Mean variance analysis
Asset valuation is the process of determining the current value of a company’s assets, such as stocks, bonds, buildings, equipment, brands, and goodwill. The book value of assets is calculated using a variety of approaches, including calculating the current values of assets and adjusting them with the present values of liabilities (Pinto 2020). To determine the fair value of assets, models such as discounted cash flow analysis, option pricing model, or similar method are used. Asset valuation is a critical component of portfolio management and plays a significant influence in determining an investment’s profitability (Hrdý 2018). The following section discusses the many approaches employed by investment managers all around the world to manage their funds:
- Net asset value method – The net asset value approach assesses a firm’s fair value by subtracting the book value of tangible assets from the book value of intangible assets and liabilities, or the amount of money obtained if the company were to be liquidated today (Deloitte.com 2022). The resultant number is the company’s fair worth, which is then compared to its market value. The firm is undervalued if its market value is less than its determined book value (Groww 2022).
- Absolute valuation methods – This method involves discounting the expected cash flow of the company with an appropriate discount rate which represents the required rate of return by the markets (Chastenet and Marion 2014). Some of the popular models are discussed below:
- Dividend Discount Model – This model involves discounting the expected dividend paid by the company to the present day using a discount rate (Sim and Wright 2017). The following chart displays the different types of the model:
- Discounted free cash flow model – This is one of the most popular models which is used in the financial world to value a company (Green, Hand and Zhang 2016). Under this method the free cash flow from the business is estimated and discounted to the present day using a weighted average cost of capital rate which includes both debt and equity cost (Sarastov 2017).
- Relative valuation approach – This model values a company using price multiples of comparable companies. Metrices like price to earnings ratio, price to book ratio and price to cash flow ratio are used to value companies (Pétursson 2016).
Mean variance analysis is a portfolio management strategy that involves comparing a stock’s estimated variation to its expected return (Garcia, González-Bueno and Oliver 2015). This strategy is used to make investment decisions in which investors evaluate the degree of profits earned while considering various levels of risk. It is an element of current portfolio theory, which asserts that investors are rational and make decisions based on available information (Kalayci, Ertenlice and Akbay 2019). Figure 1 in appendix represents a sample portfolio allocation using mean variance analysis technique.
The variance of a stock is a metric that displays the daily or weekly spread of the stock’s returns. When the returns of two stocks are almost comparable, the one with the lowest variance is chosen for investment (Martin and Wagner 2019). If the investment’s expected variance is close to the same, the stock with the higher return would be preferred.
Optimization is a strategy that aids an investor in determining the best portfolio for a specific goal. Maximization of profits and risk reduction evaluated in terms of variance at the lowest level achievable are the major objectives (towardsdatscience 2020). The utility maximization issue is another name for the portfolio optimization process. According to current portfolio theory, optimization assumes that investors are rational and want the maximum level of return for the lowest amount of risk. Assets are chosen based on their relative performance, and minimal correlations between them are required. Due to the diversity obtained, the resulting portfolio would be less risky and likely to outperform the market during stormy times (Dai and Wen 2018).
The portfolio optimization method is based on Modern Portfolio theory and the most prevalent criticism of portfolio optimization is that the method analyses a portfolio using variance of the securities instead of downside risk. Variance of a stock may seem to be high due to small losses experienced frequently (Frajtova-Michalikova, Spuch?akova and Misankova 2015). A similar stock may be having high variance due to infrequent but big falls in value. Investors in the real world are not as rationale as predicted by Modern Portfolio Theory and they are willing to tolerate small amount of losses.
Portfolio optimization
Mean variance analysis is a technique that has been criticized by a number of people since the financial model used in the approach is not representative of the actual world. The variables employed in this strategy, such as correlation, risk assessed by variance, and projected returns, are based on values estimated in the future using various statistical methods (Gotoh, Kim and Lim 2018). The statistical methods utilized in this method do not represent genuine values and are frequently excessively skewed, resulting in overstated return increases. This approach of portfolio allocation relies on historical market data that is frequently unrepresentative of future market conditions, and the risk of losses calculated is frequently unjustified in terms of the cause of the losses (Qin 2015).
Asset management is concerned with managing money for a person or an organisation by determining the client’s goals and objectives and implementing solutions through portfolio management (Davis 2016). Portfolio management is based on the notion of integrating numerous assets into a portfolio in order to achieve the clients’ objectives. Investors’ objectives are typically characterised using variables such as risk and return, and it is the portfolio manager’s responsibility to design a portfolio that focuses on either maximising return or minimising risk, depending on the client’s desire and risk tolerance. Mean variance analysis is the technique used by portfolio managers to optimally allocate assets into appropriate asset classes. This strategy evaluates the risk and return characteristics of different assets, making it simpler for a portfolio manager to choose stocks that meet the client’s goals. The portfolio as a result of mean variance analysis is then optimized incorporating client’s utility function and risk tolerance capacity. Out of hundreds of portfolios represented by an efficient frontier created using this technique, comprising of various combinations of assets, the point tangent to the investor’s indifference curve is the optimal portfolio (Gârleanu and Pederson 2018).
Lazardi Investment Management has taken a top-down strategy to investing, focusing on the idea that developing economies will outperform developed markets. There are a lot of firms in emerging economies that don’t have good accounting processes and are overvalued by the marketplace. As a result, asset valuation methodologies such as discounted cash flow are used to analyse the worth of enterprises operating in emerging markets in order to help the company obtain an informational edge over competitors. Various start-up enterprises are based in emerging economies, and their values are determined using relative valuation approaches (Köhn 2018). The firm uses mean variance analysis techniques paired with optimization approaches to disperse its money across multiple sectors and industries, allowing it to diversify and lessen the risk associated with emerging market economies.
Criticism of portfolio optimization
Conclusion
As the core approach of asset allocation, Lazardi Investment Management should use mean variance analysis techniques to identify equities in the developing market to invest the corpus of GBP 150 million. The corporation should also use neural networks, which have previously proven to be useful in identifying portfolio building flaws, since they might aid in adding the volatilities of a stock’s returns into the model. Neural networks improve the algorithm for selecting and investing in stocks (Arxiv.org 2022). When allocating cash to sectors and then to stocks, it’s important to keep your expectations for the portfolio’s outcome in control as mean variance analysis is based on historical data which may be non-representative of future events. The data to be fed into the model should be checked for biasness and regime switching problem to reduce the risk and get more optimized asset allocation suggestion. As stocks in emerging markets are risky, methods like VaR and covariance should be followed to reduce the risk inherent in the portfolio. Constraints should be used to include the goal of obtaining supernormal returns from the portfolio into the asset allocation process. The fund should invest the GBP 150 million in sectors or equities that are performing very well, which may be accomplished using mean variance portfolio optimization approaches. The mean variance analysis model should also take into account investor restraints, which should be handled equally.
When it comes to picking sectors and limiting it down to stocks that are offered for a reasonable price, the corporation should use a combination of top down and bottom up approaches. Methods such as DCF and DDM can be used to assess firms in emerging markets since market participants frequently value companies incorrectly, resulting in over or under valuation. The inherent values of freshly launched companies with well-established brands or companies in the same field are determined using relative valuation approaches. Optimization techniques are further utilized to allocate the stock into appropriate stocks which are close in risk and return parameters.
Covid 19 may have an impact on asset management firms because to a liquidity shortfall in the investment management industry. Due to severe selling pressures, asset management organisations have been vulnerable to increased redemption requests and asset valuation has been harmed. To avoid high volatility and capital loss, an asset management business needs to effectively implement risk management measures.
References
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