Understanding the Order Book: Analyzing Market Liquidity

Bochen LIU

In this article, Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025) explains the concept of the order book, how it functions in financial markets, and the insights it provides to traders.

What is an order book?

For anyone engaging in financial markets, understanding the order book is essential. The order book is a dynamic record of buy and sell orders for a given asset, reflecting the interaction between supply and demand in real time. Whether trading stocks, currencies, or digital assets, the order book allows market participants to visualize liquidity, identify potential price movements, and make informed decisions.

An order book lists all outstanding buy and sell limit orders for an asset, showing both the prices at which traders are willing to transact and the quantities they wish to trade. It provides a clear picture of market depth and the relative interest of buyers and sellers at different price levels. Unlike a simple price chart, the order book reveals where liquidity is concentrated and where potential support or resistance may occur, making it an indispensable tool for understanding short-term market dynamics.

How an order book functions

The order book is typically divided into two sections: the buy side (bid side) and the sell side (ask side). The buy side shows the highest prices buyers are willing to pay, while the sell side reflects the lowest prices sellers are willing to accept. Orders are listed by price and aggregated volume, and the book is continuously updated as trades are executed and new orders enter the market.

The difference between the best bid and best ask is known as the bid-ask spread, a key indicator of market liquidity. By monitoring changes in the spread and the distribution of orders, traders can gain insights into market sentiment and anticipate short-term price movements.

In practice, the organization of the order book allows traders to understand not just current prices but also the pressure from buyers and sellers at different levels. For example, a concentration of large buy orders may act as a support level, while clusters of sell orders can indicate resistance. The order book therefore acts as a living map of market intentions and is often used together with charts and other data sources.

Order book representation

The structure of the order book is often visualized through trading platforms that display the distribution of buy and sell orders at different price levels. A typical representation includes two columns: bids on the left and asks on the right. Each row shows the price level and the cumulative quantity available at that level.

Figure 1. Example of an order book (buy and sell parts presented side by side).
Example of an order book with buy and sell parts presented side by side
Source: screenshot from a trading platform.

Figure 1 presents one of the most common visual formats of the order book, in which bid orders are shown on the left and ask orders on the right. This side-by-side structure allows traders to compare the quantities available at different price levels and to identify the best bid and best ask immediately. It also makes the bid-ask spread visible, which is a key indicator of market liquidity and transaction cost.

Modern electronic trading platforms such as NASDAQ TotalView or cryptocurrency exchanges provide graphical representations of the order book. These interfaces often include a “depth chart,” where the cumulative buy and sell volumes are plotted against price levels. Such visualizations allow traders to quickly observe supply and demand imbalances.

Figure 2. Example of an order book (depth chart representation).
Example of an order book with a depth chart representation
Source: screenshot from a trading platform.

Figure 2 shows the order book in a format that combines tabular bid-ask information with a depth chart. The green area represents cumulative buy-side liquidity, while the red area represents cumulative sell-side liquidity. This representation helps traders visualize how orders are distributed across price levels and where supply-demand imbalances may emerge in the market.

Evolution of the order book

The order book constantly evolves as new orders arrive, existing orders are cancelled, and trades are executed. Two main types of orders influence this evolution: limit orders and market orders.

Limit orders add liquidity to the market by specifying both a price and quantity at which a trader is willing to buy or sell. When a trader places a buy limit order below the current market price or a sell limit order above it, the order enters the order book and waits to be matched.

Market orders, in contrast, remove liquidity. A market buy order immediately matches with the lowest available sell orders, while a market sell order matches with the highest available buy orders. As these trades execute, they reduce the quantities available in the order book and may shift the best bid and ask prices.

The interaction between incoming limit orders and market orders continuously reshapes the order book and drives short-term price movements.

Order priority rules

Electronic markets generally follow two key priority rules when matching orders: price priority and time priority.

Price priority means that orders offering better prices are executed first. For example, among buy orders, the highest bid has priority, while among sell orders the lowest ask has priority.

