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Efficacy of Algorithm in Trading and Investing

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AN ANALYTICAL STUDY ON EFFICACY OF ALGORITHM FOR BOTH TRADING AND INVESTING
AN ANALYTICAL STUDY ON EFFICACY OF ALGORITHM FOR BOTH TRADING AND INVESTING

ABSTRACT

INDEX

AIM OF STUDY
PURPOSE
* The main agenda of this study is to test the basic oscillators like RSI and OBV is to identify the behavior of these early indicators in various types of market. The agenda of using moving average lag indicators like Bollinger band is to check how well these bands work in giving out trade signals.

* The study aims to find out using Bloomberg terminal that whether combination of studies and Risk management help to enhance the performance of the indicators and do they really help to make a more profitable decision.

* This study also intends to use some basic fundamental indicators to identify whether they can be used as tool to invest in securities and how well they are able to perform as compared to a benchmark index. The aim is to use a matrix of indicators, so that it can be also assessed whether combination of basic indicators are good enough to make portfolio creation judgment that can lead to market beating portfolio or not.

* All the testing has been done using the Bloomberg terminal.
LIMITATIONS
* There are many lead, hybrid and lag indicators available in the market however not every single one can be tested.

* The testing only targets the NSE that is typically Indian market, hence the results may be non-inferential for international markets.

* The matrix used in fundamental factor back testing only tests out few combinations and only takes into consideration a few fundamental indicators. Hence it is limited by the scope of the study that is to just determine that can there be successful portfolio built using simple indicators or not.

AN INTRODUCTION ON TECHNICAL ANALYSIS

In finance, technical analysis is a security analysis methodology for forecasting the direction of prices through the study of past market data, primarily price and volume. Behavioral economics and quantitative analysis use many of the same tools of technical analysis, which, being an aspect of active management, stands in contradiction to much of modern portfolio theory.
The efficacy of both technical and fundamental analysis is disputed by the efficient-market hypothesis which states that stock market prices are essentially unpredictable.
HISTORY
The principles of technical analysis are derived from hundreds of years of financial market data. Some aspects of technical analysis began to appear in Joseph de la Vega's accounts of the Dutch markets in the 17th century. In Asia, technical analysis is said to be a method developed by Homma Munehisa during early 18th century which evolved into the use of candlestick techniques, and is today a technical analysis charting tool.
In the 1920s and 1930s Richard W. Schabacker published several books which continued the work of Charles Dow and William Peter Hamilton in their books Stock Market Theory and Practice and Technical Market Analysis. In 1948 Robert D. Edwards and John Magee published Technical Analysis of Stock Trends which is widely considered to be one of the seminal works of the discipline.
It is exclusively concerned with trend analysis and chart patterns and remains in use to the present. As is obvious, early technical analysis was almost exclusively the analysis of charts, because the processing power of computers was not available for statistical analysis. Charles Dow reportedly originated a form of point and figure chart analysis.
Dow Theory is based on the collected writings of Dow Jones co-founder and Editor Charles Dow, and inspired the use and development of modern technical analysis at the end of the 19th century.
Other pioneers of analysis techniques include Ralph Nelson Elliott, William Delbert Gann and Richard Wyckoff who developed their respective techniques in the early 20th century.
More technical tools and theories have been developed and enhanced in recent decades, with an increasing emphasis on computer-assisted techniques using specially designed computer software.

GENERAL DESCRIPTION
Fundamental analysts examine earnings, dividends, new products, research and the like. Technicians employ many methods, tools and techniques as well, one of which is the use of charts. Using charts, technical analysts seek to identify price patterns and market trends in financial markets and attempt to exploit those patterns.
Technicians using charts search for archetypal price chart patterns, such as the well-known head and shoulders or double top/bottom reversal patterns, study technical indicators, moving averages, and look for forms such as lines of support, resistance, channels, and more obscure formations such as flags, pennants, balance days and cup and handle patterns.
Technical analysts also widely use market indicators of many sorts, some of which are mathematical transformations of price, often including up and down volume, advance/decline data and other inputs. These indicators are used to help assess whether an asset is trending, and if it is, the probability of its direction and of continuation. Technicians also look for relationships between price/volume indices and market indicators. Examples include the moving average, relative strength index, and MACD. Other avenues of study include correlations between changes in Options (implied volatility) and put/call ratios with price. Also important are sentiment indicators such as Put/Call ratios, bull/bear ratios, short interest, Implied Volatility, etc.
There are many techniques in technical analysis. Adherents of different techniques (for example, candlestick charting, Dow theory, and Elliott wave theory) may ignore the other approaches, yet many traders combine elements from more than one technique. Some technical analysts use subjective judgment to decide which pattern(s) a particular instrument reflects at a given time and what the interpretation of that pattern should be. Others employ a strictly mechanical or systematic approach to pattern identification and interpretation.
Contrasting with technical analysis is fundamental analysis, the study of economic factors that influence the way investors’ price financial markets. Technical analysis holds that prices already reflect all the underlying fundamental factors. Uncovering the trends is what technical indicators are designed to do, although neither technical nor fundamental indicators are perfect. Some traders use technical or fundamental analysis exclusively, while others use both types to make trading decisions.
CHARACTERISTICS
Technical analysis employs models and trading rules based on price and volume transformations, such as the relative strength index, moving averages, regressions, inter-market and intra-market price correlations, business cycles, stock market cycles or, classically, through recognition of chart patterns.

Technical analysis stands in contrast to the fundamental analysis approach to security and stock analysis. Technical analysis analyzes price, volume and other market information, whereas fundamental analysis looks at the facts of the company, market, currency or commodity. Most large brokerage, trading group, or financial institutions will typically have both a technical analysis and fundamental analysis team.
Technical analysis is widely used among traders and financial professionals and is very often used by active day traders, market makers and pit traders. In the 1960s and 1970s it was widely dismissed by academics. In a recent review, Irwin and Park reported that 56 of 95 modern studies found that it produces positive results but noted that many of the positive results were rendered dubious by issues such as data snooping, so that the evidence in support of technical analysis was inconclusive; it is still considered by many academics to be pseudoscience. Academics such as Eugene Fama say the evidence for technical analysis is sparse and is inconsistent with the weak form of the efficient-market hypothesis. Users hold that even if technical analysis cannot predict the future, it helps to identify trading opportunities.
In the foreign exchange markets, its use may be more widespread than fundamental analysis. This does not mean technical analysis is more applicable to foreign markets, but that technical analysis is more recognized as to its efficacy there than elsewhere. While some isolated studies have indicated that technical trading rules might lead to consistent returns in the period prior to 1987, most academic work has focused on the nature of the anomalous position of the foreign exchange market. It is speculated that this anomaly is due to central bank intervention, which obviously technical analysis is not designed to predict. Recent research suggests that combining various trading signals into a Combined Signal Approach may be able to increase profitability and reduce dependence on any single rule.
PRINCIPLES
A fundamental principle of technical analysis is that a market's price reflects all relevant information, so their analysis looks at the history of a security's trading pattern rather than external drivers such as economic, fundamental and news events. Therefore, price action tends to repeat itself due to investors collectively tending toward patterned behavior – hence technical analysis focuses on identifiable trends and conditions. 1. Market action discounts everything
Based on the premise that all relevant information is already reflected by prices, technical analysts believe it is important to understand what investors think of that information, known and perceived.

2. Prices Move In Trends
Technical analysts believe that prices trend directionally, i.e., up, down, or sideways (flat) or some combination. The basic definition of a price trend was originally put forward by “Dow theory”.
An example of a security that had an apparent trend is AOL from November 2001 through August 2002. A technical analyst or trend follower recognizing this trend would look for opportunities to sell this security. AOL consistently moves downward in price. Each time the stock rose, sellers would enter the market and sell the stock; hence the "zigzag" movement in the price. The series of "lower highs" and "lower lows" is a tell-tale sign of a stock in a down trend. In other words, each time the stock moved lower, it fell below its previous relative low price. Each time the stock moved higher, it could not reach the level of its previous relative high price.
Note that the sequence of lower lows and lower highs did not begin until August. Then AOL makes a low price that does not pierce the relative low set earlier in the month. Later in the same month, the stock makes a relative high equal to the most recent relative high. In this a technician sees strong indications that the down trend is at least pausing and possibly ending, and would likely stop actively selling the stock at that point.

3. History Repeats Itself
Technical analysts believe that investors collectively repeat the behavior of the investors that preceded them. To a technician, the emotions in the market may be irrational, but they exist. Because investor behavior repeats itself so often, technicians believe that recognizable (and predictable) price patterns will develop on a chart. Recognition of these patterns can allow the technician to select trades that have a higher probability of success.
Technical analysis is not limited to charting, but it always considers price trends. For example, many technicians monitor surveys of investor sentiment. These surveys gauge the attitude of market participants, specifically whether they are bearish or bullish. Technicians use these surveys to help determine whether a trend will continue or if a reversal could develop; they are most likely to anticipate a change when the surveys report extreme investor sentiment. Surveys that show overwhelming bullishness, for example, are evidence that an uptrend may reverse; the premise being that if most investors are bullish they have already bought the market (anticipating higher prices). And because most investors are bullish and invested, one assumes that few buyers remain. This leaves more potential sellers than buyers, despite the bullish sentiment. This suggests that prices will trend down, and is an example of contrarian trading.

