a spread sheet or double entry bookkeeping. This is pretty depressing for the heirs of Newton, Euler, Riemann, Poincaré and Kolmogorov.

The mathematical study of probability is usually thought to have begun in the mid-sixteenth century, with Cardano’s Liber de Ludo Alea (‘Book on Games of Chance’), where there is the first explicit statement that the chance of rolling a six on a fair dice is 1 in 6. Shortly after making this statement, Cardano makes the perceptive observation that

Quotation
These facts contribute a great deal to understanding but hardly anything to practical play.1


Cardano’s work was ignored for centuries, the problem was, despite Cardano’s status as a mathematician, his ‘Book on Games of Chance’ didn’t fit in to what modern mathematicians regard as proper mathematics. The fact is that Cardano did not see his work on probability as principally a mathematical work, but as an investigation of the ethics of gambling, a point made recently by the mathematician David Bellhouse2.

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Python in Matlab Week in Review 250312

April 27, 2012 by tigergb   Comments (0)

pymex - Matlab in Python: pymex embeds a Python interpreter in Matlab, allowing Matlab programmers write parts of their scripts in Python. Programmers are also able to use Python modules in Matlab.

The Short Term Prediction of Analysts' Forecast Error: a short term trading strategy based on predicting the error in analysts' earnings per share forecasts using publicly available information generates an annual gross abnormal return of 16.56%.

R in Google Summer of Code 2012: participating in a program receives US$5,000 for successful completion of a GSoC project using R language sponsored by Google.

Comovement and Predictability Relationships Between Bonds and the Cross-Section of Stocks: we find that bonds are robustly related to the cross-section of stock returns in both comovement and predictability patterns.

10 Things the Public Need to Know About Quantitative Trading: Infographic: 10 Things the Public Need to Know About Quantitative Trading.
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