If multiple orders are placed at the same price level, time priority applies. The order that was submitted earlier will be executed before later orders. This rule encourages traders to submit orders quickly if they want to secure execution.

These priority mechanisms ensure fairness and transparency in electronic trading systems.

Price impact and transaction prices

The execution of orders can influence market prices, a phenomenon known as price impact. When large market orders consume multiple levels of liquidity in the order book, the transaction price may move significantly.

For example, if a large buy market order exceeds the quantity available at the best ask price, the trade will continue matching with higher ask prices. This process pushes the transaction price upward and illustrates how large orders can move markets.

Transaction prices and traded volumes therefore provide important information about market activity. High trading volumes often indicate strong participation and may reinforce price trends.

Liquidity characteristics of the order book

The order book provides several indicators that help measure market liquidity.

Bid-ask spread is the difference between the best bid and best ask price. A narrow spread typically indicates a liquid market with low transaction costs.

Market depth refers to the total quantity of buy and sell orders available at different price levels. A deep order book allows large trades to be executed without significantly affecting prices.

Market breadth describes how widely orders are distributed across price levels. A broad distribution indicates active participation from many traders.

Figure 3. Example of an order book (used to assess liquidity).
Example of an order book used to assess liquidity
Source: screenshot from a trading platform.

Figure 3 provides a mobile-style visualization of the order book, showing the best bid, the best ask, and the quantities available on both sides of the market. It is particularly useful for illustrating liquidity measures such as bid-ask spread, visible depth, and market breadth. By comparing the quoted quantities at different prices, traders can better evaluate the strength of buying and selling pressure.

Resilience measures how quickly the order book replenishes after large trades remove liquidity. A resilient market quickly attracts new orders and stabilizes prices.

These liquidity measures help traders evaluate the quality and stability of a market.

Why should I be interested in this post?

For ESSEC students interested in business and finance, understanding the order book is fundamental to analyzing financial markets and trading behavior. It provides practical insight into how prices are formed, how liquidity affects execution, and how real-time data informs strategic decisions.

Mastering order book analysis strengthens financial reasoning, improves understanding of market microstructure, and supports more informed investment or trading strategies. This knowledge is directly relevant for careers in finance, trading, investment analysis, and quantitative research.

Related posts on the SimTrade blog

   ▶ Federico DE ROSSI Understanding the Order Book: How It Impacts Trading

   ▶ Jayna MELWANI The impact of market orders on market liquidity

   ▶ Lokendra RATHORE Good-til-Cancelled (GTC) order and Immediate-or-Cancel (IOC) order

   ▶ Clara PINTO High-frequency trading and limit orders

Useful resources

SimTrade course — Trade orders

SimTrade course — Market making

SimTrade simulation — Market orders

SimTrade simulation — Limit orders

About the author

The article was written in April 2026 by Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025).

   ▶ Discover all posts by Bochen LIU

AMM

Calculateur AMM

Calculateur AMM à produit constant

Cette application calcule le prix moyen de transaction et le prix final (prix marginal après transaction) pour un AMM de type x × y = k avec la convention suivante : achat = l’utilisateur achète l’actif x et paie en y, vente = l’utilisateur vend l’actif x et reçoit en y.

Paramètres du pool

Transaction

Résultats

Graphique

My Internship Experience as a Marketing Intern at XING QI Educational Institution

Bochen LIU

In this article, Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025) shares his professional experience as a Marketing Intern at XING QI Educational Institution in Beijing, China.

About the company

XING QI is a private educational institution based in Beijing, China, specializing in after-school programs and supplemental learning for primary and secondary school students. Operating in a highly competitive market, the institution focuses on attracting students, improving enrolment, and maintaining high-quality educational services.

I worked within the marketing team, which was responsible for managing digital campaigns, promoting institutional events, analyzing marketing performance, and supporting student recruitment initiatives. The department ensured that promotional strategies reached potential students effectively and that marketing resources were allocated efficiently to support enrollment growth.

