The modern approach to portfolio management is a common sense and valuable methodological starting point to the portfolio management process (Phayre 63). Quantitative processes refer to combination of fundamental or traditional evaluation a human agent performs using information and judgment with quantitative outputs that computer-driven models generate following fixed rules (“Challenges in Quantitative Equity Management” 657). Effective quantitative management is one that interfaces information ratios with skill, efficiency and diversification (Khan 5). Performance is the measure of some desirable characteristic (“Challenges in Quantitative Equity Management” 653). Portfolio management entails investing capital in a variety of investment instruments and opportunities (Mau 12). Risk is the emergent phenomenon that depends on the complexity of a given situation. Data describe the empirical patterns that researchers observe, while theory explains why these patterns are so.
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The rationale of quantitative fund management is to capture market inefficiencies to inform investment decisions (“Trends in Quantitative Equity Management” 115). Different professionals in the equity market believe that quantitative processes give an edge whenever there is a complex problem to solve (“Challenges in Quantitative Equity Management” 662). A large amount of electronic information concerning companies’ financial performance is available in databases accessible online.
A majority of firms managing assets eschew forecasting models (“Trends in Quantitative Equity Management” 116). A survey on the efficacy of optimization in relation to portfolio management revealed that optimization presents a challenge in differentiating quantitative products and improving on perforce (“Trends in Quantitative Equity Management” 116). Experts anticipate that the trend in quantitative management of funds will continue albeit with variations in the extent to which various firms apply this approach (“Challenges in Quantitative Equity Management” 649). The period of 2004 to 2005 saw an increase the equities under quantitative management a trend experts anticipate will continue (“Trends in Quantitative Equity Management” 116, 117).
The modus operandi in equity management, and in particular the fact that nowadays traders eschew thresholds of leveraging above 100%, has contributed to the positive results the equity market is realizing (“Trends in Quantitative Equity Management” 117). Models utilizing the time series of prices and returns are subject to self-indicative deductions with a potential for mispricing (“Challenges in Quantitative Equity Management” 650). Where there is a lack of full information on some parameters that characterize an investment environment, agents may rationally deviate from the empirical distribution of the state variables (Massimo 3). One study showed that investment professionals rely less on quantitative methods as predictability declines (Mau 16). Data and text mining enable financial agents study hidden indications about future performance of companies from various financial reports of the same.
Financial Economic Theories
Quantitative investing involves rigorous analysis using scientific methods to investing (Khan 5). According to one expert (cited in “Challenges in Quantitative Equity Management” ) strategies that are driven by fundamentals make for improved market outcomes compared to technical model-driven strategies because the latter rely on trend following (“Challenges in Quantitative Equity Management” (650).
Optimization is the engineering of portfolio construction with the use of quantitative models (“Trends in Quantitative Equity Management” 120). Investment strategies are not static but change overtime (“Challenges in Quantitative Equity Management” 650). Operationally, quantitative investing is narrow because it employs financial rations agents have established to optimize portfolios, a factor that has seen this approach performing poorly overtime (Khan 5). Qualitative methods are a set of data collection and analysis techniques that find utility in providing description, and in building and testing theory.
Mathematical and Technical Approaches or Principles
On approach, the dynamic stochastic programming (DSP), enables the combining of practical details with the rapid optimization necessary for gainful financial planning overtime (Dempster and Medova 4). Many situations require the employment of a mixture of experiments to investigate the effects of different proportions of input factors. An important and basic component is to determine the asset allocation with minimum risks that come with market volatilities. DSP is necessarily a decision support tool that enables interactivity between modifications of personal preferences and data inputs as market variables prompt (Dempster and Medova 4).
Financial problems are usually not exact or correct but find utility in exploring solutions to problems (“Challenges in Quantitative Equity Management” 660). Because quantitative models are contingent on the optimization, which technology has made possible given its inherent large data handling capabilities (“Trends in Quantitative Equity Management” 118). The fundamental role of financial markets is to facilitate the raising of capital and most efficiently allocate this capital to productive activities (“Challenges in Quantitative Equity Management” 655). Multifactor financial models determine the utility of how history influences stock position and specifically, they provide a starting point that gives analysts latitude on what portfolios to act on (Phayre 63).
The easily accessible technological capabilities of quantitative methods, the high volume and quality of data these generate and data portability are factors that are contributing to the increasing adoption of quantitative methods (“Trends in Quantitative Equity Management” 117). Objectives for investment are contingent on numerous factors non-technical factors such as personal priorities, aspirations, human capital, and family status, among other similar variables (Dempster and Medova 17). Excellent short-term performance may be due to large positions in takeover situations where the acquirer has bid at a substantial premium to the prevailing market price or a market bias for that portfolio (Phayre 62).
For share prices to accurately reflect their actual values, a large cluster of investors who are attempting to value them in absolute terms and also relative to other share prices need to be present. Of necessity, if there are tools available that help improve decision making in risky management situations, learning is possible and with this, improved risk management strategies carried forward (Mau 16). The rigorous mathematical models that quantitative management employs do not mitigate, in terms of unfavorable outcomes, the shortfalls of poor investment decisions.
