Stochastic Oscillator for Financial Analysis
When using the Stochastic Indicator your basic aim is to buy (go long) when the stochastic becomes oversold (goes below 20%) and then the %K line crosses above the %D line at a market low, and to sell (go short) when the stochastic becomes overbought (goes above 80%) and then the %K line crosses below the %D line at a market high. Other indicators or candlestick patters can also be used to further confirm the trade. Below is a chart of a trade I recently took on GUD.AX using the Slow Stochastic in combination with support and resistance levels as well as a candlestick pattern.  When I conducted my stock search on the evening of 28th July 2015, GUD was one of the results from the search that I particularly liked. The Slow Stochastic had just made a crossover in the oversold area just as the price was bouncing off its recent lows at the support line. But what I really like about this opportunity was that there was also a bullish reversal candle (a Hammer) at this short term market bottom. The high for the day was $8.34, so I placed a stop buy order to buy the stock tomorrow if the price opened or moved above this high of today. So I would only buy if the stock hit or moved above $8.35 during the next days trading. So if the stock opened and stayed below the previous day's high I would not buy the stock and my order would be canceled at the end of the day. This is very important because it stops you from getting into trades that don't go in the direction you want. If this happens then you could check the chart again after market close to see if it is still worthwhile to place a new order for the next day. On the 29th July 2015 the stock did open higher at $8.42 (which is where my order got executed) and closed at $8.46. So I ended up buying slightly above my target price but it did move higher on the day, so a successful entry overall. I had placed my initial stop loss at $8.09 (just below the low of the previous day of $8.10). If the trade had gone against me in the following days the most I would have lost was 1% of my total account capital. My target for this trade was $9.86 (just below the resistance line near $10), which would represent a 5% gain against my total account capital (or approx. 16.7% gain on the trade). So my win to loss ratio was 5:1. As you can see from the chart, the next day gapped up at the open and prices continued moving up strongly during the day. The next day was a slightly negative day, and then a few days later, on the 5th August 2015 my target price of $9.86 was reached (with a high of $9.88 for that day), so my order was closed for a total profit of 16.7% on the face value of the trade. However, as I bought on margin (using CFDs) my actual profit on the initial margin I had invested was 167%. My total time in the trade was 7 days, and I spent about an hour in the evening doing my searches and placing my orders, then less than 10 minutes managing the trade each evening after market close. The stock did go up a bit further after my profit target was reached, but the day after that the price started to fall as the price hit the resistance line and the Slow Stochastic did a crossover in the overbought area. Overall, this was a very ideal trade and I was very happy with it. Not all my trades reach my profit targets and I may get stopped out at a smaller profit or I might make a small loss (as I move my stops up as the price moves up). The key to successful trading over the long term is to keep you losses small and let your profits run (with my longer term trend trading I don't have profit targets and let my profits run until I get stopped out, but with my shorter term trading I do use a profit target usually 5 or 6 times the size of my initial stop). If you have an average win to average loss ratio of 3:1 or higher and have a success rate of 50% winners you will make money over the long term. While trading in stochastic I've understood, one needs reference (SMA/EMA/Bolinger Band and even RSI) to verify trade prior entering it. Stochastic is nothing to do with price or volume it is about speed. Adjusting K% has ability to turn you from Day trader to -> swing trader to -> long term investor. So you adjust your k% according to chart time-frame. Stochastic setup for 1 min, 5 min ,15, 30, 60 min, daily, weekly, monthly, quarterly, half yearly and yearly are all different. If you try hopping from one time-frame to another just because it is below oversold or above overbought region with same K%, you may get confused. Worst you may not square-off your loss making trade. And rather not use excel; charts gives better visual for oscillators.
