Two Sides of the Market: Managing $20 Million Under Pressure

Finance in Real Time

Diversification, transaction costs, and risk management are easy to define on paper. Using them inside a 30 minute round, with news arriving on a rolling feed and a ranking waiting at the end, is a different exercise.

On October 13, 2026, I competed in the CFA Institute Investing Simulator Challenge 2026, presented by CFA Institute and powered by AmplifyME's simulation platform, as part of CFA Group 2. The challenge put me in two roles. I started as a sell side investment bank trader, handling client orders and the risk they create, then switched to the buy side as an asset manager investing a client's money.

In Asset Management, I finished 12th of 58 ranked participants in CFA Group 2, growing $20,000,000 of simulated capital to $21,227,765.27. Investment Banking, the round I started with, was harder for me: I finished 32nd of 59. The gap between those two results, and the gaps inside each one, say more than either ranking does on its own.

The order matters for how the two rounds fit together. An asset manager who wants to buy needs someone willing to sell, often a bank's trading desk, and that desk then has to manage the position it just took on. In the first round I was the desk. In the second, I was the client placing the orders.

The $20 Million Mandate

The Asset Management round started with a client brief. The client was hypothetical: a 40 year old founder and CEO of a successful technology company, with a moderately high tolerance for risk, a preference for a diversified portfolio, and an interest in ESG investing, meaning companies judged on environmental, social, and governance factors alongside financial ones.

The client mandate

Simulated capital

$20,000,000

Strategy

Long only: no short selling

Investment universe

NVIDIA (NVDA), Tesla (TSLA), Électricité de France (EDF), Siemens (SIE), Royal Dutch Shell (RDS)

Benchmark

An equally weighted portfolio of the five stocks, 20% in each

Risk limit

A maximum stop loss of $500,000 on any one trade

Time

A 30 minute round representing a fictional three month investment period

Objective

Maximize return and outperform the benchmark

On paper the objective sounds simple. In practice, the constraints pull against each other:

• Return versus risk tolerance. Moderately high risk tolerance allows meaningful stock exposure, but it isn't unlimited. A portfolio built only to maximize return would ignore half of what the client asked for.
• Diversification versus conviction. With only five stocks, every position is a large part of the portfolio. Concentrating in the one or two companies with the best story might raise expected return, but it also concentrates the risk this client wanted spread out.
• ESG preferences versus financial risk. Several of the companies had clear sustainability angles. That is a reason to consider them for this client, not a reason to overlook their valuation, operating, or governance risks.
• The benchmark. Beating an equally weighted benchmark requires holding something different from it. Owning 20% of everything would roughly match the benchmark before costs, so every deviation is a bet that needs a reason.
• Liquidity and execution costs. Every trade costs something: commissions, the spread between buying and selling prices, and the price movement a large order can cause. A strategy that looks good on paper can lose much of its edge if it's expensive to carry out.
• The stop loss limit. The $500,000 cap on any one trade puts a ceiling on how much a single bad call can cost, which shapes how large and how aggressive any one position can be.

That is why portfolio construction here couldn't be reduced to picking whichever stock had performed best recently. The real question was which mix of exposures fit this client, at a cost that could be justified, given what was known at that moment.

Navigating the Market

Once the round started, news arrived on a rolling feed. Some headlines were macroeconomic or geopolitical; others were about a single company. Each one raised the same question: does this change the investment case, and if so, by how much?

Macroeconomic and geopolitical

• Russia's invasion of Ukraine
• Rising global inflation and interest rate concerns
• The European Chip Act
• European investment in offshore wind energy
• Germany announcing its withdrawal from the Paris Climate Agreement
• Supply chain bottlenecks affecting vehicle components

NVIDIA NVDA

Could support

• ▲ Growth in artificial intelligence and semiconductors
• ▲ Plans for an AI supercomputer for climate research

Could pressure

• ▼ Collapse of the proposed Arm acquisition

Tesla TSLA

Could support

• ▲ Strong electric vehicle deliveries
• ▲ Approval of its first European gigafactory

Could pressure

• ▼ Supply chain concerns
• ▼ Questions about product reliability and corporate governance

Électricité de France EDF

Could support

• ▲ Renewable energy contracts and growing ESG interest
• ▲ An improved ESG rating

Could pressure

• ▼ Nuclear operating concerns

Siemens SIE

Could support

• ▲ Expanding wind energy opportunities

Could pressure

• ▼ Higher turbine manufacturing costs
• ▼ Supply chain and governance challenges

Royal Dutch Shell RDS

Cuts both ways

• ◆ Geopolitical changes affecting energy markets
• ◆ Withdrawal from Russia

Could pressure

• ▼ Environmental and emissions concerns

Growth versus downside

NVIDIA and Tesla carried the most obvious growth stories: demand for artificial intelligence and semiconductors for one, strong electric vehicle deliveries and approval of its first European gigafactory for the other. Both also faced the kind of risk that tends to hit growth stocks hardest when inflation and interest rates rise, because more of their value depends on profits expected far in the future. The scenario added company specific risks on top: supply chain and governance questions at Tesla, and a failed acquisition at NVIDIA.