There are two categories of models, those with a basis on fundamentals and those utilizing the analysis of time series of past prices and returns (“Challenges in Quantitative Equity Management” 650). Respondents to the utility of quantitative methods attribute their increasing use to the positive results these methods realize (“Trends in Quantitative Equity Management” 117). Scholarship has not paid adequate attention to the mechanisms by which investors form and update their beliefs.
Models underpinned by fundamentals use characteristics of firms to make forecasts and in principal favor market efficiency (“Challenges in Quantitative Equity Management” 650). Systematic market inefficiencies are detectable for analytical purposes using various models that accommodate departures from efficiency (“Trends in Quantitative Equity Management” 115). Because fallible human agents control markets, profit opportunities exist because cumulatively, residual imperfections invariably add up to delays and distorted responses to news (“Challenges in Quantitative Equity Management” 650).
Quantitative methods in managing continue to win favor because of the availability of computing power and the resulting accurate data (“Trends in Quantitative Equity Management” 117). Kahn (2010) asserts that skills that have insights as their basis are ephemeral because market dynamics demand that investors develop new insights with a leaning towards developing proprietary data (6). A previous survey projected that quantitative management has increasingly find favor in the management of equity funds over the past three decades (“Trends in Quantitative Equity Management” 116). Attempts to reconcile investor behavior theories with practice have so far been unsuccessful (Dempster and Medova 3).
Equity markets are tending towards reduced efficiency that the rise of indexation, a reduction in the number of major participating agents and closet indexing precipitate. Lo (cited in “Challenges in Quantitative Equity Management”), argues that markets are adaptive structures in a state of continuous change in which agents strive to optimize profits from the ephemeral opportunities that present themselves (650). Quantitative models now find employment in seeking out in not only stock ranking and risk control functions, but also positive market trends (“Trends in Quantitative Equity Management”117).
Basic approaches to managing risks while being easy to implement and appeal to intuition may yield implausible estimates of economic capital. Some major risk characteristics for portfolio managers must contend with (Mau 15). These include large losses such as a significant drop in price or negative returns; returns below target or nonpayment of interest and knowledge or lack of it of quality, timely and comprehensive information about the firm (15). Additionally, another risk is the ability to manage unfavorable developments such as losses (15). For risky portfolios to perform highly on the short term, an investing agent has to combine them with market-timing abilities that are very difficult to cultivate (Phayre 62).
For gainful investment over the long term, there is need to adopt a tractable technology that enables the optimization of complex stochastic dynamic systems that are capable of coping with a myriad of details over time (Dempster and Medova 3). Agents miss opportunities when they do not fully appreciate the true drivers of opportunities that may arise or may have limited liquidity that makes it difficult for them to trade out of positions (Phayre 62). Financial markets are open systems where differentials in accumulation or destruction of capital can potentially result in unrecoverable assets, bankruptcies and even an exit from the system (“Challenges in Quantitative Equity Management” 654).
Since learning is stronger in the aftermath of structural breaks, the data should display deviations from unconditional properties (Massimo 18). If agents are on a recursive-learning path, tail events may produce long-lasting effects on equilibrium prices (Massimo 21). Even seasoned technocrats find it difficult to predict returns in a volatile market. The effectiveness of models and trading strategies hinges on macroeconomic laws and the policy and regulatory climate as these determine overall performance in fields of finance such as asset management (“Challenges in Quantitative Equity Management” 650).
A growth manager is far more reliant on his own insight than on objective criteria. Financial advisors mostly lean towards employing rules of the thump as opposed to academic solutions for investment decisions (Dempster and Medova 3). A moving average system leaves an agent locked in a trend-following structure and the associated rigid buy and sell cycles. The psychology of investing impairs the making of good market-timing decisions that are necessary for portfolios to profit from economic factor exposures (Phayre 62). The economic value of different risk management strategies remains a question and these new approaches do not predict extreme events (Mau 14). Whereas developing theories is important, equally important is developing these theories further.
Dempster, M. A. H. and E. A. Medova. “Asset Liability Management for Individual Households” British Actuarial Journal (2011). 1-42. Print.
Fabozzi, Frank J., Sergio Forcadi and Caroline Jonas. “Trends in Quantitative Equity Management.” Quantitative Finance 7.2 (2007): 115-122.
Fabozzia, Frank, Sergio Focardi and Caroline Jonas. “On the Challenges in Quantitative Equity Management.” Quantitative Finance 8.7 (2008): 649-655.
Kahn, Ronald N. “Quantitative Equity Investing: Out of Style?” Journal of Portfolio Management 36.2 (2010): 5-6. Print.
Massimo Guidolin. “Pessimistic Beliefs under Rational Learning: Quantitative Implications for the Equity Premium Puzzle.” Federal Reserve Bank of St. Louis Working Paper 2005-005A (2004). 1-33.
Mau, Ronald, R. “Back to Basics: A Process Approach for Managing Portfolio Risk.” International Journal of Economic and Finance 1.2 (2009): 12-20.
Phayre, Sean. “Dismissing the Critics of Modern Portfolio Theory.” Investment Week Nov. 2011: 62-63. Print.