My internship

During my studies at ESSEC Business School, I joined XING QI Educational Institution as a Marketing Intern from 2021 to 2022. This experience provided hands-on exposure to digital marketing, campaign management, and event organization, offering insight into how strategic marketing decisions influence organizational growth.

The internship allowed me to observe how marketing activities are planned, executed, and evaluated, and how data-driven adjustments can improve performance and business outcomes.

My missions

I managed online promotions and social media campaigns, contributing to a 35% increase in inquiries and a conversion rate of approximately 20% into enrollments. By redesigning advertising materials and conducting A/B testing, I helped improve campaign return on investment by about 18%, ensuring marketing resources were used efficiently.

In addition to digital campaigns, I supported campus events that attracted over 300 students and generated more than 50 new registrations. Organizing these events required coordination with team members, preparation of promotional materials, and direct engagement with students and parents. These activities demonstrated how marketing strategies directly influence customer behavior and institutional growth.

Required skills and knowledge

This internship required both technical marketing competencies and interpersonal communication skills. I used digital advertising tools, analytics platforms, and performance tracking methods to monitor campaign effectiveness and optimize promotional strategies. Applying marketing principles helped ensure campaigns were targeted and efficient.

Collaboration and communication were equally important, as I worked closely with the marketing team to coordinate campaigns, collect feedback, and refine event planning processes. Critical thinking and problem-solving were necessary when analyzing performance data and proposing improvements.

What I learned

This internship deepened my understanding of how marketing contributes to organizational growth. I learned the importance of continuously measuring campaign performance, understanding target audiences, and applying data insights to improve outcomes.

I also developed project management and coordination skills by working with multiple stakeholders during campaigns and events. These experiences strengthened my ability to organize tasks, manage timelines, and support team objectives effectively.

Furthermore, the internship highlighted the connection between marketing and finance. Digital campaigns and events generate revenue and influence institutional profitability, while evaluating campaign performance involves metrics similar to ROI calculations. My prior exposure to financial concepts through SimTrade helped me interpret marketing data quantitatively and understand how business decisions are assessed in terms of returns.

Business and financial concepts related to my internship

I present below three business and financial concepts related to my internship: marketing return on investment (ROI), conversion rate analysis, and data-driven strategic decision-making.

Marketing return on investment (ROI)

Marketing return on investment (ROI) measures the effectiveness of promotional spending relative to the results generated. By redesigning advertising materials and testing campaign variations, I contributed to improving ROI by increasing the efficiency of marketing expenditures and maximizing enrollment outcomes.

Conversion rate

Conversion rate analysis evaluates how effectively inquiries or leads are transformed into actual customers. Tracking inquiry growth and enrollment conversion rates allowed the marketing team to assess campaign performance and refine targeting strategies, demonstrating how quantitative metrics guide operational improvements.

Data-driven strategic decision-making

Data-driven strategic decision-making involves using performance metrics and analytical insights to guide organizational actions. Through analyzing campaign results and event outcomes, I observed how marketing data supports planning, resource allocation, and long-term institutional growth strategies.

Why should I be interested in this post?

This post provides insight into how marketing internships contribute to business performance and strategic development. Students interested in finance, business strategy, or management can understand how campaign analytics, performance metrics, and event coordination influence revenue generation and organizational growth.

The experience illustrates how analytical thinking, data interpretation, and structured planning are transferable skills valuable across marketing, finance, and broader business careers.

Related posts on the SimTrade blog

   ▶ All posts about Professional experiences

   ▶ Alexandre VERLET Classic brain teasers from real-life interviews

   ▶ Guylan ABBOU My Personal Experience in Marketing, and How It Links to Finance

   ▶ Fatimata KANE My internship experience as a marketing intern at Amazon

   ▶ Ines ILLES MEJIAS My professional experience as a marketing assistant at Auris Gestion

Useful resources

Beijing Weiqi Association official website

Kotler, P., & Keller, K. L. (2016) Marketing Management, 15th Edition, Pearson.

