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Quant investing has the same basic problem as any approach to asset management: capacity for capital invested. Unlike quant trading, quant investing deals with large assets. For this reason, the type of arbitrage opportunities pursued by quant traders are not feasible for investing - those strategies simply do not have the capacity necessary for asset ...

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If you look in the portfolio management sections of the CFA (chartered financial analyst) curriculum, you'll find a listing of commonly used portfolio management techniques. It is by no means exhaustive, but the content in the CFA curriculum comes directly from industry professionals, so it is reasonable current and applicable. CFA Candidate Body of ...

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Yes, a Monte Carlo simulation (MC) is what you need. It is a well known and documented approach with many uses in finance, science and engineering. MC simulations are used to simulate the returns of complex financial assets or in your case returns of business ventures under uncertainty. Your input variables ($x_1, x_2,\cdots, x_n$) are uncertain. If you ...

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In kamikaze_pilot's defense, the question is not that naive or simple. First of all, you need to define what options you are talking about. Consider a digital option for example (which is really fairly vanilla since you can proxy it as a combination of two European calls), which pays 1 of the stock is beyond a certain level at maturity and nothing otherwise....

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Off the top of my head, credit default swaps (CDS) on sovereign debt -- and perhaps on large companies in the sector you care about in that country, if the CDS's exist -- leap to mind. Check out the Wikipedia article on them. They are something along the lines of "insurance rates" (not exactly, but this is a reasonable first-pass understanding) on a country'...

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You did not carefully read the article you yourself linked to. Dollar cost averaging is a generalized concept. What the author compares is a full-sized investment or time-specific partial investments. So, dca is a concept and you draw conclusions from one single approach to dca. There is no mathematical proof that dca works or not because it is one single ...

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You answered your own question with the statement it began with: "Since if the option's price is lower than its intrinsic value (eg. strike price - current stock price for puts), then an arbitrage opportunity arises from buying the option at bargain and then exercising it..." An options price cannot be lower than its intrinsic value (for any discernab,le ...

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It is common for the Bid (and sometimes the average of the Bid/Ask) price of deep in the money options to be below the Intrinsic Price. Download some data and try it. http://www.cboe.com/delayedquote/QuoteTableDownload.aspx

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There are a number of papers in the literature which show that Dollar Cost Averaging is suboptimal, in the sense that, given a DCA investment strategy, then there exists an alternative investment strategy which will be strictly preferred by a utility maximising agent. This preferred strategy may not necessarily be a "lump-sum" strategy, but a better strategy ...

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Equity is for the bulls; debt is for the bears. It depends on what kind of capital is available for financing and what the group needing capital can offer in terms of security. Early stage startups have nothing to offer but future returns (especially if they are cash-flow negative). A high risk investment with little collateral and a high burn rate may not ...

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The classic text for machine learning is 'The Elements of Statistical Learning' by Tibshirani et al. I believe the term "data mining" is often used synonymously with "machine learning".

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It is true that strategies with higher trading frequencies have Sharpe ratios that appear implausibly high by the standards of Fama-French factors. The strong law of large numbers really helps them, as they realize profits repeatedly while not increasing the standard deviation very quickly. The real barriers to entry for them are the costs that ...

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MBS are securities which represent ownership in a pool of mortgages ABS are securities which represent ownership in a pool of assets other than mortgages (for example auto loans or credit card loans) Collateralized Debt Obligation are complex entities which issue tranches of securities to investors and use the proceeds to buy MBS, ABS or other assets. The ...

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In general, PPN is the short form for principal protected notes. Here, the principal, or notional, $N$ is generally return in full. I am a little confused why only 80 % is returned. It may be a contractual specification, and it is also called a PPN. Regarding the variable interest, or premium in your term, is the return that the investor will achieve. In ...

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I do not think such figure exist in its per-canned form. However, for the most part, any pension/social security/annuity provider needs to have an idea of that, since it is their current and future liability that we are talking about. A quick and dirty way would be to look at US Social Security's funds, and get an estimate of how underfunded they are (so you ...

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This may not directly answer your questions. There's a class offered by Georgia Tech called Machine Learning for Trading, you might find it useful. https://www.udacity.com/course/machine-learning-for-trading--ud501

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Why don't you calculate the IRR of each investment? (aside from all the issues with IRR).

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I agree with the implicit idea behind your question that "on the paper, high frequency fluctuations of prices should not affect long term moves". One point is for sure: the volatility we have in mind when we talk about Value At Risk and similar systemic measure has nothing to do with the potential increases of volatility due to high frequency activity. ...

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Assume we start at $t=0$ with $P_0$, there are $t=1...N$ subsequent periods, and at each period-end $t$ an (entirely arbitrary) portion $c$ of our portfolio $P_t$ is churned and $(1-c)$ remains untouched. $P$ grows over each period by a factor $(1+g)$: $P_t = P_{t-1}(1+g)$. We can partition $P_t$ into sub-portfolios, each with its own churn history, as in: ...

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What does it mean that you will optime portfolio "without programming"? Does that mean that you will do calculations by hand??? Articles will not help you since every article you will be able find is based on optimization models in which market data will somehow be involved. That you cannot do "without programming". Maybe you should think about finding ...

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The scope of your question is quite unclear to me. You seem to mention trading. If you have multiple trading strategies (that you think are good, and reasonably uncorrelated) and you want to trade them as a portfolio, a commonly used criterion is to allocate capital to each strategy in proportion to the inverse of the strategy's standard deviation. So if ...

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All of the DMS returns are adjusting for dividends. Hence dividends are accounted in the sample. Moreover, DMS have also accounted for inflation. Hence, the real total net equity index return, now and hereafter, $e_{t}$, may be mathematically defined as \begin{equation*} e_{t}=\frac{1+\frac{P_{t}+D_{t}}{P_{t-1}}}{1+\pi_{t}}-1 \end{equation*} ...

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This is more of an introductory finance/financial statement analysis question (which isn't really home in quantfin). However, I'm happy to walk you through the analysis. From the article When including cash and equivalents and short- and long-term investments on its balance sheet, the iPhone maker is sitting on 233 billion in cash and liquid assets. ...

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Another example might be, if companies that exhibit certain revenue growth metrics, or margin improvement, would that signal a potential buy opportunity? Or perhaps if certain words in their annual report, quarterly filings, press releases indicate this company is likely to do well? The keys to quantitative investment are research and data analysis. For ...

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HFT firms are liquidity providers. They post bids and offers at prices around what they believe the fair price of the stock is at the current moment. The distance between those bids and offers can be thought of as a confidence interval. So, to put it quite simply, they can use machine learning to better estimate the fair price of the asset or better estimate ...

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That is what XIRR does or can you read this answer. Basically it tries to find an interest rate that works out to the same numbers. I think Excel and Google Docs use the Newton approximation http://www.mftransparency.org/calculating-interest-rates-using-newtons-method/ There's also a java implementation available on github called jxirr.

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I second Tibshirani's book. There is an another edition you can download free on internet : http://www-bcf.usc.edu/~gareth/ISL/

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The problem is the way you compute profit, in which you are not accounting for the timing of cashflows. If you compute NPV's you get a better comparison. Also if you check the IRR, it will be 5% for both investments.

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Your question is little broad and has two aspect: Theory and Application. If you are interested in scientific approach and academic literature this kind of thing is called Mathematical_optimization which is branch of Multi-objective optimization which is again a branch of Operations_research. In terms of mathematics of solving these problems multivariate ...

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