We are in the PRESCIENTIFIC era of finance. The preeminent financial services companies of the world, such as Goldman Sachs, doesn’t know finance. What do I mean by this? Being lazy, I refer you to the book by George Soros, ‘The Alchemy of Finance’ because why repeat good work already done. He will explain to you why financial markets are not like natural phenomena studied in physics because thinking participants affect the system being studied. So Goldman Sachs does not know finance. Neither do economists at Yale and Harvard. The academics are still confused from 1970s championing random walks (which was postulated by Bachelier in 1900 in the more sophisticated continuous time Brownian motion). Then the 1990s they are slowly talking about behavioral finance which unfortunately is in a messy state where no one has clear quantitative models that can be used.
Now Soros is a genius of first order but his theory of reflexivity is quite elaborate and not quite quantitative in usable forms. I have been thinking about finance since 1995 from my first job at Lehman and it is becoming clearer to me that this is actually completely open terrain. Consider volatility. No one really knows what volatility is. I mean it’s easy to compute standard deviation of returns and define this as volatility but there is a way in which the WORD means something more and something deeper about, well the measure of volatile-ity. Well, if you want to measure volatile-ity, standard deviation of returns is a pretty stupid measure. So Mandelbrot brought into the fray the beautiful empirical observation of long memory in the markets. This is interesting because long memory originally was discovered in hydrology, with measurements of water levels of Nile. To make a long story short, standard deviation volatility (say proxied by return^2) has long memory. There are many studies about this. I FINALLY realized today that long memory of volatility is the totally wrong thing to consider. The right thing to consider is the long memory of the VOLUME OF TRADE. In particular if you take say the percent of total shares being traded (volume/shares outstanding) for stocks, you can see the long memory of this quantity directly say by calculating the autocorrelation sequence at different lags. THIS is the right object for long memory. In other words, the total volume traded has long memory, and this is a direct measurement while it SO HAPPENS that volume and the standard deviation of returns volatility HAPPEN to be correlated and so it is a CONSEQUENCE that there is long memory of volatility. The volume traded has long memory and this is the deeper issue because whatever we mean by volatility is happening under the volume or behind it. Yes yes Goldman folks know this but they don’t know finance anyway because there are no clear models that include reflexivity phenomena that figure as doctrine in any investment bank. Besides, they cannot break out of the religions that they earn their living on, which are completely wrong things.
The first thing we need to understand from Soros is that various disequilibria that are the fundamental features of financial markets find their most basic examples in the volume series concretely. Soros’ concepts are based not on equilibrium prices (which he explains are meaningless) and on a reflexivity of two tendencies (he borrows heavily from Hegelian dialectics of thesis/antithesis) he calls the cognitive function and the participative function of market participants. The first place to look for this is of course VOLUME TRADED. So we immediately find that the long memory effects are all to be found here as well. So to the extent that Goldman Sachs does not explicitly model the markets with these sorts of features (they can’t because they sell index funds etc.) they don’t know finance.
First I will share with you my latest great discovery, which I can say without much fake humility is absolute genius: even the most ELEMENTARY use of quantitative measures of reflexivity ideas leads very quickly to the discovery that almost EVERY oil stock (and probably much of the equities markets) has NEGATIVE autocorrelation at 1 day lag which while not super strong is strong enough that we can generate extremely good mean reversion strategies for them without doing pairs. So while I had a slightly more involved Alpha for Every Sector strategies before, here is a concrete and deeper example of how to produce alpha for every stock in the oil sector (XLE components) using mean reversion and trend following. The period is 2007-2017 and it is quite good in almost every single stock in the oil sector. So that’s the first great discovery, that alpha is quite common although it takes a bit of care to get the quantitative strategy to be in good order (see the R code for details; you will need the code in the attached file to get this to work in your machine):
meanRev4<-function(rets,volchanges){
n<-length(rets);
vRef<-rep(0,n)
v<-rep(0,n)
updown<-1
lastChange<-10000
for(k in 302:n){
lastChange<-lastChange+1
q<-quantile(volchanges,c(0.5,0.5))
CVRef<-cumsum(vRef)
CV<-cumsum(v)
CVRefSmooth<-savgol(CVRef,301,forder=3,dorder=0)