Farris, P. W., Bendle, N. T., Pfeifer, P. E., & Reibstein, D. J. (2010) Marketing Metrics: The Definitive Guide to Measuring Marketing Performance, Pearson.

About the author

The article was written in February 2026 by Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025).

   ▶ Discover all posts by Bochen LIU

Deal Structuring in Investment Banking: How Earn-Outs, Rollover Equity, and Contingent Consideration Shape M&A Outcomes

Ian DI MUZIO

In this article, Ian DI MUZIO (ESSEC Business School, Master in Finance, 2025–2027) examines how investment banks structure consideration in M&A deals through earn-outs, rollover equity, and other forms of contingent consideration, and how these tools redistribute risk and return between buyer and seller.

Context and objective

In most introductory valuation courses, M&A is presented as if deals were paid in a single block of cash at closing, with maybe some stock mixed in. In practice, especially for private targets, the consideration structure can be highly engineered: part cash, part vendor rollover, part earn-out, sometimes with ratchets, performance-based options, or contingent value rights. These instruments are not cosmetic. They shift economic exposure to future performance, mitigate information asymmetry, and can literally decide whether a deal is financeable and acceptable to both sides.

The objective of this article is to provide a practical, technical lens on deal structuring from an investment banking perspective. We will:

  • Define earn-outs, rollover equity, and other forms of contingent consideration.
  • Explain how they affect valuation, incentives, and risk allocation between buyer and seller.
  • Show, via simple numerical illustrations, how these structures change internal rate of return (IRR) profiles and downside protection.
  • Discuss how investment banks help clients choose among structures, negotiate terms, and document them.

The target reader is a student or junior analyst who already understands basic discounted cash-flow (DCF) analysis and valuation multiples (e.g., EV/EBITDA) and wants to see how real‑world M&A uses structuring to solve problems that pure valuation cannot.

Why should I be interested in this post?

For ESSEC students targeting investment banking or private equity, deal structuring is one of the clearest markers of “on-the-job” knowledge. Many candidates can explain EV/EBITDA; far fewer can articulate when you would propose an earn-out instead of a price cut, how much rollover equity is typical in sponsor-backed deals, or how contingent payments are discounted and recorded.

Understanding these tools matters for three reasons:

  1. Interviews: Questions on earn-outs and vendor rollover appear frequently in technical and case interviews. Being able to speak in terms of incentives and risk, not just definitions, differentiates you.
  2. Live work: As a junior in M&A, you will build models where 10–40% of consideration is contingent. Mis-modelling that leg can distort valuation, internal rate of return (IRR), and leverage metrics.
  3. Client dialogue: CEOs and founders often care more about earn-out mechanics, governance, and downside protection than about abstract DCF outputs. Structuring is where banking becomes advisory, not just arithmetic.

Earn-outs – pricing uncertainty with contingent payments

An earn-out is a contractual arrangement where part of the purchase price is paid in the future if the target achieves predefined performance metrics (such as revenue, EBITDA, or users) over a measurement period. Economically, it converts part of the fixed price into a state-contingent claim on future outcomes.

Suppose a buyer and seller disagree on the sustainable EBITDA level. The seller believes the business can reach EUR 20m of EBITDA in three years; the buyer is only comfortable underwriting EUR 15m. An earn-out can bridge this gap by paying a base purchase price consistent with EUR 15m, plus a contingent payment if actual EBITDA falls within (or above) a specified range.

From a valuation perspective, the earn-out has three key components:

  • Performance metric and definition (EBITDA, revenue, gross profit; GAAP vs adjusted; FX treatment).
  • Pay-out function mapping metric values to consideration (for example, linear, step, or capped).
  • Discounting and probability-weighting of future pay-outs to compute present value.

The payout curve below shows that earnout payments rise as EBITDA improves, with a floor below the threshold and a cap beyond which additional performance does not yield further payment.

