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:
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.
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.
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
(2022) Arxiv.org. Available at: https://arxiv.org/ftp/cs/papers/0501/0501005.pdf (Accessed: 3 March 2022).
(2022) Www2.deloitte.com. Available at: https://www2.deloitte.com/content/dam/Deloitte/xe/Documents/About-Deloitte/mepovdocuments/mepovissue22/reading-between-the-lines_mepov22.pdf (Accessed: 2 March 2022).
Chastenet, E. and Marion, A., 2015. Valuation using industry multiples: How to choose the most relevant multiples. Business Valuation Review, 34(4), pp.173-183.
Dai, Z. and Wen, F., 2018. A generalized approach to sparse and stable portfolio optimization problem. Journal of Industrial & Management Optimization, 14(4), p.1651.
Davis, R., 2016. An introduction to asset management. Retrieved November, 20, p.2016.
Frajtova-Michalikova, K., Spuch?akova, E. and Misankova, M. (2015) “Portfolio Optimization”, Procedia Economics and Finance, 26, pp. 1102-1107. doi: 10.1016/s2212-5671(15)00936-3.
García, F., González-Bueno, J. and Oliver, J. (2015) “Mean-variance investment strategy applied in emerging financial markets: Evidence from the Colombian stock market”, Intellectual Economics, 9(1), pp. 22-29. doi: 10.1016/j.intele.2015.09.003.
Gârleanu, N. and Pedersen, L.H., 2018. Efficiently inefficient markets for assets and asset management. The Journal of Finance, 73(4), pp.1663-1712.
Gotoh, J.Y., Kim, M.J. and Lim, A.E., 2018. Robust empirical optimization is almost the same as mean–variance optimization. Operations research letters, 46(4), pp.448-452.
Green, J., Hand, J.R. and Zhang, X.F., 2016. Errors and questionable judgments in analysts’ DCF models. Review of Accounting Studies, 21(2), pp.596-632.
Hrdý, M., 2018. Valuation standards for commercial banks in the financial theory and their analysis. Prague Economic Papers, 27(5), pp.541-553.
Kalayci, C.B., Ertenlice, O. and Akbay, M.A., 2019. A comprehensive review of deterministic models and applications for mean-variance portfolio optimization. Expert Systems with Applications, 125, pp.345-368.
Köhn, A., 2018. The determinants of startup valuation in the venture capital context: a systematic review and avenues for future research. Management Review Quarterly, 68(1), pp.3-36.
Martin, I.W. and Wagner, C., 2019. What is the Expected Return on a Stock?. The Journal of Finance, 74(4), pp.1887-1929.
NAV – What is Net Asset Value, Types, Formula and its Roles (2022). Available at: https://groww.in/p/nav (Accessed: 2 March 2022).
Pétursson, E., 2016. Relative Valuation–Accuracy of Corporate Valuation Using Multiples.
Pinto, J.E., 2020. Equity asset valuation. John Wiley & Sons.
Qin, Z., 2015. Mean-variance model for portfolio optimization problem in the simultaneous presence of random and uncertain returns. European Journal of Operational Research, 245(2), pp.480-488.
Sarastov, Z.G., 2017. A stochastic DCF model for valuing and stress testing banks and its application. In International Conference on Economic Research, Alanya, Turkey, 20-21 October 2017. Proceedings (pp. 140-150). Alanya Alaaddin Keykubat University.
Sim, T. and Wright, R.H., 2017. Stock valuation using the dividend discount model: An internal rate of return approach. In Growing Presence of Real Options in Global Financial Markets. Emerald Publishing Limited.
Understanding Portfolio Optimization (2020). Available at: https://towardsdatascience.com/understanding-portfolio-optimization-795668cef596 (Accessed: 2 March 2022).
Essay Writing Service Features
Our Experience
No matter how complex your assignment is, we can find the right professional for your specific task. Contact Essay is an essay writing company that hires only the smartest minds to help you with your projects. Our expertise allows us to provide students with high-quality academic writing, editing & proofreading services.
Free Features
Free revision policy
$10Free bibliography & reference
$8Free title page
$8Free formatting
$8How Our Essay Writing Service Works
First, you will need to complete an order form. It's not difficult but, in case there is anything you find not to be clear, you may always call us so that we can guide you through it. On the order form, you will need to include some basic information concerning your order: subject, topic, number of pages, etc. We also encourage our clients to upload any relevant information or sources that will help.
Complete the order form
Once we have all the information and instructions that we need, we select the most suitable writer for your assignment. While everything seems to be clear, the writer, who has complete knowledge of the subject, may need clarification from you. It is at that point that you would receive a call or email from us.
Writer’s assignment
As soon as the writer has finished, it will be delivered both to the website and to your email address so that you will not miss it. If your deadline is close at hand, we will place a call to you to make sure that you receive the paper on time.
Completing the order and download
