What are the quantitative investment strategies
Quantitative Investment - Introduction to data-driven investment and quantitative investment strategies
The area of quantitative investments that only emerged in the 1980s and 1990s is now an integral part of the wealth management industry. While many areas of the financial services industry are undergoing sustained change due to technology and new sales channels, quantitative investment strategies and innovative FinTech companies are flourishing.
Quantitative portfolio management based on empirical evidence eliminates the negative impact of emotions on decision making. It is cheaper than fundamental analysis and allows even small teams to cover a wide range of stocks. Quantitative investing has not yet reached its full potential and several strides are underway. This article will give you an introduction to the world of quantitative investing.
What is quantitative investing?
Quantitative wealth management uses statistical and mathematical models to study the behavior of stocks and other asset classes. Quantitative investing consists of two different parts: research and implementation. Research can be based on proprietary research or using published scientific work. The research is used to create a model that identifies stocks with a greater than average probability of outperforming a benchmark index.
To implement a model, securities such as stocks are typically assigned a rating based on one or more characteristics (or factors) and then classified. A quantitative investment portfolio typically holds the highest-rated stocks and is then rebalanced periodically or when it no longer matches the model. Quantitative techniques can be used to manage both long-only portfolios and long / short portfolios.
Why does quantitative investing make sense?
When an active asset manager makes an investment decision, it is usually based on an assessment of how companies will perform in the future, assuming that good company performance will lead to positive stock price development. These decisions are based on a subjective analysis of the company's management and products, as well as the market and economic environment in which it operates.
Actively managed funds have been compared to indices since the 1960s. Over time, it has been shown that the majority of actively managed funds rarely outperform their benchmark indices over the long term. Technological advances in the 1970s made it possible for analysts to analyze very large data sets for the first time in the early 1980s. This quantitative analysis enabled investors to find out which types of stocks have performed better over time.
Quantitative investing enabled three things - evaluating a large number of stocks at once, making decisions based on empirical evidence rather than subjective projections, and a systematic approach to portfolio management. Early research found that certain anomalies existed to partially explain the stock price performance. Value, momentum and size were the first factors that led to an explainable outperformance. Over time, other factors and combinations of factors have emerged that result in regular outperformance.
Quantitative investment analysis is also useful for asset allocation and risk management. It enables the construction or analysis of a portfolio based on long-term expected returns and volatilities. In this way, portfolios can be put together according to the individual needs of the investors. Most funds today use a quantitative approach for at least part of their portfolio management process. While not used for stock selection, it is typically used for risk management or asset allocation.
Quantitative vs. fundamental investment
The more traditional active and fundamental investment approaches are usually based on bottom-up analysis and projections of corporate earnings and economic growth. Fundamental analysis also looks at qualitative factors such as the quality of management and the strength of the balance sheet. In contrast, when using quantitative factors in investment decisions, portfolio managers look for those factors that have been proven to reliably lead to outperformance. You do not invest on the basis of subjective prognoses, but on the basis of empirical knowledge.
Quantitative investment models are based on probabilities and an expected distribution of returns. This means that the expected risk and return can be predicted more accurately. However, this also requires a sufficiently large sample size to be effective. Quantitative funds therefore generally hold a higher number of securities than actively managed funds.
Investment decisions for an actively managed fund are made by the fund manager with great discretion. With quantitative funds, buying and selling decisions are made according to a model, with very little discretion on the part of the fund manager.
Types of quantitative investment strategies
While most quantitative investment models overlap and may have their own specifics, most strategies incorporate elements from some of the following strategies:
Factor models are used to select stocks that have one or more characteristics that have resulted in outperformance in the past. General factors include value, momentum, size, and growth. More specific factors are key figures such as price / book value, price to free cash flow and return on equity. Quantitative Investment Factor Models typically rate each stock against a series of metrics and then calculate an overall score that is then used to value stocks.
Event driven arbitrage strategies use price patterns that typically occur before or after certain events. Events include earnings announcements, economic data announcements, corporate actions, and legal changes. Portfolios are constructed by buying or selling securities in order to generate profits when the price development follows a typical pattern.
Systematic global macro strategies are based on a quantitative analysis of the economies in the individual countries and regions. This analysis is used to allocate capital to countries, regions, asset classes and sectors with favorable fundamentals.
Risk Parity Fund balance the risk of a portfolio across different asset classes based on how each asset class behaves in different market situations. The idea is that volatility and losses in one asset class are always offset by the other asset classes. This approach doesn't necessarily outperform equity funds, but can deliver better risk-adjusted returns over time.
Statistical arbitrage is one of the most active quant trading strategies. This is a "Mean Reversion" Approach that looks for mispricing based on the relationships between securities. Long and short positions are opened in related stocks in order to profit when prices rise again "normalize", so return to the long-term expected average rating. Statistical arbitrage also uses financial metrics to identify mispriced assets.
