# Tag Info

9

Since the stock is listed on NASDAQ, you have access to fairly standard 10Q and 10K financial statements. So you can apply the analysis pioneered by Ed Altman in his Z-score paper - compare this company's fundamental ratios with those of other companies, and see how many of them went bankrupt historically. For example, Moody's KMV uses this approach to ...

8

In the academic literature it is extremely widely applied in the last 20 years. I would estimate maybe 200 empirical papers, or more. For example a common finding is that higher frequency (daily) wavelet correlations have been high since 2007, attributable either to increasing financial interation or the financial crisis. It is also popular to estimate the ...

8

I have been told: Bankruptcy is very controversial Google Scholar Researchers. You might track companies ratios (e.g., debt to equity ratio, EPS, net income, cash per share (cash/sh), etc.). For instance, GE looks almost bankrupt. But, it is not and there is a very low probability that GE would file for any bankruptcy chapter, I'm just guessing. There ...

7

The top chart is called a 'candle stick chart' or 'OHLC candlestick' or 'OHLC bar chart' http://multicharts.com/trading-charts When the price goes down during a time interval (from O to C) the box is filled in orange, when the price goes up it is green bordered with black inside. The exact colors are a matter of taste, as long as they are clearly different ...

6

SMM stands for single-month mortality and CPR stands for constant (or conditional) prepayment rate. They're both units of voluntary prepayment rates ($CPR = 1-(1-SMM)^{12}$). They could be based on either estimated or actual prepayments. Where to get actual MBS prepayment data will depend on what type(s) of MBS pools you're modeling (e.g. agency, ...

6

The concept of a tradable asset is closely related to the principle of (no-)arbitrage. Much of quant finance is about the connection between the price of a derivative and the price of its underlying. The fundamental reason that there is a connection at all, is the possibility to set up self-financing trading strategies in the underlying(s) which replicate ...

6

Stock market indices fall faster than they rise, in part, due to leveraged long investors. As individual stocks fall, investors must de-risk due to margin calls, and those margin calls may need to be met by selling other stocks. This causes correlations to increase as markets fall. This also causes indices to fall more quickly than they rise, since the ...

