Next-gen Investing: 40+ Web 2.0 Tools, Networks and Mashups for Today’s Internet has moved beyond flat content and into websites that offer collaboration, interaction, and lots of customization. This is great news for investors, because now it’s easier than ever to get the information you need to make wise investment decisions. Check out these innovative tools that are designed to give your workflow and investment portfolio an upgrade. Social Investing Have you ever been baffled by a market turn that everyone but you saw coming? Information Hubs There’s a lot of investment information out there, but not all of it is actually useful. NestEggr: On NestEggr, you can ask and answer questions on finance, including topics on trading strategies, risk, and forex. Recommendations If you’re just starting out or realigning your portfolio, you could use some advice and recommendations. Social Media With all of the investment news out there, it’s often hard to see what’s really important. Tracking SaneBull: This market monitor is sort of like Yahoo! Start Pages Other
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. 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. I never thought about buying a stock purely because of its P/E ratio or tangible book value or PEG ratio or earnings growth or whatever because I hadn’t read any investing books and I hadn’t heard the only person I knew in business – my mom – ever use words like that. What did I know? · Activision (ATVI) · Like
The Evolution of The Corporation and Its Impact on Investing - S The invention of the corporation gave way to a new era of financial dealings, melding individuals together to increase their monies, especially with marketable securities. But first, what is a corporation? It is an intangible legal being designed to assure an organization unlimited life and limited liability. The corporation first appeared in Europe around the fourteenth century when Queen Elizabeth I was the moving light in the East India Company. Now these corporations and others like them have another feature giving them a great advantage over an individual proprietorship or partnership. Government and municipal corporations (whose main revenues are from taxes) do their security financing almost entirely by borrowing. After making rewarding gains in the market many investors like to salt them away for dependable income and, if their bracket status is sufficiently high, they seek some form of tax shelter. Stock corporations may raise money both by borrowing and by selling stock.
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%. 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. Good.
Everything You Ever Really Needed to Know Ab A few days ago, I had lunch with an individual who is considering hiring me to give a multi-hour seminar to a business convention on personal finance. This person knows me from the local community and is a reader of The Simple Dollar and he felt that I might be the right person to give such a presentation. During the lunch, out of the blue, he asked me to give a five minute nutshell version of what I would present to the group. I saved the business cards, scanned them in, and thus, for your enjoyment, is my presentation (with some extensive helper notes so you can know what I was actually saying while drawing these cards). 1. In the end, this is the fundamental rule of personal finance: spend less than you earn. There are two avenues to achieving this goal: spending less and earning more. 2. So how does one earn more? Once you dig past that, though, there are some common things that anyone can do, regardless of their financial state, to earn more money. 1. 2. 3. 4. 5. 6. 3. 1. 2. 3. 4.
Learn to Trade Forex (Currencies), Stocks, & CFDs | InformedTrades 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. Consider a local utility company whose stock is selling for $10 a 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. The P/E ratio is one common measurement used to place stocks on the value to growth continuum. Some measurements use the past four quarters of earnings, which is often called the trailing P/E ratio. Is this author on the ball? Follow and be the first to know when they publish. Follow Marotta on Money (111 followers) Investment advisor, portfolio strategy, long-term horizon
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 .
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. Note that these concerns have nothing to do with the importance of messages. Historical background[edit] With it came the ideas of This is justified because
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. 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. Side information[edit] where Y is the side information, X is the outcome of the betable event, and I is the state of the bookmaker's knowledge. The nature of side information is extremely finicky. Doubling rate[edit] Doubling rate in gambling on a horse race is [3] where there are (e.g if the where
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. 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. If a compression scheme is lossless—that is, you can always recover the entire original message by decompressing—then a compressed message has the same quantity of information as the original, but communicated in fewer characters. Shannon's theorem also implies that no lossless compression scheme can compress all messages. Named after Boltzmann's H-theorem, Shannon defined the entropy H (Greek letter Eta) of a discrete random variable X with possible values {x1, ..., xn} and probability mass function P(X) as: Here E is the expected value operator, and I is the information content of X.[8][9] I(X) is itself a random variable. . The average uncertainty , with