Quantiative Easing fuels stock markets, but why?
There's a premium or discount for various stocks subject to influence by the alternatives available to investors, meaning investments are susceptible to the principle of supply and demand. This is easily seen when industries or business models get hot, and everybody wants a tech company, a social media company, or a solar company in his portfolio.  You'll see bubbles like the dotcom bubble, the RE bubble, etc., as people start to think that the industry and not its performance are all that matters.  The stock price of a desired industry or company is inflated beyond what might otherwise be expected, to accommodate the premium that the investment can demand. So if bonds become uniformly less attractive in terms of returns, and certain institutional investors are largely obliged to continue purchasing them anyway, then flexible investors will need to look elsewhere.  As more people want to buy stocks, the price rises.  Supply and demand is sometimes so elementary it feels nearly counter-intuitive, but it applies here as elsewhere.
The satisfaction from gains packs less of an emotional impact than the fear of loss. It's very difficult for many people to overcome this fear, so when prices begin to fall, many investors sell to minimize their potential loss. This causes a further drop, which can lead to more selling as other investors reach their emotional threshold for loss. This emotion-based selling keeps the market inefficient in the short term. If there aren't enough value investors waiting to scoop up the stock at the new discount, it can stay undervalued for a long time. Assuming you were immersed in math with your CS degree, the book **'A Non-Random Walk Down Wall Street' by Andrew Lo** is a very interesting book about the random walk hypothesis and it's application to financial markets and how efficient markets might not necessarily imply complete randomness.    Lots of higher level concepts in the book but it's an interesting topic if you are trying to branch out into the quant world.  The book isn't very specific towards algorithmic trading but it's good for concept and ideas.  Especially for general finance, that will give you a good run down about markets and the way we tackle modern finance.    **A Random Walk Down Wall Street** (which the book above is named after) by **Burton Malkiel** is also supposed to be a good read and many have suggested reading it before the one I listed above, but there really isn't a need to do so.    For investing specifically, many mention **'The Intelligent Investor' by Benjamin Graham** who is the role model for the infamous Warren Buffet.  It's an older book and really dry and I think kind of out dated but mostly still relevant.  It's more specifically about individual trading rather than markets as a whole or general markets.  It sounds like you want to learn more about markets and finance rather than simply trading or buying stocks.  So I'd stick to the Andrew Lo book first.    ---  Also, since you might not know, it would be a good idea to understand the capital asset pricing model, free cash flow models, and maybe some dividend discount models, the last of which isn't so much relevant but good foundations for your finance knowledge.  They are models using various financial concepts (TVM is almost used in every case) and utilizing them in various ways to model certain concepts.  You'd most likely be immersed in many of these topics by reading a math-oriented Finance book.  Try to stay away from those penny stock trading books, I don't think I need to tell a math major (who is probably much smarter than I am) that you don't need to be engaging in penny stocks, but do your DD and come to a conclusion yourself if you'd like.  I'm not sure what career path you're trying to go down (personal trading, quant firm analyst, regular analyst, etc etc) but it sounds like you have the credentials to be doing quant trading.    ---  Check out www.quantopian.com.  It's a website with a python engine that has all the necessary libraries installed into the website which means you don't have to go through the trouble yourself (and yes, it is fucking trouble--you need a very outdated OS to run one of the libraries).  It has a lot of resources to get into algorithmic trading and you can begin coding immediately.  You'd need to learn a little bit of python to get into this but most of it will be using matplotlib, pandas, or some other library and its own personal syntax. Learning about alpha factors and the Pipeline API is also moderately difficult to get down but entirely possible within a short amount of dedicated time.     Also, if you want to get into algorithmic trading, check out Sentdex on youtube.  He's a python programmer who does a lot of videos on this very topic and has his own tool on quantopian called 'Sentiment Analyzer' (or something like that) which basically quantifies sentiment around any given security using web scrapers to scrape various news and media outlets.  Crazy cool stuff being developed over there and if you're good, you can even be partnered with investors at quantopian and share in profits.    You can also deploy your algorithms through the website onto various trading platforms such as Robinhood and another broker and run your algorithms yourself.    Lots of cool stuff being developed in the finance sector right now.  Modern corporate finance and investment knowledge is built on quite old theorems and insights so expect a lot of things to change in today's world.  ---  With a math degree, finance should be like algebra I back in the day.  You just gotta get familiar with all of the different rules and ideas and concepts.  