Research Analyst

In the 18th century, Munehisa Homma, a Japanese rice trader, pioneered the development of candlestick charts for financial instruments. These charts were later introduced to the Western world by Steve Nison in his book, “Japanese Candlestick Charting Techniques.”

Candlestick charts serve as a powerful technical tool, condensing a wealth of data across various time frames into individual price candles or bars. These charts provide a visual method for analyzing and predicting price movements in financial markets, utilizing historical price data and market statistics. Unlike traditional open-high, low-close bars or simple connecting lines, candlesticks offer enhanced usability. They effectively illustrate trends in both price and volume for a specific stock or index over a defined period. Interpreting a candlestick chart is more intuitive than sifting through extensive numerical data, making it easier to grasp market trends, with each candlestick representing a trading day, for instance, in a one-month chart.

Candlesticks typically consist of a body (black or white) and upper and lower shadows, referred to as the “wick.” The real body, the space between the open and close, is flanked by shadows that depict price fluctuations above and below the real body. The wick signifies the high and low prices of a traded security. The body reflects opening and closing trades. A white or green body indicates a close higher than the open, with the opening price at the bottom and the closing price at the top. Conversely, a black or red body signifies a close lower than the open, with the opening price at the top and the closing price at the bottom. Candlesticks may also appear as “doji,” lacking either a body or a wick.

Candlestick charts find frequent application in technical analysis, particularly for assessing patterns in equity, derivative, commodity, and currency prices. Although they may bear a superficial resemblance to box plots, they are unrelated. Candlestick patterns visually represent price movements on a chart, aiding in the identification of historical support and resistance levels, crucial for predicting market movements. These patterns fall into two categories: A) SIMPLE and B) COMPLEX.

A) SIMPLE Candlestick Patterns include: Big White Candle, Big Black Candle, Doji, Hammer, Hanging Man, Inverted Hammer, Shooting Star, Long Upper Shadow, Long Lower Shadow, Marubozu, Spinning Top, Shaven Head, and Shaven Bottom.
B) COMPLEX Candlestick Patterns encompass: Three White Soldiers, Three Black Crows, Bullish or Bearish Harami, Morning Star, Evening Star, Dark Cloud Cover, Engulfing Bullish or Bearish Line, On Neckline, Tweezer Tops or Bottoms, Hikkake Pattern, Piercing Line, Rising Window, Judas Candle, and Darth Maul.

Charles Henry Dow (November 6, 1851 – December 4, 1902) was an American newspaper journalist who co-founded Dow Jones & Company alongside Edward Jones and Charles Bergstresser. Dow is credited with establishing the stock price average on July 3, 1884, within the “Customer’s Afternoon Letter,” featuring 11 initial companies—9 railroads and 2 non-rail companies, Pacific Mail Steamship and Western Union Telegraph. Serving as the newspaper’s first editor from 1889 to 1902, Dow earned acclaim as a financial expert, with his writings forming the basis for the “Dow Theory” in market analysis.

Founded by Dow, The Wall Street Journal stands as one of the world’s most esteemed financial publications. Dow’s contribution extended to creating the Dow Jones Industrial Average as part of his research into market movements. His insights led to the development of six tenets, known as Dow Theory, which laid the foundation for understanding and analyzing market behavior, becoming a cornerstone of technical analysis.

1) Stock and index prices incorporate all available information, adhering to the principles of the Efficient Market Hypothesis.
2) Primary trends, lasting a year or more, dictate whether a market is bullish or bearish. Secondary trends, enduring three weeks to one quarter, represent corrective movements, while minor trends capture short-term fluctuations in stock prices, spanning from one day to one week.
3) Primary trends persist until a discernible reversal takes place.
4) Primary trends guide investors on profiting from both bull and bear markets, with the public subsequently engaging in buying or selling.
5) To validate a trend, volume should increase in the direction of that trend.
6) Two conflicting primary trends cannot coexist on two different market indices. A primary trend identified in one market must always be corroborated by a similar trend in another market, such as pairing railroad trends with industrial manufacturing indices.

Ralph Nelson Elliott (July 28, 1871 – January 15, 1948) was an American accountant and author known for his analysis of stock market data. He developed the Wave Principle, a form of technical analysis that identifies specific patterns in financial market price trends, known today as Elliott waves.
Five wave pattern (dominant trend)
Three wave pattern (corrective trend)
A correct Elliott wave count must have three rules:
Wave 2 never retraces more than 100% of wave 1.
Wave 3 cannot be the shortest of the three impulse waves, namely waves 1, 3 and 5.
Wave 4 does not overlap with the price territory of wave 1, except in the rare case of a diagonal triangle formation.

Wave 1: The inception of wave one is typically inconspicuous. In the initiation of a new bull market, fundamental news tends to be overwhelmingly negative, accompanied by low-volume stock trading. The prevailing trend is perceived to be still dominant. Fundamental analysts persist in downward revisions of earnings estimates, and the overall economic outlook appears lackluster. Bearish sentiment prevails, reflected in trending put options and high implied volatility in the options market. Although prices may rise, volume shows only minimal increases, often escaping notice by technical analysts.