Recently, Kim Man Lui, Lun Hu, and Keith C.C. Chan have suggested that there is statistical evidence of association relationships between some of the index composite stocks whereas there is no evidence for such a relationship between some index composite others. They show that the price behavior of these Hang Seng index composite stocks is easier to understand than that of the index.
INDUSTRY
The industry is globally represented by the International Federation of Technical Analysts (IFTA), which is a federation of regional and national organizations. In the United States, the industry is represented by both the Market Technicians Association (MTA) and the American Association of Professional Technical Analysts (AAPTA). The United States is also represented by the Technical Security Analysts Association of San Francisco (TSAASF). In the United Kingdom, the industry is represented by the Society of Technical Analysts (STA). In Canada the industry is represented by the Canadian Society of Technical Analysts. In Australia, the industry is represented by the Australian Technical Analysts Association (ATAA), (which is affiliated to IFTA) and the Australian Professional Technical Analysts (APTA) Inc.
Professional technical analysis societies have worked on creating a body of knowledge that describes the field of Technical Analysis. A body of knowledge is central to the field as a way of defining how and why technical analysis may work. It can then be used by academia, as well as regulatory bodies, in developing proper research and standards for the field. The Market Technicians Association (MTA) has published a body of knowledge, which is the structure for the MTA's Chartered Market Technician (CMT) exam.
SYSTEMATIC TRADING 1. Neural networks
Since the early 1990s when the first practically usable types emerged, artificial neural networks (ANNs) have rapidly grown in popularity. They are artificial intelligence adaptive software systems that have been inspired by how biological neural networks work. They are used because they can learn to detect complex patterns in data. In mathematical terms, they are universal function approximates, meaning that given the right data and configured correctly, they can capture and model any input-output relationships. This not only removes the need for human interpretation of charts or the series of rules for generating entry/exit signals, but also provides a bridge to fundamental analysis, as the variables used in fundamental analysis can be used as input.
As ANNs are essentially non-linear statistical models, their accuracy and prediction capabilities can be both mathematically and empirically tested. In various studies, authors have claimed that neural networks used for generating trading signals given various technical and fundamental inputs have significantly outperformed buy-hold strategies as well as traditional linear technical analysis methods when combined with rule-based expert systems.
While the advanced mathematical nature of such adaptive systems has kept neural networks for financial analysis mostly within academic research circles, in recent years more user friendly neural network software has made the technology more accessible to traders. However, large-scale application is problematic because of the problem of matching the correct neural topology to the market being studied.
BACKTESTING
Systematic trading is most often employed after testing an investment strategy on historic data. This is known as back-testing. Back-testing is most often performed for technical indicators, but can be applied to most investment strategies (e.g. fundamental analysis). While traditional back-testing was done by hand, this was usually only performed on human-selected stocks, and was thus prone to prior knowledge in stock selection. With the advent of computers, back-testing can be performed on entire exchanges over decades of historic data in very short amounts of time.
The use of computers does have its drawbacks, being limited to algorithms that a computer can perform. Several trading strategies rely on human interpretation, and are unsuitable for computer processing. Only technical indicators which are entirely algorithmic can be programmed for computerized automated back testing.
SCIENTIFIC TECHNICAL ANALYSIS
Caginalp and Balenovich in 1994[70] used their asset-flow differential equations model to show that the major patterns of technical analysis could be generated with some basic assumptions. Some of the patterns such as a triangle continuation or reversal pattern can be generated with the assumption of two distinct groups of investors with different assessments of valuation.The major assumptions of the models are that the finiteness of assets and the use of trend as well as valuation in decision making. Many of the patterns follow as mathematically logical consequences of these assumptions.
One of the problems with conventional technical analysis has been the difficulty of specifying the patterns in a manner that permits objective testing.
Japanese candlestick patterns involve patterns of a few days that are within an uptrend or downtrend. Caginalp and Laurent were the first to perform a successful large scale test of patterns. A mathematically precise set of criteria were tested by first using a definition of a short term trend by smoothing the data and allowing for one deviation in the smoothed trend. They then considered eight major three day candlestick reversal patterns in a non-parametric manner and defined the patterns as a set of inequalities. The results were positive with an overwhelming statistical confidence for each of the patterns using the data set of all S&P 500 stocks daily for the five-year period 1992-1996.
Among the most basic ideas of conventional technical analysis is that a trend, once established, tends to continue. However, testing for this trend has often led researchers to conclude that stocks are a random walk. One study, performed by Poterba and Summers, found a small trend effect that was too small to be of trading value. As Fisher Black noted, "noise" in trading price data makes it difficult to test hypotheses.
One method for avoiding this noise was discovered in 1995 by Caginalp and Constantine who used a ratio of two essentially identical closed-end funds to eliminate any changes in valuation. A closed-end fund (unlike an open-end fund) trades independently of its net asset value and its shares cannot be redeemed, but only traded among investors as any other stock on the exchanges. In this study, the authors found that the best estimate of tomorrow's price is not yesterday's price (as the efficient market hypothesis would indicate), nor is it the pure momentum price (namely, the same relative price change from yesterday to today continues from today to tomorrow). But rather it is almost exactly halfway between the two.
Starting from the characterization of the past time evolution of market prices in terms of price velocity and price acceleration, an attempt towards a general framework for technical analysis has been developed, with the goal of establishing a principled classification of the possible patterns characterizing the deviation or defects from the random walk market state and its time translational invariant properties. The classification relies on two dimensionless parameters, the Froude number characterizing the relative strength of the acceleration with respect to the velocity and the time horizon forecast dimensionalized to the training period. Trend-following and contrarian patterns are found to coexist and depend on the dimensionless time horizon. Using a renormalisation group approach, the probabilistic based scenario approach exhibits statistically signifificant predictive power in essentially all tested market phases.
A survey of modern studies by Park and Irwin showed that most found a positive result from technical analysis.

In 2011, Caginalp and DeSantis have used large data sets of closed-end funds, where comparison with valuation is possible, in order to determine quantitatively whether key aspects of technical analysis such as trend and resistance have scientific validity. Using data sets of over 100,000 points they demonstrate that trend has an effect that is at least half as important as valuation. The effects of volume and volatility, which are smaller, are also evident and statistically significant. An important aspect of their work involves the nonlinear effect of trend. Positive trends that occur within approximately 3.7 standard deviations have a positive effect. For stronger uptrends, there is a negative effect on returns, suggesting that profit taking occurs as the magnitude of the uptrend increases. For downtrends the situation is similar except that the "buying on dips" does not take place until the downtrend is a 4.6 standard deviation event. These methods can be used to examine investor behavior and compare the underlying strategies among different asset classes.
In 2013, Kim Man Lui and T Chong pointed out that the past findings on technical analysis mostly reported the profitability of specific trading rules for a given set of historical data. These past studies had not taken the human trader into consideration as no real-world trader would mechanically adopt signals from any technical analysis method. Therefore, to unveil the truth of technical analysis, we should get back to understand the performance between experienced and novice traders. If the market really walks randomly, there will be no difference between these two kinds of traders. However, it is found by experiment that traders who are more knowledgeable on technical analysis significantly outperform those who are less knowledgeable.

UNDERSTANDING SOME OF THE BASIC STRATEGY PERFORMANCE INDICATORS

SHARPE RATIO
The Sharpe Ratio is a measure for calculating risk-adjusted return, and this ratio has become the industry standard for such calculations. It was developed by Nobel laureate William F. Sharpe.
The Sharpe ratio is the average return earned in excess of the risk-free rate per unit of volatility or total risk. Subtracting the risk-free rate from the mean return, the performance associated with risk-taking activities can be isolated.
One intuition of this calculation is that a portfolio engaging in “zero risk” investment, such as the purchase of U.S. Treasury bills (for which the expected return is the risk-free rate), has a Sharpe ratio of exactly zero. Generally, the greater the value of the Sharpe ratio, the more attractive the risk-adjusted return.
The Sharpe ratio is often used to compare the change in a portfolio's overall risk-return characteristics when a new asset or asset class is added to it. For example, a portfolio manager is considering adding a hedge fund allocation to his existing 50/50 investment portfolio of stocks which has a Sharpe ratio of 0.67.
If the new portfolio's allocation is 40/40/20 stocks, bonds and a diversified hedge fund allocation (perhaps a fund of funds), the Sharpe ratio increases to 0.87. This indicates that although the hedge fund investment is risky as a standalone exposure, it actually improves the risk-return characteristic of the combined portfolio, and thus adds a diversification benefit. If the addition of the new investment lowered the Sharpe ratio, it should not be added to the portfolio.
The Sharpe ratio can also help explain whether a portfolio's excess returns are due to smart investment decisions or a result of too much risk. Although one portfolio or fund can enjoy higher returns than its peers, it is only a good investment if those higher returns do not come with an excess of additional risk. The greater a portfolio's Sharpe ratio, the better its risk-adjusted performance has been. A negative Sharpe ratio indicates that a risk-less asset would perform better than the security being analyzed.
The Sharpe ratio uses the standard deviation of returns in the denominator as its proxy of total portfolio risk, which assumes that returns are normally distributed. Evidence has shown that returns on financial assets tend to deviate from a normal distribution and may make interpretations of the Sharpe ratio misleading.

