nhaliday + orfe   74

Abstract: It is well known that the set of correlated equilibrium distributions of an n-player noncooperative game is a convex polytope that includes all the Nash equilibrium distributions. We demonstrate an elementary yet surprising result: the Nash equilibria all lie on the boundary of the polytope.
pdf  nibble  papers  ORFE  game-theory  optimization  geometry  dimensionality  linear-algebra  equilibrium  structure  differential  correlation  iidness  acm  linear-programming  spatial  characterization  levers 
may 2019 by nhaliday
Vladimir Novakovski's answer to What financial advice would you give to a 21-year-old? - Quora
Learn economics and see that investment and consumption levels (as percentages) depend only marginally on age and existing net worth and mostly on your risk preferences and utility function.
qra  q-n-a  oly  advice  reflection  personal-finance  ORFE  outcome-risk  investing  time-preference  age-generation  dependence-independence  economics 
february 2019 by nhaliday
The rate of return on everything - Marginal REVOLUTION
Here is what I learned from the paper itself:

1. Risky assets such as equities and residential real estate average about 7% gains per year in real terms.  Housing outperformed equity before WWII, vice versa after WWII.  In any case it is a puzzle that housing returns are less volatile but about at the same level as equity returns over a broader time span.
2. Equity and housing gains have a relatively low covariance.  Buy both!
3. Equity returns across countries have become increasingly correlated, housing returns not.
4. The return on real safe assets is much more volatile than you might think.
5. The equity premium is volatile too.
6. The authors find support for Piketty’s r > g, except near periods of war.  Furthermore, the gap between r and g does not seem to be correlated with the growth rate of the economy.

I found this to be one of the best and most interesting papers of the year.
econotariat  marginal-rev  commentary  study  summary  economics  macro  investing  ORFE  securities  data  street-fighting  objektbuch  scale  time-preference  cost-benefit  outcome-risk  housing  money  monetary-fiscal  debt  history  mostly-modern  world-war  trends  correlation  moments  growth-econ  inequality  piketty  stylized-facts  war  meta:war 
december 2017 by nhaliday
'P' Versus 'Q': Differences and Commonalities between the Two Areas of Quantitative Finance by Attilio Meucci :: SSRN
There exist two separate branches of finance that require advanced quantitative techniques: the "Q" area of derivatives pricing, whose task is to "extrapolate the present"; and the "P" area of quantitative risk and portfolio management, whose task is to "model the future."

We briefly trace the history of these two branches of quantitative finance, highlighting their different goals and challenges. Then we provide an overview of their areas of intersection: the notion of risk premium; the stochastic processes used, often under different names and assumptions in the Q and in the P world; the numerical methods utilized to simulate those processes; hedging; and statistical arbitrage.
study  essay  survey  ORFE  finance  investing  probability  measure  stochastic-processes  outcome-risk 
december 2017 by nhaliday
Farm (revenue leasing) - Wikipedia
Tax farming was originally a Roman practice whereby the burden of tax collection was reassigned by the Roman State to private individuals or groups. In essence, these individuals or groups paid the taxes for a certain area and for a certain period of time and then attempted to cover their outlay by collecting money or saleable goods from the people within that area.[5] The system was set up by Gaius Gracchus in 123 BC primarily to increase the efficiency of tax collection within Rome itself but the system quickly spread to the Provinces.[6] Within the Roman Empire, these private individuals and groups which collected taxes in lieu of the bid (i.e. rent) they had paid to the state were known as publicani, of whom the best known is the disciple Matthew, a publicanus in the village of Capernaum in the province of Galilee. The system was widely abused, and reforms were enacted by Augustus and Diocletian.[7] Tax farming practices are believed to have contributed to the fall of the Western Roman Empire in Western Europe.[8]
concept  finance  ORFE  taxes  political-econ  institutions  government  leviathan  wiki  reference  history  iron-age  mediterranean  the-classics  gibbon  roots  britain  broad-econ  economics  rent-seeking 
october 2017 by nhaliday
Kelly criterion - Wikipedia
In probability theory and intertemporal portfolio choice, the Kelly criterion, Kelly strategy, Kelly formula, or Kelly bet, is a formula used to determine the optimal size of a series of bets. In most gambling scenarios, and some investing scenarios under some simplifying assumptions, the Kelly strategy will do better than any essentially different strategy in the long run (that is, over a span of time in which the observed fraction of bets that are successful equals the probability that any given bet will be successful). It was described by J. L. Kelly, Jr, a researcher at Bell Labs, in 1956.[1] The practical use of the formula has been demonstrated.[2][3][4]