Earn-out payout profile as a function of EBITDA performance
Figure 1 – Example earn-out pay-out curve linked to EBITDA: below a threshold, the earn-out pays zero; between the threshold and the cap, pay-out increases with EBITDA; above the cap, additional performance does not increase consideration.
Il would add value for your post if you can provide the Excel (with the parameters to play with) that you used to create the figure

As Figure 1 illustrates, the earn-out can be seen as a call option written by the buyer on the future performance of the business. The seller receives upside if results exceed the base case, but bears downside if performance disappoints. For the buyer, this reduces the risk of overpaying based on optimistic projections and aligns seller incentives to support post-closing integration and growth.

In practice, the main challenges with earn-outs are not mathematical but behavioural and legal: defining metrics that cannot be easily manipulated, setting governance rules (who controls capex, pricing, hiring), and designing mechanisms for dispute resolution. Investment banks help by modelling multiple scenarios, benchmarking structures to market practice, and ensuring that legal drafting matches the economics in the spreadsheet.

Rollover equity – keeping the seller in the game

Rollover equity refers to the portion of the seller’s equity that is not sold for cash at closing, but reinvested into the new capital structure. In sponsor-backed deals, it is common for founders and management to roll over 20–40% of their pre-deal ownership. The rationale is twofold:

  • The buyer reduces the immediate cash outlay and increases alignment: the seller remains exposed to future value creation.
  • The seller keeps a “second bite of the apple”: if the PE fund executes its value-creation plan, rolled equity may be sold at a higher multiple at exit.

From a modelling standpoint, rollover equity affects both valuation and IRR attribution. Consider a deal where the implied enterprise value is EUR 200m, funded by EUR 120m of debt, EUR 50m of new equity from the sponsor, and EUR 30m of seller rollover. If the business is later sold for EUR 300m, the allocation of proceeds between sponsor and seller depends on their respective equity stakes and any preferred or ratchet instruments.

IRR comparison between all-cash sale and partial rollover equity for the seller
Figure 2 – Stylised IRR for the seller in two structures: (i) all-cash sale; (ii) 70% cash + 30% rollover equity. With strong post-deal value creation, the rollover structure produces a higher overall IRR for the seller.

As Figure 2 suggests, for sellers who believe in the buyer’s ability to grow the business, accepting rollover can increase expected IRR, even though it reduces immediate liquidity. For buyers, requiring some rollover is a signalling device: if the seller refuses to keep any skin in the game, that may indicate scepticism about the forecast.

Investment banks advising the seller will therefore frame the decision not just in terms of headline price, but in terms of risk-adjusted value and liquidity preferences. For founder-led companies, personal risk tolerance and diversification needs matter as much as expected uplift.

Contingent consideration in the valuation model

From the perspective of a valuation or LBO model, contingent consideration (earn-outs, contingent value rights (CVRs), deferred payments with performance triggers) must be integrated explicitly into the cash-flow profile for both parties. Conceptually, you proceed in three steps:

  1. Define states of the world (for example, downside, base, upside) with associated performance metrics (EBITDA, revenue, net promoter score (NPS)).
  2. Apply the contractual pay-out function to each state to compute the contingent leg of consideration.
  3. Probability-weight and discount each state back to closing, using a discount rate consistent with the risk of the contingent claim (typically higher than the buyer’s WACC).

On the buyer’s side, the expected cost of contingent consideration affects both sources & uses at closing and post-deal leverage metrics. On the seller’s side, it determines expected proceeds and IRR, but with higher dispersion than a pure cash deal.

Sources and uses diagram including cash, rollover equity, and contingent consideration
Figure 3 – Simplified sources & uses for a deal combining cash, seller rollover equity, and contingent consideration. The expected value of the earn-out is modelled separately and may be financed from future operating cash flows rather than funded entirely at closing.

Figure 3 shows a stylized sources & uses table where the base cash consideration is funded at closing, while the expected value of the earn-out is treated as an off-balance-sheet liability that will be funded over time from cash flows. Modelers must decide whether to treat this as debt-like (affecting leverage) or equity-like (affecting valuation but not covenants), depending on accounting treatment and negotiation.

