Can anybody explain the terms “levered beta riders”, “equity long-short” and “the quant process driven discipline” for me, please?
"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." "Translation : Funds managers that use traditionnal methods to select stocks will have less success than those who use artificial intelligence and computer programs to select stocks.  Meaning : The use of computer programs and artificial intelligence is THE way to go for hedge fund managers in the future because they give better results.  ""No man is better than a machine, but no machine is better than a man with a machine."" Alternative article : Hedge-fund firms, Wall Street Journal. A little humour : ""Whatever is well conceived is clearly said, And the words to say it flow with ease."" wrote Nicolas Boileau in 1674."
Just adding on a touch, when market participants refer to swaps they are talking about the fixed leg.   So for example, if I said a 5y Receiver, it means I am receiving fixed, paying floating. Ie I want yields to fall. Opposite for a Payer.  Swaption is just an option on these swaps, so basic swaptions:  Long Payer Short Payer Long Receiver Short Receiver Citing the Yahoo Finance Help page, Beta: The Beta used is Beta of Equity. Beta is the monthly price   change of a particular company relative to the monthly price change of   the S&P500. The time period for Beta is 3 years (36 months) when   available. Regarding customised time periods, I do not think so. The quant aspect '''''. This is the kind of math I was wondering if it existed, but now it sounds like it is much more  complex in reality then optimizing by evaluating different cost of capital. Thank you for sharing If you have your long positions established and are investing responsibly (assuming you know the risks and can accept them), the next step IMO is typically learning hedging - using options or option strategies to solidify positions. Collars (zero cost) and put options are a good place to start your education and they can be put to use to both speculate (what you are effectively doing with short-position trading) or long-term oriented edging. Day trading equites can be lucrative but it is a difficult game to play - learning options (while more complex on speculation) can provide opportunities to solidify positions. Options trading is difficult grounds. I just think the payoff long term to knowing options is much greater than day-trading tactics (because of versatility) Finance noob here.  Am I reading the article right that he's saying MPT bad, active management good?    If so, what is that saying about how I should manage my portfolio (assuming I am only dedicating a few hours a month)?    &gt;he suggests that if you don't have an edge, no one needs to play the game  So what do I do then? Are there specific strategies?  Also, could you suggest a good explanation of why MPT is bad? This isn't totally wrong- there are hedge funds that are long 150% of AUM and short 50%. However, Rentech has said that holding a position for 8 seconds is long for them, so that's not what they're doing. I'd assume the 4X leverage most just refers to option positions that have delta 4 on average. They also may be borrowing money, which they can probably do extremely cheaply since they have a 35 year track record showing they're essentially risk-free. "A straddle is an options strategy in which one ""buys"" or ""sells"" options of the same maturity (expiry date) that allow the ""buyer"" or ""seller"" to profit based on how much the price of the underlying security moves, regardless of the direction of price movement. IE: A long straddle would be: You buy a call and a put at the same strike price and the same expiration date. Your profit would be if the underlying asset(the stock) moves far enough down or up(higher then the premiums you paid for the put + call options) (In case, one waits till expiry) Profit = Expiry Level - Strike Price - (Premium Paid for Bought Options) Straddle" long deep ITM calls is equivalent to owning the equity.  You're going to pay alot and hence will start off in a hole already, and you aren't getting too much leverage there at all depending how deep ITM you go.  Covariance scales, but assuming B-S in order to get nice scaling and ignoring the risks you are actually taking with options (unlimited down-size ie you can lose your entire investment in the option, people forget this) will screw you unless you really know what you are dong.  Leverage means increasing your risk.  long dep ITM is not obtaining much leverage and therefore not risking too much.  but you aren't going ot get 3-4x leverage this way.  you get leverage by saying: oh, i have 100, i could invest in 1 share of stock OR I could buy 100 worth of some option.  If I pick a deep ITM (think strike = 0) it's identical to owing the stock.  If i pick ATM, i have a ~50/50 chance of wining, so i should be able to double my upside.  If I go OTM, i can increase my exposure to the upside while increasing hte chance that my options expire worthless.  So really, i have no idea why deep ITM do what you are trying to do.  and If you don't either, you probably shouldn't do it. 