Fina 408 Chap 6-7-8 PDF

LECTURE 6 - Momentum & Volume Indicators  Volume is the amount of shares or contracts traded over a specified period and is portrayed as a vertical bar below the price bar.  Volume is used to confirm price, therefore volume is secondary to price analysis.  However, the increase in a...

LECTURE 6 - Momentum & Volume Indicators  Volume is the amount of shares or contracts traded over a specified period and is portrayed as a vertical bar below the price bar.  Volume is used to confirm price, therefore volume is secondary to price analysis.  However, the increase in arbitrage, HFT and program trading has made some volume statistics sometimes misleading  Volume also refers to a change in volume. Standard Guidelines on Using Volume  High volume should go in the direction of the trend.  Volume not going with the trend is a warning of impending trend reversal.  Exceptionally high volume is a signal of an important change. When prices are rising: Volume increasing is confirming Volume decreasing is questionable (puts upward trend in question) When prices are declining: Volume increasing is confirming Volume decreasing is questionable (puts downward trend in question) 1|Page When a price advance halts with high volume, it is a potential top When a price decline halts with high volume, it is a potential bottom Other Methods for Analyzing Volume Confirmation 1. Volume Indexes  Indexes are comprised of cumulative sums of data measuring supply and demand over time, rather than a specific period.  Compare price with the index looking for divergences between highs and lows in each.  More useful in trending markets. On Balance Volume  Cumulative index of plus or minus volume days based on change of closing prices.  It is calculated as a cumulative total: If price close is higher than the previous day, add the day’s volume. If price close is lower than the previous day, deduct the day’s volume.  High volume should confirm price trend, therefore compare price to OBV. If price reaches a new high, confirmation of price strength comes from OBV hitting a new high.  Negative divergence between price and OBV warn of a potential price reversal. 2|Page Other volume indices (AD index) offer calculations with similar results:  AD – Accumulation/Distribution index  Similar to OBV, but uses a more specific calculation to calculate the midpoint for the day’s volume: = Volume X {(Close - Low) – (High - Close)} / (High - Low)  If the close occurs above the midpoint for the day the result is positive accumulation (vice versa).  Each figure is then cumulated into the index, similar to OBV, and the same rules of divergences apply. 2. Volume Related Oscillators  Oscillate between an upper and lower bound. A move to high upper bound level creates an “overbought” condition and a move to a lower bound level produces an “oversold” condition.  Are more useful for trading range markets. Volume Oscillator  Is a ratio between two moving averages of volume and is used to determine when volume is expanding or contracting.  Expanding volume implies strength in the trend, contracting volume implies weakness in the trend.  It is useful for confirming trend and for giving advanced warning in a trading range of direction for the next breakout. Chaikin Money Flow  Uses the accumulation/distribution calculation for each day.  It is calculated by summing the ADs (accumulation/distribution) over the past 21 days and dividing that sum by the total volume over the past 21 days.  This produces an oscillator that rises above zero when an upward trend begins and declines below zero when the trend turns downward. Volume spikes  Usually a test of a significant support or resistance level.  A sign of a sudden change in information (gap) or other pattern breakout.  Most common at the beginning and end of a trend. 3|Page Momentum  The case for momentum is that momentum leads price action. This gives the technical analyst advanced warning of a potential trend change.  Momentum is the rate of change of price, measures how quickly prices are rising or how steeply the trend line is sloping.  Similar to acceleration and deceleration of a car, rate of speed of price (price velocity). Before a car actually begins to slow down, its rate of acceleration decreases.  There are many different types of momentum indicators:  Rate of Change (ROC)  Moving Average Convergence Divergence (MACD)  Stochastic  Relative strength Index (RSI). 1. MACD – Moving Average Convergence Divergence  Uses two lines; MACD and signal line plotted together below a price chart.  MACD line is calculated by finding the difference between two exponential moving averages (EMAs) of closing prices: the last 26 and 12 periods (faster EMA line).  