Wave 2: Serving as a correction to wave one, wave two is constrained from extending beyond the starting point of wave one. Despite ongoing bearish sentiments in the news, a retest of the previous low prompts the building of bearish sentiment once again, reinforcing the notion that the bear market persists. However, for those observing momentum or volume indicators, a few positive signs emerge. Volume during wave two is typically lower than in wave one, prices seldom retract beyond 61.8% of the wave one gains (as indicated by the Fibonacci retracement level), and the decline in prices consistently follows a three-wave pattern.

Wave 3: Regarded as the largest and most potent wave in all three trends (though some research suggests wave five might be the largest in commodity markets), wave three marks a shift to positive news. Fundamental analysts, influenced by improved financial ratios, raise earnings estimates. Prices surge rapidly, and corrections, when they occur, are brief and accompanied by low volume. Those seeking to capitalize on pullbacks are likely to miss the trend. At the onset of wave three, the news or prevailing trend may still be bearish, with most market players and analysts maintaining a negative outlook in the short term. However, by the midpoint of wave three, “the crowd” undergoes a shift in mindset and analysis towards a new bullish trend. Wave three often surpasses the length of wave one by a ratio of 1.618:1.

Wave 4: Distinguished as a clearly corrective phase, wave four often sees prices moving sideways for an extended period. Typically, this wave retraces less than 38.2% of wave three (in line with Fibonacci relationships). Characterized by lower volume compared to wave three, wave four presents an opportune moment to buy a pullback for those who comprehend the potential for wave 5. However, fourth waves can be frustrating due to their limited progress within the larger trend.

Wave 5: Serving as the final leg in the direction of the prevailing trend, wave five witnesses overwhelmingly positive news, and a pervasive bullish sentiment prevails, often resembling herd mentality. Unfortunately, this surge often coincides with a large number of investors buying stocks right before the market’s peak. Volume in wave five tends to be lower than in wave three, and momentum indicators like the Relative Strength Index (RSI), Stochastic Oscillator, True Strength Index (TSI), Price Rate of Change, Commodity Channel Index (CCI), Advance/Decline (A/D) line, etc., begin showing divergences—where prices reach a new high, but the indicators do not—indicating a potential upcoming profit-taking phase. As a major bull market concludes, bearish traders may find themselves discredited, having incurred losses during the bull market (recall the skepticism toward forecasts of a market top during 2005 to 2007).

Wave A: Corrections in trends are often more challenging to discern than impulse moves. During wave A of a bear market, fundamental news tends to remain positive. Analysts commonly interpret the decline as profit booking or a correction within an ongoing bull market. Certain technical indicators may signal wave A, such as higher volume (indicating a potential trend reversal), a surge in implied volatility in the options market (leading to higher option costs), and a potential rise in open interest in the futures market option chain.

Wave B: Prices undergo a reversal to the upside, often interpreted as the beginning of a new bullish market phase. Technical analysts might identify the peak as the right shoulder of a head and shoulders reversal pattern or a breakout from a channel. Volume during wave B should typically be lower than in wave A. At this juncture, fundamental indicators, such as company earnings, Industrial Production (IP) data, and inflation, may cease improvement, yet they haven’t shifted into negative territory, discouraging a short position.

Wave C: Prices exhibit a decisive downward impulse in five waves. Volume begins to increase, and by the third leg of wave C, widespread recognition of a firmly established bear market sets in. Wave C typically matches or surpasses the size of wave A, often extending to 1.618 times wave A or beyond.

Elliott wave rules and guidelines 
A precise Elliott wave “count” must adhere to three rules:
According to Ralph, Wave 2 should never retrace more than 100% of Wave 1.
Wave 3 must not be shorter than the three impulse waves, namely waves 1, 3, and 5.
Wave 4 should not intersect or overlap with the price territory of Wave 1, except in the rare case of a diagonal triangle.

In a five-wave pattern, a fundamental guideline notes that waves 2 and 4 often assume different forms. For instance, a sharp move in wave 2 may indicate a mild move in wave 4. Corrective wave patterns manifest in various forms such as zigzags, flats, or triangles. These corrective patterns can combine to form more intricate corrections. Notably, ascending, descending, and symmetrical triangular corrective patterns usually emerge in wave 4 (occasionally in wave 2) and signal the conclusion of a correction, suggesting a potential opportunity for a long position.

William Delbert Gann (June 6, 1878 – June 18, 1955) was a renowned finance trader credited with developing technical analysis tools such as Gann angles, Square of 9, Hexagon, and Circle of 360, collectively known as Master charts. Gann achieved significant success as a stock and commodity trader. His market forecasting methods were rooted in geometry, astronomy, astrology, and ancient mathematics. Opinions on the value and relevance of his work vary sharply. Gann’s approach involves studying natural time cycle trends and utilizing natural geometric shapes and ancient mathematics to analyze patterns, historical data, and trends as predictors for an asset’s future price movements. Various books by Gann explore different trading methodologies.