ESG is not the same as low risk

EDF and Siemens fit the client's sustainability interest most directly, through renewable energy contracts, an improved ESG rating, and offshore wind demand. But the same scenario raised nuclear operating concerns at EDF and rising turbine costs and supply chain problems at Siemens. Shell sat at the other end: shifts in energy markets and its exit from Russia mattered financially, while its emissions profile worked against the client's ESG preferences. A strong ESG story doesn't remove operating, cost, or governance risk, and a weak ESG profile doesn't automatically make a stock a poor investment over three months. For this client, both had to be weighed together.

Macro news moves everything at once

Some headlines didn't belong to any one company. The European Chip Act and offshore wind investment pointed toward particular sectors, while the war in Ukraine and concerns about inflation and interest rates affected the whole market. Diversification is partly a defense against exactly that kind of news.

Updating the thesis

A recurring part of the round was deciding how much a headline should change a view. A single headline rarely settles an investment case. The useful questions were whether news changed a company's long run earning power or only its short term sentiment, whether the market had probably priced it in already, and whether acting on it was worth the cost of trading.

I'm deliberately not reconstructing which trades I made after which headlines. I don't have a trade by trade record of the round, and rebuilding one from memory would make this case study less accurate, not more.

Results: Portfolio Performance

The Asset Management round ended with my simulated portfolio worth $21,227,765.27, up $1,227,765.27 from its $20,000,000 starting value. That is growth of about 6.14% over the round's fictional three months, and it placed me 12th of 58 in CFA Group 2 with a session score of 72.96%.

Starting capital Exact: $20,000,000

$20.00M

Final portfolio value Exact: $21,227,765.27

$21.23M

Net increase Exact: +$1,227,765.27

+$1.23M

Portfolio growth Calculated, start to end

+6.14%

Three numbers that look like return

The dashboard reports more than one figure that looks like a return, and they aren't the same measurement:

The 6.7% return on initial funds is consistent with trading P&L divided by the $20 million starting capital, before commissions ($1,340,757 ÷ $20,000,000 ≈ 6.7%). Subtracting the reported commissions leaves $1,239,200, which is still about $11,435 more than the actual change in portfolio value. The dashboard doesn't itemize that difference, so I'm not going to guess at it. I treat the change in what the portfolio was actually worth, $1,227,765.27, as the clearest measure of the round, and I report the platform's figures alongside it exactly as given.

Two more cautions. First, I can't say whether I beat the benchmark: the equally weighted benchmark's final return isn't in the materials I have, so I'm not claiming outperformance or alpha. Second, the 0.38 Sharpe ratio (a measure of return relative to volatility) describes one 30 minute session standing in for three months. It says very little about investment skill over years, and neither 6.14% nor 6.7% is a return I'd expect to repeat or annualize.

How the round was scored

My 72.96% session score equals the simple average of these five category scores. The platform doesn't publish exactly how each category is calculated, so I read them as its assessment of different skills rather than precise measurements. One distinction matters more than the rest: the 84% ROI assessment score is a grade, not an 84% return. My portfolio actually grew about 6.14%, and the platform separately reported a 6.7% return on initial funds. I also made zero fat finger errors (mistyped orders, like an extra zero on a quantity), consistent with the 100% accuracy score.

One detail I noticed: my Risk Appetite score, 67.2%, is the same number as my average equity allocation over the round, covered below. The dashboard doesn't say so directly, but that suggests the metric reflects how fully invested the portfolio was on average.

Execution Quality: The Hidden Cost of Investing

The most useful number on my dashboard is the lowest one.

My ROI assessment score (84%) and buy side risk management score (86%) suggest that the decisions about what to own, and how much risk to take for this client, held up reasonably well. My execution score, 27.59%, says that turning those decisions into actual trades did not. It sits more than 56 points below the average of my other four categories (84.3%), which makes it the biggest drag on my session score.

Why a good idea can still be an expensive trade

An asset manager can be right about a stock and still lose part of that edge in how the trade is carried out. In general, the cost comes through a few channels:

• The spread. Every market has a bid (the highest price a buyer will pay) and an ask (the lowest price a seller will accept). Buying at the ask and later selling at the bid loses the difference, even if the price never moves.
• Market depth and liquidity. Only so many shares are usually offered at the best price. An order bigger than that supply keeps filling at worse prices.
• Order size and market impact. The bigger an order is compared with what's available at the current price, the more buying it quickly can push the price up while you are still buying.
• Price discovery with brokers. Asking a broker for a price is part of finding a fair one. Comparing quotes, and choosing when to trade with a broker versus directly on the exchange, affects the price you end up with.
• Commissions. The visible cost. My round's reported commissions were $101,557, about 0.5% of the starting capital.

Apart from commissions, these are general mechanics, not measurements from my round. The dashboard doesn't break execution cost down by spread, depth, or impact, so I can't say how much each one contributed.

Total trades

29

Chat and voice trades

17

Exchange trades

12

Fat finger errors

0

I made 29 trades in the Asset Management round: 17 through the platform's chat and voice channel and 12 directly on the exchange, with none through the other two channels the dashboard tracks. Aggregate data can't tell me which of those trades were executed well or poorly, or how much any single one affected the 27.59% score.

