According to US quantitative fund manager Two Sigma, the world produces a billion gigabytes of data per hour. There are massive amounts of data sets out there. There are numerous examples of how fund managers are applying big data analysis – from using AI programmes to trawl through collated credit card statements to predict quarterly Netflix subscribers to designing software that captures satellite imagery to count the number of cranes in Guangzhou and produce accurate forecasts of Chinese housing supply. The data needs to be relevant and accurate for the machines to get to the right conclusions – and there needs to be lots of it. As with the old method of an analyst tinkering with his Excel spreadsheet model, the “rubbish in, rubbish out” theory is still crucial. What they all have in common is a need for data. Most of these models are ‘black box’ or secretive, as individual fund managers try to maintain their edge over the competition. These quant and ‘quantamental’ funds, which have benefited from the significant growth in cloud storage capacity and Artificial Intelligence (AI) programming to crunch through enormous datasets, invariably boast proprietary models and different methodologies to analyse inputs. This figure ignores the increasing number of hedge funds using ‘quantamental’ techniques, which add quant analysis and big data inputs to support traditional fund management teams in search of investment ideas. By the end of 2016, funds run by systematic strategies made up close to 20% of total hedge fund assets. Quantitative investment is gaining increasing importance in the fund management industry. The requirement for quality data is becoming even more central to their work as they move into quantitative, or systematic, investment strategies that augment or replace human judgement with data, algorithms and machine learning to manage funds. The need for relevant inputs and accurate data has been fundamental to the work of equity analysts and fund managers since the days of modelling future financial performance on an Excel spreadsheet. And it is common for the best financial analysts, and models, to get things very wrong because they are being fed irrelevant or bad assumptions. Whether an analyst or fund manager’s financial modelling is basic or intricate, the forecasts and conclusions that come out at the end can only be as good as the data or assumptions that are input at the beginning.Īnalysts have always been in a constant battle to find, and verify, the right inputs into their financial models to reasonably predict the future performance of a company or asset. Ltd.“Rubbish in, rubbish out” is an old adage in equity research. Their efficiency in market management, keen market research, and wealth distribution has been formed upon solid expertise derived from their sponsor, Quant Capital Finance and Investment Pvt. In fact, in the last two decades, Quant Mutual Fund has emerged successful in providing financial management services that utilize its intelligent cross-market and cross-asset investment tactics to create wealth for its customers/investors. Some of the famous products they offer are the Quant Absolute Fund, Quant Active fund, Quant Midcap and Large Fund, Quant Focused Fund, Quant Dynamic Bond Fund, and Quant Tax plan, among a plethora of other mutual funds. Their mutual fund range varies from equity and debt to hybrid and tax-saving categories. As per the terms and conditions dictated by the Investment Management Agreement, Quant Money Managers Limited has been appointed by the Trustee Company to manage mutual funds.Īs a mutual fund company, Quant Money Managers Limited is known for its wide variety of products suited to all kinds of investors. Later on October 30, 2017, it was approved to act and work as an Asset Management Company by SEBI. On December 1, 1995, Quant Money Managers Limited, famously known as QMML, was incorporated.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |