Estimize. Binomial options pricing model. Use of the model[edit] The Binomial options pricing model approach is widely used as it is able to handle a variety of conditions for which other models cannot easily be applied. This is largely because the BOPM is based on the description of an underlying instrument over a period of time rather than a single point. As a consequence, it is used to value American options that are exercisable at any time in a given interval as well as Bermudan options that are exercisable at specific instances of time. Being relatively simple, the model is readily implementable in computer software (including a spreadsheet). Although computationally slower than the Black–Scholes formula, it is more accurate, particularly for longer-dated options on securities with dividend payments.
Method[edit] The binomial pricing model traces the evolution of the option's key underlying variables in discrete-time. Option valuation using this method is, as described, a three-step process: or and ) . , we have: Where Max [ ( Entropy (information theory) 2 bits of entropy. A single toss of a fair coin has an entropy of one bit. A series of two fair coin tosses has an entropy of two bits. The number of fair coin tosses is its entropy in bits. This random selection between two outcomes in a sequence over time, whether the outcomes are equally probable or not, is often referred to as a Bernoulli process. The entropy of such a process is given by the binary entropy function. The entropy rate for a fair coin toss is one bit per toss. However, if the coin is not fair, then the uncertainty, and hence the entropy rate, is lower. This definition of "entropy" was introduced by Claude E.
Entropy is a measure of unpredictability of information content. Now consider the example of a coin toss. English text has fairly low entropy. Shannon's theorem also implies that no lossless compression scheme can compress all messages. Here E is the expected value operator, and I is the information content of X.[8][9] I(X) is itself a random variable.
. . , with. Gambling and information theory. Statistical inference might be thought of as gambling theory applied to the world around. The myriad applications for logarithmic information measures tell us precisely how to take the best guess in the face of partial information.[1] In that sense, information theory might be considered a formal expression of the theory of gambling. It is no surprise, therefore, that information theory has applications to games of chance.[2] Kelly Betting[edit] Kelly betting or proportional betting is an application of information theory to investing and gambling. Its discoverer was John Larry Kelly, Jr. Part of Kelly's insight was to have the gambler maximize the expectation of the logarithm of his capital, rather than the expected profit from each bet. This is important, since in the latter case, one would be led to gamble all he had when presented with a favorable bet, and if he lost, would have no capital with which to place subsequent bets.
Side information[edit] Doubling rate[edit] where there are. Information theory. Overview[edit] The main concepts of information theory can be grasped by considering the most widespread means of human communication: language. Two important aspects of a concise language are as follows: First, the most common words (e.g., "a", "the", "I") should be shorter than less common words (e.g., "roundabout", "generation", "mediocre"), so that sentences will not be too long. Such a tradeoff in word length is analogous to data compression and is the essential aspect of source coding. Second, if part of a sentence is unheard or misheard due to noise — e.g., a passing car — the listener should still be able to glean the meaning of the underlying message. Such robustness is as essential for an electronic communication system as it is for a language; properly building such robustness into communications is done by channel coding.
Source coding and channel coding are the fundamental concerns of information theory. Historical background[edit] With it came the ideas of Entropy[edit] . That. Latent Dirichlet allocation. In natural language processing, latent Dirichlet allocation (LDA) is a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's creation is attributable to one of the document's topics.
LDA is an example of a topic model and was first presented as a graphical model for topic discovery by David Blei, Andrew Ng, and Michael Jordan in 2003.[1] Topics in LDA[edit] In LDA, each document may be viewed as a mixture of various topics. For example, an LDA model might have topics that can be classified as CAT_related and DOG_related. Each document is assumed to be characterized by a particular set of topics. Model[edit] With plate notation, the dependencies among the many variables can be captured concisely. Is the topic distribution for document i, The 1. . , where . Value: The Third Factor Of Investing. A stock's valuation is the final factor of the Fama-French three-factor model of investment returns.
A stock's valuation is measured on a continuum from "value" to "growth. " In broad strokes, value stocks are cheap and growth stocks are expensive. But there are compelling reasons why an investor might be willing to pay more for a growth stock than a value stock. Consider a local utility company whose stock is selling for $10 a share. The price has not changed much in the past 20 years. The company only services a specific geographic area that is not experiencing population growth. It has also had consistent earnings each year and paid the entire amount to shareholders at $1 per share. This company has a price per earnings (P/E) ratio of 10. In contrast, consider a technology startup company that has shown meteoric growth in the past three years. Investors might rightly decide that the growing technology company is worth more than the static regional utility. Is this author on the ball? Learn to Trade Forex (Currencies), Stocks, & CFDs | InformedTrades.
Insider Monkey Blog - Insider Trading, Hedge Funds, Stock Picks. Interactive Investor Blog. How to Know a Stock Is Cheap Enough to Buy. Someone who reads my articles asked me this question : {*style:<i> <b>Micropac ( MPAD ) </b> sells at 83% of NCAV, has similar (slightly better) z- and f-scores, a FCF margin of 6%, but has ROA of 28%.
</i>*} <b>ADDvantage ( AEY ) </b> sells at 95% of NCAV, has similar (in the ballpark) scores and FCF and ROA of 23%. </i>*} There’s a great post over at Oddball Stocks called: “ A Stock is a Business ”. Here’s what Richard said in a post called “ Giving Up on Mastery of the Universe ”: So, if someone says simply, “At less than its book value, I’m comfortable buying DreamWorks. If you believe most decent businesses are worth at least 12 times earnings, you don’t have to drive yourself crazy trying to figure out whether Company A which is superior to Company B is a better buy at 11 times earnings than Company B is at 7 times earnings. Now, let’s look at net-nets. Yes. That leaves us with a pretty simple arbitrary rule. So… Yes. I’m not saying this because the cheapest net-nets are the worst.
How Long Does It Take to Develop an Investing Style? Someone who reads my articles asked me this question: Hey Geoff, How long did it take you to develop your own style? Tom I’m an odd case. It happened almost instantly. And here’s why. I got started investing when I was 14. I first learned about value investing when my dad brought home an article about Ben Graham.
There is a reason why I talk about things like comfort with a stock and Warren Buffett’s 20 punches. It’s not that I abstractly believe that investing like you get to make only 20 investment decisions for the rest of your life will work well. I was born in 1985. So, it’s not like I learned about stocks first and businesses second. When I was growing up, my mom was basically the second officer at a family-controlled company. What did I know? My mom’s company had moved from New Jersey to Florida when I was about 6. My mom’s home office actually doubled as my bedroom (I slept on a pull out couch). The company my mom worked for made dust control systems. It was not the only one I got. Find the smart money — AlphaClone. Value Investing | Market Insight of Investment Gurus. Covestor Investment Management.