Managed futures, also known as CTAs (Commodity Trading Advisors) and trend-following hedge funds, use systematic methods to track key market trends. Traditionally, these funds have focused on the futures markets, but increasingly they are also active in the stock market.
Smart beta Strategies are used to systematically manage passive investment vehicles such as ETFs and mutual funds. Instead of using market capitalization to weight stocks, other factors can be used to improve a portfolio's risk-adjusted return.
Quantitative value Funds use a methodical approach to analyze every line of any company's income statement and balance sheet. An aggregated value is then calculated and used to value stocks. This systematic approach to value investing can be very effective, but it requires a long-term investment horizon.
A.I. and big data based strategies are the newest category of quantum strategies. They are trying to find new sources of alpha using techniques and data that were not used in the fund management industry until recently.
Advantages of quantum strategies
Since quantitative trading decisions are made by a computer model, they are not influenced by human emotions. When people make investment decisions, fear or greed often influences them. This applies to both entering and exiting positions, where discipline is often a problem for investors. In addition, quantitative investments can also exploit the possibilities of irrational decision-making in the market.
Small teams of quant analysts can cover a very large number of stocks. You can cover multiple sectors, regions and countries without hiring new analysts. Quant teams therefore have more opportunities to find stocks that could do better. It also means the analysis is cheaper per share.
Quantitative investing is based on demonstrable patterns, which means that outcomes are more predictable, especially in terms of the expected risk and reward profile. They can therefore also be better tailored to the needs of the various investors. Once created, quantitative models can be easily and inexpensively tested in different markets, with or without modifications.
Disadvantages of quantum strategies
Since quantum strategies are based on an expected distribution of returns and probabilities, a relatively large number of data points and observations are required. However, this can dilute returns. Quantitative strategies typically take long periods of time to perform and are often below their benchmark for shorter periods of time. This does not apply to all quant funds, and new data sources are now being used to create models that can generate alpha in the short term. Most quantitative funds are often unable to take subjective factors into account.
Quant strategies are prone to sudden increases in volatility and flash crashes that can be generated by other algorithmic trading strategies. The fact that quantitative funds are managed without any discretion can be a double-edged sword. In most cases, the unemotional way of making decisions is beneficial, but there are situations when it can be disadvantageous.
Quantitative investing today
Today Wall Street is fully committed to quantitative investing. Quantitative techniques are now used to manage most types of mutual funds, including mutual funds, hedge funds, ETFs, and single portfolios. Quantitative techniques are also used for asset allocation and risk management, as well as for aligning portfolios with client needs.
The new goal for quantitative investing is to develop strategies that fully take into account the current state of technology. Artificial intelligence (A.I.) is used to find new patterns and relationships between security prices and data from other data sources. Big data is used to develop new data sources that can lead to alpha-generating ideas. User-generated data is used to measure investor sentiment, which may have an impact on stock prices.
Quant platforms such as Quantopian and Quandl exist to enable crowd-sourcing of investment ideas and to make it easier for quantitative analysts to collaborate and obtain data. Robo-advisors, which enable individual investors to invest or save for retirement or certain life situations, use quantitative models for capital allocation. And finally, social trading platforms enable traders to make their performance transparent to the outside world and to indirectly manage money for individual investors.
The future of quantitative investing
Quantitative investing is advancing on several fronts and there is likely to be a convergence of different techniques and platforms in the future. Developments in other areas, including the introduction of new investment products and asset classes (e.g. cryptocurrencies and tokenized securities), will open up new opportunities.
The advancing globalization of the markets will also play a role in the future, as investors can open up new markets. The greatest opportunities lie in the use of A.I. and big data. These technologies enable analysts to find relationships between stock prices and data that have not traditionally been used by investors. Satellite imagery, social media content, and GPS data from vehicles and devices are potential sources of information.
Sentiment, sentiment towards individual stocks or the market, is a factor that is becoming increasingly important for quantitative investments. Both A.I. as well as big data techniques are used intensively to analyze the mood and its predictive power. Finally, advances in artificial intelligence could make it possible to model qualitative factors as well. This would close the gap between quantitative and traditional active fund management, as more subjective factors can now also be taken into account.
The competitive pressure in the industry is likely to increase further. Only quantitative asset managers who are committed to developing and searching for new quantitative strategies will be able to consistently generate alpha in the future and ultimately survive on the market.
Conclusion: quantitative analysis as a systematic approach to investing
Quantitative analysis has introduced a more scientific and systematic approach to investing. There are several benefits to making investment decisions based on empirical evidence, including lower relative costs and the elimination of emotion in decision-making.
However, an investment strategy based on a quantitative model is not a panacea and there is no guarantee of performance. However, in most cases, quant funds have a better chance of achieving their goals. The recent introduction of new products, technologies and asset classes suggests that there is still a long way to go and that the industry will continue to grow and develop over the next decade.
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