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Hot answers tagged anomalies

9

This the "Joint Hypothesis Problem". Basically, any test for abnormal returns is also implicitly a test of the model you use to define "abnormal". If you see a significant and positive $\alpha$, that could either mean that you actually are generating excess risk-adjusted returns, or it could mean that your risk model is incomplete. This is basically what ...

8

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: ...

8

The best explanation I have seen so far is the so-called Adaptive Market Hypothesis by Andrew Lo: The adaptive market hypothesis, as proposed by Andrew Lo, is an attempt to reconcile economic theories based on the efficient market hypothesis (which implies that markets are efficient) with behavioral economics, by applying the principles of ...

6

Your question is too broad to give anything but a very general answer. Data mining in the raw form won't do any good. At the minimum, you will pick up thousands of spurious correlations. You cannot go from data to a solution. You have to work in the opposite direction, you have to posit some model of the world and then test it. You must have an existing ...

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

5

I would say the main difference between "risk factor" and "market anomaly" is that people demand to be compensated for risk and because there are different kinds of risks these can be systematized into risk factors whereas anomalies are results of behavioral biases. Another big difference would be that risk factors will stay because of the need for ...

5

Any anomaly that can be phrased as a "mispricing" or "relative value" opportunity can be expected to disappear as more people discover it and trade on it. For example, say that stock movements over the last 15 minutes of the day are found to be strongly mean-reverting. That is, stocks which decline over the last 15 minutes of the day tend to be undervalued, ...

4

Adding to the excellent answer of vonjd: another, more cynical interpretation is that some of these "anomalies" never would have existed ex-post and that their discovery is the inevitable result of thousands of researchers looking for patterns in the same data set. Look at ". . . and the Cross-Section of Expected Returns" by Harvey et. al. for an ...

4

A wonderful recent paper that might be of interest is Feng, Giglio, and Xiu's "Taming the Factor Zoo." First, the paper lists nearly 100 "factors" that have been proposed from 1965 through 2016. The corresponding papers that first discuss these factors are also tabulated. Second, the authors analyzed quite thoroughly whether the newly discovered factors, ...

3

That's in finance what we call a puzzle. From their follow on paper (here), they rule out many different economic explanations for such a thing to happen: We conclude that the puzzle of why high idiosyncratic volatility stocks have low returns is a global phenomenon. Further research must investigate if there are true economic sources of risk behind ...

3

Based on your linked question/answer you should carefully notice two separate concepts of the term alpha. As stated in my answer here, we have to distinguish between empirically testing asset pricing models (like the CAPM or Fama/French models) and using asset pricing models as a benckmark portfolio to evaluate the outperformance of our investment strategy. ...

3

I think a very good paper that summarizes the empirical evidence on other measures of value is the Lettau and Wachter (2007). Take a look at their tables 1, 2 and 3 for the most standard uses of value measures which indeed match with AQRs measures. Below their table 1, just for completeness:

3

Nice question! I don’t have a precise answer to it but I will try somehow to give you my thoughts. I think it depends a lot whether you have in mind an APT or an ICAPM as explained in this article by Eugene Fama. The APT is really agnostic regarding the risk premia and the factors, and basically the only prediction is that alphas are going to vanish thanks ...

2

There are a couple of nice papers about the dot-com effect by Michael Cooper: full list, paper1, paper2

2

I think the best source for the possible explanations of this anomaly is Ang's book. In there, he says: We are still searching for a comprehensive explanation for the risk anomaly. Inmy opinion, the true explanation is a combination of all of the explanations listed below, plus potentially others being developed. Data mining. Bali and Cakici (2008) ...

2

In high-frequency energy trading, it's possible for a trader to have hundreds of orders in the book, possibly on more than one exchange, e.g. ICE and NYMEX. A trader may be running dozens of algorithms simultaneously. Each of these may respond independently to the changes it detects in the market data, as well as to the timing and fill data from its own ...

1

In your case, there are five elements that aggregate into your portfolio return. Just treat the intercept as a fifth factor for now. We'll come back to what it means in a second. But you should be able to take your regression betas, multiply these by each style's period-return, to give you 5 style return contributions, that should sum to your portfolio ...

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

I like your classification (though I would possibly combine sentiment + momentum and profitability + low vol), and it seems as reasonable as any. Most classifications I've seen don't have more than 6-7 categories. For a completely different approach, you can look at the classification of Harvey, Liu, and Zhu. If you want to extend beyond "factors" into ...

1

"Size adjusted return for company X" on day t is defined as the return of company X on day t minus the equal weighted return of all stocks in the same size decile as company X. So for example if company X is in the third NYSE size decile, you average together the returns of all third size decile companies (whether NYSE or not) on day t and you subtract this ...

1

As pure speculative commentary and a non-quantitative answer, you may want to look at the short-term options volatility as a factor for possibly fine tuning your model. Your goal seems to be to find the elasticity or 'beta' factor of the price movement. The price drift you are looking at implies some form of information dissemination or perception change in ...

1

1) I'm going from memory here so someone may want to confirm that I'm thinking about this correctly - but the two models will end up with the same results and significance levels - in the first model, the intercept acts as the reference day, such that the average effect of $D_d=\beta_0+\beta_d$. In the second model you should get the same effect, however, it ...

1

There are a lot of good answers on here, but I can't help but to add my 2 cents. Of interest, a recent paper "...and the Cross-Section of Expected Returns", by Harvey, Liu, and Zhu (2015): ...is related to a recent paper by McLean and Pontiff (2015), who argue that certain stock market anomalies are less anomalous after being published. Their paper ...

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