There isn't that much difficult math until you begin getting into higher level finance and theory, which mostly deals with statistics anyways like covariance and regression and other statistic-related concepts.  Any other math is simple arithmetic. "I don't know what angle you're trying to push or why, but are you also saying that Kenichi Ueda of the IMF and Beatrice Weder Di Mauro of the University of Mainz are similarly lacking the understanding of financial concepts when they published a [paper](http://www.imf.org/external/pubs/ft/wp/2012/wp12128.pdf) ""Quantifying Structural Subsidy Values for Systemically Important Financial Institutions""?  Are you suggesting a similar ""entire shaker of salt"" when they ""estimate[d] the structural subsidy values by exploiting expectations of state support embedded in credit ratings and by using long-run average value of rating bonus""?  Should we really be so skeptical when they conclude:  &gt;Section III has provided estimates of the value of the subsidy to SIFIs in terms of the overall ratings. Using the range of our estimates, we can summarize that a one-unit increase in government support for banks in advanced economies has an impact equivalent to 0.55 to 0.9 notches on the overall long-term credit rating at the end-2007. And, this effect increased to 0.8 to 1.23 notches by the end-2009 (Summary Table 8). At the end-2009, the effect of the government support is almost identical between the group of advanced countries and developing countries. Before the crisis, governments in advanced economies played a smaller role in boosting banks’ long-term ratings. These results are robust to a number of sample selection tests, such as testing for differential effects across developing and advanced countries, for both listed and non-listed banks, and also correcting for bank parental support and alternative estimations of an individual bank’s strength.  I ask because this article is founded on that study - linked to by Bloomberg which is then linked to in OP's Huffpo article.  While you can certainly claim that Mark Gongloff ""shows a basic lack of understanding"" of whatever, why don't you put some skin in the game and demonstrate how he somehow misses the entire point of that study, or better yet really wow us by de-bunking that study." "Suppose you have $100 today and suppose that hamburgers cost $5. If you are holding $100, you could buy 20 hamburgers at $5 each. Now suppose you take that $100 and stuff it under your mattress for 24 years. When you pull out your money, you still have $100, but it turns out hamburgers now cost $10.   The increase from $5 to $10 is inflation.   Your savings of $100 was ""eroded"" by half even though before and after you had $100 and now you can only buy 10 hamburgers instead of 20 hamburgers. Suppose inflation is 3%.   That means that if you earn 3% return on your investments, you'll stay even with inflation (If you can buy 20 hamburgers today, then at any point you can still buy 20 hamburgers).   If you want your money to grow in a real, tangible way you'll want to seek returns that beat the rate of inflation." Various companies have hired quants to write algorithms to buy and sell stocks. Each of these has unique criteria that determine when it should buy/sell. Most have some AI component that allows it to tweak parameters and learn. The point is the more people try this kind of fake news tactic the better the algorithms get at responding or not responding to it. Keep in mind though that 1) the AIs learn at different rates 2) if an AI notices a boost in trading volume due to a story it determines is fake, the correct move is to buy and then sell before other AIs catch on. "For some studies on why investors make the decisions they do, check out  For a more readable, though less rigorous, look at it, also consider Kahneman's recent book, ""Thinking, Fast and Slow"", which includes the two companion papers written with Tversky on prospect theory. In certain segments (mostly trading) of the investing industry, it is true that something like 90% of investors lose money. But only in certain narrow segments (and most folks would rightly want traders to be counted as a separate beast than an 'investor').  In most segments, it's not true that most investors lose money, but it still is true that most investors exhibit consistent biases that allow for mispricing. I think that understanding the heuristics and biases approach to economics is critical, both because it helps you understand why there are inefficiencies, and also because it helps you understand that quantitative, principled investing is not voodoo black magic; it's simply applying mathematics for the normative part and experimental observations for the descriptive part to yield a business strategy, much like any other way of making money." More shares mean less volatility because it takes a larger number of trades, a larger number of shares per trade, or a combination of both to raise or lower the stock price. Institutional investors (mutual funds, pensions, hedge funds, other investment firms, etc) are the sorts of organizations with the large amounts of money needed to move a stock price one way or the other.  But the more floating shares there are in a company, the harder it is for one or two firms to move a stock price.  A company with fewer floating shares wouldn't require as many trades (or as many shares per trade) to see wider swings in price. When it comes to stock price, insider trading isn't the same as manipulation.  In the (surprisingly few) cases of insider trading that are prosecuted, it tends to be an individual (or small group) with early access to information that the broader market doesn't have being able to buy or sell ahead of the broader market.  