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Quantitative Finance Collector is a blog on Quantitative finance analysis, financial engineering methods in mathematical finance focusing on derivative pricing, quantitative trading and quantitative risk management. Random thoughts on financial markets and personal staff are posted at the sub personal blog.

Mar 10
Han, Y.F., and Zhou, G.F. have an interesting working paper on the performance of a trend factor they proposed:
In this paper, we propose a trend factor to capture cross-section stock price trends. In contrast to the popular momentum factor constructed by sorting stocks based on a single criterion of past year performance, we form our trend factor with a cross-section regression approach that makes use of multiple trend indicators containing daily, weekly, monthly and yearly information. We find that the average return on the trend factor is 1.61% per month, more than twice of the momentum factor. The Sharpe ratio is more than twice too. Moreover, during the recent financial crisis, the trend factor earns 1.65% per month while the momentum factor loses 1.33% per month. The trend factor return is robust to a variety of control variables including size, prior month return, book-to-market, idiosyncratic volatility, liquidity, etc., and is greater under greater information uncertainty. In addition, the trend factor explains well the cross-section decile portfolio returns sorted by short-term reversal, momentum, and long-term reversal as well as various price ratios (e.g. E/P), and performs much better than the momentum factor.


The basic idea is to first calculate the month-end price moving average time series of different lags, then regress cross-sectionally monthly returns at date t on all moving average series at date t-1, finally predict monthly returns at date t+1 using the regression estimates and the moving average series at date t. This procedure guarantees we forecast stock returns at t+1 with information set only up to t. We then rank all stocks based on the forecasts into five quintiles, long the quintile with highest forecast returns and short the quintile with lowest, and rebalance once per month. This strategy generates, on average, 1.61% monthly return and 0.29 sharpe ratio using all US stocks, performs especially good during recession, and outperforms several existing factors. Moreover, the good performance of this strategy cannot be explained by firm fundamentals.

I implement this strategy with Chinese stock data, adjust the rebalance frequency to weekly for convenience, and trade in extreme by always long the one stock with the highest forecast return, no short is allowed, stop loss is set at 5%. The result is amazing, it yields an annualized return at 97.15% from March, 2013 to Feb, 2014, with maximum drawdown at 30.01%. The fund curve is as follows (note: I didn't use all Chinese stocks but only 840 stocks in my stock pool with good liquidity, so there is selection bias and please accept the result cautiously...)


Nice shot. It seems to be better than the simple strategy between A-shares and H-shares.
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Feb 3
A similar article was posted at the sub-personal blog before and I paste it here in case someone is interested.

At the moment there are 84 firms listed at both A (Shanghai and Shenzhen) and H (Hongkong) stock markets, according to the law of one price, the stock prices of these firms should be at similar level. However, there are huge differences, without considering exchange rate (1 RMB = 1.28 HK$), the ratio of the price in A market to the price in H market for a same firm is as low as 52.72% and as high as 617.59% as of 02/03/2014. Is the difference mean reverting? If yes, we would expect the stock traded cheaper in A market to go up, and vice versa. So can we make profit by long the stocks with large differences?

Rigorous statistical method should be undertaken to examine whether the ratio is indeed mean reverting. For simplicity, I construct a trading strategy that each week, I go long at the opening price the stock in A market that has the smallest price ratio  of previous week, hold it one week and sell it at the weekly closing price. Short trading is not allowed for individual investor in A market. Stop loss is set arbitrarily at 5%. Transaction cost is 0.18% per trading.

The results for this simple strategy from 02.2013 to 01.2014 are:
Annualized Return         0.2070
Annualized Std Dev        0.2545
Annualized Sharpe          0.8133
Maximum Drawdown
        From     Trough         To   Depth Length To Trough Recovery
1 2013-09-13 2013-12-13           -0.1275     19        12       NA
2 2013-08-16 2013-08-23 2013-09-06 -0.0566      4         2        2
3 2013-03-22 2013-04-19 2013-05-03 -0.0488      5         3        2
4 2013-07-12 2013-07-12 2013-07-19 -0.0374      2         1        1
5 2013-05-31 2013-05-31 2013-07-05 -0.0229      6         1        5
The fund curve
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Lower line is the return for a buy-and-hold strategy of all 84 firms.

Considering the fact that 2013 is a gloomy year for A market and this strategy is long only, the performance is not bad at all. Comments are welcomed
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Jan 22
Justin, the founder of Thinknum, contacted me a few days ago about his site, I am very glad to share on this blog since it looks interesting and close related to the blog content.  

Thinknum is a web platform that enables investors to collaborate on financial analysis, it aggregates the abundance of financial data and insights on the web and presents it to our users in an intuitive format, indexing the world’s financial information in the process.

A few samples of what you can do on Thinknum:
Thinknum’s Cashflow Model allows users to value companies based on fundamentals just like Wall Street research analysts do.  All the assumptions that go into the valuation models are visible and editable.  The data for the models is also updated automatically when companies publish their quarterly filings.

The Plotter allows users to track financial data, analyze trends, and perform expressions such as regressions and correlations without having to write code.  Thinknum currently provides data from over 2,000 sources.

A few experts have written about Thinknum:
•  Jason Voss of the CFA Institute published a comprehensive overview of Thinknum’s mission.
•  Francis Smart discussed Thinknum’s integration with R on R-Bloggers.  

Thinknum was founded in 2013 by Gregory Ugwi and Justin Zhen, two friends who met at Princeton University in 2006.  After graduation, Gregory went to work for Goldman Sachs and Justin worked at a hedge fund, where they both discovered the major flaws with existing financial data analysis tools.  That’s when they decided to create a superior platform for all types of investors.

Thinknum is constantly adding new features, so join their community and sign up for free today at thinknum.com.
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Jan 4

Bloomberg Businessweek
Bloomberg Businessweek, commonly and formerly known as BusinessWeek, is a weekly business magazine published by Bloomberg L.P. Founded in 1929, the magazine was created to provide information and interpretation about what was happening in the business world. BusinessWeek was first published in September 1929, only weeks before the stock market crash of 1929. The magazine provided information and opinions on what was happening in the business world at the time. Early sections of the magazine included marketing, labor, finance, management and Washington Outlook, which made BusinessWeek one of the first publications to cover national political issues that directly impacted the business world.

I am offered a 15% off coupon for Bloomberg Businessweek, so should you are interested, you can order 16 Issues of Bloomberg Businessweek for $15! (That's an 81% Savings)!.


Bloomberg Businessweek
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Jul 28
Another interesting paper forthcoming in Journal of Finance investigates the stock picking and market timing abilities of mutual fund managers.

We propose a new definition of skill as a general cognitive ability to either pick stocks or time the market at different times. We find evidence for stock picking in booms and for market timing in recessions. Moreover, the same fund managers that pick stocks well in expansions also time the market well in recessions. These fund managers significantly outperform other funds and passive benchmarks. Our results suggest a new measure of managerial ability that gives more weight to a fund’s market timing in recessions and to a fund’s stock picking in booms. The measure displays far more persistence than either market timing or stock picking alone and can predict fund performance.


Paper.
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