Understanding Quant Fund
Quant is no longer a foreign term to the Hedge Fund world. So what are Quant Funds? Quant Funds are pooled investment vehicles that operate using computer-based models which reduce risks related to human fund management. Like any other investment funds, quant funds aim to outperform the market in generating alpha (access return), rely on research, and mathematical and statistical modelling to forecast investment performance.
In simpler terms, quant funds select securities by utilising the capabilities of advanced quantitative analysis.
Quant Funds implement quantitative theory in securities selection. They combine finance and calculus in the development of strategies such as modern portfolio theory, the Black-Scholes option pricing model and more. The quantitative models used by quant funds are designed to identify and distinguish investment opportunities in the market. This removes the need for human involvement in the buy/sell decision process. The timing of the market’s entry and exit of quant funds will be determined by the system.
Quantitative Investment Strategies
There are three clear steps in the investment process of a quant fund – input system, forecasting engine, and portfolio construction.
Input – The first stage of the necessary inputs being inserted consists of counter rules, company data and market data. Company data includes revenue, growth projections, ratios such as price to earnings and dividend yield while market data includes inflation, interest rate, GDP growth and more. During this stage, factors of high volatility, debt and inefficient capital allocation will be removed from the quantitative model. This is to reduce risks at the same time increase the probability of optimum selection.
Forecasting – In this stage, the stocks valuation process will be carried out. The system will generate several estimation factors such as expected return, risk, expected pricing and more.
Portfolio Construction – The final stage will consist of portfolio construction and optimization. An optimum portfolio of the quant model is determined by the allotment of the appropriate weight of each stock in developing a portfolio with an acceptable risk level and desired returns rate.
Quantitative investing is also known as data-driven investing. The growth of quant funds is strongly infused by the greater access to relevant data and significant solutions of ‘big data’ processing such as real-time company news, trending international asset values and economic data points.
Furthermore, the innovative developments in financial technology multiplies the amount of accessible datasets available for the quant managers to work on. Thus, this will lead to better efficient scenario analysis and reduce error probability.
Quantitative Investment Process
Below are some of the common quantitative strategies being implemented by hedge funds.
Event-driven arbitrage – This strategy analyses event-driven data. Once the model detects a specific pattern in price movement, buying and selling activities will take place.
Statistical arbitrage – This is an active trading strategy that identifies misplaced securities by analysing the relationship between them. Statistical arbitrage is commonly used to engage financial ratios to open short and long positions.
Smart-Beta Strategies – Improve the risk-adjusted returns by adopting strong beta elements (closely correlated to the market). It will also exploit market inefficiencies and underlying risk factors by implementing alternative constructed indices.
Counter Trend Strategies – In this strategy, the fund will undergo short term positions during price reversals from the dominant market trend. The sudden reversal will shake undercapitalized traders and add sparks to the counter-trend move. Stochastics and Bollinger Bands are frequently used for counter trend trading.
Risk Premia Strategies – It targets factors through long-short trades to generate absolute returns. Risk Premia Strategies discard a lot of beta elements to deliver positive returns; even during bear markets. The long-short trading strategy involves taking long positions on undervalued stocks and taking short positions on overvalued stocks. It leads to a better chance of generating alpha and capturing risk-premium from both undervalued and overvalued stocks through long-short value investing.
Pros and Cons of Quantitative Investment
Why do hedge fund managers increasingly adopt quant investment into their portfolios?
Quantitative investing offers consistency; as the datasets are analysed by high-performance computers, emotional and human error are eliminated. Quantitative investing allows for more efficient and reliable risk management. Next, it is also cost-effective; as it requires less to no human involvement. Plus, quant investing also aids hedge funds in allocating portfolios according to investors’ preferences. Since it uses only historical numbers and big data, it is easier to identify risks and expected returns that lead to better portfolio construction.
There are shortcomings of quant investing, and due to the high level of dependence on historical data, quant investing does not consider the possibility of data alteration that might lead to error in computer-system based decision making. Quant investing relies heavily on historical data but whilst history does tend to repeat itself, it usually does with multiple variations. Last but not least, unexpected situations that only humans will recognise such as a global pandemic or abrupt decisions by politicians and business leaders that computers might fail to recognise will certainly alter the performance.
In a nutshell, technological advancement, Artificial Intelligence (AI) and big data implementation drive analysts to uncover unexpected opportunities for hedge funds managers to make better decisions, minimise their shortcomings and more importantly find their Alpha.
At AxeHedge, we believe the future of investing is democratised and digital, and that’s why we have dedicated our resources to bringing hedge fund-style investing and quantitative approaches directly to our investors and clients. We’re taking away the high management and performance fees and in particular the high investment requirements
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