In 1935, Gann introduced a Forecasting Method based on the utilization of patterns and trends in the stock market. Calculating a Gann angle is akin to determining the derivative of a specific line on a chart in a straightforward manner. Each geometric angle, essentially a line extended into space, divides time and price proportionately into its parts. Gann deemed the most valuable angle as the 1×1 or the 45° angle, representing one unit of price for one unit of time. Illustratively, envision drawing a perfect square and then drawing a diagonal line from one corner to another – this elucidates the concept of the 1×1 angle, signifying a movement of one point per day.

Gann counseled his students to exercise caution against overtrading and emphasized the importance of taking positions based on sound logic rather than mere hope. He advocated the development of distinct strategies tailored to various market conditions, including bull markets, transitions from bull to bear markets, bear markets, and shifts from bear to bull markets. The question of whether Gann primarily generated profits through speculation remains a subject of general disagreement.

Richard Demille Wyckoff (November 2, 1873 – March 19, 1934) was a stock market author, the founder and editor of the Magazine of Wall Street, and also served as the editor of Stock Market Technique journal.

Implementing his methods in the financial markets, Wyckoff continued his role as a trader and educator in the stock, commodity, and bond markets during the early 1900s. Intrigued by the rationale behind market action, Wyckoff derived key principles and methodologies through his research, conversations, and interviews with successful traders of his era, aimed at providing guidance for trading.

Richard Wyckoff Given A Five-Step Approach to the Market

1] Determine the present position and probable future trend of the market.
2] Select stocks in harmony with the trend.
3] Select stocks with a “cause” that equals or exceeds your minimum objective.
4] Determine the stocks’ readiness to move.
5] Time your commitment with a turn in the stock market index.
Wyckoff analyzed stock market operators and their operations, identifying optimal points for trading in terms of risk and reward. He emphasized the critical placement of stop-losses at all times, the significance of controlling the risk associated with each trade, and demonstrated techniques for campaigning within the broader trend, whether bullish or bearish. The Wyckoff technique offers insights into the strategies employed by professional traders when buying and selling securities. Wyckoff gained renown for his expertise in analyzing trading range markets.

Three Wyckoff Laws 

1] Wyckoff adhered to the belief that the law of supply and demand dictates the direction of price movements.
2] Wyckoff employed the law of cause and effect to assist traders and investors in establishing price objectives by assessing the potential scope of a trend arising from a trading range or sideways market. In essence, when demand surpasses supply, prices ascend, and when supply outweighs demand, prices decline.
3] Wyckoff recognized the significance of the law of effort on trade versus result, offering an early warning of a potential shift in trend. Monitoring divergences between volume and price is crucial, as they often indicate an imminent change in the direction of a price trend.

Quantitative finance traces its origins back to 1900 with Louis Bachelier’s doctoral thesis, “The Theory of Speculation.” This groundbreaking work introduced a model for pricing options under a normal distribution. According to this theory, there exists only one correct price for any given security at a specific period in time.

In the realm of quantitative analysis, individuals who assess situations or events maintain an objective, numerical, and measurable approach. This is particularly evident in financial markets, where complex mathematical and statistical research methods are applied to solve business problems and measure long-term trends. Quantitative analysts, often referred to as “quants,” are employed by investment banks, asset managers, hedge funds, private equity firms, and insurance companies. They play a crucial role in identifying profitable investment opportunities over the long term and managing risks. In sales and trading, quants use mathematical tools, with stochastic calculus being a principal method in quantitative finance, to determine prices, manage risk, and uncover profitable opportunities.

A statistically oriented quantitative analyst commonly tackles challenges like developing models to distinguish between relatively expensive and cheap stocks. This involves considering metrics such as a company’s book value-to-price ratio, trailing earnings-to-price ratio, and other accounting factors. An investment manager can then utilize this analysis by purchasing undervalued stocks, selling overvalued ones, or both. Quantitative analysis comprises various types, including statistical arbitrage, quantitative investment management, algorithmic trading, and electronic market making.

In 1965 Paul Samuelson introduced stochastic calculation into the study of finance

Paul Anthony Samuelson (May 15, 1915 – December 13, 2009) was a distinguished American economist, and he achieved the distinction of being the first American to be awarded the Nobel Memorial Prize in Economic Sciences. His contributions elevated the level of scientific analysis and significantly advanced both static and dynamic economic theory on a global scale. Samuelson introduced two categories of business cycle theories based on the interaction of the multiplier and accelerator. E. Randall Parker, an economic historian, hails him as the “Father of Modern Economics,” and according to The New York Times, Paul Samuelson was recognized as the “foremost academic economist of the 20th century.”

Samuelson was instrumental in developing the neoclassical synthesis, a study that harmonizes neoclassical microeconomics with neo-Keynesian macroeconomics. His teachings emphasized the fundamental unity of economic problems and analytical techniques by systematically applying the maximization methodology to a wide array of issues. Samuelson’s contributions spanned various fields, and he believed that at its core, economics is fundamentally about people—how they earn a living and how they allocate their income.

