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# Tag Info

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The best overview I have seen so far is this paper which lists 214 (!) factors (or anomalies if you like) on over one hundred (!) pages: Harvey, Campbell R. and Liu, Yan and Zhu, Caroline, …and the Cross-Section of Expected Returns (February 3, 2015). Available at SSRN: https://ssrn.com/abstract=2249314 or http://dx.doi.org/10.2139/ssrn.2249314 Abstract: ...

7

Behavioral Finance is a wide topic, which I believe is still today underestimated by many financial professionals. How can it be used by quants? Well, in portfolio optimization it can be used "as an overlay" in the form of constraints where the optimal portfolio can not be too different from the current portfolio, because clients have behavioral biases ...

5

There is a paper by Goldstein and Taleb (2007) which tries to address this question of what number captures investors intuitive feelings of the volatility of series of returns and whether this coincides with the standard deviation of returns. What they found was that Median Absolute Deviation does a much better job of capturing this intuition in a small ...

5

You have started a huge job, an enormous number of anomalies have been reported. The web site quantpedia.com has a list, here for example is their writeup on momentum effect in stocks

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Maybe it is not exactly what you are looking for, but you can take a look at this paper by Kozak, Nagel and Santosh. Roughly speaking, we know that the first order conditions of arbitrageurs must be satisfied, i.e. the following Euler equation should be satisfied for any gross return $R_{t+1}^i$ 1 = \widetilde{E}_t[M_{t+1}R^i_{t+1}] = \sum_{\omega\in\Omega}...

3

The general effect of quantitative analysis of the markets is to enforce randomness. Suppose a strategic quant finds a predictable pattern where a stock always rises on Tuesdays. His institution will commence buying the stock every Monday, and selling on Tuesday. The trading itself pushes the stock price up on Monday and down on Tuesday (in general), so if ...

3

1) If you want to show an unsophisticated investor with like a real-time estimate of volatility, my main suggestion would be to fit a Garch model to the returns and use those estimates of the conditional volatility. Just provide a chart of it, rather than showing the model and all the details that go into calculating it. 2) If that is too complicated, you ...

3

Usually very good questions don't come with straight answers and I think this is the case here. I believe you have two (unfortunatly linked) problems here. One is to get a sense of investors aversion to risk and the other is to get a statistically "receivable" estimate of volatility. 1/ Different investors may be willing to take risk on significantly ...

2

More measurable effects to add to your list: "window dressing" - returns of the fourth quarter or 12th month (i.e. year-end) are higher on average than oher returns; the same to returns of 4th months (qtr-end) vs. others; "herding": changes in asset-classes shares of "big" funds (whatever you define "big") granger-cause changes in asset-classes shares of "...

2

There are different methodologies to detect a change in the market efficiency, both in the market and firm-specific cases. In the FIRM-SPECIFIC case, the most common procedure is the event study methodology; you can find how to construct an event-study case explained in Kothari & Warner (2006), who collected all the event study methodology implemented ...

2

Your opinion is correct. There is simply more information about risk-reward encoded in the Sharpe ratio than cumulative returns. The other thing that's important to know is that whatever ratio you choose is simply a social construct or conventional benchmark that people use to compare between each other. The ratio is only useful insofar as other people are ...

2

See also utility indifference pricing (Henderson, V., & Hobson, D. (2004) Utility Indifference Pricing - An Overview http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.321.994&rep=rep1&type=pdf is a good reference) for examples where utility functions can be used to price derivatives on non tradable assets, such as stock options on non ...

1

This is a difficult question per se, but people in the literature have tried different ways of dealing with the time-inconsistency of the mean variance problem. Basak and Chabakauri (2010) is one of the seminal references.From their paper: "In this article, we solve the dynamic asset allocation problem of a meanvariance optimizer in an incomplete-market ...

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First, the semi strong form of the EMH (prices reflect all public information) corresponds to the returns being a martingale difference; GARCH is a martingale difference, so GARCH is compliant with the EMH. Second, you can look into the stylized fact of financial returns called "leverage effect", in short it says that returns have a negative correlation ...

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Utility functions are used all the times in systematic hedge funds/systematic trading desks to perform portfolio optimization. If you are still not convinced here's a nice little one: Say you and I decide to play 100 instances of an even-money game of flip a coin where you have a probability advantage of winning (p > 1/2). Say you start with a capital of 1$... 1 Vayanos & Woolley have proposed a unified theory of momentum & reversal due to institutional fund flows, but their analysis appears to be limited to stocks. To quote: Our explanation of momentum and reversal is as follows. Suppose that a negative shock hits the fundamental value of some assets. Investment funds holding these assets realize low ... 1 Formula:$H(i) = \mid\frac{B(i)}{B(i)+S(i)}-p(t)\mid-AF(i) $(Lakonishok et al. 1992) How to calculate$AF(i)$? This answer is based on Herding and Feedback Trading by Different Types of Institutions and the Effects on Stock Prices (Jones, Lee, Weis 1999) Given that Institutions in a company are neither net-buying nor net-selling,$p=0,5\$, and there ...

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