What governs the shape of price history graphs?
"Let me see if I can restate your question: are speculative investments more volatile (subject to greater spikes and drops in pricing) than are more long-term investments which are defined by the predictability of their dividend returns? The short answer is: yes.  However, where it gets complicated is in deciding whether something is a speculative investment. Take your example of housing.  People who buy a house as an investment either choose to rent it out (so receive ""rent"" as ""dividend"") or live in it (foregoing dividends).  Either way, the scale of the investment is large and this is often the only direct investment that people manage themselves.  For this reason houses are bound up in the sentimental value people attach to a home, the difficulty of uprooting and moving elsewhere in search of cheaper housing or better employment, or the sunk cost of debt that can't be recovered by a fire-sale.  Such inertia can lead to sudden sell-offs as critical inflection points are reached (such as hoped-for economic improvements fail to materialise and cash needs become critical). At different levels that is true of just about every investment. Driving price-volatility is the ease of sale and the trade-offs involved.  A share that offers regular and dependable dividends, even if its absolute value falls, is going to be hung on to more frequently than those shares that suffer a similar decline but only offer a capital gain.  For the latter, the race is on to sell before the drop neutralises any remaining capital gain the investor may have experienced. A house with a good tenant or a share with stable dividends will be kept in preference for the quick cash-return of selling an asset that offers no such ongoing returns. This would result, visually, in more eratic curves for ""speculative"" shares while more stable shares are characterised by periods of stability interspersed with moments of mania. But I have to take your query further, since you provide graphical evidence to support your thesis.  Your charts combine varying time-scales, different sample rates and different scales (one of which is even a log scale).  It becomes impossible to draw any sort of meaningful micro-comparison unless they're all presented using exactly the same criteria." I agree with @Turukawa that the x-axes need to be the same to make a direct comparison.  However, the graphs you linked make me think of introductory calculus:  If you time averaged plots, speculative investments (gold, housing) seem to have many large concave up time periods and the dow jones has many concave down sections.  Using the concavity test: If the first derivative tells you about the rate of change, the second derivative tells you about the rate of change of rate of change.  Remember back to Physics 101:  1st derivative is velocity & second derivative is acceleration.   It would be interesting to have the same time scales for your plots & compare these accelerations between the two.  I suspect the more volatile investments would have larger (in magnitude) accelerations during boom/bust cycles than less speculative investments. "Dividend-paying securities generally have predictable cash flows. A telecom, electric or gas utility is a great example. They collect a fairly predictable amount of money and sells goods at a fairly predictable or even regulated markup. It is easy for these companies to pay a consistent dividend since the business is ""sticky"" and insulated by cyclical factors. More cyclic investments like the Dow Jones Industrial Average, Gold, etc are more exposed to the crests and troughs of the economy. They swing with the economy, although not always on the same cycle. The DJIA is a basket of 30 large industrial stocks. Gold is a commodity that spikes when people are faced with uncertainty. The ""Alpha"" and ""Beta"" of a stock will give you some idea of the general behavior of a stock against the entire market, when the market is trending up and down respectively."
"I'm not sure the term actually has a clear meaning. We can think of ""what does this mean"" in two ways: its broad semantic/metaphorical meaning, and its mechanical ""what actual variables in the market represent this quantity"". Net buying/selling have a clear meaning in the former sense by analogy to the basic concept of supply and demand in equilibrium markets. It's not as clear what their meaning should be in the latter sense. Roughly, as the top comment notes, you could say that a price decrease is because of net selling at the previous price level, while a price rise is driven by net buying at the previous price level. But in terms of actual market mechanics, the only way prices move is by matching of a buyer and a seller, so every market transaction inherently represents an instantaneous balance across the bid/ask spread. So then we could think about the notion of orders. Actual transactions only occur in balance, but there is a whole book of standing orders at various prices. So maybe we could use some measure of the volume at various price levels in each of the bid/ask books to decide some notion of net buying/selling. But again, actual transactions occur only when matched across the