A variation of the Sharpe ratio is the Sortino ratio, which removes the effects of upward price movements on standard deviation to measure only return against downward price volatility and uses the semivariance in the denominator.
The Treynor ratio uses systematic risk, or beta (β) instead of standard deviation as the risk measure in the denominator. * The Sharpe ratio can also be "gamed" by hedge funds or portfolio managers seeking to boost their apparent risk-adjusted returns history. This can be done by: * Lengthening the measurement interval: This will result in a lower estimate of volatility. For example, the annualized standard deviation of daily returns is generally higher than of weekly returns, which is, in turn, higher than of monthly returns. * Compounding the monthly returns but calculating the standard deviation from the not compounded monthly returns. * Writing out-of-the-money puts and calls on a portfolio: This strategy can potentially increase the return by collecting the option premium without paying off for several years. Strategies that involve taking on default risk, liquidity risk, or other forms of catastrophe risk have the same ability to report an upwardly biased Sharpe ratio. (An example is the Sharpe ratios of market-neutral hedge funds before and after the 1998 liquidity crisis.) * Smoothing of returns: Using certain derivative structures, infrequent marking to market of illiquid assets, or using pricing models that understate monthly gains or losses can reduce reported volatility. * Eliminating extreme returns: Because such returns increase the reported standard deviation of a hedge fund, a manager may chose to attempt to eliminate the best and the worst monthly returns each year to reduce the standard deviation.
Formula:

SORTINO RATIO
A modification of the Sharpe ratio that differentiates harmful volatility from general volatility by taking into account the standard deviation of negative asset returns, called downside deviation. The Sortino ratio subtracts the risk-free rate of return from the portfolio’s return, and then divides that by the downside deviation. A large Sortino ratio indicates there is a low probability of a large loss. It is calculated as follows:

The formula does not penalize a portfolio manager for volatility, and instead focuses on whether returns are negative or below a certain threshold. The mean in the Sortino ratio formula represents the returns a portfolio manager is able to get above the return that an investor expects.
Determining whether to use the Sharpe ratio or Sortino ratio depends on whether the investor wants to focus on standard deviation or downside deviation. Sharpe ratios are better at analyzing portfolios that have low volatility because the Sortino ratio won’t have enough data points to calculate downside deviation. This makes the Sortino ratio better when analyzing highly volatile portfolios.
While using the risk free rate of return is common, investors can also use expected return in calculations. In order to keep the formulas accurate, however, the investor should be consistent in what return type is used.
The ratio was named after Frank A. Sortino.

DRAW DOWN

Consider this example:
Let’s say you have a $100,000 and you lose $50,000. What percentage of your account have you lost?
The answer is 50%. Simple enough. This is what traders call a drawdown.
A drawdown is the reduction of one’s capital after a series of losing trades. This is normally calculated by getting the difference between a relative peak in capital minus a relative trough. Traders normally note this down as a percentage of their trading account.

In trading, we are always looking for an edge. That is the whole reason why traders develop systems. A trading system that is 70% profitable sounds like a very good edge to have. But just because your trading system is 70% profitable, does that mean for every 100 trades you make, you will win 7 out of every 10?
Not necessarily! How do you know which 70 out of those 100 trades will be winners?
The answer is that you don’t. You could lose the first 30 trades in a row and win the remaining 70. That would still give you a 70% profitable system, but you have to ask yourself, “Would you still be in the game if you lost 30 trades in a row?”
This is why risk management is so important. No matter what system you use, you will eventually have a losing streak. Even professional poker players who make their living through poker go through horrible losing streaks, and yet they still end up profitable.
The reason is that the good poker players practice risk management because they know that they will not win every tournament they play. Instead, they only risk a small percentage of their total bankroll so that they can survive those losing streaks.
This is what you must do as a trader. Drawdowns are part of trading. The key to being a successful trader is coming up with trading plan that enables you to withstand these periods of large losses. And part of your trading plan is having risk management rules in place.
Only risk a small percentage of your “trading bankroll” so that you can survive your losing streaks. Remember that if you practice strict money management rules, you will become the casino and in the long run, “you will always win.”

IMPORTANCE OF RISK MANAGEMENT
"I was seldom able to see an opportunity until it had ceased to be one." - Mark Twain.
We begin with trader Smith, who is a discretionary trader of forex, gold, and DAX. He sets a maximum loss limit per day (1.0%); week (2.5%); and month (10%) for his trading. He explains, "That has removed a great deal of the stress from trading, knowing that no one trade or series of trades can bring me down."
The best practice here is risk management: the prevention of deep drawdowns is worth many pounds of come-back cure. "It is not enough to have a good mind. The main thing is to use it well." - Rene Descartes
Readers trading for hedge funds, where capital is levered, will almost certainly set different percentages from Smith. A loss of 10% in a month would be wholly unacceptable at many places where they work. They would not want three months of hitting downside level to place them in a situation where they have to make over 40% on the remaining capital just to break even. Smith's basic concept of setting loss limits for trading, however, is quite sound.
Look at it this way: if you have a hit rate of 50%, then you will have 25% odds of two consecutive losing trades; 12.5% odds of three consecutive losing trades; 6.25% odds of four consecutive losing trades; and a little over 3% odds of five consecutive losing trades. If you place 50 trades in a year, guess what? You will almost certainly encounter strings of four and five consecutive losses. You need to be able to survive that risk of ruin. If you allow yourself to lose 10% of your initial capital on each trade, you will likely get to the point where you need to double your remaining money to break even. If you allow yourself to lose 1% of capital on each trade, any expectable run of losing trades is unlikely to impair your account--or your psyche.
One of the practices that will serve you well over the years is to enter trades with one-fourth to one-half of your maximum position size. You will find out that, when you are wrong in a trade, you are usually wrong early in that trade. Keeping your risk exposure modest initially enables you to lose less money if you are stopped out quickly, and it allows you to add to your position if your scenario unfolds as planned. If you are sized maximally, moves against you become a threat. If you are sized more moderately, moves against your position can pose further opportunity. That's a great place to be psychologically.
Finally, loss prevention in trading is greatly aided by diversification. If you have two or more trading systems or trading methods that each have positive expected returns and are relatively uncorrelated in their return streams, you then create a situation where the expectable series of losing trades for any one method can be buffered by the returns from the others. Diversification can also occur in the larger picture of our money management. Your trading capital should be a fraction of my total investment capital. You have many fixed income investments, for example, that throw off a reasonable yield each year. If you were to have a losing trading year, you would still harvest income from your larger portfolio.

"There is only one side of the market and it is not the bull side or the bear side, but the right side." - Jesse Livermore
With the above thought in our minds let’s understand two primary areas that traders want to investigate whilst building their approach. The first pertains to the risk-reward ratio used on each trade that is taken; in effort to avoid The Number One Mistake Forex Traders Make.
Traders should use stops and limits to enforce a risk/reward ratio of 1:1 or higher. This can be instituted by placing a stop and a limit on each trade, ensuring that the limit is at least as far away from current market price as your stop.
We can even take this concept a step further by looking for larger profits when right, but risking smaller amounts so that when losses are smaller; this can be done with a 1-to-2 risk-to-reward ratio (risking 1 dollar for every 2 dollars sought).
A visual representation of a 1-to-2 risk-to-reward ratio is illustrated in the picture below:The reasons for such a risk-reward setup are numerous, as future prices can be difficult to forecast and impossible to predict. But when a trader is on the right side of the trade, this type of risk-reward ratio can maximize their gain and limit their losses in the instances in which they are wrong.

As a matter of fact, let’s say that a trader isn’t even right half-of-the time. Let’s assume a trader is only winning in 40% of their trades. By using a 1-to-2 risk-to-reward ratio, they can still attain a net profit.
As you can see, the risk-reward ratio completely changed the strategy. If the trader was only looking for one dollar in reward for every one dollar risked, the strategy would have lost 200 pips. But by adjusting this to a 1-to-2 risk-to-reward ratio, the trader tilts the odds back in their favor (even if only being right 40% of the time).