avgVeryLong<-mean(CVRef[(k-30):(k-1)])
avgLong<-mean(CVRef[(k-5):(k-1)])
avgShort<-CVRef[k-1]
actualAvgLong<-mean(CV[(k-5):(k-1)])
actualAvgShort<-CV[k-1]
#if (avgLong>1.005*avgShort && lastChange>0){
# updown<- -1
# lastChange<-0
#}
#if (avgLong<0.995*avgShort && lastChange>0){
# updown<- 1
# lastChange<-0
#}
updown<- -sign(CVRefSmooth[k-1]-CVRefSmooth[k-2])
#if (abs(avgLong-avgShort)<0.0005){
# updown<- 0
# lastChange<-0
#}
if( volchanges[k-1]>q[2] || volchanges[k-1]<q[1] ){
vRef[k]<- -sign(rets[k-1])*rets[k]
v[k]<-updown*vRef[k]
if (v[k]< -0.02) {
v[k]<- -0.02
}
}
}
list(v=v,vRef=vRef)
}
performanceTrendFollowingMeanReversion<-function( symbols,startDate ) {
R<-constructComponentRets(symbols,startDate)
V<-constructVolumeChanges(symbols,startDate)
dts<-index(R)
multiseries<-NULL
nsymb<-length(symbols)
for (ns in 1:nsymb){
x<-meanRev4(coredata(R[,ns]),coredata(V[,ns]))
multiseries<-cbind(multiseries,x$v)
}
df<-data.frame(multiseries,index=dts)
names(df)<-symbols
matplot( df,type=c(‘l’))
legend(‘topleft’,legend=1:nsymb,col=1:nsymb,pch=1)
df
}
Now the second consequence of the problem of Goldman Sachs (and therefore the rest of the industry) not knowing finance. Just as Bill Gates understood that no one knew software in 1970s we must understand that despite a century of theory of finance since Bachelier and some fancy models, no one actually knows finance because the two fundamental directions (fundamental/technicals) are inadequate to have any real clue about what is going on in the actual reality. The answer is that someone (if not me someone at least) should start a TECHNOLOGY STARTUP that does this right: the only way that any serious WIDESPREAD OPEN understanding/use of finance can take shape is if we can COMMODITIZE QUANTITATIVE MODELS directly in retail so that actual models are easy to extract and EXECUTE in the markets. Finance cannot be understood without quantitative models being accessible for RETAIL USE WORLDWIDE and the right vehicle for this is a technology company that provides this sort of service. The correct quantitative models of the markets should not be considered material for the cognoscenti. It should be provided as a retail service more or less how Google and Yahoo provide stock data but with ease of EXECUTION of strategies that are solid by anyone anywhere. This is a big idea in the sense that such a company will probably make the entire financial services investment banks obsolete in time. Just as Bill Gates understood that it was SOFTWARE that was more important than hardware, we must understand that in the situation where no one knows any finance, the first company to provide PUBLIC OPEN ACCURATE MODELS that show clear alpha and which can be executed conveniently by anyone anywhere in the world will be the company that redefines all financial markets. The idea is that in the case where no one knows finance (and I don’t want to waste time arguing this since I can cite Soros’ book) the first technology company that makes concrete open easily accessible STANDARD for models of markets which can be USED easily and UNDERSTOOD easily and which which are OPEN and robust etc. will define for posterity what finance is. Alpha in the markets are ubiquitous but it is quite a bit of work to build the models for the first time by hand.
The individual stock strategies are quite good alpha so check them out by running the code. The Alchemy of Finance must be accessible to seven billion of this planet in a concrete manner. A technology company is the right way to do this. It will succeed if done properly more than Google and Facebook.
For sector strategies, I got decent results by machine learning. Some of those graphs are attached. This is original work so unpolished. QUANTITATION of reflexivity ideas may seem at first glance like technicals/chartist strategies but this is not the right way to think about this. The chartists’ concepts and viewpoint are not the right way to consider the issues of what is happening in the markets. Soros’ ideas are much more serious but at the same time, we need some concrete simple results that are clear and compelling. Alpha in the stock markets should be UNDERSTOOD, should be GLOBALLY PUBLIC, should be available to everyone in the world to trade. Until this happens, this is a great way to establish a great new company; later on we can understand whether a true science of finance has a chance of emerging. Soros is saying that this will be forever impossible in a sense but on the other hand, we can’t be sure until we try.
Financial markets should be available to everyone with convenient ways of trading without having to get a Masters in Finance. This is a great opportunity to transform the entire structure of global finance as well as a path to addressing some subtler issues such as VOLATILITY STORMS and other deeper instabilities. Academic ways of dealing with these is not going to be effective. We don’t know what volatility is; how will we be able to quell volatility storms (which I coined but you can probably make sense of these from Soros’ theory).
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