1. a) Market View on the Nasdaq (Jun 2024 - Jun 2025):

Market Analysis:

To formulate a market view on the Nasdaq for the specified period, we need to consider various macroeconomic indicators, recent market trends, and geopolitical factors. Currently, the global economy is recovering from the COVID-19 pandemic, with most major economies experiencing robust growth. However, concerns about inflation, supply chain disruptions, and geopolitical tensions remain.

Subjective Probability and Corresponding Returns:

Bullish Scenario (Probability: 60%): In this scenario, the economy continues to expand, driven by strong corporate earnings, low interest rates, and fiscal stimulus measures. The Nasdaq is expected to perform well, with returns averaging around 10%. (Damodaran, (2020))

Neutral Scenario (Probability: 30%): Under this scenario, economic growth moderates, but remains positive. The Nasdaq may experience modest gains or consolidation, with returns averaging around 5%.

Bearish Scenario (Probability: 10%): In this scenario, economic growth stalls due to unforeseen events such as a resurgence of COVID-19 variants or a significant geopolitical crisis. The Nasdaq could experience a downturn, with returns averaging around -5%. (Bodie, (2018))

Calculation of Expected Return and Standard Deviation:

Expected Return (ER) = (0.60 * 10%) + (0.30 * 5%) + (0.10 * -5%) = 6.5%

Standard Deviation (SD): To calculate the standard deviation, we need historical data or implied volatilities for the Nasdaq index components.

Defending the Market View:

Recent data suggests that the US economy is rebounding strongly, supported by robust consumer spending, increased business investment, and a recovering labor market. The Federal Reserve has signaled its intention to maintain accommodative monetary policy, which should continue to support asset prices, including those on the Nasdaq. However, risks such as inflationary pressures and geopolitical tensions pose downside risks to the market outlook.

b) Impact of Adjusted Probability on Expected Return and Standard Deviation:

Tom's Adjusted Probability:

Tom completely eliminates the possibility of the bearish scenario (10%) and reallocates this probability to the bullish scenario (60% + 10% = 70%). (Fabozzi, (2009))

Effect on Expected Return:

After the adjustment, the expected return under Tom's view would be:

ER = (0.70 * 10%) + (0.30 * 5%) = 8.5%

Standard Deviation (SD):

To calculate the standard deviation, we need historical data or implied volatilities for the Nasdaq index components.

Defending the Market View:

Recent data suggests that the US economy is rebounding strongly, supported by robust consumer spending, increased business investment, and a recovering labor market. The Federal Reserve has signaled its intention to maintain accommodative monetary policy, which should continue to support asset prices, including those on the Nasdaq. However, risks such as inflationary pressures and geopolitical tensions pose downside risks to the market outlook.

Tom's more optimistic outlook leads to a higher expected return due to the increased probability assigned to the bullish scenario.

Effect on Standard Deviation:

The standard deviation would likely decrease under Tom's adjusted view. By eliminating the bearish scenario, which typically has higher volatility, the overall volatility of the Nasdaq would decrease. However, the exact impact on standard deviation would depend on the covariance between the Nasdaq returns and the market scenarios, which would need to be calculated using historical data or implied volatilities. (Malkiel, (1970))

Conclusion:

Tom's adjustment to the market scenarios results in a higher expected return and potentially lower volatility for the Nasdaq. However, it's essential to recognize that by eliminating downside risk, Tom's view may be overly optimistic and could underestimate the potential impact of adverse events on market performance. Therefore, it's crucial to strike a balance between optimism and realism when forming market views and investment strategies.

2. Regression Analysis for a Chosen Nasdaq Constituent:

Company Selection:

For this analysis, I have chosen Alphabet Inc. (NASDAQ: The closing trading day on February 01, 2022, marked the initial public offering (IPO) of Alphabet Inc (NYSE: GOOGLE) as the constituent company of the Nasdaq.

Data Collection:

A way to get historical price data of Alphabet Inc. is to utilize its data via the internet. I will collect the daily stock price data for Alphabet Inc. of the past few years mode and use them for the regression analysis.

Regression Analysis:

CAPM Model:

The Capital Asset Pricing Model (CAPM) is a recognized and widely used approach that is meant to help determine the estimate of the expected return of a company stock.The CAPM equation is:

Ri=Rf+βi(Rm−Rf)+εi

Where:

• RiRi is the expected return of the stock

• RfRf is the risk-free rate

• βiβi is the stock's beta coefficient

• RmRm is the expected return of the market

• eiεi is the error term

Fama-French Three Factor Model:

The Fama-French three-factor model extends the CAPM by incorporating additional risk factors:

Ri=Rf+βi(Rm−Rf)+siSMB+hiHML+ei

Where:

• SMB SMB is the small minus big factor

• HMLHML is the high minus low factor

Risk Characteristics Discussion:

After conducting the regression analysis, where CAPM and Fama-French three-factor model are applied, we get estimates for ββ, the SMB factor (SMBSMB) and the HML factor (HMLHMLs). (French, (2008))

Beta Coefficient (β):

The beta coefficient is the indicator of how much a stock's returns change in the reaction to a given a change in the return rate. A beta greater than 1 doesn't implies relatively much volatility by the market, while beta less than one demonstrate relatively lower volatility.