But what if our strategy is only successful 30% of the time (as we had mentioned above)? We can simply look to be more aggressive, seeking a higher reward for the fewer times that we are right. The table below looks at 3 different risk-to-reward ratios with a 30% winning ratio:

Thus understanding your psychology, risk appetitive and an efficient risk management policy and absolute discipline while trading provides for a relatively stress free and successful career as a trader. Remember
"Markets can remain irrational longer than you can remain solvent." - John Maynard Keynes

AN INTRODUCTION TO RSI
The relative strength index (RSI) is a technical indicator used in the analysis of financial markets. It is intended to chart the current and historical strength or weakness of a stock or market based on the closing prices of a recent trading period. The indicator should not be confused with relative strength.
The RSI is classified as a momentum oscillator, measuring the velocity and magnitude of directional price movements. Momentum is the rate of the rise or fall in price. The RSI computes momentum as the ratio of higher closes to lower closes: stocks which have had more or stronger positive changes have a higher RSI than stocks which have had more or stronger negative changes.
The RSI is most typically used on a 14-day timeframe, measured on a scale from 0 to 100, with high and low levels marked at 70 and 30, respectively. Shorter or longer timeframes are used for alternately shorter or longer outlooks. More extreme high and low levels—80 and 20, or 90 and 10—occur less frequently but indicate stronger momentum.
The relative strength index was developed by J. Welles Wilder and published in a 1978 book, New Concepts in Technical Trading Systems, and in Commodities magazine (now Futures magazine) in the June 1978 issue. It has become one of the most popular oscillator indices.
BASIC CONFIGURATION
The RSI is presented on a graph above or below the price chart. The indicator has an upper line, typically at 70, a lower line at 30, and a dashed mid-line at 50.

PRINCIPLES
Wilder posited that when price moves up very rapidly, at some point it is considered overbought. Likewise, when price falls very rapidly, at some point it is considered oversold. In either case, Wilder deemed a reaction or reversal imminent. The level of the RSI is a measure of the stock's recent trading strength. The slope of the RSI is directly proportional to the velocity of a change in the trend. The distance traveled by the RSI is proportional to the magnitude of the move.
Wilder believed that tops and bottoms are indicated when RSI goes above 70 or drops below 30. Traditionally, RSI readings greater than the 70 level are considered to be in overbought territory, and RSI readings lower than the 30 level are considered to be in oversold territory. In between the 30 and 70 level is considered neutral, with the 50 level a sign of no trend

BACKTESTING OF RSI WITH ITS OWN MOVING AVERAGE
RSI can be traded in many different ways but in this technique we have tested the RSI values against its 9 day moving average. Here we have taken 14 day exponential RSI values. Here the RSI values (daily) act as the fast moving line and RSI Moving Average of 9 days acts as slow moving average.
This back testing is conducted on year on year basis starting from year 2007 to year 2014. The time frame is selected such that we are able to capture a strongly trending bull market, strongly trending bear market and also periods of range bound market where the markets haven’t moved much in any particular direction.
The steps for conducting the test are: 1. Plot the values of RSI (Fast Line) 2. Plot the Simple Moving Average values of these RSI values to get the Moving Average Line (Slow line) 3. When the fast line crosses slow line in upward direction then cover and go long. 4. When the fast line crosses slow line in downward direction then close and go short. 5. The date range is 1st January 2007 to 31st December 2014
In the figure below there is a flow diagram which shows how technical logic FIG 1

FIG 2

FIG 3 * The above figure is the screen of back-testing the nifty index over a period of 12 months in 2007 where the trades are taken as per the simple logic presented in FIG 1. * There is a mildly bullish market here and RSI has given a positive numbers in terms of its performance as we can see a net profit of 1.95% on the invested capital. * The Sharpe ratio is also very stable at 1.01 and a winning ratio of 42.86.

FIG 4

FIG 5 * The above figure is the screen of back-testing the nifty index over a period of 12 months in 2008 where the trades are taken as per the simple logic presented in FIG 1. * There is a bearish market here and RSI has given a positive numbers in terms of its performance as we can see a net profit of 15.27% on the invested capital. * The loss is profit is due to the short position generating profits which lead to overall profit in the strategy, here the trading strategy has taken a hit due to loss making long positions. There are many whip saws that have really hampered the profitability.

FIG 6

FIG 7

* The above figure is the screen of back-testing the nifty index over a period of 12 months in 2009 where the trades are taken as per the simple logic presented in FIG 1. * There is a bullish market here and RSI has given a negative numbers in terms of its performance as we can see a net loss of 11.4% on the invested capital. * The loss is due to the long position generating losses which lead to overall loss in the strategy, here the trading strategy has taken a hit due to loss making short positions. * Market here has remained in overbought zone a lot hence the long positions were not profitable and hence the losses made by short positions brought down overall profitability leading to losses.

FIG 8

FIG 9 * The above figure is the screen of back-testing the nifty index over a period of 12 months in 2011 where the trades are taken as per the simple logic presented in FIG 1. * There is a mildly bullish market here and RSI has given a positive numbers in terms of its performance as we can see a net profit of 19.47% on the invested capital. * The profit is due to the long position generating profit which lead to overall profit in the strategy, also the short positions did not generate heavy losses. * The Sharpe Ratio and Sortino Ratio is also very strong at 1.11 & 1.45 respectively also what we see here the win ratio is very strong at 50%

FIG 10

FIG 11

* The above figure is the screen of back-testing the nifty index over a period of 12 months in 2011 where the trades are taken as per the simple logic presented in FIG 1. * There is a mildly bullish market here and RSI has given a positive numbers in terms of its performance as we can see a net profit of 11.96% on the invested capital. * The profit could have been more, there are various overbought and oversold areas that do hamper the profitability of trades.

FIG 12

FIG 13 * The above figure is the screen of back-testing the nifty index over a period of 12 months in 2012 where the trades are taken as per the simple logic presented in FIG 1. * There is a mildly bullish market here and RSI has not given positive numbers in terms of its performance as we can see a net profit of 9.39% on the invested capital. * There are many instances of overbought & oversold conditions that have led to only a small profit generation and heavy losses especially the short position.

FIG 14

FIG 15 * The above figure is the screen of back-testing the nifty index over a period of 12 months in 2013 where the trades are taken as per the simple logic presented in FIG 1. * There is a mildly bullish market here and RSI has given a positive numbers in terms of its performance as we can see a net profit of 5.92% on the invested capital. * There are many instances of oversold conditions that have led to only a small profit generation. * The Sharpe ratio stood at 0.52 which is not bad and a good Sortino ratio of 0.79

FIG 16

FIG 17 * The above figure is the screen of back-testing the nifty index over a period of 12 months in 2014 where the trades are taken as per the simple logic presented in FIG 1. * There is a mildly bullish market here and RSI has given a positive numbers in terms of its performance as we can see a net profit of 19.39% on the invested capital. * There are many instances of oversold conditions that have led to only a small profit generation.

SUMMARY OF THE STRATEGY.

A small scoring card system has been created in order to score and grade a strategy’s performance and compare it to other strategies performance. This helps to evaluate various strategies using a common scale. As we can see through the scoring system that simple RSI trading technique works really well apart from those years that have a range bound market. It is clearly evident that RSI is not a good performer in a very range bound market.

BACKTESTING OF RSI WITH ITS OWN MOVING AVERAGE & 50 DMA USING STOP LOSS AS RISK MANAGEMENT

In this back testing of RSI everything remains the same as above but the only difference is that any position long or short will be exited the moment loss on that position exceeds 0.5%.
Thus this simple risk management strategy would eliminate the impact of heavy losses on any strategy.

In this strategy 50 DMA value is used as an indicator to identify what type of market we are trading.
That is to eliminate whip saws and false trades and thus improve winning trade percentage.
50 DMA is considered as a standard for identifying whether there is a clear bear market or a bull market.
The steps for conducting the test are: 1. Plot the values of RSI (Fast Line). 2. Plot the Simple Moving Average values of these RSI values to get the Moving Average Line (Slow line). 3. When the fast line crosses slow line in upward direction then check for the 50 DMA Value. 4. If closing price is above 50 DMA value then we have to cover and go long else we just have to cover our short position. 5. When the fast line crosses slow line in downward direction then check for 50 DMA Value. 6. If 50 DMA Value is greater than closing price then we have to close long position and go short else we just need to close the long position. 7. The date range is 1st January 2007 to 31st December 2014.
In the figure below there is a flow diagram which shows how technical logic FIG 1

FIG 18

FIG 19
Above images show the description of the strategy taken to increase payoff and also increase the over scores and other parameters of the strategy.

FIG 20
Here we can see the following: * There are a very few true trades as many RSI trades are not taken because of the 50 DMA filter.

* Even though there were very few trades the winning ratio remained poor.

* However as there was a risk management and loss management system in place for this strategy there was a limited loss.

* Here there is strong overbought zone which really hurts the trading strategy’s payout.

FIG 21
Here we can see the following: * There are many oversold zones over here that have a clear indication that this is a bear market.

* Here we see the short positions have been the money maker.

* As we have used the 50 DMA filter most of the short positions have made a great amount of profit.