Black–Scholes model

Fischer Black and Myron Scholes introduced the Black–Scholes model (BSM) in their 1973 paper, “The Pricing of Options and Corporate Liabilities,” published in the Journal of Political Economy. The Black–Scholes equation, derived from this model, serves as a widely employed mathematical method for calculating the theoretical value of an option contract. Utilizing factors like current stock prices, expected dividends, the option’s strike price, anticipated interest rates, time to expiration, and expected volatility, the model facilitates risk elimination through a hedging strategy known as delta hedging. This approach involves buying and selling the underlying asset in a precise manner. Delta hedging forms the basis for more intricate hedging strategies undertaken by investment banks and hedge funds.

The Black-Scholes equation relies on five essential variables, including volatility, the price of the underlying asset, the strike price of the option, the time until expiration of the option, and the risk-free interest rate.

The Black-Scholes model makes certain assumptions

1] No dividends are paid out during the life of the option contract.
2] Markets are random (i.e., market movements cannot be predicted by anyone).
3] There are no transaction costs in buying the option only price exculated.
4] The risk-free rate and volatility of the underlying asset are known and constant.
5] The returns of the underlying asset are normally distributed.
6] The option is European and can only be exercised at expiration US Option not consider.

The Black-Scholes model has limitations, notably that it exclusively applies to pricing European options and does not consider the possibility of early exercise for U.S. options before the expiration date.

In March 1976, Black and Scholes asserted that human capital and business experience “ups and downs that are largely unpredictable” due to fundamental uncertainty about future consumer preferences and the economy’s production capabilities. They noted that if future tastes and technology were known in advance, profits and wages would experience smooth and certain growth over time. According to their business cycle theory, a boom occurs when technology aligns effectively with demand, while a bust represents a period of mismatch where predictions deviate from actual market dynamics. The Black-Scholes model played an early role in contributing to the understanding of the business cycle.

The Black-Scholes Model (BSM) assumes a log-normal distribution for stock prices, as assets cannot have negative prices and are bounded by zero. However, real-world asset prices often exhibit significant right skewness (asymmetry in the distribution) and some degree of kurtosis or fat tails (non-normal distribution patterns). This suggests that high-risk downward movements occur more frequently in the market than predicted by a normal distribution.

Behavioral economics and behavioral finance explore the impact of social, psychological, cognitive, emotional, and other factors on the economic decisions made by individuals and institutions. These decisions have direct consequences for market prices, returns, and resource allocation. Behavioral economics focuses on understanding the bounds of rationality within economic agents. Behavioral models in this field integrate insights from psychology, neuroscience, and microeconomic theory, covering a diverse range of concepts and methods. Often seen as an alternative to neoclassical economics, the study of behavioral economics includes examining how market decisions are made and understanding the mechanisms that drive public choice.

There are three important themes given in behavioral finances

Heuristics refer to mental shortcuts that individuals commonly use to simplify problems and avoid cognitive overload. These are practical problem-solving methods that rely on learning from past experiences. In psychology, heuristics are recognized as simple, efficient rules that people often employ to form judgments and make decisions. Decisions are frequently made based on approximate rules of thumb rather than strict logic. For example, when deciding the best mode of transportation for a 20-kilometer distance, individuals may consider options such as a car, bus, bike, train, or metro train, using practical and efficient decision-making shortcuts.

Framing, within the social sciences, encompasses the concepts and theoretical perspectives shaping how individuals, groups, and societies organize, perceive, and communicate in reality. It involves a collection of anecdotes and stereotypes forming the mental and emotional filters individuals use to comprehend and react to events. These filters often influence how people perceive and interpret information, sometimes leading to unfair generalizations about certain individuals or groups based on limited experiences or preconceived notions.

Market inefficiencies, or market anomalies, represent distortions in returns that deviate from the predictions of the efficient market hypothesis (EMH). These anomalies manifest as discrepancies in the pricing of stocks, where market prices differ from what models anticipate. These anomalies can result from mispricings, deviations from rational pricing mechanisms influenced by the interplay of supply and demand. Additionally, non-rational decision-making processes, which are intuitive and evaluative rather than strictly based on reason, contribute to market inefficiencies. Such inefficiencies challenge the notion of a completely efficient market.

The two analytical models : Fundamental analysis vs Technical analysis

When investors aim to decide which stocks to purchase and at what price, they typically rely on two fundamental methodologies:

a] Fundamental analysis posits that markets might misjudge a security in the short term, but the “correct” price will eventually prevail in the long term. Profits can be gained by acquiring the mispriced security at a lower price, then patiently waiting for the market to acknowledge its “mistake,” allowing for profit-taking at a higher price during the repricing of the security. Fundamental analysis is employed for long-term investment and involves the examination of financial statements, ratio analysis, balance sheets, etc.

b] Technical analysis asserts that past price data and information are already embedded in the current price of a security. Analysts employing technical analysis study market trends and rely on chart patterns to anticipate changes in sentiment and predict price targets. Recognizable price chart patterns emerge from investors’ emotional or greedy responses to price movements. Technical analysis is applied for short-term trading, ranging from intraday momentum positions to one-month positions.

Investors can integrate one or both methods for stock selection. For instance, numerous fundamental investors incorporate technical analysis to determine optimal entry and exit points. Likewise, many technical investors incorporate fundamental analysis to narrow down their selection to companies considered “good.”