spread. If a significant order volume is added on one side or the other, but at a price far away from the bid/offer - far enough that an actual trade at that price is unlikely to occur - should that be included in the notion of net buying/selling? Presumably there is some price distance from the bid/offer where the orders don't matter for net buying/selling. I'm sure you'd find a lot of buyers for BRK.A at $1, but that's completely irrelevant to the notion of net buying/selling in BRK.A. Maybe the closest thing I can think of in terms of actual market mechanics is the comparative total volumes during the period that would still have been executed if forced to execute at the end of period price. Assuming that traders' valuations are fixed through the period in question, and trading occurs on the basis of fundamentals (which I know isn't a good assumption in practice, but the impact of price history upon future price is too complex for this analysis), we have two cases. If price falls, we can assume all buyers who executed above the last price in the period would have happily bought at the last price (saving money), while all sellers who executed below the last price in the period would also be happy to sell for more. The former will be larger than the latter. If the price rises, the reverse is true." I would go even farther than Victor's answer.  There is little evidence that candlestick patterns and technical analysis in general have any predictive power.   Even if they did in the past, of which there is some evidence, in modern times they are so easy to do on computers that if they worked algorithmic traders would have scanned almost all traded stocks and bought/sold the stock before you even had a chance to look at the graph. While the best technical traders who are very good at quickly using pattern recognition across many indicators as Victor mentioned might be able to add some advantage.  The odds that a pattern so simple to code such as Bullish Engulfing would have predictive power is tiny. "Stock support and resistance levels mean that historically, there was ""heavy"" buying/selling at those levels. This suggests, but does not guarantee, that ""someone"" will buy at ""support"" levels, and ""someone"" will sell at ""resistance levels. Any ""history"" is meaningful, but most analysts will say that after six months to a year, the impact of events declines the further back in time you go. They can be meaningful for periods as short as days." The last two big ones actually were kind of gradual. For example, S&amp;P was already at 18% drawdown beginning of September 2008 before it went down the cliff. (Not sure if you specifically meant the initial corrections only, rather than full-blown bear markets). Candlesticks and TA are a relic of pre-computer trading, period. Market makers use sophisticated algorithms not for trading, but manipulations. Well, you can't really have it both ways. You said that they were both using the same method, but, in fact, they aren't. You can call the weighting biased, but in fact if appears that BPP is doing little or minimal weighting, and yet still is showing that prices, in general, are mapping similarly to the BLS published CPI.   Unless you're arguing an MIT 'academic conspiracy' (and even if you are), I think you've failed to make your case.  BPP is independant, it uses a different methodology, and yet the results confirm those of the BLS. "I looked at data from Sept 2010 to present: Standard deviation is what shows the spread shape of returns over time, and it meanS that about 2/3 of the time, AAPL return was within +/- 1.65 higher/lower than the daily average return which was .21 %. Not sure where to go with this except to suggest that in fact, AAPL is more volatile than the S&P and even another random tech company. With time, I'd probably come up with a list of stock more volatile. I know that when I look at a list of stocks I track on Yahoo, there are always a few that are just as volatile on a given day.  Excel makes the above analysis easy to do for a given stock, and it's actually an interesting exercise, at least for me. Disclaimer - the shape of stock returns is not a bell curve, and STdev is just a best fit.  Edit - given more time to tinker on excel, it would be interesting to see how the stock's volatility tracked over the years, did it increase or does it feel that way due to the high price? A $20 swing on a $600 stock is the same as a $2 swing on a $60 stock, yet ""up $20"" sounds huge." "Great question!  A Yield Curve is a plot of the yields for different maturities of debt. This can be for any debt, but the most common used when discussing yield curves is the debt of the Federal Government. The yield curve is observed by its slope.  