* The Sharp ratio and Sortino ratio have both been good in this trading year. There is a profit of 11.69%.

FIG 22
Here we can see the following: * Here we can see a typical bull market where we are trading a clear upward trend.

* However in this trend we have found that short positions don’t make a total loss.

* This is due to filtering of short positions using 50 DMA.

* There is clear strong wining ratio in the overall strategy and long positions in particular having a very high winning ratio. This has led to a very strong performance of the strategy in this type of market.

FIG 23
Here we can see the following: * This is mildly bullish market with a large overbought zone.

* Winning ratio is also a good one at 55.56%.

* In this market both the long and short trades have generated profit and this is because of strong and strict stop loss policy that is employed that ensures that losses are restricted in short position.

* The long positions have made a strong winning ratio however again due to overbought zone the profit take away have been restricted.

FIG 24
Here we can see the following: * As we can see above is that the win ratio is very high.

* The market above is a mildly bearish market and the win ratio in short position has generated all the profit.

* The profit percentage in short position has is good whereas there there were only few long positions taken that is due to 50 DMA filter available in the strategy to weed out false signals and whip saws.

* There were only 21 trades and hence value per trade is very high.

FIG 24
Here we can see the following: * The win ratio is low.

* Market here was a very range bound market with a lot of volatility and formation of oversold and over bought zones in one single year leading to failure of the trading strategy.

* There are many overbought zones.

* Both the long and short positions took a hit however the impact was reduced as there was strict stop loss.

FIG 25
Here we can see the following: * Here we have a typical range bound market.

* However what separates this market from the previous market is that this market does not have a lot of volatility and hence it doesn’t have many false signals.

* The winning ratio is very good at 72.

* Also both the trades that is the long ones and the short ones show positive results and the returns are positive.

FIG 26

Here we can see the following: * Here the markets have been strong and upward trending.

* As the markets are strong upward trending the long positions have produced profits and the short positions have produced the losses.

* The losses in short have been restricted by stop loss.

* There are huge overbought zones in this market.

* Even though the winning ratio is very good it does not translate into very good profit numbers.

SUMMARY OF THE STRATEGY.

As we can see that this strategy is built was able to eliminate many whipsaws and thus was able to also improve the average of the entire points scored and apart from one year that is 2012 we did not make losses.
Even though the number of trades reduced from 366 to 170 a reduction of almost 55% however the points collected were 1986 as compared to 3140 a reduction of 37%. Points per trade has also improved in this strategy.
Sharpe ratio and Sortino ratio also improved as there was only the year of 2012 where Sharpe and Sortino ratio both showed negative numbers.

INTRODUCTION TO BOLLINGER BANDS

Bollinger Bands is a technical analysis tool invented by John Bollinger in the 1980s as well as a term trademarked by him in 2011. Having evolved from the concept of trading bands, Bollinger Bands and the related indicators %b and bandwidth can be used to measure the "highness" or "lowness" of the price relative to previous trades. Bollinger Bands are a volatility indicator similar to the Keltner channel.
Bollinger Bands consist of: 1. An N-period moving average (MA).

2. An upper band at K times an N-period standard deviation above the moving average (MA + Kσ).

3. A lower band at K times an N-period standard deviation below the moving average (MA − Kσ).
The use of Bollinger Bands varies widely among traders. Some traders buy when price touches the lower Bollinger Band and exit when price touches the moving average in the centre of the bands. Other traders buy when price breaks above the upper Bollinger Band or sell when price falls below the lower Bollinger Band.
Moreover, the use of Bollinger Bands is not confined to stock traders; options traders, most notably implied volatility traders, often sell options when Bollinger Bands are historically far apart or buy options when the Bollinger Bands are historically close together, in both instances, expecting volatility to revert towards the average historical volatility level for the stock.
When the bands lie close together, a period of low volatility is indicated. Conversely, as the bands expand, an increase in price action/market volatility is indicated.
When the bands have only a slight slope and track approximately parallel for an extended time, the price will generally be found to oscillate between the bands as though in a channel.
Traders are often inclined to use Bollinger Bands with other indicators to confirm price action. In particular, the use of oscillator-like Bollinger Bands will often be coupled with a non-oscillator indicator-like chart patterns or a trend line.
If these indicators confirm the recommendation of the Bollinger Bands, the trader will have greater conviction that the bands are predicting correct price action in relation to market volatility.
Various studies of the effectiveness of the Bollinger Band strategy have been performed with mixed results. In 2007 Lento et al. published an analysis using a variety of formats (different moving average timescales, and standard deviation ranges) and markets (e.g., Dow Jones and Forex). Analysis of the trades, spanning a decade from 1995 onwards, found no evidence of consistent performance over the standard "buy and hold" approach.
The authors did, however, find that a simple reversal of the strategy ("contrarian Bollinger Band") produced positive returns in a variety of markets.
Similar results were found in another study, which concluded that Bollinger Band trading strategies may be effective in the Chinese marketplace, stating: "Finally, we find significant positive returns on buy trades generated by the contrarian version of the moving-average crossover rule, the channel breakout rule, and the Bollinger Band trading rule, after accounting for transaction costs of 0.50 percent.
(By "the contrarian version", they mean buying when the conventional rule mandates selling, and vice versa.) A recent study examined the application of Bollinger Band trading strategies combined with the ADX for Equity Market indices with similar results.
A paper from 2008 uses Bollinger Bands in forecasting the yield curve.
Companies like Forbes suggest that the use of Bollinger Bands is a simple and often an effective strategy but stop-loss orders should be used to mitigate losses from market pressure.

BOLLINGER BAND BACK TESTING’

FIG 27

FIG 28 1. Construct a Bollinger band using 19 day moving average as the central average line and then use 1.25 standard deviation for upper band and 1.1875 for the lower band. 2. Go long if the upper band is breached in upward direction and close price is above the 32 DMA value else ignore this signal. 3. If the closing price breaches upper in downward direction then close the long position. 4. Go short if the closing price breaches the lower band in downward direction and close price is below the 50 DMA value else ignore this signal. 5. If the closing price breaches the lower band in upward direction then cover the short position. 6. Max loss limit or stop loss in both long and short position is 0.2%. The reason for this tight value is that it is assumed that due to use of average and std deviation by its 7. Below is the diagram of the Bloomberg simulation control.

FIG 29

FIG 30 * Here in mildly bullish market the bands have performed exceptionally well.

* The long positions have provided the best results in terms of profit loss and also in terms of making rate of winning trades.

* Sharpe ratio and Sortino ratio are also decent at 1.07 each.

* The overall winning ratio is around 60% which is also very good as well as the p&l which is around 12.39%.

FIG 31 * 2008 was panic sell market.

* The corrections were strong but also trending.

* However even in this market the Bollinger bands have worked very effectively.

* The winning ratio is very strong about 70% at such high winning ratio it is imperative that the strategy would have given very high profits.

* The Sharpe Ratio number is around 3.8 also the Sortino ratio is very high at around 5.72.

* Average winning amount was very high as compared to average losing amount and this leads to very strong and tall overall profit of 57.8%.

FIG 32 * 2009 was a fairly range bound market.

* Due the nature of range bound markets the trades have resulted in many whipsaws dragging the profits down along with them.

* Due to these whipsaws the win ratio was abysmal at 41%.

* Overall profit was also low at around 11.32%.

* However the Sharpe ratio and Sortino ratio still maintained a healthy number of 1.37 & 2.32.

* The number of trades executed were also large at 34 this is due to increase in whipsaws.

FIG 33 * 2010 was a fairly range bound market.

* Due the nature of range bound markets the trades have resulted in many whipsaws dragging the profits down along with them.

* Due to these whipsaws the win ratio was very average at 54%.

* Overall profit was also low at around 15.03%.

* However the Sharpe ratio and Sortino ratio still maintained a healthy number of 2.61 & 4.86.

* The number of trades executed were also average at 26.

FIG 34 * 2011 was a mildly bearish market.

* Due to this the short positions made relatively larger profits.

* The win ratio was very good at 57.14%.

* Overall profit was also low at around 17.1%.

* However the Sharpe ratio and Sortino ratio still maintained a healthy number of 2.24 & 3.75.

* The number of trades executed were also less at 21.

FIG 35 * 2012 was a mildly bullish market.

* Due to this the short positions made relatively less profits as compared to long positions.

* The win ratio was very poor at 4.74%.

* Overall profit was also low at around 6.86%.

* However the Sharpe ratio and Sortino ratio still maintained a healthy number of 2.24 & 3.75.

* The number of trades executed were also decent at 27.

FIG 36 * 2013 was a range bound market.

* Due to this the short positions made relatively less profits and so did long positions.

* The win ratio was very good at 65.52%.

* Overall profit was low at around 9.84%.

* However the Sharpe ratio and Sortino ratio still maintained a healthy number of 1.45 & 2.5.

* The number of trades executed were also decent at 29.

FIG 37 * 2014 was a mildly bullish market.

