Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading (2nd Edition) (Wiley Finance)
Rishi K. Narang
Format: PDF / Kindle (mobi) / ePub
Uploader's Note:: I ripped this book chapter-by-chapter from Wiley's online library, merged it back and bookmarked it. I took the cover image from Wiley's own website (this is why it's small).
New edition of book that demystifies quant and algo trading
In this updated edition of his bestselling book, Rishi K Narang offers in a straightforward, nontechnical style—supplemented by real-world examples and informative anecdotes—a reliable resource takes you on a detailed tour through the black box. He skillfully sheds light upon the work that quants do, lifting the veil of mystery around quantitative trading and allowing anyone interested in doing so to understand quants and their strategies. This new edition includes information on High Frequency Trading.* Offers an update on the bestselling book for explaining in non-mathematical terms what quant and algo trading are and how they work
* Provides key information for investors to evaluate the best hedge fund investments
* Explains how quant strategies fit into a portfolio, why they are valuable, and how to evaluate a quant manager
This new edition of Inside the Black Box explains quant investing without the jargon and goes a long way toward educating investment professionals.
investing is to buy a stock when it is undervalued and to sell it when it is fairly valued or overvalued. Again, this represents an effort to time the stock. The software that a quant builds and uses to conduct this timing systematically is known as an alpha model, though there are many synonyms for this term: forecast, factor, alpha, model, strategy, estimator, or predictor. All successful alpha models are designed to have some edge, which allows them to anticipate the future with enough
stagnant or negative growth, even if they are very cheap (or offer high yields) already. The justification for growth investing is that growth is typically experienced in a trending manner, and the strongest growers are typically becoming more dominant relative to their competitors. In the case of a company, you could see the case being made that a strong grower is quite likely to be 40 INSIDE THE BLACK BOX in the process of winning market share from its weaker‐growing competitors. Growth
has been some aberration just around the time of the model being run. Whether more frequent or less frequent model runs are better depends on many other aspects of the strategy, most especially the time horizon of the forecast and the kinds of inputs. In the end, most quants run their models no less than once a week, and many run continuously throughout the day. The slower‐moving the strategy, obviously, the more leeway there is, whereas shorter‐term strategies tend toward continuous, real‐time
very unstable over time. They can even be unreliable over long time periods. For example, imagine a portfolio with two investments: one in the S&P 500 and one in the Nikkei 225. Taking the data on both since January 1984, we can see that these two indices correlate at a level of 0.37 since inception. The range of correlations observed using weekly returns over any consecutive 365 calendar days (a rolling year) is shown in Exhibit 6.4. Please note that we choose to use weekly returns in this case,
be considered expensive. Meanwhile, we presume that any P/E ratio below 12 is cheap. Assume we test the strategy according to the previously discussed metrics and find that a low P/E strategy with these parameters (≥50 implies expensive, ≤12 implies cheap) delivers a 10 percent annual return and 15 percent annual variability. Now imagine that we vary the parameters only slightly so that any stock with a P/E ratio below 11 is cheap and any with a P/E ratio that is negative or above 49 is