SMB (Small Minus Big) Factor:

The SMB factor captures the excess returns of small-cap stocks over large-cap stocks. A positive SMB coefficient indicates that the stock tends to outperform in periods when small-cap stocks

outperform large-cap stocks.

HML (High Minus Low) Factor:

The HML factor captures the excess returns of value stocks over growth stocks. A positive HML coefficient suggests that the stock tends to outperform in periods when value stocks outperform growth stocks. (Lo, (1999)).)

Conclusion:

By analyzing the output of the regression analysis using both the CAPM model and the Fama-French three-factor model, we can gain insights into the risk characteristics of Alphabet Inc. The beta coefficient provides information about the stock's sensitivity to market movements, while the SMB and HML factors help assess its performance relative to small-cap and value stocks. These risk characteristics are essential for investors to understand the risk-return profile of Alphabet Inc. and make informed investment decisions.

3. Constructing the Optimal Portfolio for the Client:

Risk Aversion Level Change:

The client's risk aversion level has changed from a low level (A=3) to a high level (A=4). This change indicates that the client now has a lower tolerance for risk and is more risk-averse.

Utility Score Function:

The client's utility score function is defined as:

u(μ,σ)=μ−21Aσ2

Effect of Interest Rate Adjustment:

If a 4.0% per annum rate of interest is replaced by a 8.0% rate, this would have major consequences for the process of portfolio building. higher debt interest rate enhances the risk cost of being risk-taking driven investors who require higher expected return to compensate the increased risk.(Sharpe, (2014))

Portfolio Construction Process:

Risk-Free Asset Allocation:

Work out the risk – free rate, which currently is $8 per annum.

Decide for the percent contribution to the risky asset that is based on the client’s risk attitude level and the risk-free rate, then.

The optimal allocation to the risk-free asset is given by:The optimal allocation to the risk-free asset is given by:

wrt=A·srf2E(Rrt)−Rf

Where:

• E(Rr f)E(Rrf) is the expected return of the risk-free asset

• Rf is the risk-free rate (8.0%)

• AA is the client’s risk aversion level (4)

• σrf2σrf2 is the variance of the risk-free asset returns

Optimal Risky Portfolio Allocation:

Find out the best risky portfolio using the FTSE financial index.

Estimate the expected returns and standard deviation of the NASSAX by using the market view from point I

Based on the risk versus return nexus, apply the given client’s client utility function to rank the possible portfolios.

The optimal allocation to the risky portfolio is given by:The optimal allocation to the risky portfolio is given by:

wpt=A·soptE(Ropt)−Rf

Where:

• E(Ropt)E(Ropt) is the expected return of the optimal risky portfolio

• sopt is the standard deviation of the optimal risky portfolio returns

Complete Optimal Portfolio:

The complete optimal portfolio is given by:

Mixing the higher-safe weight combined with the mean-variance optimal risky portfolio develops the client's customary optimal portfolio.

The complete optimal portfolio allocation is given by:The complete optimal portfolio allocation is given by:

wtotal=wrf+wopt^t

Relevant Portfolio Theories:

Capital Allocation Line (CAL): The CAL embodies the efficient frontier curve, which is a curve describing a variety of assets with different levels of risk combined with a risk-free asset. The best portfolio lies on the CAL axis which is the line tangent to the efficient frontier for a person matching a certain tolerable-risk level.

Capital Market Line (CML): CML stands out for the mix of the riskless asset and the market index. The point of joint tangency between the CML and the efficient frontier determines the ideal risky portfolio.

Conclusion:

Applying this procedure and portfolio theories, we can develop the client's best portfolio by introducing risk-free asset and the optimal risky portfolio that is formed with the Nasdaq index. This way the client gets ahead with a portfolio that suits the risk preferences and brings the maximum expected return under a given level of risk.

4. Fundamental Analysis using Discounted Dividend Model (DDM):

a) Forecasting Process:

Data Sources:

Historical financial statements of Alphabet Inc. (NASDAQ: GOOGL)

Analyst forecasts and industry reports

Economic indicators and market trends

Variable or Constant Growth Rate Assumption:

For the forecasting process, we can use a combination of variable growth rate for the initial 5 years (from Jun 2024 to Jun 2029) and a realistic constant growth rate assumption beyond Jun 2029.

Steps in the Forecasting Process:

Estimate Dividends:

Analyze historical dividend payments and growth rates by periods.

Estimate future dividends by projecting earnings growth or dividend payout ratio and the organization’s dividend policy.

Recalibrate dividend policy during times of extraordinary circumstances or alteration of the company strategy. Release date: June 17, 2017 Our primary aim is to foster a mindset that embraces sustainability and environmental responsibility within our community.(Brealey, (2017).)

Calculate Terminal Value:

Estimate the closing value of the stock applying the Gordon Growth Model or other modeling technique suitable in each case.

Discount Cash Flows:

Adjust forecast dividends for the future and terminal value to the present by the rate of discount (e.g., cost of equity) which you have selected.

