Jim Simons has one thing to say: “I did a lot of math, I made a lot of money, and I gave it all away.”

The founder of Renaissance Technologies (RT), whose flagship Medallion Fund has consistently yielded an average annual gross return of 60% to its investors, “buys not on optimism but on arithmetic”.

As Gregory Zuckerman noted in his book, ‘The Man Who Solved the Market’, Simons was the pioneer of quant investing, for he brought an amalgamation of mathematical expertise and programming insight to the investing forefront. RT painstakingly dug out data from primary sources, supplemented it with virgin electronic data, and brought on board world-class mathematicians and data scientists. This association allowed for the data to be visualised within a multidimensional matrix to reveal correlations and patterns in a looping optimisation process that repeated several times per hour.

There has been a paradigm shift in investing rationale, particularly as we emerge from 2022. Traditional investment themes faltered as markets cratered, and DCF does not seem to deliver alpha (or return in layman’s terms) anymore. Hedge funds setting up shop are increasingly relying on quant-based scientific approaches.

All of this begs the question: What is quant?

A quantitative fund is an investment fund that uses mathematical and statistical techniques together with automated algorithms to make investment decisions and execute trades. An intriguing aspect of quant is that its strategies are black box in nature, i.e., its workings are hidden. Publicising algorithms and quant strategies deem it futile, and thus, even investors do not know what moves their money. A critical advantage quant has over traditional techniques is the elimination of the pitfalls of human-reliant investing, be they in the form of intuition, heuristics, or biases. As Ben Graham rightly put it, “The investor’s chief problem and even his worst enemy- is likely to be himself.” In the pursuit of alpha, where the human brain creeps judgement, past experiences, and intellect into the decision-making process, quant follows hard rules and discipline, thereby gaining an edge.

How exactly quant funds operate is a subject of minimal clarity requiring a broader understanding of its functioning. Elaboration on its operations is thus in order. Quant funds must decide upon the market cycle, sector selection, stock selection, sizing (the quantity of a stock to be bought), liquidity risks, and most importantly, factor selection. Factors are characteristics inherent in groups of financial assets that describe the different risk/return metrics in the market. Popular factors targeted by quant include low volatility, quality, high yield, liquidity, and momentum, i.e., stocks that rise with the momentum of price change. As the market moves in cycles, scrips with certain aforementioned factors tend to outperform, and the program has to identify these trends. Quant rests on cross-cycle alpha generation, and consistency in the algorithm is vital; a key USP of quant is all-weather positive returns.

Quant funds rely on algorithms that study massive amounts of data to build a model that predicts future price behaviour. Back-testing algorithms lend credibility to the strategies whilst also optimising investing frameworks. It is imperative to note that quantitative investing leverages factors corresponding to all investment strategies and deploys the most profitable strategy depending on the market regime. The question then arises: is the current cycle favouring growth, quality, value, or momentum companies?

In essence, quant funds study decades’ worth of fundamental and technical data through a quantitative architecture comprising AI and ML to learn from the past and predict the future.

The quantitative investment process comprises three steps: input system, forecasting engine, and portfolio construction. At the initial step, market and company data are fed into the system along with rules that govern code execution. The forecasting engine runs stock evaluation and estimation of company and economy-wide parameters. Finally, an optimum portfolio is constructed, with an emphasis on company weights (the proportion of a stock in a portfolio), risk-return profile, and heuristics-based systems.

Quant funds follow a plethora of strategies, many of which are out of the public domain. However, two strategies stand out given their widespread adoption: smart beta and risk premia. Smart beta blends active (actively changing constituents of a portfolio) and passive investing (buy and hold) as it rebalances index weightage based on factors to capture alpha. In this regard, quant funds can capitalise on programming and mathematical prowess to appropriately rebalance portfolios. Risk premia, on the other hand, develops on the long-short strategy by introducing leverage, derivatives, and exotic instruments to maximise gains while keeping factor risks in mind.

One would assume that quant eliminates all human involvement. But we often overlook the fact that it is humans that run quants, and humans make mistakes- Jim Simons, too. The mathematician-turned-hedge-fund-guru lost faith in his trading systems during the market bumps of 2018 and overrode it with panicked sales. In psych-speak parlance, humans exhibit “algorithm aversion.” If anyone should have been resistant to this phenomenon, it was Simons, yet one can only wonder how he would have responded to the bloodbath in 1929-1932.

Bottom-line may be that quant is big money. But, an essential consideration while playing the devil’s advocate is the operation of the quant game. The quant hedge fund business has little to do with the primary societal purpose of capital markets- the efficient allocation of capital to productive enterprises. Rather, it is a zero-sum game that transfers wealth to those endowed with skill and luck from those less well-endowed with them. In this light, a renowned quote amongst shops on Wall Street seems apt: “When you transact, it is likely against Jim Simons, so trade as little as possible.”

By Samarth Shrimal

## コメント