The signal line is the 9 period EMA of the MACD line (this is the slower EMA line)  Histogram is the difference between the MACD and its signal line and usually appears at the bottom of the chart.  When the MACD is above the 0 line, it suggests an upward trend. 4|Page How to use MACD: Buy /Sell signals:  Buy signal: when the MACD line crosses the signal line from below and both lines are below the 0 zone. Best buy signals occur below the 0 zone.  Sell signal: when MACD line crosses the signal line from above and above the 0 zone. Overbought /oversold:  Overbought: When lines are far above the 0 line.  Oversold: When lines are far below the 0 line. MACD histogram:  Provides earlier warnings that current trend is losing momentum. 2. ROC - Rate of Change - Measures the amount a stock price has changed over a given number (N) of past periods. - Simplest oscillator, but has many problems. Suffers from drop off effect, only two prices appear in calculation & these prices are equally weighted. ROC = Price today – Price N periods ago X 100 Price N periods ago How to use ROC Buy signal: when the ROC crosses the 0 line from below. Sell signal: when the ROC goes lower from above overbought level 5|Page 3. RSI – Relative Strength Index (not to be confused with Relative Strength) - Developed by J. Welles Wilder, measures a security’s price strength relative to its own past price history  Bounded by the range of 0 – 100.  Wilder suggested using a smoothing method and a 14 period RSI. RSI = 100 – (100/(1+RS)) where RS = Average of x day’s up closes/Average of x day’s down closes The average gain or loss used in this calculation is the average percentage gain or loss during a look-back period. The formula uses a positive value for the average loss. Example: How to use RSI Overbought: Above 70, Oversold: Below 30 Strong trending markets can stay overbought or oversold for a long time and just because the oscillator moved to the upper or lower bound, it may not be a reason to sell/buy. Be careful with trending stocks because in bull markets, the RSI is usually above 50 and RSI ranges from 55 – 85, in bear markets 25 – 60. Example of Positive-Negative RSI Reversals An additional price-RSI relationship that traders look for is positive and negative RSI reversals. A positive RSI reversal may take place once the RSI reaches a low that is 6|Page  lower than its previous low at the same time that a security's price reaches a low that is higher than its previous low price. Traders would consider this formation a bullish sign and a buy signal. Conversely, a negative RSI reversal may take place once the RSI reaches a high that is higher that its previous high while a security's price reaches a lower high. This formation would be a bearish sign and a sell signal. 4. Stochastic Oscillator - As prices increase, prices tend to be at the upper end of the price range, and in down trends price tends to be near the lower end of the range. - Measures current closing price versus defined past window of prices. - Works best in trading range markets but still gives valuable info in trending markets. -Signals are the same as for other oscillators. - %K and %D lines. - %K = 100 x (Close –Low) (High – Low) 14 period is the time frame usually used. Fast %D = is the 3 period SMA of the %K line (fast stochastics) Slow % D = is the 3 period SMA of the % D line (slow stochastics) 7|Page How to use Stochastics Overbought /oversold: bounded from 0 -100 Overbought: Above 80 Oversold: Below 20 Similarities Between Oscillators All are similar in use, especially oscillators with overbought and oversold bounds. Strong trending markets can stay overbought or oversold for a long time. Note: Just because an oscillator moved to the upper and lower bound it may not be a reason to sell in an overbought market or buy in an oversold market, but may mean you should actually buy overbought (very strong markets) and sell oversold (very weak markets). Conclusions Volume is used as a confirmational indicator because it is a series of independent price data. However, volume itself does not always confirm price patterns, although it adds value to an entry decision. A major source of (non) and confirmation is from momentum. Strong momentum suggests a trending market and weak momentum suggests a consolidating market. The analyst looks for the beginning of a trending market and because momentum tends to lead price direction, they are often useful in warning of such a change. Oscillators are more useful in trading range markets as overbought/oversold indications. In trending markets they are more useful as warnings of a trend change. In strong trends, they tend to be skewed in the direction of the trend and therefore fail to provide reliable entry signals. 