Fundamental analysis involves assessing a stock’s true or “fair market” value. Analysts conduct research to identify stocks trading above or below their actual value. When the fair market value exceeds the market price, the stock is considered undervalued, prompting a buy recommendation. Fundamental analysis comprises three primary components: Economic analysis, Industry analysis, and Company analysis.

There are two types of fundamental analysis – Qualitative and Quantitative.

a) Qualitative analysis incorporates non-quantifiable data to evaluate a company’s investment potential. This data encompasses aspects such as the business model, competitive advantage, management quality, corporate governance, stakeholder satisfaction, ethics, brand value, and more. It is often referred to as soft data.

b) Quantitative analysis in finance is an approach that emphasizes mathematical and statistical methods to assess the value of a financial asset, such as a stock or option. This involves scrutinizing quantitative data found in financial statements like the balance sheet, income statement, and statement of cash flows.

Fundamental analysis takes into account various factors such as interest rates, production, earnings, employment, GDP, housing, manufacturing, and management. Often regarded as the father of fundamental analysis, Benjamin Graham pioneered this approach. When scrutinizing a stock, futures contract, currency, or commodity using fundamental analysis, two fundamental approaches are commonly employed: bottom-up analysis and top-down analysis.

1) Bottom-up analysis in investing focuses on company-specific fundamentals, such as financials, supply and demand, and the types of goods and services offered by a company. This approach emphasizes a business-by-business or sector-by-sector evaluation based on fundamentals.

2) Top-down analysis begins with an examination of the broader economy, assessing macroeconomic factors, and identifying specific industries poised to thrive in that economic environment. The top-down investor then selects companies within the chosen industry.

Five steps of fundamental analysis :  A) Economic and Industrial Analysis, B] Analysis of Company Financial Statements, C] Forecasting relevant payoffs,Check the debt D] Find the company’s competitors , Formulating a security value and E] Analyse the company future prospects, Make a Buy/Sell/Hold recommendation..

Fundamental Analysis encompasses a comprehensive review, from conducting a SWOT analysis to assessing PE ratios. By leveraging fundamental data, various ratios and metrics are formulated, offering insights into a company’s performance and consumer demand relative to its peers.

In financial economics, the Efficient Market Hypothesis (EMH) posits that “beating the market” is impossible, as stocks consistently trade at their fair value on exchanges. This makes it challenging for investors to buy undervalued stocks or sell stocks at inflated prices, as the market reflects all relevant information. EMH suggests that due to market randomness, investors may fare better by investing in a low-cost, low-beta stock, or a passive portfolio.

“There are three versions of the efficient market hyothesis : Weak, Strong and Semi-Strong.”

Weak Form Efficiency, often referred to as the random walk theory, posits that publicly available information, such as past price movements, volume, and earnings data, does not influence the current price of a stock, bond, or property. In this theory, past information holds no correlation with current market prices, challenging the basis of using historical data for predicting future market directions, a concept at odds with technical analysis.

Semi-Strong Form Efficiency asserts that prices promptly adjust to reflect all publicly available information, rendering both technical and fundamental analyses ineffective for gaining insights or generating higher returns (alpha) in the market. Private information not yet made public is considered the only potential source of an informational edge.

Strong Form Efficiency posits that the moment any information, whether public or private, becomes available through conferences, interviews, or digital platforms like LinkedIn, Twitter, Instagram, Facebook, etc., it is instantaneously incorporated into stock prices. This theory asserts that prices swiftly adjust to even concealed or “insider” information.

Following are the EMH Assumptions for a market to be efficient

A large number of investors analyze and value securities for profit.
New information comes to the market independent from other news and in a random fashion.
Stock prices adjust quickly to new information.
Stock prices should reflect all available information.
Financial theories are subjective, lacking proven laws or formulas to explain market workings. However, certain theories and ideas, when applied to past data, can help understand market trends and facilitate predictions.

In 1952, economist and Nobel Prize winner Harry Markowitz penned his dissertation on “Portfolio Selection,” presenting a practical approach to maximize overall returns within an acceptable risk level. This theory is valuable for investors aiming to construct efficient and diversified portfolios using ETFs, optimizing risk for a given return. Those prioritizing downside risk may lean towards post-modern portfolio theory (PMPT) over MPT.

Markowitz outlined two types of risk

Systematic risk, stemming from economic, political, and sociological macro-level changes, is both unpredictable and unavoidable. Diversification alone cannot mitigate this risk; instead, effective strategies involve hedging or employing the right asset allocation methods to address systemic risk.

Unsystematic risk pertains to factors specific to individual stocks, such as operational issues, management changes, or shifts in consumer preferences. Risk-averse investors employ the theory to build diversified portfolios that optimize returns while maintaining an acceptable risk level. MPT underscores the advantages of diversification, emphasizing the wisdom of not concentrating investments in a single asset.

Key Assumptions of MPT: Investors, being risk-averse, prefer less risky portfolios when faced with options delivering the same expected return. Rational investors strive to select the optimal portfolio. The relationship between risk and return is direct, meaning that higher expected returns necessitate taking on more risk.