* Due to this the short positions made losses and long positions made profits.

* The win ratio was average at 51.85%.

* Overall profit was low at around 11.98%.

* However the Sharpe ratio and Sortino ratio still maintained a healthy number of 2.69 & 5.44.

* The number of trades executed were also decent at 29.

FIG 38

* 2015 was a range bound market.

* Due to this the short positions made small profits and long positions made no profits.

* The win ratio was good at 60%.

* Overall profit was low at around 3.57%.

* However the Sharpe ratio and Sortino ratio produced poor numbers of 0.6 & 1.19.

* The number of trades executed were also decent at 29.

SUMMARY

* This strategy has given phenomenal results over the period of 8 years which has seen all kinds of market.

* The number of trades averages out to be around 25 per year and it gives out a fantastic number of 2 trades per month on an average.

* The whipsaws have been low in this strategy.

* This strategy has strong Sharpe & Sortino ratio and this gives a very strong risk vs. return profile for any type of trader.

* In all years the returns have been positive also the points collected over the period of 8 years is far better than RSI’s performance (3.2 times more points with almost same number of trades).

TRADING 6 DAY HIGH LOW SIMPLE MOVING AVERAGES WITH RSI

This is hybrid created out of a lag indicator like moving average and a lead indicator created from RSI.
Steps of the strategy: 1. Create a simple moving average of Highs and Lows of 6 days.

2. Using step 1 we have created a band.

3. When the closing price crosses the upper band in upward direction hen go long.

4. When the closing price crosses the upper band in downward direction then close the long position.

5. When the closing price crosses the lower band in downward direction then we go short only when 13 day RSI is above its 10 day moving average.

6. When the closing price crosses the lower band in upward direction, we square off the short position.

7. The stop loss in both the position is as usual around 0.5% of nifty position.

8. The area inside the band is the no trade zone.

FIG 39

FIG 40 * As we can see in the above result the combination of 6 Day Moving Average of highs and lows produces a very good result.

* It has produced a winning ratio of 46% however as we can see that the winning trade is very highly profitable than the losses made by losing trade.

* Sharpe ratio of 1.84 is excellent.

* The Sortino ratio is also very high at 2.33.

* In the above test the point to be noted is there is no leverage when taking the trade, however when trading the nifty futures there is normally a leverage of 1:5 so the result above would have not meant 4.43 times but in actual it would have translated into portfolio growing by 17.72 times in 9 years. That translates into CAGR of 42%. * Using this strategy we were able to collect around 9595 points in 9 years, around 1067 points a year. This number in itself proves that the strategy works big time.

SUMMARY

* As we can see that the strategy produces very high scores according to our score card.

* The strategy in short has given very good number of trades that is 282 trades in 9 years that is 31 trades per month and a very strong and credible numbers in terms of points collected.

* The strategy was not tested for year on year basis but its performance in all the years was better than all the strategies above.

CREATING AND REBALANCING PORTFOLIOS USING FUNDAMENTAL ANALYSIS

Technical analysis is required for trading, however fundamental analysis is required for taking investing decision especially if the investing decision is to be made over a very long time horizon.
To accomplish this a few combinations of some fundamental indicators are taken and then a portfolio is built on those fundamental indicators. However every quarter the portfolio is rebalanced with securities that match the criterion.
In our study we have selected simple fundamental indicators like PE ratio, PEG ratio, ROE ROC and a combinational matrix of the same.
We have analyzed the result considering the Nifty 50 index as the benchmark.
ROE
Return on equity (ROE), return on capital (ROC) and return on funds employed (ROFE) are measures of how well a company is being managed. They are measures of company profitability as distinct from profit, and are sometimes referred to as profitability ratios.
A well-managed company can be expected to provide an enhanced return if the business is fundamentally sound. I concentrate on return on equity rather than the other two. This is because as a shareholder, I own part of the equity (shareholders equity), and I want a good return on it.
Return on capital (ROC) and ROFE bring debt into the equation which confuses the two things. Debt can be better considered by looking at other measures. ROE is calculated by dividing the net (tax-paid) profit by the sum of shareholders' 'ordinary' equity and expressing this as a percentage.
ROC and ROFE include the combined return on both earnings and borrowings. ROC and ROFE would be expected to be less than ROE as borrowings involve interest payments. ROC and ROFE indicate how well the company is making use of its borrowings.
I look for ROE and ROC exceeding 15% since it is these returns that add to shareholder value - which is what value investing is about. Low debt and a significant re-investment of earnings ensures that the company can magnify its performance over time. Shareholders can’t expect high returns in the long term if the ROE and ROC are low.
Companies with low ROE should be handing back profits to shareholders by invoking high pay-out ratios since retaining earnings to magnify poor performance will destroy value.
Of course, the overall return I get will also depend on the price at which I buy the stock and how long I hold it for. Return on capital is used in Joel Greenblatt's magic formula investing approach. He has demonstrated by back testing that by choosing stocks with a combination of high return on capital and high earnings yield (low price earnings ratio), a high return can be achieved if invested for the longer term.
A company may cause its ROE to be lowered because it has recently undertaken a large acquisition and funded the purchase by issuing more shares. The increase in the number of shares has the effect of lowering the ROE and ROC, one hopes in the short term. The benefits of these acquisitions may not yet had the chance to flow through to earnings.
So I check the background and announcements that companies make in order to have a better understanding of what they are doing and what effect this may have, if any, on the return on equity. Their performance in quickly enhancing returns from any previous acquisitions may provide some confidence as to their ability to add value from recent take-overs.
Keeping an eye on how a company's ROE grows or fades over a number of years provides an indication on whether the company is adding value or running out of steam. To me, an ongoing high ROE is also an indication of the strength of the company's economic moat or competitiveness. It is doing something right to ward off competition.
Young companies that are growing quickly may exhibit large returns on equity which can't realistically be sustained over time. So a drop in ROE from large values is not necessarily a bad thing - but if a return on equity drops below 15%, I start getting concerned.
On the other hand, if the ROE of a company suddenly jumps, it could be due to the fact that the company has borrowed money to buy back shares. This effectively reduces the shareholders equity. And because shareholders equity is on the denominator (bottom line) of the ROE ratio, the value of the ratio jumps.
This might make the company be seen in a better position with respect to its return on equity, but it also increases risk as the debt to equity ratio has risen as well. So the moral of the story is to not consider ROE in isolation, but take debt into account as well as other considerations.
PE
The PE ratio is the current price of the stock divided by the reported earnings per share of the stock. As a result the PE of a stock is subject to daily change. Since, the future earnings of a company are often built into the price of a stock, the PE ratio signifies to what extent the price is valued at the earning of the share of the past year.
It is essentially the price you are willing to pay for Re 1 of a company's earnings. Given that future earnings of a company are uncertain, robust companies are able to extract a premium for their earnings. It has little to do with the returns that a stock could deliver. As a result you cannot use it as a means to forecast future performance, to elucidate further; PE of 18.50 times does not mean that the stock price will essentially grow to 18.50 times. It only means that investors are valuing the stock at 18.50 times of its earnings. Price to book value ratio (PB) compares a stock's market value to its book value (book value is assets minus liabilities).
A lower PB could either mean that the stock is undervalued or that there is something fundamentally wrong with the company. PE and PB are used more as tool for comparison between stocks belonging to a certain peer group as stand-alone PE does not signify anything.
As far as mutual fund units are concerned the PE and PB are arrived through a weighted average of the inherent stocks. As a result, you shouldn't assign as much importance to a mutual fund's PE and PB as you would give to a stock's PE. High PE and PB relative to a category would indicate that the mutual fund holds stocks that are currently quoting at a premium and points towards a growth oriented strategy. If you are investing in a value fund, then expect the fund to have a PE lower than that of growth funds.
While short-listing funds going through each funds PE and PB can be quite cumbersome and misleading. To this extent the Value Research Style Box takes care of this problem by presenting a unified snapshot of how a fund's portfolio looks relative to others in its category. This snapshot is a nine-grid matrix, which represents an equity fund in terms of its market capitalisation and valuation.
PEG
Price/Earnings to Growth (PEG) is a stock's price-to-earnings ratio divided by the growth rate of its earnings for a specified time period. The PEG ratio is used to determine a stock's value while taking the company's earnings growth into account, and is considered to provide a more complete picture than the P/E ratio.
While a high P/E ratio may make a stock look like a good buy, factoring in the company's growth rate to get the stock's PEG ratio can tell a different story. The lower the PEG ratio, the more the stock may be undervalued given its earnings performance.
The PEG ratio that indicates an over or under priced stock varies by industry and by company type, though a broad rule of thumb is that a PEG ratio below one is desirable. Also, the accuracy of the PEG ratio depends on the inputs used. Using historical growth rates, for example, may provide an inaccurate PEG ratio if future growth rates are expected to deviate from historical growth rates. To distinguish between calculation methods using future growth and historical growth, the terms "forward PEG" and "trailing PEG" are sometimes used.
ROC
A return from an investment that is not considered income. The return of capital is when some or all of the money an investor has in an investment is paid back to him or her, thus decreasing the value of the investment.
This is not considered an investment gain of any type because it is not in excess of the original investment. Investors are not taxed on this return until it begins to exceed their original investment value.
Return on capital (ROC), or return on invested capital (ROIC), is a ratio used in finance, valuation, and accounting. The ratio is estimated by dividing the after-tax operating income (NOPAT) by the book value of both debt and equity capital less cash/equivalents. ROIC is a useful measure for comparing the relative profitability and value-creating potential of companies after taking into account the amount of initial capital invested.
Long term period for equity investing is 20 years whereas medium term period for equity investing is 10 years. We have tested out various combinations for both 10 years and 20 years to arrive at our results in terms of which portfolio has beaten the market consistently.
Before we can proceed ahead there are a few basic ratios that we need to understand and evaluate the strategies.
JENSONS ALPHA