8|Page Market Breadth Indicators  Measures internal strength of the market by considering whether stocks are gaining or losing strength in price Advance – Decline (Breadth) Line ◦ Best way to measure internal strength of the market index ◦ The cumulative sum of advancing issues minus declining ones ◦ Can be constructed for any index, industry group, exchange, or basket of stocks. Calculated daily, weekly or any period but not applicable to commodities ◦ Breadth line should follow and move to new highs and lows with the stock market index, if not there is a divergence which will lead to a price reversal ◦ Example: If S&P 500 is rising and if S&P 500 A-D line is i.e. falling, only a few stocks are participating. This is a bearish warning. 9|Page LECTURE 7 - Trading Systems Designing a system (Non-discretionary)  Markets are dynamic.  An edge is needed: “An edge in trading is an exploitable statistical advantage based on market behavior that is likely to recur in the future” –Curtis Faith, The way of the turtle  A trading system exploits a predetermined type of market activity.  A trading system has objective buy and sell rules.  Objectivity in a system removes emotion and enables a trader to stay focused on trading.  Must be based on sound theory so that trader can detect if the underlying hypothesis of a system has changed and thus rendered a system ineffective. Discretionary: Trading entry and exists determined subjectively by intuition and gut instinct.  Disadv: Your gut is many times wrong! Most people go broke or burn out, excitement and being right is more important than making profits. Nondiscretionary: Trading entry and exists determined objectively mechanically by signals (computer generated) because it is a system that runs itself based on data that is continuously fed into it.  Adv: Many successful traders use this, removes the emotion from trading, predetermine the decision rules, can back test results, prevents large losses and the risk of ruin. Provides a mathematical edge.  Disadv: Operation is sometimes boring, requires periodic updates and adjustments….history does not repeat itself precisely. Updates and modifications may be needed. Discretionary vs. Non-Discretionary systems  Some traders use a partially discretionary system based on generation signals based on some rules that are acted upon by the investor based on personal confidence or experience. Some very successful traders and investors use a type of system like this – with rules. 10 | P a g e 4 things to consider when trading 1. What type of strategy are you using?  Trend following – ex. Moving average crossover system.  Counter-trend, reversion to the mean – ex: Selling overbought markets  Swing trading – Taking short term fluctuation in direction of trend. 2. Determine a trend filter  The trend is the basis of all profits  Before determining an entry technique, you must properly define the trend – regardless of your approach.  Some entry rules include (thus assume) a trend direction – ex: N-day breakout. You thought getting in a trade was hard? Selling is harder, what conditions will end a trade? A system must identify; 1) Initial stop, 2)Trailing stops, 3) Profit Target(s), 4) opposite signals 3. Data and market selection ◦ Portfolio to test on – stocks, ETFs for futures & forex? ◦ Time period to test – 1 year, 10 years? ◦ Time frame to test -daily, weekly etc.? 4. Analyzing Results  Are the historical results representative of a “good” system  Possible adjustment to the system – 1) Abandon system completely 2) change in parameters for entry or exit criteria 3) change in tools  Need for money management: If not, risk of ruin 11 | P a g e Lecture 8 High-Frequency Trading, Temporal patterns & Cycles Role of market makers and specialists Market Making: is an activity where a firm’s trader stands ready to buy and sell a particular stock on a regular and continuous basis at a publicly quoted price on a listed exchange. Back in the day when there were only a few exchanges, human traders were prevalent as market- makers. High Frequency trading (HFT) It is a type of trading using computer algorithms to rapidly trade securities. HFT is characterized by high speeds, high turnover rates, and high order-to-trade ratios HFT uses market making and proprietary trading strategies carried out by computers to move in and out of positions in milliseconds with high volumes and high speeds aiming to capture sometimes a fraction of a cent in profit on