Susquehanna International Group (SIG) Operations, Trading, SQL, and Probability Review

Vocabulary flashcards covering SIG company overview, market making, operations rotations, SQL reconciliation commands, and probability fundamentals.

Susquehanna International Group (SIG)

A global quantitative trading firm, market maker, and tech-driven proprietary trading business based in Bala Cynwyd, PA.

SIG's Core Operational Philosophy

Game-theory driven decision science, quantitative risk management, a flat, non-hierarchical culture, and treating work like decision-making under uncertainty.

Market Maker

A firm that continuously quotes both buy (Bid) and sell (Ask) prices for securities, providing instant liquidity to the market so traders can execute orders without delay.

Bid-Ask Spread

The difference between the highest price a buyer is willing to pay (Bid) and the lowest price a seller is willing to accept (Ask).

Quantitative Arbitrage

Using mathematical models to detect mispricings across correlated products (e.g., an ETF trading at a gap from its underlying basket) and executing paired trades to lock in profit.

ByteDance

TikTok's parent company in which SIG was an early investor, resulting in one of the most profitable private equity/venture capital investments in history.

Listed Equity Options

The asset class SIG is historically famous for dominating, which requires complex probabilistic pricing, volatility surface modeling, and multi-leg risk management.

SIG Operations Department

Serves as the backbone of the trade lifecycle by ensuring millions of daily trades settle accurately, managing risk and capital efficiency, managing cash/collateral, and building automated reporting tools.

Trade Break

A discrepancy or mismatch between SIG's internal trade records (what SIG booked) and external records from an exchange, clearinghouse (such as DTCC, NSCC, OCC), or executing broker.

Clearing & Settlement (Operations Knowledge)

The understanding of how trades flow from execution to clearinghouses (DTCC, NSCC, OCC) and prime brokers, including $T+1$ settlement cycles and fail-to-deliver risks.

Corporate Actions

Events such as stock splits, dividends, mergers, and spin-offs that alter underlying options strikes, equity positions, and collateral requirements across desks.

Trade Lifecycle & Reconciliation Rotation

An operations rotation where analysts investigate trade breaks between internal systems and external brokers/exchanges, audit execution logs, and verify settlement statements across global venues.

Equity Finance & Stock Loan Rotation

An operations rotation responsible for managing short-sale locates, borrowing/lending stocks, monitoring borrow/lend and repo rates, handling collateral allocations/margins, and processing corporate action events.

Desk Support & Data Aggregation Rotation

An operations rotation focused on aggregating high-volume execution data into daily reporting metrics (capital utilization, commission, and P&L metrics) for traders to optimize capital allocation and execution efficiency.

Workflow & Process Automation Rotation

An operations rotation dedicated to auditing manual operational routines and engineering automated data pipelines using SQL, Python, and Power Query to eliminate operational risk and improve internal controls.

INNER JOIN

A SQL operation that returns only the rows that have matching values in both tables; rows without a match in either table are excluded.

LEFT JOIN

A SQL operation that returns all rows from the left table, along with matching rows from the right table; unmatched right-side fields return NULL.

RIGHT JOIN

A SQL operation that returns all rows from the right table, along with matching rows from the left table; unmatched left-side fields return NULL.

FULL OUTER JOIN

A SQL operation that returns all records when there is a match in either left or right table, filling missing side values with NULLs.

WHERE vs. HAVING

In SQL, WHERE filters individual rows before aggregation occurs, whereas HAVING filters aggregated group results after a GROUP BY clause.

GROUP BY vs. Window Functions

In SQL, GROUP BY collapses multiple rows into a single summary row, whereas Window Functions (OVER / PARTITION BY) perform calculations across rows while retaining individual row details.

COALESCE

A SQL function that returns the first non-null argument, useful for replacing NULL values with $0$ or fallback strings during reconciliation table comparisons.

UNION vs. UNION ALL

In SQL, UNION combines result sets and removes duplicate rows, whereas UNION ALL combines result sets and retains all duplicates, providing faster performance.

Expected Value (EV)

The weighted average outcome of a random event, calculated as the sum of all possible values multiplied by their respective probabilities: $EV = \sum(\text{Value} \times \text{Probability})$.

Expected Value of a Fair 6-Sided Die

The mathematical average roll calculated as: $\frac{1 + 2 + 3 + 4 + 5 + 6}{6} = \frac{21}{6} = 3.5$.

Probability of Rolling a Sum of 7 (Two Dice)

The likelihood evaluated as $6$ outcomes out of $36$ total possibilities ($1+6$, $2+5$, $3+4$, $4+3$, $5+2$, $6+1$), resulting in $\frac{6}{36} = \frac{1}{6}$ ($16.67\%$).

Bayes' Theorem

A method to update the probability of a hypothesis as more evidence or information becomes available: $P(A|B) = \frac{P(B|A) \times P(A)}{P(B)}$.

Probability of At Least 2 Heads in 3 Coin Flips

The probability determined by $4$ favorable outcomes ($HHH$, $HHT$, $HTH$, $THH$) out of $8$ total combinations, giving $\frac{4}{8} = 50\%$.

1/6=

0.16

2/6=

0.33

3/6=

1/2

4/6=

0.66

5/6

0.83

STAR for Questions around Difficult Team Work

S: Group work for a class where no one wanted to share ideas

A: A way that I like to approach a situation like this is try to get as much collaboration as possible in a group setting, but if we can not get enough

STAR for Process Automation

Situation: Manual process of entering vendor payments into the bank software weekly which took 2-3 hours.

Action: Researched ways to automate the process, which is where I learned about NACHA files which could take the payment information from the ERP system we used to log invoice information and generate a file that could be uploaded and read by the bank software to automatically generate the payments in seconds

Result: The result of this was around a two hour reduction time for the AP payment process, and a much simpler process of doing so, where the AP team did not have to worry about adding payments one-by-one, they now only needed to reconcile the payments generated by the file

STAR Process Automation with SQL

S: During my Cozen O'Connor co-op, I would audit and report on Unbilled Time and AR balances across practice groups for routine client-facing reporting

A: Instead of creating the spreadsheet manually, we had a sheet with SQL queries extracting information on AR and UBT based on an inputted client code that would break the information down by fiscal period and client matter
R: This process allowed us to respond to client audit requests faster because once the client code and fiscal period was entered into the sheet, the queries would draw from the database into the excel sheet, and all we needed to do was cross reference the data in the excel sheet with the information in our ERP system to reconcile.