Advantages: MPT is employed to enhance overall returns without escalating risk through portfolio diversification. It aids in selecting the most efficient portfolio by analyzing various potential combinations of securities. Additionally, it contributes to reducing portfolio volatility.

Limitations: The theory assumes that two portfolios may exhibit the same variance levels for different reasons. One portfolio might have variance due to small, frequent losses, while another could show similar variance because of two or three larger declines.

Active management, or active investing, involves a portfolio management strategy in which the manager seeks to outperform an investment benchmark index. Active managers aim to anticipate long-term macroeconomic trends, such as focusing on specific sectors like energy or housing stocks. They may also target stocks of companies that are temporarily undervalued or selling at a discount to their intrinsic value.

Passive management involves investors seeking returns that closely mirror the investment weighting and performance of a benchmark index, such as Nifty50 or Sensex30. Investors in passive management often opt for index funds. This approach aims to eliminate the impact of human biases, leading to potentially better performance. Passive management strategies typically entail lower fees compared to actively managed funds. Historical records indicate that investing in index funds has, in many cases, outperformed the majority of actively managed funds.

Active management offers several advantages. Managers rely on in-depth investment analysis, research, and forecasts, incorporating quantitative tools and their own judgment. The approach can range from strictly algorithmic to entirely discretionary, depending on the asset class. Active fund managers enjoy greater flexibility in fund management. Additionally, actively managed funds employ various strategies like risk arbitrage, short positions, option writing, algo trading, and asset allocation. This flexibility provides the opportunity to outperform the index.

Active management comes with some disadvantages. These funds typically have higher fees and are less tax-efficient compared to passively managed funds, mainly due to frequent changes in the portfolio. Active managers may attempt to time the market, but there is a risk of making incorrect decisions. Additionally, there is the potential for underperformance compared to the index.

Investors who follow active management strategies do not align with the stronger forms of the Efficient Market Hypothesis (EMH), which assert that beating the market consistently is impossible in the long run because all public information is already reflected in stock prices. Portfolio managers employing active management, such as those in hedge funds or mutual funds, use diversification based on investment goals. Additionally, active management aims to create portfolios with less volatility or risk compared to the benchmark index.

Options

In finance, an option is a contract that grants the buyer the right, though not the obligation, to buy or sell an underlying asset (such as a stock, index, or commodity) at a predetermined price on or before a specified date. Options provide investors with the opportunity to speculate on or hedge against the volatility of the underlying asset. Options trading is utilized for both hedging and speculation purposes, employing strategies that vary from simple to complex.

Options are American and European. Both option are difference relates to when the options can be exercised

A European option can be exercised only at its expiration date, occurring at a specific, predetermined point in time. This type of option carries lower risk due to the fixed expiration date.

An American option provides the flexibility to be exercised at any point before its expiration date. This feature increases the risk associated with American options, as the option holder can choose to exercise it whenever it proves profitable.

In India, all exchange-traded options, including both index options and stock options, follow the European option style. Previously, stock options were American options, but currently, they are exercised as European options. If a trader has an open position in a Stock Futures contract and In-The-Money Stock Options that is not squared off by the expiry date, these contracts must be physically settled. This entails the trader either giving or receiving the delivery of the underlying stocks to settle the transaction.

There are two types of options : Call Option and Put Option

Call options are financial agreements that grant the option buyer the right, though not the obligation, to purchase an underlying stock, bond, commodity, or another asset at a predetermined price within a specified time frame. Call buyers realize profits when the value of the underlying asset rises.

A put option, often referred to as a “put,” is a contractual agreement that provides the option buyer with the right, without the obligation, to sell or short a designated quantity of an underlying security, bond, or commodity at a predetermined price within a specified timeframe. Put buyers realize profits when the value of the underlying asset declines.

The option premium is the cost associated with acquiring the right, but not the obligation, to trade an underlying market at a specified price for a predetermined period, typically one month in the case of a near-month contract. This premium encompasses both intrinsic value and time value. In the context of out-of-the-money options, the premium mainly consists of time value, while for in-the-money options, it includes both intrinsic and time value. Factors influencing an option’s premium include time to expiration and implied volatility, with higher values for more extended expiration periods or increased volatility. When the market price of the underlying stock rises, Call Options Premiums increase, while Put Options Premiums decrease. For an option buyer, the maximum loss is limited to the premium paid, with the potential profit theoretically unlimited.

An option’s value has several elements

a] The value of the underlying security refers to the shares, bonds, or commodities that are obligated to be delivered by one party in the derivative contract and accepted by the other party.
b] Expiration time: the final date when derivative contracts, such as options or futures, are valid.
c] Implied volatility: The anticipated level of volatility in the underlying asset.

d] The designated strike price: The price at which the underlying asset will be sold, determined by the seller, on the contract’s expiration date, settling in either cash or physical delivery.

e] Dividends: Dividends are profits distributed by a corporation to its shareholders. When a corporation generates a profit, it can allocate a portion as a dividend to shareholders. Typically, stock prices decrease by the dividend amount on the ex-dividend date, influencing options pricing. Call options become less expensive due to the expected stock price decrease, while put options become more expensive for the same reason.

