Overview
Quantamental – a portmanteau joining the words quantitative and fundamental investing, is an investment approach incorporating quantitative strategies, which utilize data analysis, statistical methods and mathematical models to generate investment insights, and fundamental approaches, which focus on a deep dive in fundamental attributes of individual companies.
Fundamental Investing
Traditionally, fundamental investing focuses on evaluating the intrinsic value of a single company by forecasting and modelling its future cash flows and analyzing its financial statements. Analysts put great emphasis on the evaluation of core competitiveness of a firm through qualitative and quantitative methods, including ratio analysis, competitive landscape, growth prospect, and meetings with management. The ultimate goal of fundamental investing is to identify and invest in companies which are undervalued in the markets when compared with their intrinsic values in hope of long-term investment gains. Notable fundamental investors include Warren Buffet, Peter Lynch and David Tepper.
Through the fundamental approach, analysts develop in-depth knowledge on companies, generating alpha and avoiding questionable companies with red flags and doubtful behaviors. Nevertheless, one of the main disadvantages of fundamental investing is that it is subject to human emotions and cognitive biases, for instance recency bias and loss-aversion bias, which affect investment judgements and may lead to costly investment errors.
Quantitative Investing
Quantitative investing utilizes mathematical models, statistical techniques and state-of-the-art hardware to identify investment opportunities and make trading decisions. Vast amounts of financial and non-financial data are analyzed to identify exploitable patterns, characteristics and anomalies. A notable example of quantitative investing is factor investing, which identifies and invests in securities with a common set of characteristics (i.e. factors) that delivered excess risk-adjusted return historically. Well-researched and documented factors included value, size, yield and momentum. Quantitative methods are also used to pinpoint idiosyncratic factors (i.e. alpha) that generate excess return but cannot be explained by conventional factors and risk premia. AQR Capital, Two Sigma and Renaissance Technologies are some of the leaders in the quantitative investing field.
With the help of advanced technologies, high volume of data could be analyzed efficiently, which serves as valuable inputs in building robust models. All quantitative models are back-tested extensively before going-live to minimize model risk. Quantitative investing also avoids human emotions and cognitive biases as quantitative systems, instead of humans, will be responsible of making investment decisions.
While quantitative investing processes several distinctive advantages there are a few drawbacks as well. It is generally difficult for quantitative models to measure and analyze non-quantifiable and qualitative factors, such as leadership, culture and core competitiveness. Also, the robustness of the statistical models highly depends on the quality of data; should the data is flawed outputs generated by the models will be doubtful as well, i.e. garbage in garbage out. At last, changing investor behaviors and macroeconomic factors will affect the market landscape and undermine the usefulness of historical data analysis and back-testing.
Quantamental Investing – The Best of Both Worlds
Quantamental strategies aim at capitalizing on the advantages of both quantitative and fundamental strategies while overcoming their inherent flaws by complementing the two strategies. In essence, quantamental strategies seek to combine the best of both worlds and deliver better risk-adjusted return to the investors.
When implementing quantamental strategies, portfolio managers make investment decisions based on outputs from the best quantitative models and compliment the outputs with fundamental insights. In a typical quantamental workflow, the quantitative team will be collecting and maintaining a vast number of types of data. Researches will be carried out on the data to identify any exploitable factors, alphas and patterns and quantitative models will be constructed and back-tested based on the research outcomes. Meanwhile, the fundamental team will be generating insights through fundamental methods, including ratings on corporate governance, cash-flow analysis and financial health check, and these insights will serve as invaluable inputs to the quantitative models, and will be used in the next stage where the research output from both quantitative models and fundamental research are integrated to make the final trading decisions and construct the investment portfolio. This ensures that the final outputs pass both quantitative and fundamental investment criteria and minimize the risk of outputs suffering from the quantitative or fundamental biases and errors we identified above. After the investment portfolio is constructed, both quantitative and fundamental teams will continue to monitor the portfolio jointly to optimize the portfolio performance.
There are a number of intriguing areas where quantamental techniques are generating invaluable insights in the investment process. One example will be the use of natural language processing (NLP) in analyzing corporate earnings call when companies present their financial results and business outlooks. Fundamental analysts traditionally gain deeper understanding on the financial performance of a company by looking for verbal cues when company management presents the financial performance and answers questions from investors. Natural language processing utilizes algorithms to analyze every word from the earnings call transcripts to explore patterns, trends and sentiments of the company management and analysts. For instance, a large proportion of optimistic words used by the senior management might signal their confidence in the outlook of the company, hence suggesting a bullish outlook on the stock price of the company.
Conclusion
The asset management industry is at a turning point where advanced technologies and quantitative methods are gradually reshaping the industry landscape. Portfolio managers that can harness the power of machines and combine it with the brightest human minds will be able to command a key competitive edge in the long run.
Dr. Linwen Ho
Chief Director (International Business), Amber hill Group
Dr Ho is a professor of practice at the Hong Kong Polytechnic University. Mr. Ho has over 20 years of experience in the areas of investment management, investment banking, capital investment, risk management, product development, transfer pricing and business restructuring.
Amber Hill Group is a fast-growing global investment firm dedicated to providing personal financial planning to high-net-worth clients around the world. The core quantitative trading systems created by the Group have performed well, and the assets under management of the Group's subsidiaries and affiliated companies exceed US $1,000,000,000.
