“Quant” is Not a Buzzword. It’s a Discipline.
Editorial By Boreal Family Office
In this editorial we will try to demystify quantitative analysis concepts using clear explanations and simple analogies, helping you understand what “quants” do, how their strategies work and why we believe that the term is often misused.
In certain corners of the investment community — particularly online media such as TikTok or RED — there’s no shortage of talk about “quantitative analysis”. The term quantitative has become a kind of social currency. Technical traders call themselves quants. Chart readers claim to be data scientists. And influencers post model screenshots and backtests that imply institutional sophistication.
But here’s the truth: most of what passes for “quant” in the retail world isn’t quantitative at all. It’s technical analysis dressed up in the language of data science. It’s momentum-chasing with a formula. It’s intuition masquerading as objectivity.
Let’s be clear: real quantitative investing is not a style. It’s a scientific discipline. It demands a level of precision and methodological rigor that cannot be reverse engineered from a YouTube video or TradingView subscription.
We think it’s time to draw a clearer line between what’s truly quantitative — and what’s just technical analysis in a new suit.
Quantitative Investing: Discipline Over Discretion
Quantitative investing is not a style or a shortcut. It is a rules-based portfolio construction process rooted in mathematics, statistics, and behavioral finance. Rather than relying on human judgment to pick stocks or call the market, quantitative investors build systematic models that evaluate large datasets for repeatable factors.
The factors are empirically observed patterns that have persisted over time and across markets. The most studied ones include:
Value: Stocks that are cheap relative to fundamentals (e.g., earnings, book value)
Momentum: Stocks with rising prices that tend to keep rising
Quality: Companies with strong profitability, balance sheets, and stability
Low Volatility: Securities that deliver more stable returns with less downside
Quantitative investing allows us to test rules like “Buy companies with high return on equity, low valuation multiples, and upward price momentum.” These aren’t hunches — they’re hypotheses backed by decades of empirical research.
The result is a system that is repeatable, scalable, and unemotional. It removes guesswork from security selection and replaces it with process.
How Does Quantitative Trading Differ?
Many who claim to be quants are in fact quantitative traders — or, more often, discretionary traders using technical tools.
Quantitative trading, in contrast to investing, typically refers to shorter-horizon, high-frequency, or event-driven strategies. These systems attempt to:
Exploit short-term mispricings
Respond to earnings surprises in real time
Place thousands of trades per day through algorithmic execution
Both approaches use automation and data, but they diverge in purpose and timescale.
Quantitative investing aims to capture persistent risk premia over months or years.
Quantitative trading targets temporary inefficiencies lasting seconds to days.
It’s a difference in philosophy — not just frequency.
What Real Quants Do?
Contrary to popular portrayals, real quantitative investing isn’t about throwing data at a black box and hoping for something to stick. It’s a rigorous research process built on statistical reasoning. Quantitative investors rely on a shared set of disciplines:
Large data analysis — Parsing thousands of securities and fundamental metrics
Model construction — Writing algorithms that execute rules without bias
Backtesting — Testing ideas over decades of history to identify robustness and edge
Automation — Removing emotion and inconsistency from the execution layer
But here’s the litmus test: Is the strategy derived from theoretical foundations? Is it robust across regimes? Has it been stress-tested for slippage, turnover, transaction costs, and decay?
If not, it’s not quantitative investing — it’s a quant-flavored narrative. A well-designed spreadsheet with a curve-fit chart may look sophisticated. But without rigor, it’s just another story looking for buyers.
True quantitative investing doesn't just look smart. It survives contact with uncertainty.
How It Compares to Traditional Investing?
Where traditional investment managers may rely on concentrated conviction — say, 30–50 handpicked stocks — quantitative strategies invert the process. Instead of betting on a few “best ideas,” quants use breadth and scale to their advantage:
They analyze thousands of securities, not dozens
They rely on historical and cross-sectional data, not management meetings
They remain consistent across volatility spikes and narrative shifts
It is important to point out that neither approach is inherently superior. In fact, many institutions blend both.
Why Should High-Net-Worth Investors Pay Attention?
Quantitative strategies bring specific benefits that are often underappreciated in wealth management:
Diversification Across Risk Premia
Exposure to multiple systematic factors — such as value, momentum, or low beta — provides an additional layer of diversification beyond asset class.
Discipline in Execution
A rules-based model doesn’t panic in drawdowns or chase fads in euphoric markets. It sticks to its logic.
Breadth and Analytical Scale
Human managers are constrained by time and attention. A quant model can evaluate thousands of opportunities simultaneously, identifying subtle patterns that would otherwise go unnoticed.
Access to Alternative Sources of Return
From statistical arbitrage to volatility harvesting, quant strategies can target structural inefficiencies that traditional portfolios ignore.
A Final Note to the Industry
To fellow professionals: If you work in quant, you already know the tools of our trade — Python, R, SQL, Pandas, factor libraries, multi-asset covariance structures, and cross-sectional models. You also know how hard it is to make a strategy work out-of-sample, net of costs, in real size.
That’s why we owe it to the broader financial community to stop tolerating shallow imitations. A rules-based strategy is not a quant strategy. A backtest is not a portfolio. And a buzzword is not a credential.
If we want to elevate the standard of conversation in our community, we need to start by reclaiming the word “quantitative” from those who’ve stripped it of its meaning.
The Boreal Perspective:
We’re not interested in selling simplified versions of complex ideas. We’re here to raise the standard of how quant is understood and applied in a high-net-worth context — especially in a landscape where the word is increasingly used without meaning.