What happens to strike price after dividend? As per NSE, After the declaration of an “extraordinary” dividend by a company, the total dividend amount (comprising special and/or ordinary dividends) is subtracted from all the strike prices of the option contracts related to that stock. The adjusted strike prices take effect from the ex-dividend date specified by the exchange,

f) Interest rates play a crucial role in options trading, influencing the pricing and attractiveness of options contracts. Traders can speculate on interest rate directions using interest rate options. Interest rate risk is the potential impact of changing overall interest rates on the value of fixed-rate investments such as bonds. Generally, when interest rates rise, stock or bond prices tend to fall. The relationship between interest rates and options pricing is essential in the option pricing model. As interest rates increase, the appeal of buying puts diminishes slightly, leading to lower put option prices. The risk-free rate signifies the expected return from a completely risk-free investment within a specified timeframe.

The Greeks, also known as risk sensitivities, risk measures, and hedge parameters, play a crucial role in risk management within the financial markets. These quantities represent the sensitivity of derivative prices, such as options, to changes in underlying parameters. The Greeks are integral in achieving a desired exposure or rebalancing a portfolio, exemplified by strategies like delta hedging in the stock market. Delta, gamma, theta, and vega are the most common Greeks, extensively utilized in derivatives and options pricing models. Traders and portfolio managers rely on the Greeks to effectively hedge their positions and manage risks.

Delta ( Δ OR δ OR 𝛿 )

Delta, a crucial Greek in options trading, represents the rate of change in the theoretical option value concerning changes in the underlying asset’s price. It is the first derivative of the option value with respect to the underlying instrument’s price, indicating its price sensitivity. For long calls, delta is always positive, and for long puts, it is negative (unless it is zero). In terms of the hedge ratio, delta provides insight into the number of underlying shares required for a neutral position.

For call options:
Delta ranges from 0 to +1.
OTM Call (Out-of-the-Money): Delta approaches 0
ATM Call (At-the-Money): Delta approximately +0.50
ITM Call (In-the-Money): Delta is +1.
For put options:
Delta ranges from 0 to -1.
OTM Put (Out-of-the-Money): Delta approaches 0
ATM Put (At-the-Money): Delta approximately -0.50
ITM Put (In-the-Money): Delta is -1.

For example, if the stock ABC has a current market price of 100, a call option with a strike of 110 will have a delta of 1, a call with a strike of 100 will have a delta of 0.50, and a call with a strike of 90 will have a delta of 0. For put options, the delta values are similar but negative.

Gamma ( uppercase Γ, lowercase γ )

Gamma, a crucial metric in options trading, gauges the pace of delta’s transformation in response to shifts in the underlying asset’s price. It represents the second derivative of the value function concerning the underlying price. Gamma stands as positive for long options and negative for short options. It attains its maximum when the option is near or at the money, diminishing when the option is deep in or out of the money. While delta adapts based on the underlying asset price, gamma remains a constant signifying the rate of delta’s alterations. Gamma computations offer precise insights for slight variations in the underlying asset’s price, showcasing sensitivity to volatility. It specifies the extent by which delta would adjust with a Rs.1 movement in the underlying security. For instance, envision an investor holding a long call option for a hypothetical stock XYZ. If the call option has a delta of 0.50 and a gamma of 0.10, a Rs.1 rise or fall in stock XYZ would prompt the call option’s delta to increase or decrease by 0.10.

Theta (uppercase Θ or Ө, lowercase θ )

“Theta, a vital metric in options trading, quantifies the pace at which an option’s value diminishes over time, commonly known as time decay. Option traders employ theta to assess time sensitivity. When all factors remain constant, an option experiences a reduction in value as it approaches maturity. Theta holds a consistently negative value for buyers or those holding long positions in calls and puts, while it remains positive for option writers or those involved in short (written) calls and puts. Theta tends to increase when options are at-the-money and decrease when options move in- and out-of-the-money. As options approach expiration, theta exhibits accelerating time decay. For instance, if an option has a strike price of Rs.1,150 and a theta of 53.80, the option’s theoretical value will decrease by Rs.53.80 per day, according to the time decay effect.

Vega (V, α Lyr, α Lyrae, Alpha Lyrae)

Vega, a critical factor in options pricing, measures the impact of changes in the implied volatility of the underlying asset on the contract’s price. Options, both calls, and puts, witness a rise in value with increasing volatility. Vega holds a positive value for both call and put options when there is time until the expiration date. Volatility gauges the magnitude and speed of price fluctuations, relying on recent historical price changes in a trading instrument. Vega is influenced by factors such as time until expiration, the strike price relative to the underlying asset’s spot price, and implied volatility. Changes in Vega occur during significant price movements or heightened volatility in the underlying stock or asset, and it diminishes as the option approaches expiration. Vega serves as a tool to evaluate an option’s potential for value appreciation relative to the underlying asset before the expiration date. For instance, an option with a vega of 0.10 implies that the option’s value is expected to change by 10 cents with a 1% change in implied volatility.