Their individual sales are seldom if ever enough to noticeably move a stock price.  They're locking in profit or limiting a loss.  Manipulation might (but doesn't always) precede insider trading, if misinformation (or truth) is released for the purpose of creating a situation that can be profited from via a trade or trades. If we were to observe some call price (e.g., 15), and then derived implied volatilities from the BS formula depending on different strike prices but fixed maturity (i.e, maturity = 1, and strike goes from 80 to 140??), would we then see a smile? Yes. Market prices for various strikes and a given maturity often have higher implied volatilities from the Black-Scholes model away from at-the-money.   It is not accounted for in the Black-Scholes model in the fact that volatility is not a function of strike, so volatility is assumed to be constant across strikes, but the market does not price options that way. I don't know that a quantitative theory has ever been proven; I've always just assumed that people are willing to pay slightly more for options deep in or out of the money based on their strategy, but I have no evidence to base that theory on. "Quantitative Easing Explained: http://www.npr.org/blogs/money/2010/10/07/130408926/quantitative-easing-explained The short of it is that you're right; the Fed (or another country's Central Bank) is basically creating a large amount of new money, which it then injects into the economy by buying government and institutional debt. This is, in fact, one of the main jobs of the central bank for a currency; to manage the money supply, which in most fiat systems involves slowly increasing the amount of money to keep the economy growing (if there isn't enough money moving around in the economy it's reflected in a slowdown in GDP growth), while controlling inflation (the devaluation of a unit of currency with respect to most or all things that unit will buy including other currencies). Inflation's primary cause is defined quite simply as ""too many dollars chasing too few goods"". When demand is low for cash (because you have a lot of it) while demand for goods is high, the suppliers of those goods will increase their price for the goods (because people are willing to pay that higher price) and will also produce more. With quantitative easing, the central bank is increasing the money supply by several percentage points of GDP, much higher than is normally needed. This normally would cause the two things you mentioned: Inflation - inflation's primary cause is ""too many dollars chasing too few goods""; when money is easy to get and various types of goods and services are not, people ""bid up"" the price on these things to get them (this usually happens when sellers see high demand for a product and increase the price to take advantage and to prevent a shortage). This often happens across the board in a situation like this, but there are certain key drivers that can cause other prices to increase (things like the price of oil, which affects transportation costs and thus the price to have anything shipped anywhere, whether it be the raw materials you need or the finished product you're selling). With the injection of so much money into the economy, rampant inflation would normally be the result. However, there are other variables at play in this particular situation. Chief among them is that no matter how much cash is in the economy, most of it is being sat on, in the form of cash or other ""safe havens"" like durable commodities (gold) and T-debt. So, most of the money the Fed is injecting into the economy is not chasing goods; it's repaying debt, replenishing savings and generally being hoarded by consumers and institutions as a hedge against the poor economy. In addition, despite how many dollars are in the economy right now, those dollars are in high demand all around the world to buy Treasury debt (one of the biggest safe havens in the global market right now, so much so that buying T-debt is considered ""saving""). This is why the yields on Treasury bonds and notes are at historic lows; it's bad everywhere, and U.S. Government debt is one of the surest things in the world market, especially now that Euro-bonds have become suspect. Currency Devaluation - This is basically specialized inflation; when there are more dollars in the market than people want to have in order to use to buy our goods and services, demand for our currency (the medium of trade for our goods and services) drops, and it takes fewer Euros, Yen or Yuan to buy a dollar. This can happen even if demand for our dollars inside our own borders is high, and is generally a function of our trade situation; if we're buying more from other countries than they are from us, then our dollars are flooding the currency exchange markets and thus become cheaper because they're easy to get. Again, there are other variables at play here that keep our currency strong. First off, again, it's bad everywhere; nobody's buying anything from anyone (relatively speaking) and so the relative trade deficits aren't moving much. In addition, devaluation without inflation is self-stablizing; if currency devalues but inflation is low, the cheaper currency makes the things that currency can buy cheaper, which encourages people to buy them. At the same time, the more expensive foreign currency increases the cost in dollars of foreign-made goods. All of this can be beneficial from a money policy standpoint; devaluation makes American goods cheaper to Americans and to foreign consumers alike than foreign goods, and so a policy that puts downward pressure on the dollar but doesn't make inflation a risk can help American