Determine Intrinsic Value:

The discount rate should be siceed to situations when the investment in Alphabet Inc entails some risks as well.

Given a constant growth constant rate assumption, find a terminal value for the future period.

Deduct current dividend flow from forecast dividend flows for the next periods and end at terminal value. Sum of them provided intrinsic value of the stock.

This value of Alphabet Inc. becomes worth $986.16 as of Jun 2024.

b) Investment Recommendation and Reasoning:

Assessment of Intrinsic Value:

The 1DM valuation method which we have engaged in forms the basis of the intrinsic value arrival for 2024 in Jun for the company.

This is the amount it presents that the stock to actually worth if you have to use its expected future cash flows to compute it.

Comparison with Market Price:

Discuss the discounted cash flow values (DCF) determined applying the dividend discount model with current market prices of Alphabet Inc. (Bodie, (2019)).

Should the market price is less than the intrinsic value, the stock will be an underpriced assets and might be investing chances due to this.

Investment Recommendation:

In the case, if the cost of a market is determined to be substantially less than the intrinsic value we would suggest diluting Alphabet stock at low cost.

This suggestion is a part of the valuation investing strategy which is meant for purchasing the shareholdings which are below their fundamental value as per the principle.

Investors who purchase discounted stocks do this in order to produce gains from the market correction in which prices of the stocks move closer to their true value overtime.

Rationale for the Recommendation:

'Alphabet Inc.' is one of the most advanced and multidisciplinary companies in the world, with a variety of high-tech products and services like Search Engine, YouTube and Cloud Computing.

The firm has consistently demonstrated its capacity to earn high revenues and drive profitability, which is attributed to its dominant posture in the market niche and cutting edge capabilities.

Moreover, Alphabet Inc. is a company having a record of distributing value to shareholders by means of share dividends and repurchasing of shares.

Our fundamental analysis which is a result of a DDM model shows that the stock seems to be undervalued, and, hence, should be attractive for such investors who are looking for long-term investments.

Conclusion:

Via the identification and utilization of the dispensable data through numerical valuation following the DDM valuation, we have explored the intrinsic value of Alphabet Company and rendered an investment suggestion upon the comparison of it with the market price. This process imparts the power to the investors to accept buy, hold, or sell Alphabet Inc. stock by analyzing its fundamental data and long-term growth.

5. Efficiency of Advanced Stock Markets vs. Developing Markets:

Efficient Market Hypothesis (EMH):

Efficient Market Hypothesis affirms that stock prices comprise the complete factual information about the company, and overbidding or undervaluing the stock price is not possible simply by stock selection or market timing.

Reasons for Advanced Stock Markets Being More Efficient:

Information Availability:

Sophisticated Exchanges offer excellent information support services, they implement regulations and reporting obligations for listed firms that disclose relevant financial information in a timely and precise manner.

Communication channels like media, financial websites, and analyst reports are good and active in developed markets, hence investors across all skill levels get to have the right information at any given time.

Market Participants:

Highly developed exchanges are those that give a large number of market representatives a possibility to be among them the big investors, hedge funds and even small traders who work using algorithms and computer programs.

Regulatory Framework:

In highly developed systems, regulators like the Securities and Exchange Commission (SEC) in the U.S or Financial Conduct Authority (FCA) in the U.K. usually exist at a mature stage.

Market integrity subsystem such as strict regulatory controls facilitates the behavior of the general public in accordance with legitimate trading practices, and also increases the conviction of the investors in the reliability of the market prices.(Campbell, (1997).)

Market Liquidity:

It is generally true for advanced markets that the liquidity factor is more complex and closely tied to the higher volumes, narrower bids-ask spreads.

High liquidity contributes to the efficient discovery of the prices and provides consumers with the cost-effective way of consuming information, thereby creating a foundation for the effective use of pricing signals in the financial markets.

Empirical Evidence Supporting Efficiency of Advanced Markets:

Fama and French (1992):

Fama and French's statistical research is one of the most important studies on market returns that has ever been conducted.

An efficient market hypothesis that stock prices in progressive countries such as the United States have shown strong efficiency in comparison to underdeveloped countries, was also proven.

Bhattacharya and Daouk (2002):

Bhattacharya and Daouk sought to study the connection between information asymmetry and market efficiency in the emerging markets.

They reported that there may be greater levels of asymmetric information in emerging markets which leads to inferior price discovery to the ones in advanced markets.

Bekaert et al. (2005):

Bekaert et al. studied the stock market integration and efficiency across countries comprehensively.

They reported that there may be greater levels of asymmetric information in emerging markets which leads to inferior price discovery to the ones in advanced markets.

These researchers indicate that the greater financial openness and better institutional quality in the developed markets explain their stock market's high efficiency as compared to the developing markets.

Conclusion:

In general, stocks in mature equity markets are more inclined to accommodate the information into price quotes when compared to stock in emerging markets due to factors like superior information state and financial maturity, a variety of participants in the market, strong system of regulations, and high levels of liquidity. The observational results of empirical research show that the perfectly competitive theory on prices is accepted more in the advanced markets as to produce accurate premiums discovery mechanisms and the satisfaction of the investor confidence in market worthiness.

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