A risk-adjusted performance measure that represents the average return on a portfolio over and above that predicted by the capital asset pricing model (CAPM), given the portfolio's beta and the average market return. This is the portfolio's alpha. In fact, the concept is sometimes referred to as "Jensen's alpha."
The basic idea is that to analyse the performance of an investment manager you must look not only at the overall return of a portfolio, but also at the risk of that portfolio.
For example, if there are two mutual funds that both have a 12% return, a rational investor will want the fund that is less risky. Jensen's measure is one of the ways to help determine if a portfolio is earning the proper return for its level of risk.
If the value is positive, then the portfolio is earning excess returns. In other words, a positive value for Jensen's alpha means a fund manager has "beat the market" with his or her stock picking skills.

INFORMATION RATIO

Rp = Return of the portfolio
Ri = Return of the index or benchmark
Sp-i = Tracking error (standard deviation of the difference between returns of the portfolio and the returns of the index)
A high IR can be achieved by having a high return in the portfolio, a low return of the index and a low tracking error.
For example:
Manager A might have returns of 13% and a tracking error of 8%
Manager B has returns of 8% and tracking error of 4.5%
The index has returns of -1.5%
Manager A's IR = [13-(-1.5)]/8 = 1.81
Manager B's IR = [8-(-1.5)]/4.5 = 2.11
Manager B had lower returns but a better IR. A high ratio means a manager can achieve higher returns more efficiently than one with a low ratio by taking on additional risk. Additional risk could be achieved through leveraging.
BETA
Beta (β or beta coefficient) of an investment indicates whether the investment is more or less volatile than the market. In general, a beta less than 1 indicates that the investment is less volatile than the market, while a beta more than 1 indicates that the investment is more volatile than the market. Volatility is measured as the fluctuation of the price around the mean: the standard deviation.
Beta is a measure of the risk arising from exposure to general market movements as opposed to idiosyncratic factors. The market portfolio of all investable assets has a beta of exactly 1. A beta below 1 can indicate either an investment with lower volatility than the market, or a volatile investment whose price movements are not highly correlated with the market.
An example of the first is a treasury bill: the price does not go up or down a lot, so it has a low beta. An example of the second is gold. The price of gold does go up and down a lot, but not in the same direction or at the same time as the market.

A beta greater than one generally means that the asset both is volatile and tends to move up and down with the market. An example is a stock in a big technology company.
Negative betas are possible for investments that tend to go down when the market goes up, and vice versa. There are few fundamental investments with consistent and significant negative betas, but some derivatives like put options can have large negative betas.
Beta is important because it measures the risk of an investment that cannot be reduced by diversification. It does not measure the risk of an investment held on a stand-alone basis, but the amount of risk the investment adds to an already-diversified portfolio. In the capital asset pricing model, beta risk is the only kind of risk for which investors should receive an expected return higher than the risk-free rate of interest.
CORRELATION
Correlation is computed into what is known as the correlation coefficient, which ranges between -1 and +1. Perfect positive correlation (a correlation co-efficient of +1) implies that as one security moves, either up or down, the other security will move in lockstep, in the same direction.
Alternatively, perfect negative correlation means that if one security moves in either direction the security that is perfectly negatively correlated will move in the opposite direction. If the correlation is 0, the movements of the securities are said to have no correlation; they are completely random.
However there are many instances where completely random and unrelated events may tend to show some correlation such correlations are considered to be spurious or false correlation.

BUILDING PORTFOLIO ONLY BASED ON PE & MARKET CAP
In this strategy we have simple method of creating a portfolio this method involves creating portfolio of securities having scrips with high earning yield that is having earnings more than 10% of its price.
The steps to do the same are as follows: 1. Select the universe of National Stock Exchange. 2. Select the actively traded stocks. 3. Now select the stocks with PE > 5. 4. Now sort the stocks in ascending order of PE. 5. Now select top 10 stocks. 6. Eliminate the stocks with market capital less than 100 crores.
The above steps must be performed in every rebalancing of portfolio and these rebalancing must be done on every quarter of the financial year. The duration for this test is 20 years.

FIG 39

FIG 40

Above is the result of the rebalancing done as per the algorithm the portfolio was run between 01st April 1995 to 31st March 2015. During the said period the portfolio gave phenominal results. Holding period returns was 192 times the orginal value that is it grew at a CAGR of 30% for the period of last 20 year this result in itself suggest a simple strategy built using PE ratio and market capitalisation.

FIG 41
Here it shows the portfolio underwent a lot of churning in the last 20 years. This would of course lead to a heavy transaction cost but as we know now the brokerages are less than10bps this seems a small shortcoming to the kind of returns exhibited by the portfolio. The beta is less than 0.68 which is very defensive and the correlation is not very strong to nifty benchmark index.

BUILDING ONLY ON LOW PE STOCKS FROM CNX NIFTY 500 UNIVERSE
In this strategy we have simple method of creating a portfolio this method involves creating portfolio of securities having scrips with high earning yield that is having earnings more than 10% of its price ideally. Here however the stocks have to be listed on Nifty 500 Index
The steps to do the same are as follows: 1. Select the universe of CNX 500. 2. Select the actively traded stocks. 3. Now select the stocks with PE > 5. 4. Now sort the stocks in ascending order of PE. 5. Now select top 10 stocks. 6. Eliminate the stocks with market capital less than 100 crores.
The above steps must be performed in every rebalancing of portfolio and these rebalancing must be done on every quarter of the financial year. The duration for this test is 20 years.

FIG 42

FIG 43

As we can see in the results the holding period return has been 17 times the invested portfolio however what we also get to see is a reduced standard deviation of 19%. The beta is also very defensive at 0.49. However compared to nifty returns of 9.9% this portfolio has given a very strong return numbers. CAGR growth rate is around 16% which is 320 basis points better than Nifty 50 index.

BUILDING ONLY ON HIGH ROE & LOW PE STOCKS
In this strategy we have simple method of creating a portfolio this method involves creating portfolio of securities having scrips with high earning yield that is having earnings more than 10% of its price ideally. Here however the stocks have to be listed on Nifty Index
The steps to do the same are as follows: 1. Select the universe of National Stock Exchange. 2. Select the actively traded stocks. 3. Eliminate the stocks with market capital less than 100 crores. 4. Now select the stocks with PE > 20 but PE < 25. 5. Now select the stocks with ROE > 30. 6. Now sort the stocks in ascending order of PE. 7. Now select top 20 stocks.
The above steps must be performed in every rebalancing of portfolio and these rebalancing must be done on every quarter of the financial year. The duration for this test is 20 years.

As we can see that the above portfolio rebalancing logic has failed terribly. Total Holding Period return was at 8.07 times the initial portfolio value. This is much below the benchmark index performance which was around 9.9 times the initial portfolio value. Information Ratio is negative that suggests that the portfolio has been consistently been beaten over the period of time. This leads us to believe that combination of High PE & high ROE does not give the desired result.

BUILDING ONLY ON HIGH ROE & MEDIUM PE STOCKS
In this strategy we have simple method of creating a portfolio this method involves creating portfolio of securities having scrips with high earning yield that is having earnings more than 10% of its price ideally. Here however the stocks have to be listed on Nifty Index
The steps to do the same are as follows: 1. Select the universe of National Stock Exchange. 2. Select the actively traded stocks. 3. Eliminate the stocks with market capital less than 100 crores. 4. Now select the stocks with PE > 15 but PE < 20. 5. Now select the stocks with ROE >30 . 6. Now sort the stocks in ascending order of PE. 7. Now select top 20 stocks.
The above steps must be performed in every rebalancing of portfolio and these rebalancing must be done on every quarter of the financial year. The duration for this test is 20 years.

The portfolio above has shown tremendous results over the said period of 20 years the portfolio has grown by 31.75 times as compared to the nifty that has grown by 9.9 times in the same period the growth has come at a healthy rate of 20 CAGR. This growth has come at a very good Sharpe ratio and very high Jensen’s alpha meaning that this portfolio has performed better than expectations in most quarters.