Gartner, Forester, etc., data and analysis are not worth shit.   I learned that from 20+ years as a financial analyst: my wild ass guess was no worse than their wild ass guess on anything but my wild ass guess was a lot cheaper.  Its a pity so many investment decisions and strategic plans are based on the garbage cranked out by industry analysts. My 2 cents:As I understand it, stochastic math doesn't seem to cut it for pricing derivatives. Ito's Lemma and Feynman-Kac SDE solutions both impose constraints on higher order moments of the PDF. So you can't just drop in your forecasted return distribution and get a price. All the fixes seem somewhat ad hoc. Shit gets real when you move from options to their first cousin: term structure of interest rates. The Heath-Jarrow-Morton framework was intractable in the original paper, but there are some promising simplifying assumptions. I think there's real money there for someone who works that out and comes up with new arbitrage possibilities. On the other hand, you get some phenomenally bad math like the Gaussian copula that was used to value the subprime tranches and bring them a mind-boggling AAA rating. A lot of folks here do fancy nonlinear time series analysis to forecast the 1st, 2nd &amp; higher moments of asset returns. Not a lot of theoretical basis (well neuroscience), but if you can do it better you win. Yea read a few books or watch some videos on YouTube on fundamental analysis and try it using excel. It's probably not what you'll be doing if you get a degree in finance, and you might even end up in accounting like I did, but its a good place to start to see if you like it or not.  It also exposes you to accounting, business, the politics of business, taxation, financial statements, EDGAR and all the other interesting and important stuff.  You might want to pick up a study guide for the CPA exam BEC. Lots of very interesting stuff in there about business in general including how the board of directors works, and taxation. It's an awesome read. The government bailed out the financial system because it was in its best interest to. Letting the system all crash on top of yourself is definitely not smart. So we loan financial institutions money, shore up any short term issues, and we get most of the money back, if not all of it. If I'm not mistaken, all the bail out money was paid back.  But beyond the point, there may be merit in basic income ideas, but this is definitely not the kind of rhetoric, thinking, analysis, or insight that it needs. "I was wondering how ""future cash flows of the asset"" are predicted? Are they also predicted using fundamental and/or technical analysis? There are a many ways to forecast the future cash flows of assets. For example, for companies: It seems like calculating expected/required rate using CAPM does not belong to either fundamental or technical analysis, does it? I would qualify the CAPM as quantitative analysis because it's mathematics and statistics. It's not really fundamental since its does not relies on economical data (except the prices). And as for technical analysis, the term is often used as a synonym for graphical analysis or chartism, but quantitative analysis can also be referred as technical analysis. the present value of future cash flows [...] (called intrinsic price/value, if I am correct?) Yes you are correct. I wonder when deciding whether an asset is over/fair/under-valued, ususally what kind of price is compared to what other kind of price? If it's only to compare with the price, usually, the Net asset value (which is the book value), the Discount Cash flows (the intrinsic value) and the price of comparable companies and the CAPM are used in comparison to current market price of the asset that you are studying. Why is it in the quote to compare the first two kinds of prices, instead of comparing the current real price on the markets to any of the other three kinds? Actually the last line of the quote says that the comparison is done on the observed price which is the market price (the other prices can't really be observed). But, think that the part: an asset is correctly priced when its estimated price is the same as the present value of future cash flows of the asset means that, since the CAPM gives you an expected rate of return, by using this rate to compute the present value of future cash flows of the asset, you should have the same predicted price. I wrote this post explaining some valuation strategies. Maybe you can find some more information by reading it." No need for Fabozzi yet. His stuff gets cover in plenty of college textbooks, old and new.   Keep browsing /r/finance, /r/personalfinance, and /r/investing and you'll find the usual recommendations.  Here's goldman sach's recommended reading list: http://www.stat.unc.edu/faculty/cji/890-11/Goldman-Sachs-Suggested-Reading-List.pdf  No need to read it all. Follow your interests. Standard Markowitz's portfolio optimization takes trend into account, not mean reversion. Otherwise, since a portfolio is a linear combination of your individual assets, you could 1st model them separately and than establish a second-layer criteria for weighting. For the 1st layer, mean reversion (as well as trend) of returns can be captured with a ARIMA model, and for the 2nd layer, you could use the Kelly criterion, for instance. A more direct approach to mean-reversion portfolio selection is working with pairs trading. I'm not linking any materials as those topics are plentiful on the web. ...If that's still not the answer you're looking for... The problem with predicting economic cycles is that, they are long, and we hardly have a sufficient measured history to forecast anything reliable. In order to predict the mean reversion of a stock or bond market cycle, you've got to measure their long-term mean first. And there you'll have disagreements right on the start... Some researchers (see Jeremy Siegel) have tried to measure the long-term mean of returns for various asset classes. Some argue that stocks are the long-run winners and that CAPM explains that, but others say that's just questionable, since measurements go just as far as the western countries (US, UK, etc.) have thrived. Other countries have much more recent economic records. Quant has been an interesting part of finance, but I always thought they missed something really important.   The standard methods of statistical analysis can’t explain what’s going on with economy and firms. Otherwise, quant hedge funds would have been making money all the time.   The conventional quants find patterns in data, test them then go live. These models work until some fundamental (or not so fundamental) shifts in the market. Then they find themselves back in the drawing board looking for their next model. This kind of analysis seems like a lot of grind for fairly  unpredictable returns.   There are more and more companies that don’t lend themselves well to fundamental analysis as we know it. Financial data and some poor flawed human analysts insights alone can no longer explain much.   This is a unique set of challenges that Data Science, more specifically AI could solve. The speed at which AI can learn and process information is just mind boggling. AI can learn the entire financial history of decision making in minutes. It can recognize patterns, make sophisticated analysis and constantly update its probability tables as the new information comes in. This is something no human can compete with.    Imagine a quantitative engine that adapts as the economy changes. It could draw heaps of unstructured data about a company, learn from it in real time, make course corrections and spit out its recommendations.   To the degree that human decision making is involved in the strategic decision making of companies, we will need the depth of context that can be provided by humans, but I see the field moving away from excel spreadsheets with fairly poor predicting capacity to AI models with strong predictive capacity.