Updated July 15, 2023
What is Quantamental?
The term “Quantamental” refers to the investment strategy commonly used by hedge fund managers wherein the traders’ decisions are aided by the results generated using machine learning and artificial intelligence.
Combining a quantitative approach and human intervention in buying and selling various financial instruments, such as bonds, stocks, derivatives, etc., improves portfolio performance. Effectively, this investment strategy helps overcome the flaws of broken models and human bias.
Explanation
Quantitative trading primarily refers to the computer-driven approach involving mathematics, finance, and trading experience. It helps build a computer code that can leverage an exploitable market anomaly, such as price arbitrage or momentum trading. Investors characterize these strategies by holding many securities to take advantage of the law of large numbers and the strategies’ ability to process an infinite amount of data.
On the other hand, the fundamental investment approach implicates the valuation of a business based on its forecasted future cash flows. The forecasting is typically based on the expected growth rate, current financial position, and other factors, including quantifiable and non-quantifiable information, such as economic reports, other analyst valuations, earnings call transcripts, news events, etc.
Now, the word quanta mental combines both the abovementioned approaches – quantitative and fundamental. As the name suggests, asset managers intermix various quantitative approaches with traditional fundamentally driven investment strategies to improve their portfolio’s performance in searching for new alpha sources. A large pool of potential stocks is assessed using accounting information coupled with statistics and financial econometrics; necessitating increased use of machine learning.
Quantamental Process
There are several steps involved in a quanta mental investing process, and they can be categorized as below:
- Step 1: First, identify the existing process’s need or gap. Once done, develop an idea to fill the gap and offer a smoother process.
- Step 2: Gather as much data as possible to build a strong database. Then, analyze the data and draw meaningful insights, such as patterns or trends.
- Step 3: Create a model based on the available data and drawn insights. There will be several rounds of iterations to refine the model before the final model comes out.
- Step 4: Next, use the model to quantitatively screen a large universe of potential stocks to identify some investment opportunities.
- Step 5: Next, employ fundamental valuation techniques for the selected assets and develop the final selection. This where quantitative methods merges with the fundamental approach.
- Step 6: Next, build a portfolio based on the finally selected assets and optimize it using the analyst’s experience.
- Step 7: Finally, the system is ready to trade and monitor using algorithms. It becomes a continuous process to analyze the forthcoming data and improve the algorithms as and when required.
Examples of Quantamental
Now, let us take the example of the alpha surprise model used by Merrill Lynch as an instrument for quantamental investing. The wealth management firm developed a new research product- ML Alpha Surprise Model Index. This index helps track the performance of many stocks taken from the S&P500 using the Alpha Surprise Model, one of the models developed by the US Equity Research Quantitative Strategy group. The ML Alpha Surprise Model Index has been constructed with equal weightage to each stock in the Alpha Surprise Model, and these stocks are updated monthly.
Over time, the index has outperformed the S&P500 index – higher return at a lower level of risk. From 1999 to 2007, the index generated an annual return of 11.4%vis-à-vis S&P500, which could generate only 1.2%, while the index’s volatility was lower than that of S&P500.
Risks of Quantamental
Traders sometimes face the risk of curve fitting in quantitative trading, where they use a specific pattern in the available data to build the final model. But the model may not be fit for some future data points, which will fail the investment strategy based on the model. Nevertheless, this risk can be mitigated by testing the model before finalization using data from outside the sample.
Advantages
Some of the major advantages of quanta mental are as follows:
- All investments are based on a large volume of the well-analyzed data set.
- Amalgamating quantitative and fundamental methods is a step towards a more improved financial market.
- It is a better valuation method than the quantitative or fundamental method.
- Historically, investment strategies based on quantamental methods have performed better than most other investment strategies.
Disadvantages
Some of the major disadvantages of quantamental are as follows:
- Machine learning algorithms are still an enigma for humankind, and it is very difficult to predict how they will behave under certain unanticipated events. E.g., the trillion-dollar flash crash on May 6, 2010.
- Unlike humans, machines react purely to triggers as they lack intuitive sense.
- Over time, the need for human intervention may gradually decrease as machine learning becomes more and more advanced. This may result in the risk of higher unemployment.
Conclusion
So, it can be seen that quantamental is an investment strategy wherein statistical methods and mathematical principles go hand in hand with traditional fundamental investment methods. In short, it offers the benefits of both quantitative and fundamental investing. However, like most technologies, it has merits and demerits, and it is up to humans to leverage their potential to the maximum.
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