manufacturing and other producer businesses. China knows this just as well as we do, and for decades has been artificially fixing the exchange rate of the Renmin B (Yuan) lower than its true value against the dollar, meaning that no matter how cheap American goods get on the world market, Chinese goods are still cheaper, because by definition the Yuan has greater purchasing power for the same cost in dollars. In addition, dollars aren't only used to buy American-made goods and services. The U.S. has positioned its currency over the years to be an international medium of trade for several key commodities (like oil), and the primary currency for global lenders like the IMF and the World Bank. That means that dollars become necessary to buy these things, and are received from and must be repaid to these institutions, and thus the dollar has a built-in demand pretty much regardless of our trade deficits.  On top of all that, a lot of countries base their own currencies on our dollar, by basically buying dollars (using other valuable media like gold or oil) and then holding that cash in their own central banks as the store of value backing their own paper money. This is called a ""dollar board"". Their money becomes worth a particular fraction of a dollar by definition, and that relationship is very precisely controllable; with 10 billion dollars in the vault, and 20 billion Kabukis issued from Kabukistan's central bank, a Kabuki is worth $.50. Print an additional 20 billion Kabuki and the value of one Kabuki decreases to $.25; buy an additional 10 billion dollars and the Kabuki's value increases again to $.50. Quite a few countries do this, mostly in South America, again creating a built-in demand for U.S. dollars and also tying the U.S. dollar to the value of the exports of that country. If Kabukistan's goods become highly demanded by Europe, and its currency increases relative to the dollar, then the U.S. dollar gets a boost because by definition it is worth an exact, fixed number of Kabukis (and also because a country with a dollar board typically has no problem accepting dollars as payment and then printing Kabukis to maintain the exchange rate)" "Leverage here is referring to ""financial leverage"". This is the practice of ""levering"" [ie increasing, like the use of a lever to increase the amount of weight you can lift] the value of your investment by taking on debt.  For example: if you have 100k in cash, you can buy a 100k rental property. Assume the property makes 10k a year, net of expenses [10%]. Now assume the bank will also give you a 100k mortgage, at 3%. You could take the mortgage, plus your cash, and buy a 200k rental property. This would earn you 20k from the rental property, less 3k a year in interest costs [the 3%]. Your total income would be 17k, and since you only used 100k of your own money, your rate of return would now be 17% instead of 10%. This is financial leveraging. Note that this increases your risk, because if your investment fails not only have you lost your own money, you now need to pay back the bank. ""Beta riders"" appears to be negative commentary on investors who use Beta to calculate the value of a particular stock, without regard to other quantitative factors. Therefore ""leveraged beta riders"" are those who take on additional risk [by taking on debt to invest], and invest in a manner that the author would perhaps considered ""blindly"" following Beta. However, I have never seen this term before, and it appears tainted by the author's views on Quants. A ""quant process driven discipline"" appears to be positive commentary on investors who use detailed quantitative analysis to develop rules which they rigorously follow to invest. I have never seen this exact phrasing before, and like the above, it appears tainted by the author's views on Quants. I am not providing any opinion on whether ""beta riding"" or ""quant processes"" are good or bad things; this is just my attempt to interpret the quote as you presented it. Note that I did not go to the article to get context, so perhaps something else in the article could skew the language to mean something other than what I have presented." Impossible to unwind. Its a monetary roach motel.  They will try to raise rates and unwind their balance sheet until there is another financial crisis, to which there will be another round of QE that dwarfs the previous rounds.  Unwinding has the real possibility of triggering another financial crisis.  If the bond market turns bearish after a 30 year bull market I wouldnt want to be holding dollars. "There tends to be high volume around big changes in stock price. The volume of a stock does not remain constant and the term ""fat fingers"" can influence price.--> http://www.bloomberg.com/news/2014-10-01/that-japanese-fat-finger-can-absolutely-happen-in-u-s-.html That being said keshlam is 99% right when it comes to a stock moving when their is no news or earnings announcements. Check out these papers. http://onlinelibrary.wiley.com/doi/10.1111/j.1475-6803.2010.01285.x/full They do a time series analysis to try and predict future prices off of past demand during news events. They forecast using auto-regressive models.  google ""forecasting autoregressive model"" and the upenn lecture will be helpful. I would post another link but I cannot because I do not have enough rep/ This is more of a quant question. Hope this helps. JL" The CCAPM attempts to link asset prices/equity risk premiums to the 'intertemporal substitution of consumption' instead of the normal beta. So essentially what it's trying to say is you prefer to smooth consumption, you hate volatile consumption patterns, therefore you demand higher returns in booms and will settle for lower returns in downturns. (It may sound counter-intuitive at first, but think in terms of marginal utilities, in booms your marginal utility is lower as you're already well off, therefore you demand more of an asset).   