every trade using insignificant amounts of capital. Given the short holding periods, HFT can potentially achieve Sharpe ratios tens of times higher than traditional buy-and-hold strategies. Presently, “market making” strategies are now implemented over many exchanges by a large range of investors and computer algorithms thanks to wide adoption of direct market access. This activity is now performed by fewer and fewer humans with exclusivity to a particular stock. HFT firms perform "Market making” activities using a set of high-frequency trading strategies that involve placing a limit order to sell (or offer) or a buy limit order (or bid) in order to earn the bid-ask spread. By doing so, they provide a counterpart to incoming market orders. However, HFT firms are under no obligation to maintain this activity during periods of extreme volatility. Computer algorithms employing market making activities may simply cease during these times. As of 2009, HFT accounted for approximately 55% of all US equity trading volume, with that number falling to approximately 30-40% in 2016 12 | P a g e Impacts of HFT, Empirical Evidence* 1. Liquidity: HFT has led to a reduction in bid-ask spreads and an increase in trading volume However, trading volume and narrower bid-ask spreads may not be a reliable indicator of liquidity during times of significant market volatility 2.Volatility More aggressive HFT strategies may increase stock volatility HFT Liquidity detection strategies “front run” ahead of large institutional orders amplifies price swings Front running is the illegal practice of having knowledge of your client’s orders and executing your own orders first. 3. Price discovery  Although HFT strategies are very rapid helping prices be more efficient to reflect new information in the short term, their effect on long term price discovery is less clear  HFT strategies are agnostic to a company’s fate and intrinsic value 4. Market Confidence Sophisticated “Algo” strategies and access to dark pools used by HFT firms give them an advantage over regular investors Market events such as the flash crash of May 6, 2010 erodes confidence and create disincentives for individuals to invest in the market Led to some market participants believing that the “markets are rigged,” with HFT having an edge at the expense of investors 13 | P a g e What to do about High-Frequency trading?  Good: HFT has made markets more liquid and decreased transaction costs  Harmful: Traders who post standing limit orders that cannot reflect changes in value due to news changes fast enough and lose to HFT.  Very harmful: HFT traders front run traders who are working large orders, making their trades more expensive.  HFT are engaged in a technology arms race by employing faster computers, locating servers closer to exchanges (co-location), using algorithmic code, employing many types of orders and paying for high-speed data feeds and dark pool access and being faster by milliseconds.  Algorithmic trading by HFT poses systematic market risks; flash crashes, algos out of control, market terrorism in the wrong hands (Market volatility has increased since the proliferation of HFT). National Best Bid and Offer (NBBO) represents the highest displayed bid price and lowest displayed offer price available for a security across the various exchanges or liquidity providers. If a portfolio manager wants to sell many shares of a stock, doing so via a dark pool would lower the impact on price of the shares they are about to sell. 14 | P a g e CYCLE ANALYSIS  CYCLE ANALYSIS attempts to find recurring major and minor peaks and troughs in price movement for better trade timing.  By adding short, medium and long term cycles together the actual price activity can be forecasted.  In a trading range, cycles are fairly regular in that the market peaks half-way through the cycle. However, when a market is trending, the cycle peak tends to shift left or right depending on the direction of the larger trend (called left or right translation).  This is consistent with the notion that in rising markets, prices should spend more time going up and in falling markets prices should spend more time going down.  For example, if a market is in a 10-point trading range with a bottom of 40 and a top of 50, the price movement from 40 to 50 and back to 40 again is one cycle. Traders can use this information to enter low-risk buys at 40 and low-risk sells at 50. 2 ARGUMENTS AGAINST THE CYCLE CONCEPT  If prices behaved in pure cycles, like radio waves, the numbers would easily fit into mathematical formulas that would give us precise predictions. This has not happened.  