A curve with a positive slope (up and to the right) or a steepening curve, i.e. one that's becoming more positively sloped or less negatively sloped, may indicate several different situations. The Kansas City Federal Reserve has a nice paper that summarizes various economic theories about the yield curve, and even though it's a bit dated, the theories are still valid. I'll summarize the major points here. A positively sloped yield curve can indicate expectations of inflation in the future. The longer a security has before it matures, the more opportunities it has to be affected by changes in inflation, so if investors expect inflation to occur in the future, they may demand higher yields on longer-term securities to compensate them for the additional inflationary risk. A steepening yield curve may indicate that investors are increasing their expectations of future inflation. A positively sloped yield curve may also reflect expectations of deprecation in the dollar. The publication linked before states that depreciation of the dollar may have increased the perceived risk of future exchange rate changes and discouraged purchases of long-term Treasury securities by Japanese and other foreign investors, forcing the yields on these securities higher. Supply shocks, e.g. decreases in oil prices that lead to decreased production, may cause the yield curve to steepen because they affect short-term inflation expectations significantly more than long-term inflation. For example, a decrease in oil prices may decrease short-term inflation expectations, so short-term nominal interest rates decline. Investors usually assume that long-term inflation is governed more by fundamental macroeconomic factors than short-term factors like commodity price swings, so this price shock may lead short-term yields to decrease but leave long-term relatively unaffected, thus steepening the yield curve. Even if inflation expectations remain unchanged, the yield curve can still change. The supply of and demand for money affects the ""required real rate,"" i.e. the price of credit, loans, etc. The supply comes from private savings, money coming from abroad, and growth in the money supply, while demand comes from private investors and the government. The paper summarizes the effects on real rates by saying Lower private saving, declines in the real money supply, and reduced capital inflows decrease the supply of funds and raise the required real rate. A larger government deficit and stronger private investment raise the required real rate by increasing the demand for funds. The upward pressure on future real interest rates contributes to the yield curve's positive slope, and a steepening yield curve could indicate an increasing government deficit, declines in private savings, or reduced capital coming in from abroad (for example, because of a recession in Europe that reduces their demand for US imports). an easing of monetary policy when is economy is already producing near its capacity ... would initially expand the real money supply, lowering required short-term real interest rates. With long-term real interest rates unchanged, the yield curve would steepen. Lower interest rates in turn would stimulate domestic spending, putting upward pressure on prices. This upward price pressure would probably increase expected inflation, and as the first bullet point describes, this can cause long-term nominal interest rates to rise. The combination of the decline in short-term rates and the rise in long-term rates steepens the yield curve.  Similarly, an inverted yield curve or a positively sloped yield curve that is becoming less steep may indicate the reverse of some or all of the above situations. For example, a rise in oil prices may increase expectations of short-term inflation, so investors demand higher interest rates on short-term debt. Because long-term inflation expectations are governed more by fundamental macroeconomic factors than short-term swings in commodity prices, long-term expectations may not rise nearly as much as short term expectations, which leads to a yield curve that is becoming less steep or even negatively sloped. Forecasting based on the curve slope is not an exact science, just one of many indicators used.  Note - Yield Curve was not yet defined here and was key to my answer for What is the ""Bernanke Twist"" and ""Operation Twist""? What exactly does it do? So I took the liberty of ask/answer." "A brief review of the financial collapses in the last 30 years will show that the following events take place in a fairly typical cycle: Overuse of that innovation (resulting in inadequate supply to meet demand, in most cases) Inadequate capacity in regulatory oversight for the new volume of demand, resulting in significant unregulated activity, and non-observance of regulations to a greater extent than normal Confusion regarding shifting standards and regulations, leading to inadequate regulatory reviews and/or lenient sanctions for infractions, in turn resulting in a more aggressive industry ""Gaming"" of investment vehicles, markets and/or buyers to generate additional demand once the market is saturated ""Chickens coming home to roost"" - A breakdown in financial stability, operational accuracy, or legality of the actions of one or more significant players in the market, leading to one or more investigations A reduction in demand due to the tarnished reputation of the instrument and/or market players, leading to an anticipation of a glut of excess product in the market ""Cold feet"" - Existing customers seeking to dump assets, and refusing to buy additional product in the pipeline, resulting in a glut of excess product ""Wasteland"" - Illiquid markets of product at collapsed prices, cratering of associated portfolio values, retirees living below subsistence incomes Such investment bubbles are not limited to the last 30 years, of course; there was a bubble in silver prices (a 700% increase through one year, 1979) when the Hunt brothers attempted to corner the market, followed by a collapse on Silver Thursday in 1980.   