The Kelly Criterion is to bet a predetermined fraction of assets and can be counterintuitive. In one study,[5][6] each participant was given $25 and asked to bet on a coin that would land heads 60% of the time. Participants had 30 minutes to play, so could place about 300 bets, and the prizes were capped at $250. Behavior was far from optimal. "Remarkably, 28% of the participants went bust, and the average payout was just $91. Only 21% of the participants reached the maximum. 18 of the 61 participants bet everything on one toss, while two-thirds gambled on tails at some stage in the experiment." Using the Kelly criterion and based on the odds in the experiment, the right approach would be to bet 20% of the pot on each throw (see first example in Statement below). If losing, the size of the bet gets cut; if winning, the stake increases.
nibble  betting  investing  ORFE  acm  checklists  levers  probability  algorithms  wiki  reference  atoms  extrema  parsimony  tidbits  decision-theory  decision-making  street-fighting  mental-math  calculation 
august 2017 by nhaliday
Skin or Skim? Inside Investment and Hedge Fund Performance by Arpit Gupta, Kunal Sachdeva :: SSRN
We find that funds with greater investment by insiders outperform funds with less "skin in the game" on a factor-adjusted basis; exhibit greater return persistence; and feature lower fund flow-performance sensitivities.
study  economics  finance  ORFE  investing  paying-rent  business  industrial-org 
june 2017 by nhaliday
Bond (finance) - Wikipedia
In finance, a bond is an instrument of indebtedness of the bond issuer to the holders. The most common types of bonds include municipal bonds and corporate bonds. It is a debt security, under which the issuer owes the holders a debt and, depending on the terms of the bond, is obliged to pay them interest (the coupon) and/or to repay the principal at a later date, termed the maturity date.[1] Interest is usually payable at fixed intervals (semiannual, annual, sometimes monthly). Very often the bond is negotiable, that is, the ownership of the instrument can be transferred in the secondary market. This means that once the transfer agents at the bank medallion stamp the bond, it is highly liquid on the second market.[2]

Thus, a bond is a form of loan or IOU: the holder of the bond is the lender (creditor), the issuer of the bond is the borrower (debtor), and the coupon is the interest. Bonds provide the borrower with external funds to finance long-term investments, or, in the case of government bonds, to finance current expenditure. Certificates of deposit (CDs) or short term commercial paper are considered to be money market instruments and not bonds: the main difference is in the length of the term of the instrument.

Bonds and stocks are both securities, but the major difference between the two is that (capital) stockholders have an equity stake in the company (i.e., they are owners), whereas bondholders have a creditor stake in the company (i.e., they are lenders). Being a creditor, bondholders have priority over stockholders. This means they will be repaid in advance of stockholders, but will rank behind secured creditors in the event of bankruptcy.[3] Another difference is that bonds usually have a defined term, or maturity, after which the bond is redeemed, whereas stocks are typically outstanding indefinitely. An exception is an irredeemable bond, such as a consol, which is a perpetuity, that is, a bond with no maturity.
concept  finance  investing  securities  ORFE  money  wiki  reference  comparison 
may 2017 by nhaliday
William Stanley Jevons - Wikipedia
William Stanley Jevons FRS (/ˈdʒɛvənz/;[2] 1 September 1835 – 13 August 1882) was an English economist and logician.

Irving Fisher described Jevons' book A General Mathematical Theory of Political Economy (1862) as the start of the mathematical method in economics.[3] It made the case that economics as a science concerned with quantities is necessarily mathematical.[4] In so doing, it expounded upon the "final" (marginal) utility theory of value. Jevons' work, along with similar discoveries made by Carl Menger in Vienna (1871) and by Léon Walras in Switzerland (1874), marked the opening of a new period in the history of economic thought. Jevons' contribution to the marginal revolution in economics in the late 19th century established his reputation as a leading political economist and logician of the time.

Jevons broke off his studies of the natural sciences in London in 1854 to work as an assayer in Sydney, where he acquired an interest in political economy. Returning to the UK in 1859, he published General Mathematical Theory of Political Economy in 1862, outlining the marginal utility theory of value, and A Serious Fall in the Value of Gold in 1863. For Jevons, the utility or value to a consumer of an additional unit of a product is inversely related to the number of units of that product he already owns, at least beyond some critical quantity.

It was for The Coal Question (1865), in which he called attention to the gradual exhaustion of the UK's coal supplies, that he received public recognition, in which he put forth what is now known as the Jevons paradox, i.e. that increases in energy production efficiency leads to more not less consumption. The most important of his works on logic and scientific methods is his Principles of Science (1874),[5] as well as The Theory of Political Economy (1871) and The State in Relation to Labour (1882). Among his inventions was the logic piano, a mechanical computer.