6

There are more ways to approach this but the method I propose should work reasonably well in practice, especially if you increase the number of assets you hold. Calculate the beta of the stocks you're holding with respect to an index Buy $N_f$ (sell when $N_f$ is negative) future contracts on that index $N_f$ can be calculated as $$N_f = \frac{\beta_T - \... 5 You can try using different approaches. Starting from something not that "heavy" like the NN. 0) Pre study - you need to prepare your data (how you will treat a negative spread (i.e. ASK - BID <0), what will you do if you will have 0 spread and then you will divide some value by it?), - plan your research ahead - how will you divide your limited data ... 5 So basically your question boils down to: How can markets be non-normal in the short run but (more) normal in the long run? The answer to that lies in the fact that certain assumptions of normality are not satisfied in the short run, one of them being independence. In the short run returns are just not independent (think e.g. volatility clustering) because ... 5 The original Vasicek paper is "An equilibrium model of the term structure". If you google for it, you'll find it and you can read in his own words his motivation for developing it. In particular, what now is called the Vasicek model basically comes from applying his results to an Ornstein-Uhlenbeck model for the spot process, which he claims was proposed by ... 5 It's really quite simple. It's just a matter of the fact that we can change measure on the stochastic volatility while not changing the fact that the stock is a martingale. Once we can do this, we have payoffs that have different values under different measures, so the market can't be complete. For clarity, just consider a stock S, a money market account ... 5 TLDR: Massive expansion of credit fuelled by rehypothecation, a general shift to repo, then the scale tips and everyone pays as credit collapses. Quants were there, but I don't think they can be simply blamed for all the ills of the world. There is a general disagreement about what caused what, so some of this is guesswork. I'm marking this a community wiki ... 5 What happened was the BoJ announcement. Such large scale news are well covered in mainstream media (ft, bloomberg, etc) and also mainstream anti-media (eg zerohedge). 5 There was a methodology change in how volume is reported. Now it is using the consolidated tape including trades on all exchanges. See: http://stockcharts.com/articles/dont_ignore_this_chart/2016/12/whats-the-deal-with-that-intraday-volume-on-the-dow-indu.html 4 This is known as a 'crossed' book, the exchange will attempt to uncross the book at the price at which the maximum amount of volume can trade. In your example at the price of 42 there's only 3533 amount of buying quantity, and there are more than enough sellers to cover this. At a price of 40, there's now 3533+425 buying quantity willing to trade, and still ... 4 Art markets typically have huge transaction costs of the order of 10%, caused by buyers premium and auction fees. Therefore long holding periods are unavoidable, with long-term returns somewhere between those of bonds and equities. By its very nature, art is not easily replicated so arbitrage or derivatives are out. The rationality of agents (aka collectors) ... 4 There are two excellent choices for implementing prediction markets: (1) Use book orders that stand until filled, just as intrade.com does. (2) Use an automated market maker (like Robin Hanson's) that stands ready to make trades. The book orders model is very simple to implement, but can suffer from very wide Bid/Ask spreads. And, it can be tough to bet ... 4 When both the spot and futures markets are open, the futures price is related to the spot S&P level via the equation F = S e^{(r-d)t}. t is the time (in years) between now and the contract expiration. The March contract is expiring soon, so the most active is actually June. The difference between S and F is called the 'basis', and with current ... 4 I have been searching for the same information as I noticed some of my students did use CSI market. What bothered me is that there are no dates attached to the numbers. The only number is the copyright year in small letters at the bottom. 4 I contacted one the authors of the original paper. He confirmed that the overlapping three day log returns are to be used on both stock and market returns. 4 The short answer: many factors. The following are some key ones: Reported Trades - Stocks are quoted "bid" and "ask" rates. These are the traders setting their prices much similar to a local farmers market trading their produce. Volume - number of shares traded. Price trend - When the bid volume is higher than the ask volume, the selling is stronger, and ... 4 Let's stick to a discrete market for simplicity. So, you have a finite number of states in this type of model. The first fundamental theorem of asset pricing says that the absence of arbitrage in such markets imply the existence of (not necessarily unique) risk-neutral measure and vice-versa. The reason it works in the second direction (the existence of a ... 3 Market beta just tells your portfolio has low covariance, scaled by variance, with the market. Remember that$$ \beta= \frac{Cov(x,y)}{Var(x)} = \rho\frac{\sigma_x \sigma_y}{\sigma_x^2}=\rho\frac{\sigma_y}{\sigma_x}  You can see that it well may be that $\sigma_x<\sigma_y$ but $\rho$ is small enough to have a beta of 0.5. By the way, you can directly ...

3

Yes, those are probably the variables that predict the better the stock market return. However, the OOS evidence is usually weak. Goyal & Welch provide a good summary on predictors: http://rfs.oxfordjournals.org/content/21/4/1455.abstract

3

I would say the financial- and the art market is very different, only the roots of the market / auctions is the same. As the art market is unique and very illiquid, alot of the strategies from the modern financial market simply does not apply. I have been building (and still maintains) a toolbox of models, which mostly try to find trends based on multiple ...

3

In addition, there is no "About Us" tab, which would provide the opportunity to say something about the company, it staff, and other pertinent informations, that would lead one to assess the value of the company's product. I was interested, as a response above noted, in using the data as a source for my managerial economics and forecasting class, but have ...

3

the point of the LMM is to evolve several different rates simultaneously. If you have rates $f_i$ from $t_i$ to $t_{i+1}$ and take a bond expiring at $t_j$ as numeraire then only the rate $f_{j-1}$ is driftless. Typically $P_{t_0}$ is used as numeraire which makes all the rates have drift. It generally gives lower variance. (see my book More ...

3

On the CRSP database, for each stock there is a field called EXCHCD. This field will indicate you the exchange. Here are the exchange codes: So if you keep only the stocks with codes 1 and 2 you should be good to go.

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Short answer: That is a common approach in empirical finance. The exclusion of financial firms is due to their business model, which is highly different from other companies. Fama/French (1992), p. 429 state: We exclude financial firms because the high leverage that is normal for these firms probably does not have the same meaning as for non-financial ...

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