Basumallick is a value investor who has been in the market for nearly two decades. He pays close attention to the economy and changes in technological trends across industries. He is a supporter of behavioral finance and a long-term approach to investing.
His investing career will be 20 years old this year. He was fortunate because He did not have much money in the market when the dot-com bubble burst in 2000, followed by the 9/11 terrorist attacks the following year, which sent the markets into a tailspin. Despite this, He was able to learn all the lessons without too many scars.
He has always been fascinated by the art and skill of investing and what makes people successful. As a result, he has been looking for and reading biographies of a significant number of investors and traders. Early in his investment career, he came across Buffett, Munger, and Lynch, greatly influencing his thought process.
As a student, he always approached the market with caution. He came across Renaissance Technologies about three years ago, known for Jim Simons, the Man Who Solved the Market (bestselling book by Gregory Zuckerman). It piqued my interest. What struck him was how a guy with no prior knowledge of markets could compound money at a rate roughly three times faster than Buffett and over decades.
Then He began researching quantitative investing. He discovered some incredible books, people, and resources. It was like a light bulb went off in his head. This overall method appealed to me because he has a computer programming background and still enjoys programming. He began to learn as much as He could as well as experimenting. His goal is to compile the practical lessons he has learnt over the course of two decades of value investing.
IT is divided into two parts: research and implementation. Quant investment management examines the behavior of stocks and other asset classes using statistical and mathematical models. The study could be based on proprietary data or scholarly publications that have already been published.
The data is used to build a model that finds equities with a higher-than-average chance of outperforming a benchmark index. Stocks are often given a score based on one or more qualities (or criteria) and then ranked to execute a model. A quantitative investing portfolio will typically hold the top-ranked companies and be rebalanced on a regular basis or when it deviates from the model. Long-only and long-short portfolios can both benefit from quantitative methodologies.
An active portfolio manager’s investment strategy is typically based on how the company is expected to perform in the future, with the premise that excellent company success will translate to solid share price performance. These choices are based on a subjective assessment of the company’s leadership and goods and the market and economic climate they operate.
Quantitative investment appealed to him for several reasons:
- It was rule-based, which meant that the procedure was crucial: You have guidelines in place for the three key aspects of investing: stock selection, allocation, and selling.
- You could eliminate behavioral biases: he comes to believe that the most critical driver of investment success is behavioral psychology, not stock selection, which sadly 99 percent of investors solely focus. The most important approach for reaping outsized gains is to reduce or eliminate biases.
- You may back-test your plan, which means you could look at how your investment strategy would have performed in the past. You might also conduct scenario analysis and test it under various conditions. The concept of “purchase and hope” might be drastically diminished if not eliminated.
- You could diversify your investments by using several investment styles: Quant investing, because it is rule-based, may assist the support in ways that he is not comfortable. As a result, he could develop techniques for playing scenarios, turnarounds, short-term super growth, momentum, and other similar factors.
He also realized that his long-term investing attitude prevented him from taking advantage of or ignoring shorter-term possibilities. Having well-defined strategies to do so would be an excellent complement to his primary competency of long-term value investing. With this in mind, He began to develop and test several tactics. Obtaining high-quality data over a lengthy period in India is a difficult task. He has persevered in constructing his database and will continue to do so. He has been actively investing in three strategies for the last six months, and the outcomes have been intriguing.
Quantitative analysis has revolutionized investing by introducing a more scientific and methodical approach. Making investment decisions based on empirical facts has various advantages, including lower relative costs and the absence of emotion from decision-making.
A quant-based approach isn’t a silver bullet, and there’s no guarantee of success, but quant funds have a better probability of achieving their goals in most cases. New products, technologies, and asset classes have recently been introduced, indicating that there is still a long way to go and that the sector will continue expanding and evolving in the following decade.
Ken is a Quantitative Trader with experience in investments, quantitative finance, financial modelling and algorithmic trading in Global Investable Markets (GIM). He enjoys using Bayesian Statistics, Time Series and Machine Learning in developing Robust consistent Alphas in Equities Market, FX, ETPs and Derivatives instruments. He enjoys deep dives in understanding High Frequency Trading infrastructures and improving how the African financial markets work. He holds a Bachelor's in Actuarial Science from Strathmore Institute of Mathematical Sciences : An Executive Program in Algorithmic Trading (EPAT) certificate in Algo Trading from QuantInsti : A current MSc student in Financial Engineering at World Quant University.