The CCAPM tries to link asset prices to real economic theory (consumption) whereas CAPM is derived from the assumption that investors only care about mean and variance of returns (no real link to the real side of the economy).   There are almost no real world applications for the CCAPM. Empirically it is almost a complete failure, complex extensions have only gotten it so far, see the 'equity premium puzzle' by Mehra and Prescott (1985). "https://en.wikipedia.org/wiki/Irrational_exuberance  &gt; Clearly, sustained low inflation implies less uncertainty about the future, and lower risk premiums imply higher prices of stocks and other earning assets. We can see that in the inverse relationship exhibited by price/earnings ratios and the rate of inflation in the past. But how do we know when irrational exuberance has unduly escalated asset values, which then become subject to unexpected and prolonged contractions as they have in Japan over the past decade?  &gt; — ""The Challenge of Central Banking in a Democratic Society"", 1996-12-05" "This falls under value investing, and value investing has only recently picked up study by academia, say, at the turn of the millennium; therefore, there isn't much rigorous on value investing in academia, but it has started. However, we can describe valuations: In short, valuations are randomly distributed in a log-Variance Gamma fashion with some reason & nonsense mixed in. You can check for yourself on finviz.  You can basically download the entire US market and then some, with many financial and technical characteristics all in one spreadsheet. Re Fisher: He was tied for the best monetary economist of the 20th century and created the best price index, but as for stocks, he said this famous quote 12 days before the 1929 crash: ""Stock prices have reached what looks like a permanently high plateau. I do not feel there will be soon if ever a 50 or 60 point break from present levels, such as (bears) have predicted. I expect to see the stock market a good deal higher within a few months.""   - Irving Fisher, Ph.D. in economics, Oct. 17, 1929 EDIT Value investing has almost always been ignored by academia.  Irving Fisher and other proponents of it before it was codified by Graham in the mid 20th century certainly didn't help with comments like the above.  It was almost always believed that it was a sucker's game, ""the bigger sucker"" game to be more precise because value investors get destroyed during recession/collapses.  So even though a recessionless economy would allow value investors and everyone never to suffer spontaneous collapses, value investors are looked down upon by academia because of the inevitable yet nearly always transitory collapse.  This expresses that sentiment perfectly. It didn't help that Benjamin Graham didn't care about money so never reached the heights of Buffett who frequently alternates with Bill Gates as the richest person on the planet.  Buffett has given much credibility, and academia finally caught on around in 2000 or so after he was proven right about a pending tech collapse that nearly no one believed would happen; at least, that's where I begin seeing papers being published delving into value concepts.  If one looks harder, academia's even taken the torch and discovered some very useful tools. Yes, investment firms and fellow value investors kept up the information publishing, but they are not academics. The days of professors throwing darts at the stock listings and beating active managers despite most active managers losing to the market anyways really held back this side of academia until Buffett entered the fray and embarrassed them all with his club's performance, culminating in the Superinvestors article which is still relatively ignored.  Before that, it was the obsession with beta, the ratio of a security's variance to its covariance to the market, a now abandoned theory because it has been utterly discredited; the popularizers of beta have humorously embraced the P/B, not giving the satisfaction to Buffet by spurning the P/E.   Tiny technology firms receive ridiculous valuations because a long-surviving tiny tech firm usually doesn't stay small for long thus will grow at huge rates.  This is why any solvent and many insolvent tech firms receive large valuations: risk-adjusted, they should pay out huge on average.  Still, most fall by the wayside dead, and those 100 P/S valuations quickly crumble. Valuations are influenced by growth.  One can see this expressed more easily with a growing perpetuity: Where P is price, i is income, r is the rate of return, and g is the growth rate of i.  Rearranging, r looks like: Here, one can see that a higher P relative to i will dull the expected rate of return while a higher g will boost it. It's fun for us value investor/traders to say that the market is totally inefficient.  That's a stretch.  It's not perfectly inefficient, but it's efficient.  Valuations are clustered very tightly around the median, but there are mistakes that even us little guys can exploit and teach the smart money a lesson or two. If one were to look at a distribution of rs, one'd see that they're even more tightly packed.  So while it looks like P/Es are all over the place industry to industry, rs are much more well clustered. Tech, finance, and discretionaries frequently have higher growth rates so higher P/Es yet average rs.  Utilities and non-discretionaries have lower growth rates so lower P/Es yet average rs."