The ""poster child"" of investment bubbles is the Tulip Mania that gripped the Netherlands in the early 1600's, in which a single tulip bulb was reported to command a price 16 times the annual salary of a skilled worker. The same cycle of events took place in each of these bubbles as well. Templeton's caution is intended to alert new (especially younger) players in the market that these patterns are doomed to repeat, and that market cycles cannot be prevented or eradicated; they are an intrinsic effect of the cycles of supply and demand that are not in synch, and in which one or both are being influenced by intermediaries. Such influences have beneficial effects on short-term profits for the players, but adverse effects on the long-term viability of the market's profitability for investors who are ill-equipped to shed the investments before the trouble starts." Support and resistance only works as a self-fulfilling prophecy. If everyone trading that stock agrees there's a resistance at so-and-so level, and it is on such-and-such scale, then they will trade accordingly and there will really be a support or resistance. So while you can identify them at any time scale (although as a rule the time scale on which you observed them should be similar to the time scale on which you intend to use them), it's no matter unless that's what all the other traders are thinking as well. Especially if there are multiple possible S/P levels for different time scales, there will be no consensus, and the whole system will break down as one cohort ruins the other group's S/P by not playing along and vice versa. But often fundamentals are expected to dominate in the long run, so if you are thinking of trades longer than a year, support and resistance will likely become meaningless regardless. It's not like that many people can hold the same idea for that long anyhow. Bonds are extremely illiquid and have traditionally traded in bulk.  This has changed in recent years, but bonds used to be traded all by humans not too long ago. Currently, price data is all proprietary.  Prices are reported to the usual data terminals such as Bloomberg, Reuters, etc, but brokers may also have price gathering tools and of course their own internal trade history. Bonds are so illiquid that comparable bonds are usually referenced for a bond's price history.  This can be done because non-junk bonds are typically well-rated and consistent across ratings. Regardless of your thoughts on what was happening in late 2012, financial prices should not be used as the sole barometer of a recovery...this is why the Fed uses tons of macroeconomic data, not just that BAC has been up 20% in the last 6 months.   My point is if you are going to tout what seems to be a market timing model, where the authors are proud of the fact that the entire thing is data mined,  you need strong out of sample evidence (over a few business cycles) that it works.  Nothing here comes close "That characterisation of arbitrage-free pricing sounds a bit like the ""relative vs. fundamental"" approaches to asset pricing that Cochrane outlines (in his text, *Asset Pricing*). Rebonato also makes this distinction with regard to term structure models in *Volatility and Correlation*.   On one extreme you have CAPM-style models in which asset prices are completely determined by investors' risk preferences; on the other extreme, you would have something like a SABR-Libor Market Model where you take everything up to and including the volatility surface as given.   What's interesting to me is the way in which these different classes of models get used in various parts of the financial industry. So, buy side firms tend to rely a lot more on equilibrium-style models, since they ultimately care about things like how the equity risk premium or the bond risk premium affect asset prices. In contrast, derivatives quants working at a big sell-side bank who are pricing exotics don't care about what the ""fundamental"" value of their underlying assets is; they just take that as given and price the exotic accordingly." It does when you argue that a single factor is causing the price chart to move a certain way.  If they start to think it's perhaps more complicated than that, then yes, it breaks down quickly, which was precisely my point. "IMO: If you look long and hard enough at your data you're going to bump into these kinds of ""patterns"". There is no ""curse of 7"".  “Looking at the chart makes you want to hide under your desk when you see it, but remember this is only a sample size of 11, and the average performance is greatly skewed by big drops late in 1907, 1937, 1957, and 1987. Also, consider that 1907, 1937, and 1957 were all recessionary years, and equities had run up 40% for the year in August 1987, so a pullback was not surprising."" [Quote](https://lplresearch.com/2017/08/11/the-curse-of-years-ending-in-7/)"