What the score does tell me is where to focus. Execution isn't paperwork that happens after the real decision. It determines the implemented return, which is the return a client actually receives. If two managers make identical calls but one pays more in spreads and market impact, the second manager's client ends up with less, even though the investment thesis was the same. I'm treating the low score as the clearest improvement target from the challenge, not as proof that execution cost me a specific amount. Without trade level data, that number can't be calculated honestly.

Capital Deployment and Risk Appetite

Over the whole round, my portfolio averaged 67.2% in equities and 32.8% in cash. By the end it was 87.9% equities and 12.1% cash. Equity exposure increased as I put capital to work, which also means a meaningful share of the portfolio sat in cash for part of the round.

Cash has real advantages. It can't fall with a stock, and it is available to act on new information. But this was a long only mandate for a client with moderately high risk tolerance, measured against a benchmark that holds 20% in each stock from the first minute. In that setup, the scenario encouraged full investment: money in cash doesn't participate when stocks rise, and the benchmark never carries that drag.

There is a reasonable case either way. Deploying slowly can avoid buying just before bad news; it can also mean missing gains. Whether holding cash helped or hurt my result can't be quantified without stock level performance and trade timing, which I don't have. What the gap between a 67.2% average and an 87.9% final allocation does show is that the pace of deployment was its own decision, separate from which stocks to buy.

The Sell Side: Inside Investment Banking

Investment Banking was the round I started with, and it was a different job. As a sell side trader, I wasn't choosing exposures to fit a client's long term goals. I was serving clients who wanted to trade: pricing their orders, earning commission on the business, and then managing the risk each client trade left on my book.

That last part is the hard one. When a client sells to the bank, the bank now owns what the client sold. When a client buys, the bank can be left short. Facilitating a trade means temporarily taking the other side of it, and that inventory is risk the trader didn't choose for investment reasons. The standard response is to hedge or unwind it, usually by trading on the exchange in sizes small enough not to move the price against yourself.

Investment Banking rank CFA Group 2

32nd of 59

Session score Simulator assessment

48.72%

Commission earned

$148,420

Trading P&L

$46,745

Combined P&L Commission + trading P&L

$195,165

Average net exposure Exact: −$4,615,833

−$4.62M

The round was profitable: $148,420 in commission plus $46,745 in trading P&L, for $195,165 combined. Exchange trading was my strongest category at 73.08%, and my average exchange market impact was 0.1%.

Three categories were clearly weak. Client trades came in at about 27% and the fat finger metric at about 31%, both read from rounded displays, and sell side risk management scored 34.62%. The dashboard also showed a 31% facilitation ratio, one client fat finger error, and an average net exposure of −$4,615,833 across the round.

The exposure figure is worth spelling out, because it describes a position rather than a grade. On average, I was carrying a short position worth more than $4.6M. A short position that size loses money if the market rises, however much commission comes in.

I made 81 exchange trades against 6 client trades, so I was clearly active. But activity isn't the same as risk reduction. If exchange trades don't consistently bring the book back toward flat, a trader can be busy all session and still carry a large exposure. That's the central lesson of this round:

High trading activity does not automatically mean effective inventory risk reduction.

Buy Side Versus Sell Side

The two rounds measured different jobs, so the results are easiest to read side by side.

Doing the sell side first changes how I read my Asset Management results. When an asset manager places a large order, someone has to take the other side and carry that risk until it's hedged. Part of what the asset manager pays in spreads and market impact is compensation for that, and I had just spent a round on the receiving end. Knowing where the cost comes from didn't make me better at avoiding it, though. Execution was still my lowest Asset Management score, at 27.59%.

It's the same split I noticed at Wolverine, where the questions on a market making desk were about spread, size, and risk rather than where the price was going. In the first round of this challenge, those were my questions too.

Lessons and Reflection

1. 01

   The client sets the problem.

   Long only, five stocks, an interest in ESG, a $500,000 stop loss limit. The mandate drew the boundaries before I looked at a single stock, and a higher return that ignored them wouldn't have been the right answer.

2. 02

   A good call can still be an expensive trade.

   My 84% ROI assessment score and 27.59% execution score came from the same round. What a client keeps is the implemented return, after spreads, market impact, and commissions.

3. 03

   Busy is not the same as hedged.

   Making 81 exchange trades in the Investment Banking round didn't stop me from averaging a short position of more than $4.6M. Every client trade creates exposure, and the follow up trades have to actually reduce it.

4. 04

   The breakdown beats the total.

   My overall Asset Management score blends a strong ROI assessment with weak execution. Split into categories, it points at one specific thing to work on.

The 12th place finish is the number people notice first, but the category scores told me more. In Investment Banking I was active without keeping my book balanced. In Asset Management my calls held up and my trading was expensive. Those are two versions of the same problem, seen from opposite sides of the market: investment performance depends not only on identifying opportunities, but also on how capital is allocated, how trades are executed, and how risk is managed.

Execution is the part I'd most like to get better at.