Below is a perfect cycle with a length of 100 days. Not all cycles are this well-defined.  The first peak is at 25 days and the second peak is at 125 days. The first cycle low is at 75 days and the second cycle low is at 175 days (also 100 days later) 15 | P a g e The three qualities of a cycle are: AMPLITUDE, PERIOD & PHASE.  AMPLITUDE measures the height of a wave from peak to trough signifying the strength of a cycle. Cycles are measured from TROUGH TO TROUGH as the tops tend to take more time to develop. Bottoms are more easily defined.  The PERIOD (length) of a wave is the time spent between troughs. The PHASE is a measure of the time location of a wave trough, ie, the difference in time between troughs of different waves 16 | P a g e There are four important principles to cycles: SUMMATION, HARMONICITY, SYNCHRONICITY & PROPORTIONALITY.  SUMMATION holds that all price movement is a simple addition of all active cycles. By combining each cycle & projecting forward, future price targets can be forecast. 17 | P a g e   HARMONICITY means that there are waves within waves and that they are usually related. Adjacent cycles are often related by small whole numbers (usually 2 – sometimes 3), there is a constant ratio applied to cycles.  Cycles tend to have period lengths a multiple of 2 or 3 longer or shorter than the next larger or smaller cycle  For example, with a 9-month cycle, the next shorter cycle will probably be 4.5 months (1/2 of 9) & the next longer cycle will probably be 18 months (2 x 9)  SYNCHRONICITY refers to the tendency for waves of differing lengths to bottom at the same time. The below is an example of non-synchronous cycles. 18 | P a g e   PROPORTIONALITY describes the relationship between cycle period and amplitude. Longer-term cycles (cycles having a longer period) should also have greater amplitude. There should be a proportional relationship between cycles of differing periods. For example, if a cycle’s period is 40 days, it should have proportional amplitude that is 2x the amplitude of a cycle that has a 20 day period. VARIATION, NOMINALITY, TRANSLATION VARIATION states that the principles stated above are just strong tendencies and that they are not hard and fast rules. Some ‘VARIATION’ does occur. NOMINALITY states that there tends to be a nominal set of harmonically related cycles that affect all markets. TRANSLATION  The reason that we study lows in cycle analysis is because longer and shorter cycles tend to synchronize at their lows. Peaks, on the other hand, almost never synchronize.  Peaks should occur at the halfway period of the cycle.  For example, a 20 day cycle should have a peak 10 days from its last low. This rarely happens.  Peaks can occur earlier or later than the halfway point. Their location away from the center point is called TRANSLATION. 19 | P a g e   A RIGHT TRANSLATION in a cycle is when the peak is beyond the halfway point. If the trend is up (BULLISH) the cycle is said to translate to the RIGHT. If the peak had occurred at the halfway point, we would be suspicious about the continuation of the uptrend.  A LEFT TRANSLATION in a cycle is when the cycle peak occurs before the halfway point. If the trend is down (BEARISH) the cycle is said to translate to the LEFT.  As cycles are nested and synchronous, the shorter cycle is dominated by the trend of the next longer cycle.  Translation is useful in checking where the overlying longer-term cycle trend direction is headed or if it is changing.  An INVERSION occurs when a peak occurs where a cycle low is expected. KONDRATIEFF WAVES (K-WAVES)  Are economic phenomena that are not necessarily observable in commodity and stock prices. The theory that, western capitalist economies have 50 – 60 year boom periods followed by a bust. (economic expansion followed by a depression)  Each wave is broken down into 4 seasonal periods (approx. 15 years each) with discernible characteristics: ◦ Winter: a period of concern, fear, panic, depression ◦ Spring: a period of fear of depression, fragile confidence ◦ Summer: a period of growing confidence ◦ Autumn: a period of increasing confidence, that turns into extreme confidence and euphoria  Each wave arises from bunching of innovations in product, services, technology, methods of production, new markets, new sources of raw materials, and new forms of business organization.  Has its own characteristic location – cotton in Manchester, GB or technology in Orange County, California – and a clear location in time that can be clearly dated. There are 19 separate waves that can be dated back to Sung China in 930 (printing and paper)  Affects the structure of the world economy into the future.  