In economics, the Jevons paradox (/ˈdʒɛvənz/; sometimes the Jevons effect) occurs when technological progress increases the efficiency with which a resource is used (reducing the amount necessary for any one use), but the rate of consumption of that resource rises because of increasing demand.[1] The Jevons paradox is perhaps the most widely known paradox in environmental economics.[2] However, governments and environmentalists generally assume that efficiency gains will lower resource consumption, ignoring the possibility of the paradox arising.[3]

The Coal Question: http://www.econlib.org/library/YPDBooks/Jevons/jvnCQ.html
people  big-peeps  history  early-modern  britain  economics  growth-econ  ORFE  industrial-revolution  energy-resources  giants  anglosphere  wiki  nihil  civilization  prepping  old-anglo  biophysical-econ  the-world-is-just-atoms  pre-ww2  multi  stylized-facts  efficiency  technology  org:econlib  books  modernity  volo-avolo  values  formal-values  decision-making  decision-theory 
may 2017 by nhaliday
Information Processing: Big, complicated data sets
This Times article profiles Nick Patterson, a mathematician whose career wandered from cryptography, to finance (7 years at Renaissance) and finally to bioinformatics. “I’m a data guy,” Dr. Patterson said. “What I know about is how to analyze big, complicated data sets.”

If you're a smart guy looking for something to do, there are 3 huge computational problems staring you in the face, for which the data is readily accessible.

1) human genome: 3 GB of data in a single genome; most data freely available on the Web (e.g., Hapmap stores patterns of sequence variation). Got a hypothesis about deep human history (evolution)? Test it yourself...

2) market prediction: every market tick available at zero or minimal subscription-service cost. Can you model short term movements? It's never been cheaper to build and test your model!

3) internet search: about 10^3 Terabytes of data (admittedly, a barrier to entry for an individual, but not for a startup). Can you come up with a better way to index or search it? What about peripheral problems like language translation or picture or video search?

The biggest barrier to entry is, of course, brainpower and a few years (a decade?) of concentrated learning. But the necessary books are all in the library :-)

Patterson has worked in 2 of the 3 areas listed above! Substituting crypto for internet search is understandable given his age, our cold war history, etc.
hsu  scitariat  quotes  links  news  org:rec  profile  giants  stories  huge-data-the-biggest  genomics  bioinformatics  finance  crypto  history  britain  interdisciplinary  the-trenches  🔬  questions  genetics  dataset  search  web  internet  scale  commentary  apollonian-dionysian  magnitude  examples  open-problems  big-surf  markets  securities  ORFE  nitty-gritty  quixotic  google  startups  ideas  measure  space-complexity  minimum-viable 
february 2017 by nhaliday
Convex Optimization Applications
there was a problem in ACM113 related to this (the portfolio optimization SDP stuff)
pdf  slides  exposition  finance  investing  optimization  methodology  examples  IEEE  acm  ORFE  nibble  curvature  talks  convexity-curvature 
december 2016 by nhaliday
What Drives Firm-Level Stock Returns?
I use a vector autoregressive model (VAR) to decompose an individual firm's stock return into two components: changes in cash-flow expectations (i.e., cash-flow news) and changes in discount rates (i.e., expected-return news). The VAR yields three main results. First, firm-level stock returns are mainly driven by cash-flow news. For a typical stock, the variance of cash-flow news is more than twice that of expected-return news. Second, shocks to expected returns and cash flows are positively correlated for a typical small stock. Third, expected-return-news series are highly correlated across firms, while cash-flow news can largely be diversified away in aggregate portfolios.
pdf  study  economics  classic  business  investing  causation  variance-components  micro  🎩  econometrics  wonkish  roots  securities  outcome-risk  ORFE 
december 2016 by nhaliday
The History of the Cross Section of Stock Returns
bad methodology (data snooping) generating fake market failures

Using accounting data spanning the 20th century, we show that most accounting-based return anomalies are spurious. When we take anomalies out-of-sample by moving either backwards or forwards in time, their average returns decrease and volatilities increase. These patterns emerge because data-snooping works through t-values, and an anomaly’s t-value is high if its average return is high or volatility low. The average anomaly’s in-sample Sharpe ratio is biased upwards by a factor of three. The data-snooping problem is so severe that we would expect to reject even the true asset pricing model when tested using in-sample data. Our results suggest that asset pricing models should be tested using out-of-sample data or, if not not feasible, that the correct standard by which to judge a model is its ability to explain half of the in-sample alpha.
study  economics  finance  investing  methodology  replication  pdf  preprint  market-failure  error  🎩  econometrics  longitudinal  generalization  s:*  securities  ORFE 
december 2016 by nhaliday

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