# Tag Info

6

I would recommend Marc Wildi's work on signal extraction.

5

Pring was (probably) simply referring to the fact that most indicators are function of price -- lots of different ways to twist and contort prices to define trends, reversal points, etc. Volume is another parameter entirely, as it doesn't depend on price; the market or share price can have an up day on average, high, or low volume, it can have a down day on ...

4

Perhaps construct a Brownian Bridge between the day's open and close, then scale it according to the day's high and low.

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

Usually stockreturns $R$ are assumed normally distributed. If market goes up 1%, the expected stockreturn is $R=\beta\cdot0.01=0.02$ (since $\beta$ being the senstivity to market). Stockprice from $100$ over $103$ requires at least $103/100-1=0.03$ return $R$. As we have now from the question $\sigma=0.02$ and $\mu=0.02$, with $R\sim N(\mu,\sigma)$ we ...

4

Yes. Check out Time-Series Analysis by Shumway and Stoffer. Spectral Analysis and Filtering is covered in Chapter 4.

3

This mean that the reason why apple stock price went from 3 to 100 in 10years is the overnight variation in price. This is quite unexpected, if there was no overnight variation the stock price would have died a long time ago... Why is that ? Have we been lying to us ? This is because many business and financial news are reported at market close, either ...

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

When volatility is high, daily volume is high. And when volatility is high, daily returns are high. That's why when volume is high, the price returns are high. Volatility (like volumes) is autocorrelated. This is the phenomenon of volatility clustering (large changes tend to be followed by large changes, of either sign) and volume clustering (large volumes ...

2

This is an example of minimum price variation (also known as the minimum price increment or the minimum price fluctuation). All public quotes for US equities are displayed to the nearest penny. (Hidden quotes may be entered at sub-penny increments.) US stock indices follow this convention and thus quote to the nearest penny. The oil listing is odd indeed. ...

2

The technical analysis point of view: an increase in volume (assuming the price has been in a downtrend) means the crowd are throwing in the towel, i.e. everyone is dumping the stock and assuming that hoped-for rise is now never going to happen. The same on the way up: everyone jumps on the bandwagon. In other words, high volume typically means crowd ...

2

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 ...

2

On exchanges, there is such orderbook with sufficient amount of limitorders, so when you place an order (market or limit), the "best" limitorders for you will be hitted and change the price last traded price. The price you see is actually just the midpoint between the currently best available bid and ask prices in the orderbook. Therefore, this price might ...

2

price went from \$200 to \$202, this is "one percent change", because $\frac{\$2}{\$200}100=1$

2

The state price vector are the prices of securities which pay \$1 if and only if that state of the world occurs. This is just a question of being able to replicate the payoffs $$\begin{pmatrix} 1 \\ 0 \\ 0 \end{pmatrix}, \begin{pmatrix} 0 \\ 1 \\ 0 \end{pmatrix}, \begin{pmatrix} 0 \\ 0 \\ 1 \end{pmatrix}$$ with payoff vectors$\vec{b} = [1,1,1]^T$and ... 2 The upper bound for the 80 call is C(90) + 10, or 30. At least assuming no arbitrage. Let's start by assuming the risk-free rate is 0 (this isn't a problem, but the math is clearer without it), so we don't have to discount the price. Then, the call price is given by$C(K) = E_t[(S_T - K)^+]$, which gives: \begin{array}$C(K - 10) &= E_t[max(S_T - (K - ...

2

Fractal spectra are covered in Multifractal Volatility: Theory, Forecasting, and Pricing. Also note that your run-of-the-mill moving average of a price series is a low-pass filter (filters out the higher frequencies), and moving averages are very used in basic financial analysis.

1

Market is efficient when all available public information gets priced-in relatively fast by market participants. This yields the fair price. Efficiency depends on the speed of the information dissemination. Equilibrium is a balance between supply and demand, which can be skewed by short term liquidity issues. So market can be efficient and not in equilibrium ...

1

This is a very broad question and a large number of issues have been discussed in the literature. As such, it's hard to give specific advice except that it is better to model returns instead of prices directly. What I would do if I were you: Read some of the available literature to get a good overview. This is an interesting paper but many more exist. ...

1

A unique state price vector does not have to exist for there to be no arbitrage. It sounds like the state price vector in question has infinitely many solutions. Try to reduce the price matrix to row echelon form and show that at least one state price vector exists.

1

Apparently this company was traded OTC/Pink sheet (and was already dubious in 1988 see "Precision Imaging Corp" http://babel.hathitrust.org/cgi/pt?id=uiug.30112058759736;view=1up;seq=175). To my knowledge Compustat database doesn't have it neither. My next best guess is to try at your library in some old books like "Walker's Manual" or Moody's. And my last ...

1

Regarding Joshua's inspired answer, I'm still not sure how you guarantee that scaling gives you the exact high and low values. I suppose that you could simulate until you get a result that is close enough. But that could be hard when, e.g., Open is near High and far from Low. An alternative solution is to construct a Brownian Bridge between Open and High, ...

1

Following references from the answer provided by @Richard, we see that the optimality condition for a continuous process in general (and therefore an OU process in particular) is covered in Section 2 concluding on page 6 of Thompson 2002, where he also represents the solution in terms of the Hamilton-Jacobi-Bellman equations. If you change the limits of the ...

1

you find theoretical results for the Ornstein-Uhlenbeck process if you search for "pairs trading". In pairs trading it is assumed that the ratio of the pair is mean reverting. Then one often models this ratio as Ornstein–Uhlenbeck process. You find something on page 11 here Further theoretical results that might be of interest can be found here. All these ...

1

I think a good way to think about your problem is the example of finding an optimal VWAP trading strategy. You basically have a finite point in time by which you must have performed your transaction and you trade a similar asset than the one you are considering, one with the same underlying assumptions of mean-reversion (I make such assumption in the same ...

1

Something like a moving average smoother is akin to a low pass filter, the 'stochastics' of technical analysis crudely akin to a band pass filter. Going up the ladder of sophistication, you can see something like http://www.jstor.org/pss/3592665 or applications of wavelet decomposition. This paper from 1963 by GRANGER, CLIVE W. J., and MORGENSTERN, 0. ...

1

Here is a gentle introduction to wavelet methods.

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