BUILDING ONLY ON HIGH ROE & LOW PE STOCKS
In this strategy we have simple method of creating a portfolio this method involves creating portfolio of securities having scrips with high earning yield that is having earnings more than 10% of its price ideally. Here however the stocks have to be listed on Nifty Index
The steps to do the same are as follows: 1. Select the universe of National Stock Exchange. 2. Select the actively traded stocks. 3. Eliminate the stocks with market capital less than 100 crores. 4. Now select the stocks with PE<15. 5. Now select the stocks with ROE >30. 6. Now sort the stocks in ascending order of PE. 7. Now select top 20 stocks.
The above steps must be performed in every rebalancing of portfolio and these rebalancing must be done on every quarter of the financial year. The duration for this test is 20 years.

This portfolio type has the worst performance over the period of 20 years. All the ratios like Sharpe, Jenson and beta are very disappointing for this portfolio.

BUILDING ONLY ON MEDIUM ROE & HIGH PE STOCKS

In this strategy we have simple method of creating a portfolio this method involves creating portfolio of securities having scrips with high earning yield that is having earnings more than 4% of its price ideally. Here however the stocks have to be listed on Nifty Index
The steps to do the same are as follows: 1. Select the universe of National Stock Exchange. 2. Select the actively traded stocks. 3. Eliminate the stocks with market capital less than 100 crores. 4. Now select the stocks with PE > 20 and PE < 25. 5. Now select the stocks with ROE between 25 and 30 . 6. Now sort the stocks in ascending order of PE. 7. Now select top 20 stocks.
The above steps must be performed in every rebalancing of portfolio and these rebalancing must be done on every quarter of the financial year. The duration for this test is 20 years.

In the above portfolio rebalancing method we can see very clearly that the results shows very clearly that the logic has failed to beat the bench mark index. Also for many years there has been no trade in the portfolio so the portfolio is a clear failure.

BUILDING ONLY ON MEDIUM ROE & MEDIUM PE STOCKS

In this strategy we have simple method of creating a portfolio this method involves creating portfolio of securities having scrips with high earning yield that is having earnings more than 5% of its price ideally. Here however the stocks have to be listed on Nifty Index
The steps to do the same are as follows: 1. Select the universe of National Stock Exchange. 2. Select the actively traded stocks. 3. Eliminate the stocks with market capital less than 100 crores. 4. Now select the stocks with PE > 15 and PE < 20. 5. Now select the stocks with ROE between 25 and 30 . 6. Now sort the stocks in ascending order of PE. 7. Now select top 20 stocks.
The above steps must be performed in every rebalancing of portfolio and these rebalancing must be done on every quarter of the financial year. The duration for this test is 20 years.

In the above portfolio rebalancing method we can see very clearly that the results shows very clearly that the logic has failed to beat the bench mark index. Also for many years there has been no trade in the portfolio so the portfolio is a clear failure.

BUILDING ONLY ON MEDIUM ROE & LOW PE STOCKS
In this strategy we have simple method of creating a portfolio this method involves creating portfolio of securities having scrips with high earning yield that is having earnings more than 6% of its price ideally. Here however the stocks have to be listed on Nifty Index
The steps to do the same are as follows: 1. Select the universe of National Stock Exchange. 2. Select the actively traded stocks. 3. Eliminate the stocks with market capital less than 100 crores. 4. Now select the stocks with PE > 5 and PE < 15. 5. Now select the stocks with ROE between 25 and 30 . 6. Now sort the stocks in ascending order of PE. 7. Now select top 20 stocks.
The above steps must be performed in every rebalancing of portfolio and these rebalancing must be done on every quarter of the financial year. The duration for this test is 20 years.

The returns beats the market portfolio by a comprehensive number where the Nifty gave a return of 9.9 times but then the portfolio gave a return of 13.46 times. The CAGR growth over a period of 20 years was 14.65% for the portfolio whereas nifty gave a return of 12.82%. The portfolio bettered the market by 183 bps. The Sharpe ratio was 0.43.

BUILDING ONLY ON LOW ROE & HIGH PE STOCKS

In this strategy we have simple method of creating a portfolio this method involves creating portfolio of securities having scrips with high earning yield that is having earnings more than 4% of its price ideally. Here however the stocks have to be listed on Nifty Index
The steps to do the same are as follows: 1. Select the universe of National Stock Exchange. 2. Select the actively traded stocks. 3. Eliminate the stocks with market capital less than 100 crores. 4. Now select the stocks with PE > 20 and PE < 25. 5. Now select the stocks with ROE > 20 & ROE < 25 . 6. Now sort the stocks in ascending order of PE. 7. Now select top 20 stocks.
The above steps must be performed in every rebalancing of portfolio and these rebalancing must be done on every quarter of the financial year. The duration for this test is 20 years.

In the above portfolio rebalancing method we can see very clearly that the results shows very clearly that the logic has failed to beat the bench mark index. Also for many years there has been no trade in the portfolio so the portfolio is a clear failure.

BUILDING ONLY ON LOW ROE & MEDIUM PE STOCKS

In this strategy we have simple method of creating a portfolio this method involves creating portfolio of securities having scrips with high earning yield that is having earnings more than 4% of its price ideally. Here however the stocks have to be listed on Nifty Index
The steps to do the same are as follows: 1. Select the universe of National Stock Exchange. 2. Select the actively traded stocks. 3. Eliminate the stocks with market capital less than 100 crores. 4. Now select the stocks with PE > 15 and PE < 20. 5. Now select the stocks with ROE > 20 & ROE < 25 . 6. Now sort the stocks in ascending order of PE. 7. Now select top 20 stocks.
The above steps must be performed in every rebalancing of portfolio and these rebalancing must be done on every quarter of the financial year. The duration for this test is 20 years.

The returns beats the market portfolio by a comprehensive number where the Nifty gave a return of 9.9 times but then the portfolio gave a return of 13.46 times. The CAGR growth over a period of 20 years was 13.56% for the portfolio whereas nifty gave a return of 12.82%. The portfolio bettered the market by 74 bps. The Sharpe ratio was 0.44.

BUILDING ONLY ON LOW ROE & LOW PE STOCKS
In this strategy we have simple method of creating a portfolio this method involves creating portfolio of securities having scrips with high earning yield that is having earnings more than 6% of its price ideally. Here however the stocks have to be listed on Nifty Index
The steps to do the same are as follows: 1. Select the universe of National Stock Exchange. 2. Select the actively traded stocks. 3. Eliminate the stocks with market capital less than 100 crores. 4. Now select the stocks with PE > 5 and PE < 15. 5. Now select the stocks with ROE > 20 & ROE < 25 . 6. Now sort the stocks in ascending order of PE. 7. Now select top 20 stocks.
The above steps must be performed in every rebalancing of portfolio and these rebalancing must be done on every quarter of the financial year. The duration for this test is 20 years.

The returns beats the market portfolio by a comprehensive number where the Nifty gave a return of 9.9 times but then the portfolio gave a return of 13.46 times. The CAGR growth over a period of 20 years was 17.03% for the portfolio whereas nifty gave a return of 12.82%. The portfolio bettered the market by 421 bps. The Sharpe ratio was 0.42.

BUILDING ONLY ON HIGH PEG & LOW ROE STOCKS

BUILDING ONLY ON HIGH PEG & MEDIUM ROE STOCKS

BUILDING ONLY ON HIGH PEG & HIGH ROE STOCKS

BUILDING ONLY ON MEDIUM PEG & MEDIUM ROE STOCKS

BUILDING ONLY ON MEDIUM PEG & MEDIUM ROE STOCKS

BUILDING ONLY ON MEDIUM PEG & HIGH ROE STOCKS

BUILDING ONLY ON LOW PEG & HIGH ROE STOCKS

BUILDING ONLY ON LOW PEG & MEDIUM ROE STOCKS

BUILDING ONLY ON LOW PEG & LOW ROE STOCKS

BUILDING ONLY ON HIGH ROC & HIGH PE STOCKS

BUILDING ONLY ON HIGH ROC & MEDIUM PE STOCKS

BUILDING ONLY ON HIGH ROC & LOW PE STOCKS

BUILDING ONLY ON MEDIUM ROC & HIGH PE STOCKS

BUILDING ONLY ON MEDIUM ROC & MEDIUM PE STOCKS

BUILDING ONLY ON MEDIUM ROC & LOW PE STOCKS

BUILDING ONLY ON LOW ROC & LOW PE STOCKS

BUILDING ONLY ON LOW ROC & MEDIUM PE STOCKS

BUILDING ONLY ON LOW ROC & HIGH PE STOCKS

FUNDAMENTAL ANALYSIS OF PHARMA SECTOR STOCKS TREND PATTERN

In this Section of the study we have taken a fundamental analysis of Pharmacy sector and have tried to find out their trend for the past 5 years. On the prima facie of it pharmacy sector has grown tremendously in the past 5 years.

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