How investment banks use these tools in practice

In live mandates, investment banks use structuring levers to solve concrete constraints:

  • Bridging valuation gaps: Earn-outs and seller notes allow deals to clear when buyer and seller have different expectations about growth or margin expansion.
  • Managing financing constraints: Deferring part of consideration via contingent payments can make a deal financeable within leverage limits and rating constraints.
  • Aligning incentives: Rollover equity and performance-based instruments keep key management motivated post-closing.
  • Signalling and negotiation: Willingness to accept rollover or contingent pay-outs signals confidence in the business to the other party and to co-investors.

On the execution side, junior bankers support this by:

  • Building flexible models where earn-out parameters, rollover percentages, and discount rates can be sensitized.
  • Preparing deal decks that show IRR profiles and downside cases across alternative structures.
  • Coordinating with legal counsel so that the SPA drafting matches the model (definitions of EBITDA, caps, floors, baskets, dispute mechanisms).

The key mindset shift is that price and structure are not independent. A buyer can pay more headline value if a larger share of that value is contingent. A seller can accept a lower base price if the earn-out and rollover offer enough upside. Good bankers are those who can use these levers to construct an efficient trade that both sides can sign.

Related posts on the SimTrade blog

   ▶ Emanuele BAROLI Interest Rates and M&A: How Market Dynamics Shift When Rates Rise or Fall

   ▶ Ian DI MUZIO Valuation in Niche Sectors: Using Trading Comps and Precedent Transactions When No Perfect Peers Exist

   ▶ Roberto RESTELLI My Internship at Valori Asset Management

Useful resources

American Bar Association (2010) Model Stock Purchase Agreement – commentary on earn-out provisions and contingent consideration, Second Edition.

American Bar Association (2010) Model Stock Purchase Agreement – commentary on earn-out provisions and contingent consideration, Second Edition.

Koller, T., Goedhart, M., & Wessels, D. (2020) Valuation: Measuring and Managing the Value of Companies (7th edition). Hoboken, NJ: John Wiley & Sons.

McKinsey & Company (2025) Valuation: Measuring and Managing the Value of Companies 8th Edition, Wiley.

Rosenbaum, J., & Pearl, J. (2021) Investment Banking: Valuation, Leveraged Buyouts, and Mergers & Acquisitions (chapters on the M&A process and deal structuring).

Taleb, N. N. (2018) Skin in the Game: Hidden Asymmetries in Daily Life, Random House Publishing Group.

About the author

The article was written in January 2026 by Ian DI MUZIO (ESSEC Business School, Master in Finance (MiF), 2025–2027).

   ▶ Read all posts written by Ian DI MUZIO

February 2026: Derivatives – Monthly Selection from the SimTrade blog

Most Read Articles about Derivatives on the SimTrade Blog

This monthly selection highlights key articles on derivatives, chosen based on their pedagogical value, practical relevance, and readership engagement.

   ▶ Jayati WALIA Brownian Motion in Finance

   ▶ Akshit GUPTA Option Greeks – Vega

   ▶ Shengyu ZHENG Pricing barrier options with analytical formulas

   ▶ Tianyi WANG Understanding Snowball Products: Payoff Structure, Risks, and Market Behavior

   ▶ Saral BINDAL Implied Volatility and Option Prices

SimTrade Editorial Picks

In addition to the most read posts, the SimTrade editorial team highlights the following articles for their strong educational value in the world of option pricing and investment banking.

   ▶ Lucas BAURIANNE The Golden Boy: Une immersion dans l’univers des banques d’investissement

   ▶ Alexandre VERLET Classic brain teasers from real-life interviews

   ▶ Saral BINDAL Measures and statistics of business activity in global derivative markets

   ▶ Marie POFF Film analysis: Rogue Trader

A solid understanding of derivatives is essential for careers in trading, risk management, and corporate finance, making these articles particularly valuable for aspiring finance professionals.