14

I am not an investment banker, but usually the procedure is something like this: (0) The IB knows the yield of existing bonds with the same maturity and credit rating, so it is not too difficult for them to estimate the yield of the new bonds. They usually announce this as a spread above a benchmark (Ex: "We estimate the new bonds will yield 25 to 50 bps ...


7

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 ...


5

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 ...


4

In addition to @AlexC answer there are 2 additional key points. 1) if the issue is oversubscribed the IB / syndicate team will choose the allocation to each client usually based on their relative importance in terms of future business. 2) There is a specific pricing call that takes place between the issuer and investment banks trading teams. This ...


3

I guess the concept you're looking for are martingales. These are stochastic processes which remain on their current level (in expectation!). Ignoring some technical conditions, a stochastic process $(X_t)$ is called a martingale if for all time points $t\geq s$, $$\mathbb{E}[X_t|\mathcal{F}_s]=X_s.$$ Here, $(\mathcal{F}_s)$ refers to a filtration, the ...


2

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 ...


2

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".


2

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 ...


2

No, it's not correct. The 1000 you invest at the beginning of the second year should also be discounted, That 1000 also has a present value. This gives: $$NPV = \frac{2200}{(1+R)^2} - \frac{1000}{(1+R)} - 1000$$ with $R$ the annual rate. Remember, you cannot simply add incoming or outgoing cash flows that occur at different times.


2

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 ...


2

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 ...


2

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 ...


2

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


2

False. It is not always the case for European options which cannot be exercised early and for Americans when you include transaction costs.


2

To me, that smelled like dynamic programming too. After implementing a dynamic programming solution according to http://www.cs.rpi.edu/~magdon/courses/cf/notes/optimal.pdf and other sources from the same author, it dawned on me that dynamic programming might not really be necessary at all. In the end, what you want is to put all your value into the single ...


2

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


2

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. ...


2

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: ...


2

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 ...


2

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 ...


2

I am not an expert on GIPS, with its many pages of rules, but I do remember that under GIPS Private Equity results are to be given in terms of IRR (Internal Rate of Return). In most other cases (stock/bond portfolios for example) GIPS requires TWR (Time Weighted Return) and forbids the use of IRR. To compute the IRR we need the dates and amounts of cash ...


2

The assumption that the discount rate should be derived from the IRR of an alternative investment is not correct. Commonly the WACC of the company (or the WACC of the funds needed for the investment if it is standalone) is used. If this is not available, you could make use of a combination of publicly available rates and some risk-adjustments: risk-free ...


1

Not really sure about papers, but it seems you are looking for a stock screener, which these two are my favorites, that you can filter stocks based on your parameters (e.g., ROI, ROE, net profit, debt, market cap, volatility, etc.): TradingView Finviz


1

Yes, there are sound ways to address this problem. And, depending on the level of realism required and your goals, you will need to think a lot more to devise an acceptable strategy. Bird's eye view Let us first make the assumption that each asset indeed has exactly the same growth, each period. Even in this most simple case you can follow different ...


1

Technical analysis is not quite in my wheelhouse, but it's been an interesting topic to me, so hopefully I can lend a hand. Let's start with some basic assumptions: Because OBV is based on volume, there is obviously a huge range as you've pointed out. This makes comparison straight across companies impossible. To compare companies, you need to take out ...


1

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*} ...


1

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. ...


1

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 ...


1

Read Max Dama on Automated trading (PDF) - This is the best introduction to algorithmic trading out there: http://www.decal.org/download/2582


1

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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