NBER data

INTERNATIONAL FINANCE AND MACROECONOMICS CATALOGUE OF DATA SOURCES: "All data sources are provided as is. See the cited paper or reference for details. Please cite the appropriate paper when using these data. There is no informational support, nor support of any other type, for the use of these data by the NBER nor by the researchers who are making the data available."



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Why Is Spoofing Bad? - Bloomberg View

Why Is Spoofing Bad? - Bloomberg View:  An overview of market microstructure.

"Who cares if the price is right? Well, you might want the price to be right if you are an individual investor buying stock in your E*Trade account on your lunch break. You're buying at 12:45 not because you think the stock is unusually undervalued at 12:45, but because it's your lunch break. It would be reassuring if the market price was as accurate as possible, so that you'd know you've got a good chance of getting a fair price. Index funds, which are similarly value-agnostic, might have similar preferences. Accurate prices probably also make spreads smaller, so trading costs for both big and small investors are lower."

The battle between short-term speculators and final money investors.

Crossing the river by feeling each stone

Chris Blattman (HT Brad DeLong) has an excellent overview of the trial and error approach:

"I’d make a different point: the way I’ve learned how things operate is to work with a government or organization to try out a policy and succeed or fail. This kind of trial and error seems crucial to me. Karl Popper called this the piecemeal social engineer. Deng Xiaoping called it crossing the river by feeling each stone.

"

This seems to be the way to overcome over-confidence and avoid the hubris that is associated with defending a defeated strategy.  This, of course, is the method of successful investment strategy:  try it out and be ready to change when it does not work.  Of course, ;-), you need to make sure that you do not sell at the low and buy at the top.

Tony Yates on John Taylor

Weekend Reading: Tony Yates on John Taylor (Brad DeLong's Grasping Reality...): There is a discussion of the use and abuse of the Taylor rule.  One of the weaknesses of the rule is that it does not include the financial sector.

 "Modifications of the rule such that central bank rates respond to spreads can be shown to deliver good results in prototype financial-inclusive DSGE models.  But these models are just a beginning, and certainly not the last word, on how to describe the financial sector. "

This suggests one of the ways that the rule could be modified.  Clearly, this may take the rule from being a description to being a prescription.  What other factor could be included in a model for each of these uses?   Where are the examples of these financial modifications?

Moneyness: Liquidity as static

Moneyness: Liquidity as static:  How market-priced indicators can provide false signals due to the liquidity premium.

"Government debt instruments like TIPS are useful as media of exchange, specifically as collateral, goes Williamson's argument. Those who own these instruments therefore enjoy a stream of liquidity services that gets embodied in their price as a liquidity premium. Rising TIPS prices (and falling yields) could therefore be entirely unrelated to returns on capital and wholly a function of widening liquidity premia. Bernanke and Summers can't make broad assumptions about returns on capital on the basis of market-driven yields without knowing something about these invisible premia. (Assiduous readers may remember that I've used a version of the liquidity premium argument to try to explain the three decade long bond bull market, as well as the odd twin bull markets in bond and equity prices.)
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Sampling distribution of the mean

Echoing these recommendations, articles describe how to simulate the SDM using a wide variety
of physical objects, a graphing calculator, or a computer. The demonstrations tend to use skewed
or bimodal populations, so that students are impressed with the counter-intuitive result.
Invariably, the authors anticipate that “the student will observe that the center of the distribution
remains about the same and the distribution becomes narrower. That is, as sample size gets larger
the approximations to the mean do not get better, but the variability about the mean decreases.”
(Koehler 2006, pp. 264-265).

Factor models

A step-by-step explanation on how to build a factor model - Quantitative Finance Stack Exchange:

 "Economically, the use of factor models can be either motivated using the ICAPM or the APT. Although there are some theoretical differences between the model, for empirical and practical work these differences are irrelevant. In the end, both models stipulate that returns and expected returns are linear functions of the factors:"

This is an overview of some of the issues involved in building a factor model.