“量化”不是一个流行词，而是一门严谨的学科
Boreal Family Office 编辑部
本文版权归属Boreal Family Office 所有，未经授权不得转载。 旨在分享讨论关于金融理财方面的一般信息，具体案例请联系专业人士。
数据不等于纪律：呼吁量化投资中的严谨性
在这篇文章中，我们希望通过清晰的解释和简单的类比，揭开“量化”分析的神秘面纱，帮助你理解量化投资者在做什么、他们的策略如何运作，以及为什么我们认为这个词常常被滥用。
在投资圈的某些角落——尤其是在 TikTok 或小红书等线上媒体上——充斥着关于“量化分析”的讨论。量化这个词似乎成了一种社交货币。技术交易者自称是量化专家，看图表的人号称自己是数据科学家，各种博主晒出模型截图和回测结果，摆出一副机构级的姿态。
但事实是：在零售投资世界里，大多数被称为“量化”的东西根本算不上真正的量化。那只是披着数据科学外衣的技术分析，只是用公式包装的追涨杀跌，只是伪装成客观性的直觉而已。
我们必须明确一点：真正的量化投资不是一种“风格”，而是一门科学学科。它要求的精确性和方法论严谨性，不可能从一段 YouTube 视频或一个 TradingView 订阅里逆向学出来。
现在，是时候划清真正量化与换了马甲的技术分析之间的界限了。
量化投资：纪律高于直觉
量化投资不是一种风格，更不是捷径。它是一种基于规则的投资组合构建过程，根植于数学、统计学和行为金融学。
不同于依赖人类主观判断来选股或择时的传统做法，量化投资者构建系统化模型，从庞大的数据集中提取出可重复的因子。
这些因子是经过实证验证、长期存在于不同市场的模式。最常见的因子包括：
价值：相对于基本面便宜的股票（如低市盈率、低市净率）
动量：价格持续上涨的股票倾向于继续上涨
质量：盈利能力强、财务稳健、经营稳定的公司
低波动：风险较低、回报更稳定的证券
量化投资让我们能够测试诸如“买入高股本回报率、低估值、价格动能上升的公司”这样的规则。这些不是拍脑袋的直觉，而是经由几十年实证研究支持的假设。
结果是一个可重复、可扩展且不带情绪的系统。它剔除了猜测，用流程替代了感性。
量化交易又有何不同？
许多自称“量化”的人其实是量化交易者——更常见的是使用技术工具的主观交易者。
相比之下，量化交易通常指短周期、高频或事件驱动的策略。这类系统旨在：
利用短期价格失衡
实时响应财报“惊喜”
通过算法每天执行成千上万笔交易
两者都利用自动化和数据，但目的和时间尺度不同：
量化投资追求的是跨越数月乃至数年的持续风险溢价。
量化交易针对的是持续几秒到几天的暂时性低效。
这不仅仅是频率的差别，更是理念上的不同。
真正的量化投资者在做什么？
与流行的刻板印象不同，真正的量化投资并不是往黑箱里扔一堆数据然后期待好运。它是一种建立在统计推理基础上的严谨研究过程。
量化投资者依赖于以下几个共同的学科要素：
大数据分析：处理成千上万的证券和基本面指标
模型构建：编写无偏执行规则的算法
回测：在几十年的历史数据上检验想法的稳健性
自动化：在执行层面剔除情绪和不一致性
检验的标准很简单：策略是否有理论基础？是否跨不同市场环境依然稳健？是否经过滑点、换手、交易成本和衰减的压力测试？
如果答案是否定的，那它就不是量化投资——只是一个打着量化幌子的故事罢了。
一个设计精美、曲线拟合的表格看起来很“高大上”，但如果没有严谨性，它不过是一个待售的故事。
真正的量化投资不仅看起来聪明，还能经受住不确定性的考验。
与传统投资相比
传统的投资经理可能依赖于高度集中的信念，比如挑选 30–50 只“精选”股票。
而量化策略则颠倒了这个过程。与其押注几个“最佳想法”，量化投资者利用广度和规模来获得优势：
分析成千上万只证券，而非几十只
倚赖历史和横截面数据，而非管理层会议
在波动加剧和叙事变化时依然保持一致
需要指出的是，两种方法并无高下之分。事实上，许多机构会融合两者。
为什么高净值投资者应当关注？
在财富管理中，量化策略带来了一些常被低估的优势：
跨风险溢价的多样化
暴露于多个系统化因子（如价值、动量或低贝塔），为资产配置之外提供额外的分散化。
执行中的纪律性
基于规则的模型在市场下跌时不会恐慌，在市场亢奋时不会追逐热点，而是始终坚持其逻辑。
广度和分析规模
人类经理受限于时间和注意力，而量化模型可以同时评估成千上万个机会，发现微妙的模式。
获取另类收益来源
从统计套利到波动率捕捉，量化策略能够锁定传统组合忽略的结构性低效。
给行业的最后一段话
致同行们：如果你在量化领域工作，你早已熟悉我们的工具——Python、R、SQL、Pandas、因子库、多资产协方差结构和横截面模型。你也知道，要让一个策略在样本外、扣除成本、在真实规模下运作是多么艰难。
这正是为什么我们有义务停止容忍那些浅薄的仿冒品。基于规则的策略不等于量化策略，一个回测不等于一个投资组合，一个流行词不等于一纸凭证。
如果我们希望提升这个社区的讨论标准，就必须从把“量化”这个词从那些剥夺它意义的人手中夺回来。
Boreal 的观点
我们无意贩卖那些被简化的复杂概念。我们致力于在高净值背景下提升人们对量化的理解和应用标准——尤其是在这个“量化”被频繁滥用的时代。
本文版权归属Boreal Family Office 所有，未经授权不得转载。 旨在分享讨论关于金融理财方面的一般信息，具体案例请联系专业人士。