Volatility Analysis

“Volatility, a statistical metric, assesses the extent of data dispersion around its mean (average) during a specified time frame. Typically measured through standard deviation, range, or variance among returns, volatility reflects the level of risk associated with a security or market index. Elevated stock price volatility indicates increased risk in adopting a position. Conducting volatility analysis aids investors in anticipating potential future fluctuations. Market volatility results from diverse factors, including economic conditions, financial news, shifts in interest rates, and changes in fiscal policies. These elements consistently contribute to the dynamic nature of market volatility.

The India Volatility Index, known as the “fear index,” is a dynamic indicator calculated by the NSE to gauge the market’s expectations for volatility in the near term, specifically over the next 30 days, based on Nifty 50 options trading. Introduced by the NSE in 2003, the India VIX serves as a reliable measure of market volatility, providing intraday traders with insights into market fluctuations. Commonly utilized tools to assess relative volatility levels include standard deviation, India Volatility Index (VIX), average true range (ATR), Bollinger Bands, Donchian Channel, and Keltner Channel. Volatility analysis factors in elements such as time to expiry, interest rates, forward index level, and bid-ask spreads.

There are two types of Historic Volatility and Implied Vloatility

Historic Volatility

Historical Volatility (HV) is a statistical finance indicator that gauges the distribution of returns for a particular security, bonds, or market index over a specified period. HV is determined by calculating the average deviation of the instrument from its mean price, typically using standard deviation. It does not provide guidance on when to buy or sell an asset and does not identify overbought or oversold levels. Increased price fluctuation leads to higher historical volatility, indicating heightened uncertainty and risk in the market over a period. When volatility is low, options’ premiums are also low. HV is calculated by dividing the implied volatility of an option by the historical volatility of the security during a specific timeframe. A ratio of 1.0 indicates fair value, while a ratio of 1.3 suggests the option is overpriced, quoting at 30% higher than its actual value.

Implied Volatility : (IV)

Implied Volatility (IV) signifies the anticipated level of volatility in the underlying asset or security’s price. Traders commonly refer to it as IV or simply volatility. A higher IV suggests increased uncertainty regarding the stock’s price. With rising IV, one can anticipate more significant fluctuations in both the underlying price and options prices. Expressed as a percentage change associated with one standard deviation on an annualized basis, an implied volatility of 20% indicates an expected 20% change in price over the next year. In option pricing models, implied volatility is a crucial parameter, offering insight into potential future price variations.

Implied Volatility (IV) is influenced significantly by supply and demand dynamics and time value. It plays a crucial role in pricing options contracts, leading to higher premiums and increased uncertainty in periods of elevated implied volatility. Generally, IV decreases in bullish markets and rises in bearish markets. High volatility is identified when a stock, typically exhibiting a 1% to 2% daily price range, suddenly experiences a 3-5% range, signaling increased market turbulence.

The put-call ratio (PCR)

PCR, or Put/Call Ratio, serves as a key options market indicator employed by traders to gauge market sentiment. It is computed by dividing the total number of put options traded over a specific timeframe by the total number of call options.

A put-call ratio of 1 suggests an equal number of buyers for calls and puts, which is uncommon based on historical data. An increasing put-call ratio, exceeding 1, signifies that traders are acquiring more puts than calls, reflecting a bearish sentiment in the stock market. Conversely, if traders are purchasing more call options than put options, indicating a bullish sentiment, the PCR may fall below 1 or approach 0.5.

Max Pain Strike Price

The max pain strike price is the option contract’s strike price with the highest open interest, representing the level at which traders anticipate the stock or index to remain. If the price exceeds this level, it may lead to financial losses for the majority of option holders at expiration. Historical data shows that around 90% of options expire worthless, making option writing a more consistent strategy for sellers. Towards the contract’s expiry, especially on Thursdays, there tends to be significant market movement towards these strike prices, causing potential losses for option buyers. Some believe that large institutions may manipulate index prices by writing options, avoiding contractual obligations and hedging their payouts to buyers.

Rho (/ˈroʊ/; uppercase Ρ, lowercase ρ or ϱ; Greek: ῥῶ)

Rho measures how an option’s value responds to changes in the risk-free interest rate, indicating the amount an option may gain or lose with a 1% interest rate change. At-the-money options with longer expiration times typically exhibit the highest Rho. Long call options have a positive Rho that increases with the stock price, while long put options have a negative Rho that approaches zero with higher stock prices. Short put options have a positive Rho, while short call options have a negative Rho. For instance, if a call option with a rho of 0.05 and a price of Rs.1.25 experiences a 1% interest rate increase, its value may rise to Rs.1.30, assuming other factors remain constant.

BETA ( β OR ϐ OR B )

Beta is a measure of a stock or portfolio’s expected movement relative to the overall market. A beta at or below 1.0 indicates a low-volatility stock for risk-averse investors, while a beta above 2.0 suggests a high-volatility stock for those seeking excitement. For instance, a beta of 1.5 means the portfolio’s value changes by 1.5% for every 1% change in the benchmark. Beta is more indicative of short-term rather than long-term risk. It’s worth noting that negative beta stocks tend to increase when the market falls and vice versa.