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'm searching for a master's thesis topic in equity investment or portfolio management and I'd be grateful if someone could tell me what are the hot ""trends"" going on right now on the market? Any new phenomenons (like the rise of blockchain, etf... but more relate to the equity side) or debates ( the use of the traditional techniques such as Beta to calculate WACC for example ...) ?" Thanks very much.  12b1 is a form that explains how a fund uses that .25-1% fee, right? So that's part of the puzzle im getting at.  I'm not necessarily trying to understand my net fees, but more who pays who and based off of what.   For a quick example, betterment bought me a bunch of vanguard ETFs. That's cool. But vanguard underperformed vs their blackrock and ssga etfs. I get that vanguard has lower fees, but the return was less even taking those into account.  I'm wondering, first what sort of kickback betterment got for buying those funds, inclusive of wholesale deals, education fees etc.  I'm also wondering how this food chain goes up and down the sponsor, manager tree.  I'm sure it's more than just splitting up that 1% Yeah, the past 4-5 years have been rough on fundamental guys (not to say companies like AAPL didn't fare well), but EVERYTHING has been macro. When correlations go to 1 across the board and central bank legislation has have a bigger impact on earnings than new products/good mgmt/etc. it's hard to be a fundamentalist. Like your style, this market environment is ripe for HF's though that can use leverage/hedges/and short-term positioning to create alpha while the mutual funds are stuck in long-term structured investment objective ruts and reduced risk.   Not to say you can't create alpha through selecting better/undervalued stocks, it's just been damn near impossible the last 5 years to do it. "Let me start by hedging a little bit: our industry (finance, I mean) is cyclical, and disciplines tend to surge, to fall, then to rise reborn from the ashes. Fixed income was dead, then fixed income was the place to be, then everyone got laid off, then there was a huge rally, etc. etc.  BUT... if you get into the wrong area at the wrong time, well it doesn't really matter if it recovers in 20 years, does it? As the great man wrote: **""in the long run, we're all dead.""**  Regarding equity research (and here I especially mean **sell side** equity research), the super-volatile markets have made it harder for traditional equity funds to eke out a living, much less to meet investor expectations, so margins have gotten thinner. The increased correlations and increased volatility has just made stock picking less productive as a strategy.  As a result, traditional equity funds have cut back on their trading activity, have consolidated their business to one or two brokers, and have stopped explicitly paying for research. This means fewer soft and hard dollars flowing to less research. Furthermore, sell side research is less productive these days, it was just easier in the ""old"" bull market, there was more room to find value and pick stocks.   All of these factors are contributing to a decline in the research business, as evidenced by the layoffs we can all find by searching google news.  All that having been said, buy side research is a different story, but the strategies are more complex and you really have to deliver value to your PM in a timely manner." great example of levered tracking error is any 2x/3x VIX etf. during periods of high volatility (like last week) you will be able to realize much higher returns on the underlying index as the levered and inverse ETFs are unable to replicate their intended performance using market securities.  it is not uncommon to see the VIX up ~30% with levered ETFs only netting ~10-12% as opposed to the intended 60 or 90%, for example CFDs (Contracts for Difference) are basically a contract between you and the broker on the difference in price of the underlying between the time you open a position and close a position. You are not actually buying the underlying. With share CFDs, the outcome is a bit like buying the underlying shares on margin. You pay interest for every day you hold the CFDs overnight for long CFDs. However, with short positions, you get paid interest for every day you hold your short position overnight. Most people use CFDs for short term trading, however they can be used for medium to longer term trading just as you would hold a portfolio on margin. What you have to remember is that because you are buying on margin you can lose more than your initial contract amount. A way to manage this risk is by using position sizing and stop loses. With your position sizing, if you wanted to invest $10,000 in a particular share trading at $10 per share, you would then buy 1000 shares or 1000 CFDs in that share. Your initial expense with the CFDs might be only $1000 (at a margin rate of 10%). So instead of increasing your risk by having an initial outlay of $10,000 with the CFDs you limit your risk to the same as you were buying the shares directly.