Are usually accompanied by a major war. 20 | P a g e 34 YEAR HISTORICAL CYCLES  Historical data suggests that 34 year cycles, composed of 17 year period of dormancy followed by a 17 year period of intensity, also appear to exist.  Notice that during the 1966 to 1982 dormant period, volatility was high. Declines of 35% and 50% occurred within the period, as well as sharp advances, yet the overall longer-term trend remained relatively flat.  This is a period when technical analysis outperforms fundamental analysis (trading versus buy and hold)  Warren Buffett, has been known to use this theory in his planning. Widely viewed as not being a technician, he does not attribute the cycle to growth in GNP, noting that in the dormant cycle from 1966 to 1982, the GNP grew at twice the rate as during the intensive 1982 to 2000 period. He attributes this to changes in interest rates, corporate profits and investor confidence. 21 | P a g e DECENNIAL PATTERNS  Are made up ten 12-month periods and three 40-month cycles, coinciding every ten years.  This pattern states that years ending in 3, 7, 10 and sometimes 6 are often down years in the market.  Years ending in 2, 5, 8 and most of 9 are advancing years. FOUR YEAR or PRESIDENTIAL CYCLE (KITCHIN CYCLE)  The National Bureau of Economic Research showed that from 1796 to 1923, the US economy suffered a recession on average every 40 months or approximately every 4 years.  Some have argued that this follows the four year presidential cycle, but this phenomenon occurs in countries that do not have presidential elections every four years.  Today the four-year cycle, from price bottom to price bottom, is the most widely accepted and most easily recognized cycle in the stock market. Occasionally it strays from four years, but only by a portion of the year.  Tops sometimes fail to occur as regularly as the bottoms do. They may occur before or after when their 4-year period would occur. ELECTION YEAR PATTERN  Stock market returns can be related as a function of the US presidential electoral timeline.  Equity markets are: ◦ weak during the POST ELECTION & MID YEAR (Years 1 & 2) ◦ strong during MID YEAR 3 and PRE ELECTION YEAR (4) 22 | P a g e   Many attribute this to incumbent administrations trying to sway voters with good economic policies and favorable interest rates which in turn would stimulate the economy and favor them in getting re-elected.  Some would argue that we are confusing ‘cause’ with ‘correlation’ and that there may not be a direct link between politics and the stock market. We are not concerned with the why – but we should be concerned with the fact that this relationship is there, and we should use it to possibly profit from it. SEASONAL PATTERNS Are most apparent in agricultural prices and commodities such corn, hogs and oil. This relationship is beginning to wane due to global trade. Allows the investor to profit from certain price trends that occur year after year. Though the price levels and the extent of their moves may vary – we can expect certain commodities to increase and decrease at certain times of the year. For example: ◦ Orange juice contracts have been profitable 74% of the time over the past 35 years for short trades to be initiated on June 4 and closed on July 1. ‘Sell in May and go away’ this is an old stock market adage that refers to the tendency for the stock market to decline from May to September and rise from October to April. August and September are generally the worst months for the stock market and October, November and January are the best. This may be attributable to the fact that most fund managers establish their targets at the beginning of their fiscal year (October or November) and once they attain their targets (usually by May they tend to back off on their trading, as their targets have been met) Essential for analysts to know of these strategies, however it is not guaranteed that they will be always occur on a constant basis year after year. There are always periods where they don’t occur. 23 | P a g e JANUARY BAROMETER & EFFECT  JANUARY BAROMETER is an old stock market adage that states, ‘as the S&P goes in January, so goes the year’. Therefore, an up January will foreshadow a year of positive equity returns.  JANUARY EFFECT is another theory that states that small cap equities tend to outperform the broader market in the first few days of the new trading year due to investors buying back stocks that were sold for tax loss reasons at the end of the previous fiscal year. ◦ This has not worked as well in recent years because arbitrageurs have essentially ‘arbed’ it out of the market. 24 | P a g e