---
title: "Major changes in the U.S. corporate bond market: Trading \"stockification\""
type: "News"
locale: "zh-CN"
url: "https://longbridge.com/zh-CN/news/265314408.md"
description: "Barclays believes that driven by algorithmic trading and basket trading, the U.S. corporate bond market is accelerating its \"equity-like\" characteristics, with the speed of market absorption of shocks being five times faster than in 2002. This benefits systematic strategies but smooths out price discrepancies, compressing the excess return space for active investors. The trend is not simply about machines replacing humans, but rather placing algorithms at the core of workflows, freeing traders to focus on complex trades and narrative value extraction, achieving human-machine collaboration"
datetime: "2025-11-11T10:26:40.000Z"
locales:
  - [zh-CN](https://longbridge.com/zh-CN/news/265314408.md)
  - [en](https://longbridge.com/en/news/265314408.md)
  - [zh-HK](https://longbridge.com/zh-HK/news/265314408.md)
---

> 支持的语言: [English](https://longbridge.com/en/news/265314408.md) | [繁體中文](https://longbridge.com/zh-HK/news/265314408.md)


# Major changes in the U.S. corporate bond market: Trading "stockification"

A profound structural transformation is quietly unfolding in the U.S. corporate bond market. Driven by algorithmic trading and basket trading models, this market, which has traditionally relied on interpersonal relationships and over-the-counter transactions, is accelerating its shift towards "stock-like" characteristics—trading speed, liquidity, and pricing mechanisms are all aligning more closely with those of the stock market.

According to a report released by Barclays on November 9, **the current credit market absorbs shocks five times faster than in 2002;** price discrepancies that once took 10 days to digest now only take two. This shift places systematic credit strategies in a "sweet spot" characterized by better liquidity and yet to be overcrowded.

However, this efficiency has also brought direct market impacts. Barclays analysis indicates that while algorithmic and portfolio trading enhance liquidity, they also smooth out price differences between bonds, **making it more difficult to uncover pricing errors.** For the high-yield bond market, the increase in portfolio trading share has compressed the market's realized volatility by 3% to 7%. This is beneficial for passive investors, but for active investors seeking excess returns (alpha), it means a need for deeper exploration of opportunities.

This transformation is not simply about machines replacing humans, but rather a reshaping of the human-machine collaboration model. The report suggests that the "stock-like" speed alone is far from sufficient for a market that still relies on interpersonal relationships. **The future core lies in placing algorithms at the center of workflows, thereby freeing traders to focus on handling more complex trades and uncovering the narrative value behind the market.**

## Speed and Volatility: Changes in Market Pricing Models

One of the most significant changes brought about by the "stock-like" trend is the revival of previously illiquid bonds. Barclays' data shows that the impact of this liquidity revolution is particularly pronounced for smaller bonds.

Since 2015, the weekly non-trading ratio of the least liquid half of investment-grade (IG) bonds has plummeted from 50% to 10%. **In the high-yield (HY) bond market, this figure has improved even more dramatically, dropping from 35% to 5%.** In contrast, larger bonds, which already had better liquidity, saw their non-trading ratio decrease only from 10% to 1%, reflecting a more moderate improvement.

The increase in trading speed is fundamentally changing the market's pricing logic. According to Barclays' analysis, technological advancements have enabled the market to digest information such as macroeconomic changes and rating adjustments much faster than before. This efficient pricing, in turn, affects market volatility.

**The report finds that in the high-yield credit market, due to the prevalence of portfolio trading, the pricing focus is shifting from analyzing the "specificity" of individual bonds to assessing broader "portfolio risks."** This shift in model has led to smoother price fluctuations, compressing realized volatility by 3% to 7%. While this reduces extreme risks in the market, it also poses challenges for traditional strategies that rely on mispricing of individual bonds for profit, requiring active investors to exert more effort to seek excess returns

## Human-Machine Integration: Core Algorithms and Relational Value

With the evolution of the market's "stockification," the talent structure in the trading field is also changing.

Barclays pointed out that from 2017 to 2025, the proportion of personnel with artificial intelligence skills in market front office positions will jump from 1% to nearly 5%. Nevertheless, machines have not completely dominated the market. Data shows that the electronic trading ratio of U.S. Treasury bonds has stagnated at 60%, while corporate bonds stand at 50%. This is not due to market resistance to technology, but because algorithms have created new space for human trading (Voice Trading). In times of severe market fluctuations or complex trading structures, **"investors prefer to communicate with people rather than face an algorithm that only quotes the mid-price."**

Barclays concluded that the future trend is not to pursue pure speed, but to embed algorithms at the core of workflows, allowing traders to focus on uncovering narrative value and handling complex trades that require human judgment. As defined in its "A.L.G.O." concept—"Alpha exists in the combination of online and offline" (Alpha Lives in Going On(Off) line)

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